Distribution
Associated Constructors
Distribution
Syntax: Distribution( Column() )
Description: Shows the distribution and univariate summary statistics for each variable. Results and options depend on the modeling type of each variable. Some options include histograms, box plots, quantile plots, fitting distributions, and capability analysis.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
colref = Column( "age" );
// Correct way to use the colref
Distribution( Column( colref ) );
// This will not work
Distribution( colref );
Columns
By
Syntax: obj = Distribution(...<By( column(s) )>...)
Description: Performs a separate analysis for each level of the specified column.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Distribution( Column( :Age, :Weight ), By( _bycol ) );
Column
Syntax: obj = Distribution(...<Column( column(s) )>...)
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
Freq
Syntax: obj = Distribution(...<Freq( column )>...)
Description: Specifies a column whose values assign a frequency to each row for the analysis.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_freqcol", Numeric, Continuous, Formula( Random Integer( 1, 5 ) ) );
obj = dt << Distribution( Column( :Age, :Weight ), Freq( _freqcol ) );
Weight
Syntax: obj = Distribution(...<Weight( column )>...)
Description: Specifies a column whose values assign a weight to each row for the analysis.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_weightcol", Numeric, Continuous, Formula( Random Beta( 1, 1 ) ) );
obj = dt << Distribution( Column( :Age, :Weight ), Weight( _weightcol ) );
Y
Syntax: obj = Distribution(...Y( column(s) )...)
Description: Specifies the categorical or continuous columns to be analyzed.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Columns( :Age, :Weight ) );
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Y( :Age, :Weight ) );
Item Messages
Apply Preset
Syntax: Apply Preset( preset ); Apply Preset( source, label, <Folder( folder {, folder2, ...} )> )
Description: Apply a previously created preset to the object, updating the options and customizations to match the saved settings.
JMP Version Added: 18
Anonymous preset
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ), Normal Quantile Plot( 1 ) ),
Nominal Distribution( Column( :age ), Mosaic Plot( 1 ) )
);
preset = obj[1] << New Preset();
dt2 = Open( "$SAMPLE_DATA/Aircraft Incidents.jmp" );
obj2 = dt2 << Distribution(
Nominal Distribution( Column( :Aircraft Damage ) ),
Continuous Distribution( Column( :Total Minor Injuries ) )
);
Wait( 1 );
obj2[2] << Apply Preset( preset );
Search by name
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ) ),
Nominal Distribution( Column( :age ) )
);
Wait( 1 );
obj[1] << Apply Preset( "Sample Presets", "Check Normality" );
Arrange in Rows
Syntax: obj << Arrange in Rows( number )
Description: Specifies the number of distribution reports to display across the window.
dt = Open( "$SAMPLE_DATA/Blood Pressure.jmp" );
obj = dt << Distribution( Column( :BP 8M, :BP 12M, :BP 6M, :BP 8W, :BP 12W ) );
obj << ArrangeInRows( 3 );
Axes on Left
Syntax: obj << Axes on Left( state=0|1 )
Description: Moves the count, probability, density, and normal quantile plot axes to the left side of a horizontal graph.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ), Horizontal Layout( 1 ), Count Axis( 1 ) );
obj << Axes on Left( 1 );
CDF Plot
Syntax: obj << CDF Plot( state=0|1 )
Description: Shows or hides a plot of the empirical cumulative distribution function.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << CDF Plot( 1 );
Capability Analysis
Syntax: obj << Capability Analysis( LSL( number ), Target( number ), USL( number ) )
Description: Performs a capability analysis given the stated lower specification limit (LSL), target, and upper specification limit (USL).
dt = Open( "$SAMPLE_DATA/Quality Control/Coating.jmp" );
obj = dt << Distribution( Column( :Weight ) );
obj << Capability Analysis( LSL( 16 ), USL( 24 ), Target( 20 ) );
Confidence Interval
Syntax: obj << Confidence Interval( number, <Upper | Lower>, <Sigma( number )> )
Description: Computes the specified confidence intervals around both the mean and the standard deviation. If you specify sigma, the specified value is used to compute the confidence interval around the mean.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Confidence Interval( 0.98 );
obj << Confidence Interval( 0.95, Lower );
obj << Confidence Interval( 0.95, Sigma( 4 ) );
Count Axis
Syntax: obj << Count Axis( state=0|1 )
Description: Shows or hides the count axis for the histogram.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Count Axis( 1 );
Custom Quantiles
Syntax: obj << Custom Quantiles( fraction, [quantile1, quantile2, ... quantileN] )
Description: Creates a report of the quantile rank estimates and a report of the smoothed empirical likelihood quantile estimates for the specified quantiles. Uses the fraction as the confidence level for confidence intervals in both reports.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Custom Quantiles( 0.975, [0.075, 0.1, 0.125, 0.975, 0.99] );
Customize Summary Statistics
Syntax: obj << Customize Summary Statistics(statistic1( state=0|1 ), statistic2( state=0|1 ), ..., statisticN( state=0|1 ), <Set Trimmed Mean Percent(number)>, <Set Alpha Level(number)>)
Description: Customizes the summary statistics that are displayed in the Summary Statistics report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Customize Summary Statistics( N( 0 ), Variance( 1 ), Skewness( 1 ) );
Density Axis
Syntax: obj << Density Axis( state=0|1 )
Description: Shows or hides the density axis for the density curve on the histogram.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Density Axis( 1 );
Fit All
Syntax: obj << Fit All
Description: Compares all possible distributions.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :CO ) );
obj << Fit All;
Fit Beta
Syntax: obj << Fit Beta
Description: Fits a two-parameter beta distribution to data between 0 and 1 (not inclusive).
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :OZONE ) );
obj << Fit Beta;
Fit Beta Binomial
Syntax: obj << Fit Beta Binomial( Sample Size( n | column ) )
Description: Fits a beta binomial distribution given a specified constant sample size or a column that contains the sample sizes. This distribution is a more flexible version of the binomial distribution.
JMP Version Added: 15
Example 1
dt = Open( "$SAMPLE_DATA/Quality Control/Shirts.jmp" );
obj = dt << Distribution( Column( :"# Defects"n ) );
obj << Fit Beta Binomial( Sample Size( 10 ) );
Example 2
dt = Open( "$SAMPLE_DATA/Quality Control/Shirts.jmp" );
obj = dt << Distribution( Column( :"# Defects"n ) );
obj << Fit Beta Binomial( Sample Size( :Box Size ) );
Fit Binomial
Syntax: obj << Fit Binomial( Sample size( n | column ) )
Description: Fits a binomial distribution given a specified constant sample size or a column that contains the sample sizes. This distribution models the total number of successes in n independent trials.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Quality Control/Shirts.jmp" );
obj = dt << Distribution( Column( :"# Defects"n ) );
obj << Fit Binomial( Sample Size( :Box Size ) );
Fit Cauchy
Syntax: obj << Fit Cauchy
Description: Fits a Cauchy distribution to the data. The Cauchy distribution is robust to outliers and is equivalent to a t distribution with one degree of freedom.
JMP Version Added: 15
Random Reset( 15 );
d = J( 75, 1, Random Normal() );
d[1] = 10;
d[2] = 9;
d[3] = 8;
As Table( d );
Column( 1 ) << set name( "X" );
Distribution( Column( :X ), Fit Normal, Fit Cauchy );
Fit ExGaussian
Syntax: obj << Fit ExGaussian
Description: Fits an exponentially modified Gaussian distribution to the data.
Example 1
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Distribution( Column( :Y ) );
obj << Fit ExGaussian;
Example 2
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Distribution( Column( :Y ) );
obj << Fit ExGaussian;
obj << Fit Normal;
obj << Fit Exponential;
Fit Exponential
Syntax: obj << Fit Exponential
Description: Fits an exponential distribution to nonnegative data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :POP ) );
obj << Fit Exponential;
Fit Gamma
Syntax: obj << Fit Gamma
Description: Fits a two-parameter gamma distribution to positive data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :Max deg. F Jan ) );
obj << Fit Gamma;
Fit Handle
Syntax: obj << (Fit Handle[number] << {option}); obj << (Fit Handle["Distribution Name"] << {option})
Description: Array of handles to the fitted distributions. This enables you to send commands to specific distributions that have been fit.
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :CO ) );
obj << Fit Lognormal;
obj << Fit Weibull;
obj << (Fit Handle[2] << Goodness of Fit( 1 ));
obj << (Fit Handle["Lognormal"] << QQ Plot( 1 ));
Fit Johnson
Syntax: obj << Fit Johnson
Description: Fits a Johnson distribution to the data. The most appropriate of the three types of Johnson distributions (Su, Sb, and Sl) is chosen based on the quantiles.
JMP Version Added: 15
Example 1
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Distribution( Column( :Y ) );
obj << Fit Johnson;
Example 2
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :CO ) );
obj << Fit Johnson;
Fit Largest Extreme Value
Syntax: obj << Fit Largest Extreme Value
Description: Fits a largest extreme value distribution to the data.
JMP Version Added: 19
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :NO ) );
obj << Fit Largest Extreme Value;
Fit Lognormal
Syntax: obj << Fit Lognormal
Description: Fits a lognormal distribution to positive data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :CO ) );
obj << Fit Lognormal;
Fit Negative Binomial
Syntax: obj << Fit Negative Binomial
Description: Fits a negative binomial distribution to the data. This distribution is equivalent to the Gamma Poisson distribution.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Quality Control/Airport.jmp" );
obj = dt << Distribution( Column( :Delay ) );
obj << Fit Negative Binomial;
Fit Normal
Syntax: obj << Fit Normal
Description: Fits a normal distribution to the data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Weight ) );
obj << Fit Normal;
Fit Normal 2 Mixture
Syntax: obj << Fit Normal 2 Mixture
Description: Fits a mixture of two normal distributions. This distribution is capable of fitting bimodal data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cytometry.jmp" );
obj = dt << Distribution( Column( :CD8 ) );
obj << Fit Normal 2 Mixture;
Fit Normal 3 Mixture
Syntax: obj << Fit Normal 3 Mixture
Description: Fits a mixture of three normal distributions. This distribution is capable of fitting multi-modal data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cytometry.jmp" );
obj = dt << Distribution( Column( :CD8 ) );
obj << Fit Normal 3 Mixture;
Fit Poisson
Syntax: obj << Fit Poisson
Description: Fits a Poisson distribution to the data. This distribution is a popular choice for count data. The fitted mean of the Poisson distribution is equal to the variance.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Quality Control/Airport.jmp" );
obj = dt << Distribution( Column( :Delay ) );
obj << Fit Poisson;
Fit SHASH
Syntax: obj << Fit Shash
Description: Fits a sinh-arcsinh (SHASH) distribution to the data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :CO ) );
obj << Fit Shash;
Fit Smallest Extreme Value
Syntax: obj << Fit Smallest Extreme Value
Description: Fits a smallest extreme value distribution to the data.
JMP Version Added: 19
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :NO ) );
obj << Fit Smallest Extreme Value;
Fit Smooth Curve
Syntax: obj << Fit Smooth Curve( <Bandwidth( number )> )
Description: Fits a smooth curve to the data using nonparametric density estimation. You can set the smoothness by specifying the bandwidth.
