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Dataplot: Macros

Introduction The Dataplot installation comes with a large number of Dataplot programs and macros. Dataplot macros are ASCII text files that contain Dataplot commands. We make a slight distinction between macros and programs. Macros are files that are meant to be run with a user's data set while programs are files that analyze a specific data file. This is an organizational distinction and macros and programs are run in the same manner. The term Dataplot macro will often be used to refer to both macros and programs.

The links for these file are to the NIST ftp site. If you have downloaded and installed Dataplot, local copies of the files are available in the "MACROS" subdirectory of the Dataplot auxillary directory. For Windows, the default auxillary directory is "C:\DATAPLOT". For Unix/Linux, the default auxillary directory is "/usr/local/lib/dataplot".

Purpose of Programs and Macros These programs and macros serve two purposes:
  1. Provide useful capabilities that can be applied to your own data sets.
  2. Serve as guides for writing your own Dataplot macros.
CALL Command You can run these macros by entering
    CALL <name>
where <name> is the name of the macro as given below.
LIST Command The contents of the file can be displayed on the screen by entering
    LIST <name>
Specifying the Location of the Dataplot Auxillary Directory If Dataplot cannot find the requested file when you enter a CALL or LIST command, this indicates that the Dataplot auxillary directory is not installed in the expected location on your local platform. Contact your local system installer to determine the location of the Dataplot auxillary directory on your local platform.

If the Dataplot auxillary directory is not in the default location, you can define the environment variable DATAPLO$ (on Windows and Unix/Linux platforms) to tell Dataplot where the Dataplot auxillary directory is actually located. The Dataplot installation notes contain instructions for defining this variable for Windows and Unix/Linux platforms. For other platforms, contact your local system installer for guidance.

Menu Macros For most of these macros, you define a few relevant parameters, strings, or variables using LET commands before calling the macro. You can use the LIST command to find out what the needed LET commands are (these are typically described in comment lines at the beginning of the file).

The last table lists a number of macros that are described as "Menu Macros". These macros explicitly prompt the analyst for the needed information. The use of menu macros is particularly helpful for macros that you are writing for others to use.

Note that menu macros should not be called if you are running the graphical interface (GUI) version of Dataplot. The GUI will hang if you try to do any "terminal reads", and terminal reads are a key component of menu macros. As an alternative for the GUI, you can extend the GUI menus. This is described in the Extending Dataplot web page.

File Associations The files listed on this page have a ".DP" file extension. The action taken when you click on a specific file is operating system and browser dependent. You may want to create a file association for ".DP" to specify what action you want to occur when you click on these files. Also note that Linux/Mac OS and Windows have different end-of-line conventions for files.

Windows Operating System

There are several ways to create file associations. If you are unfamiliar with how to create file associations, the following is one way to do it (you only need to do this once).

  1. Right click on one of the files and select the "Save link as ..." option. This option allows you to save the file. Select a convenient location to save the file (for our purposes here, the exact location is not important).

  2. Once you have saved the file, open the File Explorer and bring up the directory containing the saved file, right click on the saved file and then select "Properties".

  3. Under the "General" menu, look for the "Opens with" line and left click on the "Change" menu.

  4. This menu will let you select what application to open the file with. You will most likely want to select a program that allows you to edit ASCII files. We do not recommend using Notepad as it is sensitive to the end-of-line convention used in the file. Most other applications (e.g., WordPad, Notepad++) are not sensitive to the end-of-line convention used.

Linux Operating System

Under Linux, the method for specifying a file association will depend on the variant of Linux and the specific desktop/browser you are using. If you are unfamiliar with how to create a file association for ".DP" file extensions, we recommend doing a google search based on your specific Linux variant (e.g., Fedora, Ubuntu) and the specific file manager and browser being used. Most of the common applications for editing ASCII files will not be sensitive to the end-of-line convention used in the file.

Contents The following tables lists the available built-in Dataplot macros.