JMP Version Added: 15
Example 1
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :SO2 ) );
obj << Fit Smooth Curve;
Example 2
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :SO2 ) );
obj << Fit Smooth Curve( Bandwidth( 0.02 ) );
Fit Student's t
Syntax: obj << Fit Student's t
Description: Fits a Student's t distribution to the data. This distribution is a robust option that spans the space between a normal distribution and a Cauchy distribution.
JMP Version Added: 16
Random Reset( 15 );
d = J( 75, 1, Random Normal() );
d[1] = 10;
d[2] = 9;
d[3] = 8;
As Table( d );
Column( 1 ) << set name( "X" );
Distribution( Column( :X ), Fit Normal, Fit Student's t );
Fit Weibull
Syntax: obj << Fit Weibull
Description: Fits a two-parameter Weibull distribution to positive data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :NO ) );
obj << Fit Weibull;
Fit ZI Beta Binomial
Syntax: obj << Fit ZI Beta Binomial( Sample Size( n | column ) )
Description: Fits a zero inflated beta binomial distribution given the specified constant sample size or a column that contains the sample size. This distribution models the total number of successes in n independent trials where there are more zeros observed than would be expected for the beta binomial distribution.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Quality Control/Shirts.jmp" );
obj = dt << Distribution( Column( :"# Defects"n ) );
obj << Fit ZI Beta Binomial( Sample Size( :Box Size ) );
Fit ZI Binomial
Syntax: obj << Fit ZI Binomial( Sample Size( n | column ) )
Description: Fits a zero inflated binomial distribution given the specified constant sample size or a column that contains the sample size. This distribution models the total number of successes in n independent trials where there are more zeros observed than would be expected for the binomial distribution.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Quality Control/Shirts.jmp" );
obj = dt << Distribution( Column( :"# Defects"n ) );
obj << Fit ZI Binomial( Sample Size( :Box Size ) );
Fit ZI Negative Binomial
Syntax: obj << Fit ZI Negative Binomial
Description: Fits a zero-inflated negative binomial distribution to data that contain values of zero.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/CrabSatellites.jmp" );
dt << Distribution( Column( :satell ), Fit ZI Negative Binomial );
Fit ZI Poisson
Syntax: obj << Fit ZI Poisson
Description: Fits a zero-inflated Poisson distribution to data that contain values of zero.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/CrabSatellites.jmp" );
dt << Distribution( Column( :satell ), Fit ZI Poisson );
Fit ZI SHASH
Syntax: obj << Fit ZI SHASH
Description: Fits a SHASH distribution with a point mass at zero to the data.
Random Reset( 18 );
d = J( 250, 1, Random SHASH( 0, 1, 3, 5 ) );
For( i = 1, i <= 250, i++,
If( Random Uniform() < .2,
d[i] = 0
)
);
As Table( d );
Column( 1 ) << set name( "X" );
Distribution( Column( :X ), Fit ZI SHASH, Fit SHASH );
Frequencies
Syntax: obj << Frequencies( state=0|1 )
Description: Shows or hides the Frequencies report, which lists the counts and probabilities for each level. On by default.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
Wait( 1 );
obj << Frequencies( 0 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
Wait( 1 );
obj << Frequencies( 0 );
Histogram
Syntax: obj << Histogram( state=0|1 )
Description: Shows or hides the histogram. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
Wait( 1 );
obj << Histogram( 0 );
Histogram Color
Syntax: obj << Histogram Color( color )
Description: Changes the color of the histogram bars.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Histogram Color( "Red" );
Horizontal Layout
Syntax: obj << Horizontal Layout( state=0|1 )
Description: Changes the orientation of the histogram and the reports to be horizontal.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Horizontal Layout( 1 );
Mosaic Plot
Syntax: obj << Mosaic Plot( state=0|1 )
Description: Shows or hides a mosaic bar chart for each nominal or ordinal response variable. A mosaic plot is a stacked bar chart where each segment is proportional to its group's frequency count.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Mosaic Plot( 1 );
New JSL Preset
Syntax: New JSL Preset( preset )
Description: For testing purposes, create a preset directly from a JSL expression. Like <<New Preset, it will return a Platform Preset that can be applied using <<Apply Preset. But it allows you to specify the full JSL expression for the preset to test outside of normal operation. You will get an Assert on apply if the platform names do not match, but that is expected.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ) ),
Nominal Distribution( Column( :age ) )
);
preset = obj[1] << New JSL Preset(
Continuous Distribution( Column( :A ), Normal Quantile Plot( 1 ) )
);
Wait( 1 );
obj[1] << Apply Preset( preset );
New Preset
Syntax: obj = New Preset()
Description: Create an anonymous preset representing the options and customizations applied to the object. This object can be passed to Apply Preset to copy the settings to another object of the same type.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ), Normal Quantile Plot( 1 ) ),
Nominal Distribution( Column( :age ), Mosaic Plot( 1 ) )
);
preset = obj[1] << New Preset();
Normal Quantile Plot
Syntax: obj << Normal Quantile Plot( state=0|1 )
Description: Shows or hides a plot that can be used to visualize the extent to which a variable is normally distributed.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Normal Quantile Plot( 1 );
Order By
Syntax: obj << Order By( "Default"|"Count Descending"|"Count Ascending" )
Description: Orders the histogram, mosaic plot, and Frequencies report in ascending or descending order, by count. You can also revert back to the default ordering.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Order By( "Count Descending" );
Outlier Box Plot
Syntax: obj << Outlier Box Plot( state=0|1 )
Description: Shows or hides a box plot that enables you to see the distribution and identify possible outliers. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
Wait( 1 );
obj << Outlier Box Plot( 0 );
Outlier Box Plot Row Cutoff
Syntax: obj << Outlier Box Plot Row Cutoff( number )
Description: Sets the launch option for the maximum number of rows before the outlier box plot is turned off initially. "100000" by default.
dt = Open( "$SAMPLE_DATA/Seasonal Flu.jmp" );
obj = dt << Distribution( Column( :Flu Cases ) );
obj << Outlier Box Plot Row Cutoff( 10000 );
PpK Capability Labeling
Syntax: obj << PpK Capability Labeling( state=0|1 )
Description: In Process Capability output, switches the labeling of the overall capability indices to use the prefix Pp instead of Cp. On by default.
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :PM10 ) );
obj << PpK Capability Labeling( 0 );
obj << Process Capability( LSL( 5 ), Target( 40 ), USL( 75 ) );
Prediction Interval
Syntax: obj << Prediction Interval( Alpha, N Samples, <Lower | Upper> )
Description: Computes the prediction intervals for an individual future observation and the mean of a specified number (N Samples) of future observations. You can create one-sided or two-sided prediction intervals.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Prediction Interval( 0.95, 20 );
Prob Axis
Syntax: obj << Prob Axis( state=0|1 )
Description: Shows or hides the probability or proportion axis for this histogram.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Prob Axis( 1 );
Process Capability
Syntax: obj << Process Capability( LSL( number ), Target( number ), USL( number ) )
Description: Computes a process capability analysis given the lower specification limit (LSL), target, and upper specification limit (USL). The Process Capability report includes a histogram, summary details, capability indices, and nonconformance statistics.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :PM10 ) );
obj << Process Capability( LSL( 5 ), Target( 40 ), USL( 75 ) );
Quantile Box Plot
Syntax: obj << Quantile Box Plot( state=0|1 )
Description: Shows or hides a box plot with the following quantiles: 0%, 0.5%, 2.5%, 10%, 25%, 50%, 75%, 90%, 97.5%, 99%, and 100%.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Outlier Box Plot( 0 );
obj << Quantile Box Plot( 1 );
Quantiles
Syntax: obj << Quantiles( state=0|1 )
Description: Shows or hides the Quantiles report, which lists the values of selected quantiles. By default, the quantiles listed are 0%, 0.5%, 2.5%, 10%, 25%, 50%, 75%, 90%, 97.5%, 99.5%, and 100%. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
Wait( 1 );
obj << Quantiles( 0 );
Render Preset
Syntax: Render Preset( preset )
Description: For testing purposes, show the platform rerun script that would be used when applying a platform preset to the platform in the log. No changes are made to the platform.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ) ),
Nominal Distribution( Column( :age ) )
);
obj[1] << Render Preset(
Expr(
Continuous Distribution( Column( :A ), Normal Quantile Plot( 1 ) )
)
);
Save
Syntax: obj << Save( "Level Numbers"|"Level Midpoints"|"Ranks"|"Ranks averaged"|"Prob Scores"|"Normal Quantiles"|"Standardized"|"Centered"|"Robust Standardized"|"Robust Centered"|"Spec Limits"|"Script to Log" )
Description: Saves the specified observation-specific statistic to a new column in the data table. There is also an option to print the script commands that generate the current report to the log window.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Save( "Ranks" );
Separate Bars
Syntax: obj << Separate Bars( state=0|1 )
Description: Adds space between the bars of the histogram. This option is available only for categorical variables.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Separate Bars( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Separate Bars( 1 );
Set Bin Width
Syntax: obj << Set Bin Width( number )
Description: Sets the histogram bin width, using the axis as the origin. This option is available only for continuous variables.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Set Bin Width( 5 );
Set Quantile Increment
Syntax: obj << Set Quantile Increment( fraction | "revert to default quantiles" )
Description: Sets the increment used in the Quantiles report to the specified fraction or changes back to the default quantiles. This option is available only for continuous variables.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Set Quantile Increment( 0.05 );
Wait( 1 );
obj << Set Quantile Increment( "revert to default quantiles" );
Shadowgram
Syntax: obj << Shadowgram( state=0|1 )
Description: Shows or hides a smooth shadowgram in place of the histogram. A shadowgram overlays histograms with different bin widths. This option is available only for continuous variables.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Shadowgram( 1 );
Show Counts
Syntax: obj << Show Counts( state=0|1 )
Description: Shows or hides the bar counts on the histogram, which give the frequency of column values represented by each histogram bar.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Show Counts( 1 );
Show Percents
Syntax: obj << Show Percents( state=0|1 )
Description: Shows or hides the bar percents on the histogram, which give the percentage of column values represented by each histogram bar.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Show Percents( 1 );
Stack
Syntax: obj << Stack( state=0|1 )
Description: Changes the orientation of the histogram and reports to horizontal and stacks the individual distribution reports vertically.
dt = Open( "$SAMPLE_DATA/Blood Pressure.jmp" );
obj = dt << Distribution( Column( :BP 8M, :BP 12M, :BP 6M, :BP 8W, :BP 12W ) );
obj << Stack( 1 );
Std Error Bars
Syntax: obj << Std Error Bars( state=0|1 )
Description: Shows or hides standard error bars on each of the histogram bars.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Std Error Bars( 1 );
Stem and Leaf
Syntax: obj << Stem and Leaf( state=0|1 )
Description: Shows or hides a stem-and-leaf plot.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Stem and Leaf( 1 );
Summary Statistics
Syntax: obj << Summary Statistics( state=0|1 )
Description: Shows or hides the Summary Statistics report, which lists the mean, standard deviation, and other summary statistics for continuous variables. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
Wait( 1 );
obj << Summary Statistics( 0 );
Test Equivalence
Syntax: obj << Test Equivalence( Target( number ), Practical Difference( number ), <Confidence( fraction )> )
Description: Tests whether the sample mean is equivalent to a hypothesized value (Target) using the Two One-Sided Tests (TOST) approach.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Test Equivalence( Target( 62 ), Practical Difference( 1 ), Confidence( 0.95 ) );
Test Mean
Syntax: obj << Test Mean( number, <Sigma( number )>, < Wilcoxon Signed Rank( 0|1 ) >, <PValue Animation>, <Power Animation> )
Description: Performs a one-sample test for the mean. If you specify a value for the standard deviation (Sigma), a z test is performed. Otherwise, the sample standard deviation is used to perform a t test. There is also an option to perform an additional nonparametric Wilcoxon Signed-Rank test.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Test Mean( 60 );
obj << Test Mean( 60, Sigma( 4 ) );
obj << Test Mean( 60, Wilcoxon Signed Rank( 1 ) );
Test Probabilities
Syntax: obj << Test Probabilities( Test( Hypothesized|Greater than|Less than ), Fix( Hypothesized|Omitted ), p1, <f>, p2, <f>, p3, <f>, etc. )
Description: Tests the estimated probabilities of the levels of a categorical variable against the specified hypothesized probabilities (p1, p2, p3, and so on). For variables with two levels, use the Test option to specify the sign for the alternative hypothesis of the test. For variables with more than two levels, use the Fix option to specify how missing hypothesized values are handled. Note that f is an optional argument that specifies that the preceding level is treated as fixed.
Multiple Levels Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :age ) ) );
obj << Test Probabilities(
Test( Hypothesized ),
0.8,
0.04375,
0.075,
0.04375,
0.01875,
0.01875
);
Two Level, One-Sided Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :sex ) ) );
obj << Test Probabilities( Test( Less than ), 0.5, f, 0.5 );
Two Level, Two-Sided Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :sex ) ) );
obj << Test Probabilities( Test( Hypothesized ), 0.4, f, 0.6, f );
Test Std Dev
Syntax: obj << Test Std Dev( number )
Description: Performs a chi-squared test for the standard deviation, given the hypothesized value (number).
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Test Std Dev( 3 );
Tolerance Interval
Syntax: obj << Tolerance Interval( Alpha(number), Proportion(number), <Lower | Upper>, <Normal|Lognormal|Gamma|Exponential|Weibull|Smallest Extreme Value|Largest Extreme Value|Nonparametric> )
Description: Computes an interval that contains at least a specified portion of the population. A standard normal distribution is assumed. You can also specify other nonnormal distributions including lognormal, Gamma, exponential, Weibull, smallest extreme value, largest extreme value, and nonparametric distributions. There are also options for computing one-sided intervals.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Tolerance Interval( Alpha( 0.95 ), Proportion( 0.85 ) );
obj << Tolerance Interval( Alpha( 0.95 ), Proportion( 0.9 ), Lower );
obj << Tolerance Interval( Alpha( 0.95 ), Proportion( 0.9 ), Upper, Lognormal );
obj << Tolerance Interval( Alpha( 0.95 ), Proportion( 0.8 ), Lower, Nonparametric );
Uniform Scaling
Syntax: obj << Uniform Scaling( state=0|1 )
Description: Sets all histogram axes to the same minimum, maximum, and increment values so that distributions can be easily compared.
dt = Open( "$SAMPLE_DATA/Blood Pressure.jmp" );
obj = dt << Distribution( Column( :BP 8M, :BP 12M, :BP 6M, :BP 8W, :BP 12W ) );
obj << Uniform Scaling( 1 );
Vertical
Syntax: obj << Vertical( state=0|1 )
Description: Changes the orientation of the histogram, box plots, and quantile plots to be vertical. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Vertical( 0 );
Shared Item Messages
Action
Syntax: obj << Action
Description: All-purpose trapdoor within a platform to insert expressions to evaluate. Temporarily sets the DisplayBox and DataTable contexts to the Platform.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Bivariate(
Y( :height ),
X( :weight ),
Action( Distribution( Y( :height, :weight ), Histograms Only ) )
);
Automatic Recalc
Syntax: obj << Automatic Recalc( state=0|1 )
Description: Redoes the analysis automatically for exclude and data changes. If the Automatic Recalc option is turned on, you should consider using Wait(0) commands to ensure that the exclude and data changes take effect before the recalculation.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
obj << Automatic Recalc( 1 );
dt << Select Rows( 5 ) << Exclude( 1 );
Broadcast
Syntax: obj << Broadcast(message)
Description: Broadcasts a message to a platform. If return results from individual objects are tables, they are concatenated if possible, and the final format is identical to either the result from the Save Combined Table option in a Table Box or the result from the Concatenate option using a Source column. Other than those, results are stored in a list and returned.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Quality Control/Diameter.jmp" );
objs = Control Chart Builder(
Variables( Subgroup( :DAY ), Y( :DIAMETER ) ),
By( :OPERATOR )
);
objs[1] << Broadcast( Save Summaries );
Column Switcher
Syntax: obj << Column Switcher(column reference, {column reference, ...}, < Title(title) >, < Close Outline(0|1) >, < Retain Axis Settings(0|1) >, < Layout(0|1) >)
Description: Adds a control panel for changing the platform's variables
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Contingency( Y( :size ), X( :marital status ) );
ColumnSwitcherObject = obj << Column Switcher(
:marital status,
{:sex, :country, :marital status}
);
Copy ByGroup Script
Syntax: obj << Copy ByGroup Script
Description: Create a JSL script to produce this analysis, and put it on the clipboard.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Distribution( Column( :Age, :Weight ), By( _bycol ) );
obj[1] << Copy ByGroup Script;
Copy Script
Syntax: obj << Copy Script
Description: Create a JSL script to produce this analysis, and put it on the clipboard.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
obj << Copy Script;
Data Table Window
Syntax: obj << Data Table Window
Description: Move the data table window for this analysis to the front.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
obj << Data Table Window;
Get By Levels
Syntax: obj << Get By Levels
Description: Returns an associative array mapping the by group columns to their values.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( X( :height ), Y( :weight ), By( :sex ) );
biv << Get By Levels;
Get ByGroup Script
Syntax: obj << Get ByGroup Script
Description: Creates a script (JSL) to produce this analysis and returns it as an expression.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Distribution( Column( :Age, :Weight ), By( _bycol ) );
t = obj[1] << Get ByGroup Script;
Show( t );
Get Container
Syntax: obj << Get Container
Description: Returns a reference to the container box that holds the content for the object.
General
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
t = obj << Get Container;
Show( (t << XPath( "//OutlineBox" )) << Get Title );
Platform with Filter
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
gb = Graph Builder(
Show Control Panel( 0 ),
Variables( X( :height ), Y( :weight ) ),
Elements( Points( X, Y, Legend( 1 ) ), Smoother( X, Y, Legend( 2 ) ) ),
Local Data Filter(
Add Filter(
columns( :age, :sex, :height ),
Where( :age == {12, 13, 14} ),
Where( :sex == "F" ),
Where( :height >= 55 ),
Display( :age, N Items( 6 ) )
)
)
);
New Window( "platform boxes",
H List Box(
Outline Box( "Report(platform)", Report( gb ) << Get Picture ),
Outline Box( "platform << Get Container", (gb << Get Container) << Get Picture )
)
);
Get Data Table
Syntax: obj << Get Data Table
Description: Returns a reference to the data table.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
t = obj << Get Datatable;
Show( N Rows( t ) );
Get Group Platform
Syntax: obj << Get Group Platform
Description: Return the Group Platform object if this platform is part of a Group. Otherwise, returns Empty().
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( Y( :weight ), X( :height ), By( :sex ) );
group = biv[1] << Get Group Platform;
Wait( 1 );
group << Layout( "Arrange in Tabs" );
Get Script
Syntax: obj << Get Script
Description: Creates a script (JSL) to produce this analysis and returns it as an expression.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
t = obj << Get Script;
Show( t );
Get Script With Data Table
Syntax: obj << Get Script With Data Table
Description: Creates a script(JSL) to produce this analysis specifically referencing this data table and returns it as an expression.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
t = obj << Get Script With Data Table;
Show( t );
Get Timing
Syntax: obj << Get Timing
Description: Times the platform launch.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
t = obj << Get Timing;
Show( t );
Get Web Support
Syntax: obj << Get Web Support
Description: Return a number indicating the level of Interactive HTML support for the display object. 1 means some or all elements are supported. 0 means no support.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
s = obj << Get Web Support();
Show( s );
Get Where Expr
Syntax: obj << Get Where Expr
Description: Returns the Where expression for the data subset, if the platform was launched with By() or Where(). Otherwise, returns Empty()
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( X( :height ), Y( :weight ), By( :sex ) );
biv2 = dt << Bivariate( X( :height ), Y( :weight ), Where( :age < 14 & :height > 60 ) );
Show( biv[1] << Get Where Expr, biv2 << Get Where Expr );
Ignore Platform Preferences
Syntax: Ignore Platform Preferences( state=0|1 )
Description: Ignores the current settings of the platform's preferences. The message is ignored when sent to the platform after creation.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Bivariate(
Ignore Platform Preferences( 1 ),
Y( :height ),
X( :weight ),
Action( Distribution( Y( :height, :weight ), Histograms Only ) )
);
Local Data Filter
Syntax: obj << Local Data Filter
Description: To filter data to specific groups or ranges, but local to this platform
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Distribution(
Nominal Distribution( Column( :country ) ),
Local Data Filter(
Add Filter( columns( :sex ), Where( :sex == "Female" ) ),
Mode( Show( 1 ), Include( 1 ) )
)
);
Paste Local Data Filter
Syntax: obj << Paste Local Data Filter
Description: Apply the local data filter from the clipboard to the current report.
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
dist = Distribution( Continuous Distribution( Column( :POP ) ) );
filter = dist << Local Data Filter(
Add Filter( columns( :Region ), Where( :Region == "MW" ) )
);
filter << Copy Local Data Filter;
dist2 = Distribution( Continuous Distribution( Column( :Lead ) ) );
Wait( 1 );
dist2 << Paste Local Data Filter;
Redo Analysis
Syntax: obj << Redo Analysis
Description: Rerun this same analysis in a new window. The analysis will be different if the data has changed.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
obj << Redo Analysis;
Redo ByGroup Analysis
Syntax: obj << Redo ByGroup Analysis
Description: Rerun this same analysis in a new window. The analysis will be different if the data has changed.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Distribution( Column( :Age, :Weight ), By( _bycol ) );
obj[1] << Redo ByGroup Analysis;
Relaunch Analysis
Syntax: obj << Relaunch Analysis
Description: Opens the platform launch window and recalls the settings that were used to create the report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
obj << Relaunch Analysis;
Relaunch ByGroup
Syntax: obj << Relaunch ByGroup
Description: Opens the platform launch window and recalls the settings that were used to create the report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Distribution( Column( :Age, :Weight ), By( _bycol ) );
obj[1] << Relaunch ByGroup;
Remove Column Switcher
Syntax: obj << Remove Column Switcher
Description: Removes the most recent Column Switcher that has been added to the platform.
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Contingency( Y( :size ), X( :marital status ) );
ColumnSwitcherObject = obj << Column Switcher(
:marital status,
{:sex, :country, :marital status}
);
Wait( 2 );
obj << Remove Column Switcher;
Remove Local Data Filter
Syntax: obj << Remove Local Data Filter
Description: If a local data filter has been created, this removes it and restores the platform to use all the data in the data table directly
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dist = dt << Distribution(
Nominal Distribution( Column( :country ) ),
Local Data Filter(
Add Filter( columns( :sex ), Where( :sex == "Female" ) ),
Mode( Show( 1 ), Include( 1 ) )
)
);
Wait( 2 );
dist << remove local data filter;
Report
Syntax: obj << Report;Report( obj )
Description: Returns a reference to the report object.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
r = obj << Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );
Report View
Syntax: obj << Report View( "Full"|"Summary" )
Description: The report view determines the level of detail visible in a platform report. Full shows all of the detail, while Summary shows only select content, dependent on the platform. For customized behavior, display boxes support a <<Set Summary Behavior message.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
obj << Report View( "Summary" );
Save ByGroup Script to Data Table
Syntax: Save ByGroup Script to Data Table( <name>, < <<Append Suffix(0|1)>, < <<Prompt(0|1)>, < <<Replace(0|1)> );
Description: Creates a JSL script to produce this analysis, and save it as a table property in the data table. You can specify a name for the script. The Append Suffix option appends a numeric suffix to the script name, which differentiates the script from an existing script with the same name. The Prompt option prompts the user to specify a script name. The Replace option replaces an existing script with the same name.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Distribution( Column( :Age, :Weight ), By( _bycol ) );
obj[1] << Save ByGroup Script to Data Table;
Save ByGroup Script to Journal
Syntax: obj << Save ByGroup Script to Journal
Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Distribution( Column( :Age, :Weight ), By( _bycol ) );
obj[1] << Save ByGroup Script to Journal;
Save ByGroup Script to Script Window
Syntax: obj << Save ByGroup Script to Script Window
Description: Create a JSL script to produce this analysis, and append it to the current Script text window.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Distribution( Column( :Age, :Weight ), By( _bycol ) );
obj[1] << Save ByGroup Script to Script Window;
Save Script for All Objects
Syntax: obj << Save Script for All Objects
Description: Creates a script for all report objects in the window and appends it to the current Script window. This option is useful when you have multiple reports in the window.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
obj << Save Script for All Objects;
Save Script for All Objects To Data Table
Syntax: obj << Save Script for All Objects To Data Table( <name> )
Description: Saves a script for all report objects to the current data table. This option is useful when you have multiple reports in the window. The script is named after the first platform unless you specify the script name in quotes.
Example 1
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Distribution( Column( :Age, :Weight ), By( _bycol ) );
obj[1] << Save Script for All Objects To Data Table;
Example 2
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Distribution( Column( :Age, :Weight ), By( _bycol ) );
obj[1] << Save Script for All Objects To Data Table( "My Script" );
Save Script to Data Table
Syntax: Save Script to Data Table( <name>, < <<Prompt(0|1)>, < <<Replace(0|1)> );
Description: Create a JSL script to produce this analysis, and save it as a table property in the data table.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
obj << Save Script to Data Table( "My Analysis", <<Prompt( 0 ), <<Replace( 0 ) );
Save Script to Journal
Syntax: obj << Save Script to Journal
Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
obj << Save Script to Journal;
Save Script to Report
Syntax: obj << Save Script to Report
Description: Create a JSL script to produce this analysis, and show it in the report itself. Useful to preserve a printed record of what was done.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
obj << Save Script to Report;
Save Script to Script Window
Syntax: obj << Save Script to Script Window
Description: Create a JSL script to produce this analysis, and append it to the current Script text window.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
obj << Save Script to Script Window;
SendToByGroup
Syntax: SendToByGroup( {":Column == level"}, command );
Description: Sends platform commands or display customization commands to each level of a by-group.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Distribution(
By( :Sex ),
SendToByGroup(
{:sex == "F"},
Continuous Distribution( Column( :weight ), Normal Quantile Plot( 1 ) )
),
SendToByGroup( {:sex == "M"}, Continuous Distribution( Column( :weight ) ) )
);
SendToEmbeddedScriptable
Syntax: SendToEmbeddedScriptable( Dispatch( "Outline name", "Element name", command );
Description: SendToEmbeddedScriptable restores settings of embedded scriptable objects.
dt = Open( "$SAMPLE_DATA/Reliability/Fan.jmp" );
dt << Life Distribution(
Y( :Time ),
Censor( :Censor ),
Censor Code( 1 ),
<<Fit Weibull,
SendToEmbeddedScriptable(
Dispatch(
{"Statistics", "Parametric Estimate - Weibull", "Profilers", "Density Profiler"},
{1, Confidence Intervals( 0 ), Term Value( Time( 6000, Lock( 0 ), Show( 1 ) ) )}
)
)
);
SendToReport
Syntax: SendToReport( Dispatch( "Outline name", "Element name", Element type, command );
Description: Send To Report is used in tandem with the Dispatch command to customize the appearance of a report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Distribution(
Nominal Distribution( Column( :age ) ),
Continuous Distribution( Column( :weight ) ),
SendToReport( Dispatch( "age", "Distrib Nom Hist", FrameBox, {Frame Size( 178, 318 )} ) )
);
Sync to Data Table Changes
Syntax: obj << Sync to Data Table Changes
Description: Sync with the exclude and data changes that have been made.
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
dist = Distribution( Continuous Distribution( Column( :POP ) ) );
Wait( 1 );
dt << Delete Rows( dt << Get Rows Where( :Region == "W" ) );
dist << Sync To Data Table Changes;
Title
Syntax: obj << Title( "new title" )
Description: Sets the title of the platform.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
obj << Title( "My Platform" );
Top Report
Syntax: obj << Top Report
Description: Returns a reference to the root node in the report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Age, :Weight ) );
r = obj << Top Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );
Transform Column
Syntax: obj = <Platform>(... Transform Column(<name>, Formula(<expression>), [Random Seed(<n>)], [Numeric|Character|Expression], [Continuous|Nominal|Ordinal|Unstructured Text], [column properties]) ...)
Description: Create a transform column in the local context of an object, usually a platform. The transform column is active only for the lifetime of the platform.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Distribution(
Transform Column( "age^2", Format( "Fixed Dec", 5, 0 ), Formula( :age * :age ) ),
Continuous Distribution( Column( :"age^2"n ) )
);
View Web XML
Syntax: obj << View Web XML
Description: Returns the XML code that is used to create the interactive HTML report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
xml = obj << View Web XML;
Window View
Syntax: obj = Distribution(...Window View( "Visible"|"Invisible"|"Private" )...)
Description: Set the type of the window to be created for the report. By default a Visible report window will be created. An Invisible window will not appear on screen, but is discoverable by functions such as Window(). A Private window responds to most window messages but is not discoverable and must be addressed through the report object
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( Window View( "Private" ), Y( :weight ), X( :height ), Fit Line );
eqn = Report( biv )["Linear Fit", Text Edit Box( 1 )] << Get Text;
biv << Close Window;
New Window( "Bivariate Equation",
Outline Box( "Big Class Linear Fit", Text Box( eqn, <<Set Base Font( "Title" ) ) )
);
Capability Analysis
Item Messages
Capability Animation
Syntax: obj << Capability Animation
Description: Opens a separate window that shows an animation of a normal distribution that uses parameters and capability statistics from the current sample.
dt = Open( "$SAMPLE_DATA/Quality Control/Coating.jmp" );
obj = Distribution( Column( :Weight ) );
obj << Capability Analysis( LSL( 16 ), USL( 24 ), Target( 20 ), Capability Animation );
Z Bench
Syntax: obj << Z Bench( state=0|1 )
Description: Shows or hides Z statistics, which are described by AIAG as the number of standard deviation units from the process average to a specification.
dt = Open( "$SAMPLE_DATA/Quality Control/Coating.jmp" );
obj = Distribution( Column( :Weight ) );
obj << Capability Analysis( LSL( 16 ), USL( 24 ), Target( 20 ), Z Bench( 1 ) );
Continuous Distribution
Columns
Column
Syntax: obj = Quantiles(...<Column( column(s) )>...)
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
Wait( 1 );
obj << Quantiles( 0 );
Item Messages
Apply Preset
Syntax: Apply Preset( preset ); Apply Preset( source, label, <Folder( folder {, folder2, ...} )> )
Description: Apply a previously created preset to the object, updating the options and customizations to match the saved settings.
JMP Version Added: 18
Anonymous preset
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ), Normal Quantile Plot( 1 ) ),
Nominal Distribution( Column( :age ), Mosaic Plot( 1 ) )
);
preset = obj[1] << New Preset();
dt2 = Open( "$SAMPLE_DATA/Aircraft Incidents.jmp" );
obj2 = dt2 << Distribution(
Nominal Distribution( Column( :Aircraft Damage ) ),
Continuous Distribution( Column( :Total Minor Injuries ) )
);
Wait( 1 );
obj2[2] << Apply Preset( preset );
Search by name
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ) ),
Nominal Distribution( Column( :age ) )
);
Wait( 1 );
obj[1] << Apply Preset( "Sample Presets", "Check Normality" );
Axes on Left
Syntax: obj << Axes on Left( state=0|1 )
Description: Moves the count, probability, density, and normal quantile plot axes to the left side of a horizontal graph.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ), Horizontal Layout( 1 ), Count Axis( 1 ) );
obj << Axes on Left( 1 );
CDF Plot
Syntax: obj << CDF Plot( state=0|1 )
Description: Shows or hides a plot of the empirical cumulative distribution function.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << CDF Plot( 1 );
Capability Analysis
Syntax: obj << Capability Analysis( LSL( number ), Target( number ), USL( number ) )
Description: Performs a capability analysis given the stated lower specification limit (LSL), target, and upper specification limit (USL).
dt = Open( "$SAMPLE_DATA/Quality Control/Coating.jmp" );
obj = dt << Distribution( Column( :Weight ) );
obj << Capability Analysis( LSL( 16 ), USL( 24 ), Target( 20 ) );
Confidence Interval
Syntax: obj << Confidence Interval( number, <Upper | Lower>, <Sigma( number )> )
Description: Computes the specified confidence intervals around both the mean and the standard deviation. If you specify sigma, the specified value is used to compute the confidence interval around the mean.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Confidence Interval( 0.98 );
obj << Confidence Interval( 0.95, Lower );
obj << Confidence Interval( 0.95, Sigma( 4 ) );
Count Axis
Syntax: obj << Count Axis( state=0|1 )
Description: Shows or hides the count axis for the histogram.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Count Axis( 1 );
Custom Quantiles
Syntax: obj << Custom Quantiles( fraction, [quantile1, quantile2, ... quantileN] )
Description: Creates a report of the quantile rank estimates and a report of the smoothed empirical likelihood quantile estimates for the specified quantiles. Uses the fraction as the confidence level for confidence intervals in both reports.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Custom Quantiles( 0.975, [0.075, 0.1, 0.125, 0.975, 0.99] );
Customize Summary Statistics
Syntax: obj << Customize Summary Statistics(statistic1( state=0|1 ), statistic2( state=0|1 ), ..., statisticN( state=0|1 ), <Set Trimmed Mean Percent(number)>, <Set Alpha Level(number)>)
Description: Customizes the summary statistics that are displayed in the Summary Statistics report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Customize Summary Statistics( N( 0 ), Variance( 1 ), Skewness( 1 ) );
Density Axis
Syntax: obj << Density Axis( state=0|1 )
Description: Shows or hides the density axis for the density curve on the histogram.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Density Axis( 1 );
Fit All
Syntax: obj << Fit All
Description: Compares all possible distributions.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :CO ) );
obj << Fit All;
Fit Beta
Syntax: obj << Fit Beta
Description: Fits a two-parameter beta distribution to data between 0 and 1 (not inclusive).
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :OZONE ) );
obj << Fit Beta;
Fit Beta Binomial
Syntax: obj << Fit Beta Binomial( Sample Size( n | column ) )
Description: Fits a beta binomial distribution given a specified constant sample size or a column that contains the sample sizes. This distribution is a more flexible version of the binomial distribution.
JMP Version Added: 15
Example 1
dt = Open( "$SAMPLE_DATA/Quality Control/Shirts.jmp" );
obj = dt << Distribution( Column( :"# Defects"n ) );
obj << Fit Beta Binomial( Sample Size( 10 ) );
Example 2
dt = Open( "$SAMPLE_DATA/Quality Control/Shirts.jmp" );
obj = dt << Distribution( Column( :"# Defects"n ) );
obj << Fit Beta Binomial( Sample Size( :Box Size ) );
Fit Binomial
Syntax: obj << Fit Binomial( Sample size( n | column ) )
Description: Fits a binomial distribution given a specified constant sample size or a column that contains the sample sizes. This distribution models the total number of successes in n independent trials.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Quality Control/Shirts.jmp" );
obj = dt << Distribution( Column( :"# Defects"n ) );
obj << Fit Binomial( Sample Size( :Box Size ) );
Fit Cauchy
Syntax: obj << Fit Cauchy
Description: Fits a Cauchy distribution to the data. The Cauchy distribution is robust to outliers and is equivalent to a t distribution with one degree of freedom.
JMP Version Added: 15
Random Reset( 15 );
d = J( 75, 1, Random Normal() );
d[1] = 10;
d[2] = 9;
d[3] = 8;
As Table( d );
Column( 1 ) << set name( "X" );
Distribution( Column( :X ), Fit Normal, Fit Cauchy );
Fit ExGaussian
Syntax: obj << Fit ExGaussian
Description: Fits an exponentially modified Gaussian distribution to the data.
Example 1
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Distribution( Column( :Y ) );
obj << Fit ExGaussian;
Example 2
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Distribution( Column( :Y ) );
obj << Fit ExGaussian;
obj << Fit Normal;
obj << Fit Exponential;
Fit Exponential
Syntax: obj << Fit Exponential
Description: Fits an exponential distribution to nonnegative data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :POP ) );
obj << Fit Exponential;
Fit Gamma
Syntax: obj << Fit Gamma
Description: Fits a two-parameter gamma distribution to positive data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :Max deg. F Jan ) );
obj << Fit Gamma;
Fit Handle
Syntax: obj << (Fit Handle[number] << {option}); obj << (Fit Handle["Distribution Name"] << {option})
Description: Array of handles to the fitted distributions. This enables you to send commands to specific distributions that have been fit.
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :CO ) );
obj << Fit Lognormal;
obj << Fit Weibull;
obj << (Fit Handle[2] << Goodness of Fit( 1 ));
obj << (Fit Handle["Lognormal"] << QQ Plot( 1 ));
Fit Johnson
Syntax: obj << Fit Johnson
Description: Fits a Johnson distribution to the data. The most appropriate of the three types of Johnson distributions (Su, Sb, and Sl) is chosen based on the quantiles.
JMP Version Added: 15
Example 1
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Distribution( Column( :Y ) );
obj << Fit Johnson;
Example 2
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :CO ) );
obj << Fit Johnson;
Fit Largest Extreme Value
Syntax: obj << Fit Largest Extreme Value
Description: Fits a largest extreme value distribution to the data.
JMP Version Added: 19
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :NO ) );
obj << Fit Largest Extreme Value;
Fit Lognormal
Syntax: obj << Fit Lognormal
Description: Fits a lognormal distribution to positive data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :CO ) );
obj << Fit Lognormal;
Fit Negative Binomial
Syntax: obj << Fit Negative Binomial
Description: Fits a negative binomial distribution to the data. This distribution is equivalent to the Gamma Poisson distribution.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Quality Control/Airport.jmp" );
obj = dt << Distribution( Column( :Delay ) );
obj << Fit Negative Binomial;
Fit Normal
Syntax: obj << Fit Normal
Description: Fits a normal distribution to the data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Weight ) );
obj << Fit Normal;
Fit Normal 2 Mixture
Syntax: obj << Fit Normal 2 Mixture
Description: Fits a mixture of two normal distributions. This distribution is capable of fitting bimodal data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cytometry.jmp" );
obj = dt << Distribution( Column( :CD8 ) );
obj << Fit Normal 2 Mixture;
Fit Normal 3 Mixture
Syntax: obj << Fit Normal 3 Mixture
Description: Fits a mixture of three normal distributions. This distribution is capable of fitting multi-modal data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cytometry.jmp" );
obj = dt << Distribution( Column( :CD8 ) );
obj << Fit Normal 3 Mixture;
Fit Poisson
Syntax: obj << Fit Poisson
Description: Fits a Poisson distribution to the data. This distribution is a popular choice for count data. The fitted mean of the Poisson distribution is equal to the variance.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Quality Control/Airport.jmp" );
obj = dt << Distribution( Column( :Delay ) );
obj << Fit Poisson;
Fit SHASH
Syntax: obj << Fit Shash
Description: Fits a sinh-arcsinh (SHASH) distribution to the data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :CO ) );
obj << Fit Shash;
Fit Smallest Extreme Value
Syntax: obj << Fit Smallest Extreme Value
Description: Fits a smallest extreme value distribution to the data.
JMP Version Added: 19
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :NO ) );
obj << Fit Smallest Extreme Value;
Fit Smooth Curve
Syntax: obj << Fit Smooth Curve( <Bandwidth( number )> )
Description: Fits a smooth curve to the data using nonparametric density estimation. You can set the smoothness by specifying the bandwidth.
JMP Version Added: 15
Example 1
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :SO2 ) );
obj << Fit Smooth Curve;
Example 2
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :SO2 ) );
obj << Fit Smooth Curve( Bandwidth( 0.02 ) );
Fit Student's t
Syntax: obj << Fit Student's t
Description: Fits a Student's t distribution to the data. This distribution is a robust option that spans the space between a normal distribution and a Cauchy distribution.
JMP Version Added: 16
Random Reset( 15 );
d = J( 75, 1, Random Normal() );
d[1] = 10;
d[2] = 9;
d[3] = 8;
As Table( d );
Column( 1 ) << set name( "X" );
Distribution( Column( :X ), Fit Normal, Fit Student's t );
Fit Weibull
Syntax: obj << Fit Weibull
Description: Fits a two-parameter Weibull distribution to positive data.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :NO ) );
obj << Fit Weibull;
Fit ZI Beta Binomial
Syntax: obj << Fit ZI Beta Binomial( Sample Size( n | column ) )
Description: Fits a zero inflated beta binomial distribution given the specified constant sample size or a column that contains the sample size. This distribution models the total number of successes in n independent trials where there are more zeros observed than would be expected for the beta binomial distribution.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Quality Control/Shirts.jmp" );
obj = dt << Distribution( Column( :"# Defects"n ) );
obj << Fit ZI Beta Binomial( Sample Size( :Box Size ) );
Fit ZI Binomial
Syntax: obj << Fit ZI Binomial( Sample Size( n | column ) )
Description: Fits a zero inflated binomial distribution given the specified constant sample size or a column that contains the sample size. This distribution models the total number of successes in n independent trials where there are more zeros observed than would be expected for the binomial distribution.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Quality Control/Shirts.jmp" );
obj = dt << Distribution( Column( :"# Defects"n ) );
obj << Fit ZI Binomial( Sample Size( :Box Size ) );
Fit ZI Negative Binomial
Syntax: obj << Fit ZI Negative Binomial
Description: Fits a zero-inflated negative binomial distribution to data that contain values of zero.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/CrabSatellites.jmp" );
dt << Distribution( Column( :satell ), Fit ZI Negative Binomial );
Fit ZI Poisson
Syntax: obj << Fit ZI Poisson
Description: Fits a zero-inflated Poisson distribution to data that contain values of zero.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/CrabSatellites.jmp" );
dt << Distribution( Column( :satell ), Fit ZI Poisson );
Fit ZI SHASH
Syntax: obj << Fit ZI SHASH
Description: Fits a SHASH distribution with a point mass at zero to the data.
Random Reset( 18 );
d = J( 250, 1, Random SHASH( 0, 1, 3, 5 ) );
For( i = 1, i <= 250, i++,
If( Random Uniform() < .2,
d[i] = 0
)
);
As Table( d );
Column( 1 ) << set name( "X" );
Distribution( Column( :X ), Fit ZI SHASH, Fit SHASH );
Histogram
Syntax: obj << Histogram( state=0|1 )
Description: Shows or hides the histogram. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
Wait( 1 );
obj << Histogram( 0 );
Histogram Color
Syntax: obj << Histogram Color( color )
Description: Changes the color of the histogram bars.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Histogram Color( "Red" );
Horizontal Layout
Syntax: obj << Horizontal Layout( state=0|1 )
Description: Changes the orientation of the histogram and the reports to be horizontal.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Horizontal Layout( 1 );
New JSL Preset
Syntax: New JSL Preset( preset )
Description: For testing purposes, create a preset directly from a JSL expression. Like <<New Preset, it will return a Platform Preset that can be applied using <<Apply Preset. But it allows you to specify the full JSL expression for the preset to test outside of normal operation. You will get an Assert on apply if the platform names do not match, but that is expected.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ) ),
Nominal Distribution( Column( :age ) )
);
preset = obj[1] << New JSL Preset(
Continuous Distribution( Column( :A ), Normal Quantile Plot( 1 ) )
);
Wait( 1 );
obj[1] << Apply Preset( preset );
New Preset
Syntax: obj = New Preset()
Description: Create an anonymous preset representing the options and customizations applied to the object. This object can be passed to Apply Preset to copy the settings to another object of the same type.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ), Normal Quantile Plot( 1 ) ),
Nominal Distribution( Column( :age ), Mosaic Plot( 1 ) )
);
preset = obj[1] << New Preset();
Normal Quantile Plot
Syntax: obj << Normal Quantile Plot( state=0|1 )
Description: Shows or hides a plot that can be used to visualize the extent to which a variable is normally distributed.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Normal Quantile Plot( 1 );
Outlier Box Plot
Syntax: obj << Outlier Box Plot( state=0|1 )
Description: Shows or hides a box plot that enables you to see the distribution and identify possible outliers. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
Wait( 1 );
obj << Outlier Box Plot( 0 );
Outlier Box Plot Row Cutoff
Syntax: obj << Outlier Box Plot Row Cutoff( number )
Description: Sets the launch option for the maximum number of rows before the outlier box plot is turned off initially. "100000" by default.
dt = Open( "$SAMPLE_DATA/Seasonal Flu.jmp" );
obj = dt << Distribution( Column( :Flu Cases ) );
obj << Outlier Box Plot Row Cutoff( 10000 );
PpK Capability Labeling
Syntax: obj << PpK Capability Labeling( state=0|1 )
Description: In Process Capability output, switches the labeling of the overall capability indices to use the prefix Pp instead of Cp. On by default.
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :PM10 ) );
obj << PpK Capability Labeling( 0 );
obj << Process Capability( LSL( 5 ), Target( 40 ), USL( 75 ) );
Prediction Interval
Syntax: obj << Prediction Interval( Alpha, N Samples, <Lower | Upper> )
Description: Computes the prediction intervals for an individual future observation and the mean of a specified number (N Samples) of future observations. You can create one-sided or two-sided prediction intervals.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Prediction Interval( 0.95, 20 );
Prob Axis
Syntax: obj << Prob Axis( state=0|1 )
Description: Shows or hides the probability or proportion axis for this histogram.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Prob Axis( 1 );
Process Capability
Syntax: obj << Process Capability( LSL( number ), Target( number ), USL( number ) )
Description: Computes a process capability analysis given the lower specification limit (LSL), target, and upper specification limit (USL). The Process Capability report includes a histogram, summary details, capability indices, and nonconformance statistics.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :PM10 ) );
obj << Process Capability( LSL( 5 ), Target( 40 ), USL( 75 ) );
Quantile Box Plot
Syntax: obj << Quantile Box Plot( state=0|1 )
Description: Shows or hides a box plot with the following quantiles: 0%, 0.5%, 2.5%, 10%, 25%, 50%, 75%, 90%, 97.5%, 99%, and 100%.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Outlier Box Plot( 0 );
obj << Quantile Box Plot( 1 );
Quantiles
Syntax: obj << Quantiles( state=0|1 )
Description: Shows or hides the Quantiles report, which lists the values of selected quantiles. By default, the quantiles listed are 0%, 0.5%, 2.5%, 10%, 25%, 50%, 75%, 90%, 97.5%, 99.5%, and 100%. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
Wait( 1 );
obj << Quantiles( 0 );
Render Preset
Syntax: Render Preset( preset )
Description: For testing purposes, show the platform rerun script that would be used when applying a platform preset to the platform in the log. No changes are made to the platform.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ) ),
Nominal Distribution( Column( :age ) )
);
obj[1] << Render Preset(
Expr(
Continuous Distribution( Column( :A ), Normal Quantile Plot( 1 ) )
)
);
Save
Syntax: obj << Save( "Level Numbers"|"Level Midpoints"|"Ranks"|"Ranks averaged"|"Prob Scores"|"Normal Quantiles"|"Standardized"|"Centered"|"Robust Standardized"|"Robust Centered"|"Spec Limits"|"Script to Log" )
Description: Saves the specified observation-specific statistic to a new column in the data table. There is also an option to print the script commands that generate the current report to the log window.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Save( "Ranks" );
Set Bin Width
Syntax: obj << Set Bin Width( number )
Description: Sets the histogram bin width, using the axis as the origin. This option is available only for continuous variables.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Set Bin Width( 5 );
Set Quantile Increment
Syntax: obj << Set Quantile Increment( fraction | "revert to default quantiles" )
Description: Sets the increment used in the Quantiles report to the specified fraction or changes back to the default quantiles. This option is available only for continuous variables.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Set Quantile Increment( 0.05 );
Wait( 1 );
obj << Set Quantile Increment( "revert to default quantiles" );
Shadowgram
Syntax: obj << Shadowgram( state=0|1 )
Description: Shows or hides a smooth shadowgram in place of the histogram. A shadowgram overlays histograms with different bin widths. This option is available only for continuous variables.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Shadowgram( 1 );
Show Counts
Syntax: obj << Show Counts( state=0|1 )
Description: Shows or hides the bar counts on the histogram, which give the frequency of column values represented by each histogram bar.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Show Counts( 1 );
Show Percents
Syntax: obj << Show Percents( state=0|1 )
Description: Shows or hides the bar percents on the histogram, which give the percentage of column values represented by each histogram bar.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Show Percents( 1 );
Std Error Bars
Syntax: obj << Std Error Bars( state=0|1 )
Description: Shows or hides standard error bars on each of the histogram bars.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Std Error Bars( 1 );
Stem and Leaf
Syntax: obj << Stem and Leaf( state=0|1 )
Description: Shows or hides a stem-and-leaf plot.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Stem and Leaf( 1 );
Summary Statistics
Syntax: obj << Summary Statistics( state=0|1 )
Description: Shows or hides the Summary Statistics report, which lists the mean, standard deviation, and other summary statistics for continuous variables. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
Wait( 1 );
obj << Summary Statistics( 0 );
Test Equivalence
Syntax: obj << Test Equivalence( Target( number ), Practical Difference( number ), <Confidence( fraction )> )
Description: Tests whether the sample mean is equivalent to a hypothesized value (Target) using the Two One-Sided Tests (TOST) approach.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Test Equivalence( Target( 62 ), Practical Difference( 1 ), Confidence( 0.95 ) );
Test Mean
Syntax: obj << Test Mean( number, <Sigma( number )>, < Wilcoxon Signed Rank( 0|1 ) >, <PValue Animation>, <Power Animation> )
Description: Performs a one-sample test for the mean. If you specify a value for the standard deviation (Sigma), a z test is performed. Otherwise, the sample standard deviation is used to perform a t test. There is also an option to perform an additional nonparametric Wilcoxon Signed-Rank test.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Test Mean( 60 );
obj << Test Mean( 60, Sigma( 4 ) );
obj << Test Mean( 60, Wilcoxon Signed Rank( 1 ) );
Test Std Dev
Syntax: obj << Test Std Dev( number )
Description: Performs a chi-squared test for the standard deviation, given the hypothesized value (number).
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Test Std Dev( 3 );
Tolerance Interval
Syntax: obj << Tolerance Interval( Alpha(number), Proportion(number), <Lower | Upper>, <Normal|Lognormal|Gamma|Exponential|Weibull|Smallest Extreme Value|Largest Extreme Value|Nonparametric> )
Description: Computes an interval that contains at least a specified portion of the population. A standard normal distribution is assumed. You can also specify other nonnormal distributions including lognormal, Gamma, exponential, Weibull, smallest extreme value, largest extreme value, and nonparametric distributions. There are also options for computing one-sided intervals.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Tolerance Interval( Alpha( 0.95 ), Proportion( 0.85 ) );
obj << Tolerance Interval( Alpha( 0.95 ), Proportion( 0.9 ), Lower );
obj << Tolerance Interval( Alpha( 0.95 ), Proportion( 0.9 ), Upper, Lognormal );
obj << Tolerance Interval( Alpha( 0.95 ), Proportion( 0.8 ), Lower, Nonparametric );
Vertical
Syntax: obj << Vertical( state=0|1 )
Description: Changes the orientation of the histogram, box plots, and quantile plots to be vertical. On by default.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Vertical( 0 );
Distribution Fit
Item Messages
Density Curve
Syntax: obj << Fit Distribution Name( Density Curve( state=0|1 ) ); obj << ( Fit Handle[number] << Density Curve( state=0|1 ))
Description: Shows or hides a density curve on the histogram. The estimated parameters from the specified fit are used to create the density curve. On by default.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :CO ) );
obj << Fit Lognormal( Density Curve( 0 ) );
Distribution Profiler
Syntax: obj << Fit Distribution Name( Distribution Profiler( state=0|1 ) ); obj << ( Fit Handle[number] << Distribution Profiler( state=0|1 ) )
Description: Shows or hides a prediction profiler of the cumulative distribution function for the specified fitted distribution.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :CO ) );
obj << Fit Lognormal( Distribution Profiler( 1 ) );
Fitted CDF
Syntax: obj << Fit Distribution Name( Fitted CDF( vector )); obj << ( Fit Handle[number] << Fitted CDF( vector ))
Description: Shows or hides the specified fitted probabilities for the fitted distribution.
JMP Version Added: 19
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :CO ) );
obj << Fit Lognormal( Fitted CDF( [5 8 11] ) );
Fitted Quantiles
Syntax: obj << Fit Distribution Name( Fitted Quantiles( vector )); obj << ( Fit Handle[number] << Fitted Quantiles( vector ))
Description: Shows or hides the specified quantiles for the specified fitted distribution.
JMP Version Added: 19
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :CO ) );
obj << Fit Lognormal( Fitted Quantiles( [.9 .95 .99] ) );
Fix Parameters
Syntax: obj << Fit Distribution Name( Fix Parameters( vector )); obj << ( Fit Handle[number] << Fix Parameters( vector ))
Description: Fixes the specified parameters as constants and re-estimates the non-fixed parameters.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :CO ) );
obj << Fit Normal( Fix Parameters( [. 2.8] ) );
Goodness of Fit
Syntax: obj << Fit Distribution Name( Goodness of Fit( state=0|1 )); obj << ( Fit Handle[number] << Goodness of Fit( state=0|1 ))
Description: Shows or hides a report that contains a goodness-of-fit test for the specified fitted distribution.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :CO ) );
obj << Fit Lognormal( Goodness of Fit( 1 ) );
PP Plot
Syntax: obj << Fit Distribution Name( PP Plot( state=0|1 ) ); obj << (Fit Handle[ number ] << PP Plot( state=0|1 ) )
Description: Shows or hides a percentile-percentile (PP) plot that shows the relationship between the empirical cumulative distribution function (CDF) and the CDF of the specified fitted distribution.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Distribution( Column( :Y ) );
obj << Fit Gamma( PP Plot( 1 ) );
Process Capability
Syntax: obj << Fit Distribution Name( Process Capability( LSL( number ), Target( number ), USL( number ))); obj << (Fit Handle[number] << ( Process Capability( LSL( number ), Target( number ), USL( number ))))
Description: Computes a process capability analysis given the lower specification limit (LSL), target, and upper specification limit (USL). The Process Capability report includes a histogram, summary details, capability indices, and nonconformance statistics.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = dt << Distribution( Column( :OZONE ) );
obj << Fit Lognormal( Process Capability( LSL( .03 ), Target( .15 ), USL( .27 ) ) );
QQ Plot
Syntax: obj << Fit Distribution Name( QQ Plot( state=0|1 ) ); obj << ( Fit Handle[number] << QQ Plot( state=0|1 ) )
Description: Shows or hides a quantile-quantile (QQ) plot that shows the relationship between the observed data and the quantiles of the specified fitted distribution.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Distribution( Column( :Y ) );
obj << Fit Gamma( QQ Plot( 1 ) );
Quantile Profiler
Syntax: obj << Fit Distribution Name( Quantile Profiler( state=0|1 ) ); obj << ( Fit Handle[number] << Quantile Profiler( state=0|1 ) )
Description: Shows or hides a prediction profiler of the quantile function for the specified fitted distribution.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :CO ) );
obj << Fit Lognormal( Quantile Profiler( 1 ) );
Remove Fit
Syntax: obj << (Fit Handle[number] << Remove Fit )
Description: Removes the fit and JSL object of the specified distribution.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :CO ) );
obj << Fit Weibull;
obj << Fit Lognormal;
Wait( 1 );
obj << (Fit Handle[1] << Remove Fit);
Save Density Formula
Syntax: obj << Fit Distribution Name( Save Density Formula ) ; obj << ( Fit Handle[number] << Save Density Formula )
Description: Saves a column to the data table that contains the density formula of the specified fitted distribution.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :CO ) );
obj << Fit Lognormal( Save Density Formula );
Save Distribution Formula
Syntax: obj << Fit Distribution Name( Save Distribution Formula ) ; obj << ( Fit Handle[number] << Save Distribution Formula )
Description: Saves a column to the data table that contains the cumulative distribution function of the specified fitted distribution.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :CO ) );
obj << Fit Lognormal( Save Distribution Formula );
Save Simulation Formula
Syntax: obj << Fit Distribution Name( Save Simulation Formula ) ; obj << ( Fit Handle[number] << Save Simulation Formula )
Description: Saves a column to the data table that contains a formula that generates simulated values from the specified fitted distribution.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :CO ) );
obj << Fit Lognormal( Save Simulation Formula );
Save Transformed
Syntax: obj << Fit Distribution Name( Save Transformed ); obj << ( Fit Handle[number] << Save Transformed )
Description: Saves a column to the data table that contains a formula used to transform the analysis column to normality using the specified fitted distribution.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :CO ) );
obj << Fit Shash( Save Transformed );
Distribution Process Capability
Item Messages
Color Out of Spec Values
Syntax: obj << Color Out of Spec Values
Description: Colors the cells in the data table for values that are out of spec. Cells with values below the lower specification limit (LSL) are colored red, and cells with values above the upper specification limit (USL) are colored blue.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :OZONE ) );
obj << Process Capability(
LSL( 0.12 ),
Target( 0.18 ),
USL( 0.24 ),
Color Out of Spec Values
);
Save Distribution as a Column Property
Syntax: obj << Process Capability( Save Distribution as a Column Property )
Description: Saves the Process Capability Distribution type as a column property within the column of the original data table.
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :OZONE ) );
obj << Process Capability(
LSL( 0.03 ),
Target( 0.15 ),
USL( 0.27 ),
Dist( Lognormal ),
Save Distribution as a Column Property
);
Save In Spec Indicator Formula
Syntax: obj << Save In Spec Indicator Formula
Description: Creates a new formula column in the data table. The new column contains a value that indicates whether or not a row is within the specification limits.
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :OZONE ) );
obj << Process Capability(
LSL( 0.12 ),
Target( 0.18 ),
USL( 0.24 ),
Save In Spec Indicator Formula
);
Save Spec Limits and Distribution to Column Properties without Report
Syntax: obj << Fit Distribution Name( Process Capability(Save Spec Limits and Distribution to Column Properties without Report))
Description: Saves the calculated specification limits and process capability distribution type for the fitted distribution as column properties within the column of the original data table and shows no capability report.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :OZONE ) );
obj << Fit Lognormal(
Process Capability(
Set Sigma Multiplier for Quantile Spec Limits( 4 ),
Save Spec Limits and Distribution to Column Properties without Report
)
);
Save Spec Limits as a Column Property
Syntax: obj << Fit Distribution Name( Process Capability( Save Spec Limits as a Column Property )); obj << Process Capability( Save Spec Limits as a Column Property )
Description: Saves the specification limits as a column property within the column of the original data table.
JMP Version Added: 15
Example 1
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :OZONE ) );
obj << Fit Lognormal(
Process Capability(
LSL( 0.03 ),
Target( 0.15 ),
USL( 0.27 ),
Save Spec Limits as a Column Property
)
);
Example 2
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :OZONE ) );
obj << Process Capability(
LSL( 0.03 ),
Target( 0.15 ),
USL( 0.27 ),
Save Spec Limits as a Column Property
);
Set Probabilities for Quantile Spec Limits
Syntax: obj << Fit Distribution Name( Process Capability(Set Probabilties for Quantile Spec Limits( LSL Prob(p1), Target Prob(p2), USL Prob(p3)))); obj << Process Capability(Set Probabilties for Quantile Spec Limits( LSL Prob(p1), Target Prob(p2), USL Prob(p3)))
Description: Sets the probabilities that are used to calculate the quantile specification limits for the fitted distribution.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :OZONE ) );
obj << Fit Lognormal(
Process Capability(
Set Probabilities for Quantile Spec Limits(
LSL Prob( .0001 ),
Target Prob( .5 ),
USL Prob( .9999 )
)
)
);
Set Sigma Multiplier for Quantile Spec Limits
Syntax: obj << Fit Distribution Name( Process Capability(Set Sigma Multiplier for Quantile Spec Limits(K, <sided=1|2>))); obj << Process Capability(Set Sigma Multiplier for Quantile Spec Limits(K, <sided=1|2>))
Description: Sets a sigma multiplier, K, that is used to calculate the quantile specification limits for the fitted distribution. The optional sided argument equals 1 for LSL only or 2 for USL only.
JMP Version Added: 15
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
obj = Distribution( Column( :OZONE ) );
obj << Fit Lognormal(
Process Capability( Set Sigma Multiplier for Quantile Spec Limits( 4 ) )
);
Distribution Summary Statistics
Item Messages
Customize Summary Statistics
Syntax: obj << Customize Summary Statistics(statistic1( state=0|1 ), statistic2( state=0|1 ), ..., statisticN( state=0|1 ), <Set Trimmed Mean Percent(number)>, <Set Alpha Level(number)>)
Description: Customizes the summary statistics that are displayed in the Summary Statistics report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Distribution( Column( :Height ) );
obj << Customize Summary Statistics( N( 0 ), Variance( 1 ), Skewness( 1 ) );
Show All Modes
Syntax: obj << Customize Summary Statistics( Show all Modes( state=0|1 ))
Description: Shows or hides all modes in the Summary Statistics report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Distribution( Column( :Height ) );
obj << Customize Summary Statistics( Mode( 1 ), Show All Modes( 1 ) );
Multiple Response Distribution
Item Messages
Apply Preset
Syntax: Apply Preset( preset ); Apply Preset( source, label, <Folder( folder {, folder2, ...} )> )
Description: Apply a previously created preset to the object, updating the options and customizations to match the saved settings.
JMP Version Added: 18
Anonymous preset
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ), Normal Quantile Plot( 1 ) ),
Nominal Distribution( Column( :age ), Mosaic Plot( 1 ) )
);
preset = obj[1] << New Preset();
dt2 = Open( "$SAMPLE_DATA/Aircraft Incidents.jmp" );
obj2 = dt2 << Distribution(
Nominal Distribution( Column( :Aircraft Damage ) ),
Continuous Distribution( Column( :Total Minor Injuries ) )
);
Wait( 1 );
obj2[2] << Apply Preset( preset );
Search by name
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ) ),
Nominal Distribution( Column( :age ) )
);
Wait( 1 );
obj[1] << Apply Preset( "Sample Presets", "Check Normality" );
Axes on Left
Syntax: obj << Axes on Left( state=0|1 )
Description: Moves the count, probability, density, and normal quantile plot axes to the left side of a horizontal graph.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution(
Multiple Response Distribution(
Column( :Brush Delimited ),
Horizontal Layout( 1 ),
Count Axis( 1 )
)
);
obj << Axes on Left( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Nominal Distribution( Column( :Age ), Horizontal Layout( 1 ), Count Axis( 1 ) )
);
obj << Axes on Left( 1 );
Confidence Interval
Syntax: obj << Confidence Interval( "0.90"|"0.95"|"0.99"|"Other…" )
Description: Computes score confidence intervals about the probabilities.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Confidence Interval( 0.95 );
Count Axis
Syntax: obj << Count Axis( state=0|1 )
Description: Shows or hides the count axis for the histogram.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Count Axis( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Count Axis( 1 );
Density Axis
Syntax: obj << Density Axis( state=0|1 )
Description: Shows or hides the density axis for the density curve on the histogram.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Density Axis( 1 );
Frequencies
Syntax: obj << Frequencies( state=0|1 )
Description: Shows or hides the Frequencies report, which lists the counts and probabilities for each level. On by default.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
Wait( 1 );
obj << Frequencies( 0 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
Wait( 1 );
obj << Frequencies( 0 );
Histogram
Syntax: obj << Histogram( state=0|1 )
Description: Shows or hides the histogram. On by default.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
Wait( 1 );
obj << Histogram( 0 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
Wait( 1 );
obj << Histogram( 0 );
Histogram Color
Syntax: obj << Histogram Color( color )
Description: Changes the color of the histogram bars.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Histogram Color( "Blue" );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Histogram Color( "Red" );
Horizontal Layout
Syntax: obj << Horizontal Layout( state=0|1 )
Description: Changes the orientation of the histogram and the reports to be horizontal.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Horizontal Layout( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Horizontal Layout( 1 );
Mosaic Plot
Syntax: obj << Mosaic Plot( state=0|1 )
Description: Shows or hides a mosaic bar chart for each nominal or ordinal response variable. A mosaic plot is a stacked bar chart where each segment is proportional to its group's frequency count.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Mosaic Plot( 1 );
New JSL Preset
Syntax: New JSL Preset( preset )
Description: For testing purposes, create a preset directly from a JSL expression. Like <<New Preset, it will return a Platform Preset that can be applied using <<Apply Preset. But it allows you to specify the full JSL expression for the preset to test outside of normal operation. You will get an Assert on apply if the platform names do not match, but that is expected.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ) ),
Nominal Distribution( Column( :age ) )
);
preset = obj[1] << New JSL Preset(
Continuous Distribution( Column( :A ), Normal Quantile Plot( 1 ) )
);
Wait( 1 );
obj[1] << Apply Preset( preset );
New Preset
Syntax: obj = New Preset()
Description: Create an anonymous preset representing the options and customizations applied to the object. This object can be passed to Apply Preset to copy the settings to another object of the same type.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ), Normal Quantile Plot( 1 ) ),
Nominal Distribution( Column( :age ), Mosaic Plot( 1 ) )
);
preset = obj[1] << New Preset();
Order By
Syntax: obj << Order By( "Default"|"Count Descending"|"Count Ascending" )
Description: Orders the histogram, mosaic plot, and Frequencies report in ascending or descending order, by count. You can also revert back to the default ordering.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Order By( "Count Descending" );
Prob Axis
Syntax: obj << Prob Axis( state=0|1 )
Description: Shows or hides the probability or proportion axis for this histogram.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Prob Axis( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Prob Axis( 1 );
Render Preset
Syntax: Render Preset( preset )
Description: For testing purposes, show the platform rerun script that would be used when applying a platform preset to the platform in the log. No changes are made to the platform.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ) ),
Nominal Distribution( Column( :age ) )
);
obj[1] << Render Preset(
Expr(
Continuous Distribution( Column( :A ), Normal Quantile Plot( 1 ) )
)
);
Save
Syntax: obj << Save( "Level Numbers"|"Value Ordering"|"Script to Log" )
Description: Saves the Level Numbers to a new column in the data table or the script to the log.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Save( "Level Numbers" );
Separate Bars
Syntax: obj << Separate Bars( state=0|1 )
Description: Adds space between the bars of the histogram. This option is available only for categorical variables.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Separate Bars( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Separate Bars( 1 );
Show Counts
Syntax: obj << Show Counts( state=0|1 )
Description: Shows or hides the bar counts on the histogram, which give the frequency of column values represented by each histogram bar.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Show Counts( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Show Counts( 1 );
Show Percents
Syntax: obj << Show Percents( state=0|1 )
Description: Shows or hides the bar percents on the histogram, which give the percentage of column values represented by each histogram bar.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Show Percents( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Show Percents( 1 );
Std Error Bars
Syntax: obj << Std Error Bars( state=0|1 )
Description: Shows or hides standard error bars on each of the histogram bars.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Std Error Bars( 1 );
Test Probabilities
Syntax: obj << Test Probabilities( Test( Hypothesized|Greater than|Less than ), Fix( Hypothesized|Omitted ), p1, <f>, p2, <f>, p3, <f>, etc. )
Description: Tests the estimated probabilities of the levels of a categorical variable against the specified hypothesized probabilities (p1, p2, p3, and so on). For variables with two levels, use the Test option to specify the sign for the alternative hypothesis of the test. For variables with more than two levels, use the Fix option to specify how missing hypothesized values are handled. Note that f is an optional argument that specifies that the preceding level is treated as fixed.
Multiple Levels Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :age ) ) );
obj << Test Probabilities(
Test( Hypothesized ),
0.8,
0.04375,
0.075,
0.04375,
0.01875,
0.01875
);
Two Level, One-Sided Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :sex ) ) );
obj << Test Probabilities( Test( Less than ), 0.5, f, 0.5 );
Two Level, Two-Sided Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :sex ) ) );
obj << Test Probabilities( Test( Hypothesized ), 0.4, f, 0.6, f );
Vertical
Syntax: obj << Vertical( state=0|1 )
Description: Changes the orientation of the histogram, box plots, and quantile plots to be vertical. On by default.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Vertical( 0 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Vertical( 0 );
Nominal Distribution
Item Messages
Apply Preset
Syntax: Apply Preset( preset ); Apply Preset( source, label, <Folder( folder {, folder2, ...} )> )
Description: Apply a previously created preset to the object, updating the options and customizations to match the saved settings.
JMP Version Added: 18
Anonymous preset
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ), Normal Quantile Plot( 1 ) ),
Nominal Distribution( Column( :age ), Mosaic Plot( 1 ) )
);
preset = obj[1] << New Preset();
dt2 = Open( "$SAMPLE_DATA/Aircraft Incidents.jmp" );
obj2 = dt2 << Distribution(
Nominal Distribution( Column( :Aircraft Damage ) ),
Continuous Distribution( Column( :Total Minor Injuries ) )
);
Wait( 1 );
obj2[2] << Apply Preset( preset );
Search by name
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ) ),
Nominal Distribution( Column( :age ) )
);
Wait( 1 );
obj[1] << Apply Preset( "Sample Presets", "Check Normality" );
Axes on Left
Syntax: obj << Axes on Left( state=0|1 )
Description: Moves the count, probability, density, and normal quantile plot axes to the left side of a horizontal graph.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution(
Multiple Response Distribution(
Column( :Brush Delimited ),
Horizontal Layout( 1 ),
Count Axis( 1 )
)
);
obj << Axes on Left( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Nominal Distribution( Column( :Age ), Horizontal Layout( 1 ), Count Axis( 1 ) )
);
obj << Axes on Left( 1 );
Confidence Interval
Syntax: obj << Confidence Interval( "0.90"|"0.95"|"0.99"|"Other…" )
Description: Computes score confidence intervals about the probabilities.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Confidence Interval( 0.95 );
Count Axis
Syntax: obj << Count Axis( state=0|1 )
Description: Shows or hides the count axis for the histogram.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Count Axis( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Count Axis( 1 );
Density Axis
Syntax: obj << Density Axis( state=0|1 )
Description: Shows or hides the density axis for the density curve on the histogram.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Density Axis( 1 );
Frequencies
Syntax: obj << Frequencies( state=0|1 )
Description: Shows or hides the Frequencies report, which lists the counts and probabilities for each level. On by default.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
Wait( 1 );
obj << Frequencies( 0 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
Wait( 1 );
obj << Frequencies( 0 );
Histogram
Syntax: obj << Histogram( state=0|1 )
Description: Shows or hides the histogram. On by default.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
Wait( 1 );
obj << Histogram( 0 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
Wait( 1 );
obj << Histogram( 0 );
Histogram Color
Syntax: obj << Histogram Color( color )
Description: Changes the color of the histogram bars.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Histogram Color( "Blue" );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Histogram Color( "Red" );
Horizontal Layout
Syntax: obj << Horizontal Layout( state=0|1 )
Description: Changes the orientation of the histogram and the reports to be horizontal.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Horizontal Layout( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Horizontal Layout( 1 );
Mosaic Plot
Syntax: obj << Mosaic Plot( state=0|1 )
Description: Shows or hides a mosaic bar chart for each nominal or ordinal response variable. A mosaic plot is a stacked bar chart where each segment is proportional to its group's frequency count.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Mosaic Plot( 1 );
New JSL Preset
Syntax: New JSL Preset( preset )
Description: For testing purposes, create a preset directly from a JSL expression. Like <<New Preset, it will return a Platform Preset that can be applied using <<Apply Preset. But it allows you to specify the full JSL expression for the preset to test outside of normal operation. You will get an Assert on apply if the platform names do not match, but that is expected.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ) ),
Nominal Distribution( Column( :age ) )
);
preset = obj[1] << New JSL Preset(
Continuous Distribution( Column( :A ), Normal Quantile Plot( 1 ) )
);
Wait( 1 );
obj[1] << Apply Preset( preset );
New Preset
Syntax: obj = New Preset()
Description: Create an anonymous preset representing the options and customizations applied to the object. This object can be passed to Apply Preset to copy the settings to another object of the same type.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ), Normal Quantile Plot( 1 ) ),
Nominal Distribution( Column( :age ), Mosaic Plot( 1 ) )
);
preset = obj[1] << New Preset();
Order By
Syntax: obj << Order By( "Default"|"Count Descending"|"Count Ascending" )
Description: Orders the histogram, mosaic plot, and Frequencies report in ascending or descending order, by count. You can also revert back to the default ordering.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Order By( "Count Descending" );
Prob Axis
Syntax: obj << Prob Axis( state=0|1 )
Description: Shows or hides the probability or proportion axis for this histogram.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Prob Axis( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Prob Axis( 1 );
Render Preset
Syntax: Render Preset( preset )
Description: For testing purposes, show the platform rerun script that would be used when applying a platform preset to the platform in the log. No changes are made to the platform.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution(
Continuous Distribution( Column( :weight ) ),
Nominal Distribution( Column( :age ) )
);
obj[1] << Render Preset(
Expr(
Continuous Distribution( Column( :A ), Normal Quantile Plot( 1 ) )
)
);
Save
Syntax: obj << Save( "Level Numbers"|"Value Ordering"|"Script to Log" )
Description: Saves the Level Numbers to a new column in the data table or the script to the log.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Save( "Level Numbers" );
Separate Bars
Syntax: obj << Separate Bars( state=0|1 )
Description: Adds space between the bars of the histogram. This option is available only for categorical variables.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Separate Bars( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Separate Bars( 1 );
Show Counts
Syntax: obj << Show Counts( state=0|1 )
Description: Shows or hides the bar counts on the histogram, which give the frequency of column values represented by each histogram bar.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Show Counts( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Show Counts( 1 );
Show Percents
Syntax: obj << Show Percents( state=0|1 )
Description: Shows or hides the bar percents on the histogram, which give the percentage of column values represented by each histogram bar.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Show Percents( 1 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Show Percents( 1 );
Std Error Bars
Syntax: obj << Std Error Bars( state=0|1 )
Description: Shows or hides standard error bars on each of the histogram bars.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Std Error Bars( 1 );
Test Probabilities
Syntax: obj << Test Probabilities( Test( Hypothesized|Greater than|Less than ), Fix( Hypothesized|Omitted ), p1, <f>, p2, <f>, p3, <f>, etc. )
Description: Tests the estimated probabilities of the levels of a categorical variable against the specified hypothesized probabilities (p1, p2, p3, and so on). For variables with two levels, use the Test option to specify the sign for the alternative hypothesis of the test. For variables with more than two levels, use the Fix option to specify how missing hypothesized values are handled. Note that f is an optional argument that specifies that the preceding level is treated as fixed.
Multiple Levels Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :age ) ) );
obj << Test Probabilities(
Test( Hypothesized ),
0.8,
0.04375,
0.075,
0.04375,
0.01875,
0.01875
);
Two Level, One-Sided Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :sex ) ) );
obj << Test Probabilities( Test( Less than ), 0.5, f, 0.5 );
Two Level, Two-Sided Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :sex ) ) );
obj << Test Probabilities( Test( Hypothesized ), 0.4, f, 0.6, f );
Vertical
Syntax: obj << Vertical( state=0|1 )
Description: Changes the orientation of the histogram, box plots, and quantile plots to be vertical. On by default.
Multiple Response Distribution Example
dt = Open( "$SAMPLE_DATA/Consumer Preferences.jmp" );
obj = dt << Distribution( Multiple Response Distribution( Column( :Brush Delimited ) ) );
obj << Vertical( 0 );
Nominal Distribution Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Nominal Distribution( Column( :Age ) ) );
obj << Vertical( 0 );
Test Mean
Item Messages
PValue animation
Syntax: obj << Test Mean( PValue Animation )
Description: Opens a separate window that shows an animation of how p-values change as the mean changes.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Distribution( Column( :Height ) );
obj << Test Mean( 60, PValue Animation );
Power animation
Syntax: obj << Test Mean( Power Animation )
Description: Opens a separate window that shows an animation of how the power changes as the mean changes and whether the test is one-sided or two-sided.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Distribution( Column( :Height ) );
obj << Test Mean( 60, Power Animation );
Tolerance Interval
Item Messages
Save Distribution as a Column Property
Syntax: obj << Tolerance Interval( Save Distribution as a Column Property )
Description: Saves the Tolerance Interval Distribution type as a column property within the column of the original data table.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = Distribution( Column( :Height ) );
obj << Tolerance Interval(
Alpha( 0.95 ),
Proportion( 0.90 ),
Lognormal,
Save Distribution as a Column Property
);
Save to Spec Limits Column Property
Syntax: obj << Save to Spec Limits Column Property( Alpha(number), Proportion(number), <Lower | Upper>, <Nonparametric>, <Save to Spec Limits Column Property> )
Description: Saves the tolerance interval as specification limits in the Spec Limits column property in the data table.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Distribution( Column( :Height ) );
obj << Tolerance Interval(
Alpha( 0.95 ),
Proportion( 0.85 ),
Save to Spec Limits Column Property
);