Basic Statistics and Graphics Dataplot Macros
Basic Statistics and Graphics Macros
2COLUMN.DP generate a graphics version of a 2-column table
CASCADEP.DP generate a cascade (= waterfall) plot
CONFLIMD.DP compute a two-sided confidence limit for the difference of means, this is now a built-in command
CONFLIMI.DP compute a confidence limit for the mean and produce a graphics summary table
CONFLIMM.DP compute a two-sided confidence limit for the mean, this is now a built-in command
CONFLIMS.DP compute a two-sided confidence limit for the standard deviation, this is now a built-in command
CONVERT_SCREEN_TO_DATA.DP convert (0,100) screen units into data units of the pre-existing plot
CHECKSYS.DP check for the operating system, whether device 1 and device 2 have been set and whether you are running in the GUI or the command line mode
CPUTIME.DP print the cumulative CPUTIME usage on the current plot
DEFINE_FILES.DP define standardized input, data and output files (used by several general purpose macros), uses DEFINE_FILES_EXTRACT.DP
DEFINE_HEADERS.DP define standardized headers and trailers for plots (used by several general purpose macros), uses DEFINE_HEADERS_EXTRACT.DP
DIGIT.DP strip out the individual digits of a positive integer
JJFERROR.DP if Dataplot encounters an error, then stop execution and prompt the user before proceeding
JJFERRORIGNORE.DP if Dataplot encounters an error, then ignore it and continue execution
JJFERROROFF.DP if Dataplot encounters an error, then ignore it and continue execution
JJFSIZE.DP set label, tic mark label and title size to 2.6
JJFTICOFFSET.DP set tic mark offset units to SCREEN and set to 5 in both directions for both X and Y axes, can also use JJFYOFFSET.DP
MDPLOT.DP generate a Tukey mean-difference plot (this is now a built-in Dataplot command)
MORTGAG2.DP perform some basic mortgage calculations
OVERLAP_SCORE.DP compute the overlap score (= number of overlaps out of k=choose-2)
PLOTTEXT.DP superimpose text strings on a pre-existing plot
SUBPLOT.DP generate subseries plot of raw data
SUBPLOTR.DP generate subseries plot of residuals
Dataplot  / Dataplot Macros ]

Design of Experiment Dataplot Macros
Design of Experiment Macros
CHECKCLASSIC2LEVEL.DP for a given row to be printed out horizontally, check to see if have a classic 2-level design (with or without center points), also called CHECKCLASSICAL2LEVEL.DP
CHECKCORNERS.DP for a gDEX contour plot, check the mean values at the four corner points
CHECKKYX.DP check for the existence of K, Y, X1, X2, ..., XK
COMPUTESTATS.DP generate (via "offline" plots) statistics in connection with dex mean plots (motivation is error bars for dex mean plots)
CONF.DP compute confounding structure for arbitrary 2-level orthogonal designs, this is now a built-in command, CONFOUND.DP is a variant of this macro
CONVERT_TO_CLASSICAL_UNITS.DP convert the entire design matrix X to (-1,+1)
CORE.DP determine the core vectors spanning the (n-1) space (or a large part of it) for effect estimation in the 10-step analysis, this is now a built-in command
DEXBOXCO.DP generates Yates analysis effects plot for various members of the Box_Cox transformational family
DEXCASE.DP perform the I/O for a variety of special cases for the DEX 10-step analysis
DEXCLAS1.DP define NIST dex class table funnel data for 2**3 full factorial design
DEXCLASS2002.DP define the 2002 dex class team data (8 teams) for 2**3 full factorial design (only)
DEXCLASS2007.DP define the 2007 dex class team data (8 teams) for 2**3 full factorial design and the 2**(4-1) fractional factorial design
DEXCOMB.DP generate combinatorials for groups size 1, 2, and 3
DEXKN.DP write out k (number of factors) and n (number of runs) in a shaded box in the upper left corner of the plot
DEXCONT.DP generate a design of experiments contour plot
DEXCONT2.DP generate a design of experiments contour plot with SUBSET specifications
DEXCONTQ.DP generate a design of experiments contour plot for 2-level designs
DEXFACT2.DP create all 2-term interaction factors
DEXFACT3.DP create all 2-term and 3-term interaction factors
DEXPARET.DP generate a Pareto plot of absolute estimated effects
DEXPLOT1.DP carry out DEX 10-step analysis for a single data set
DEXPLOT.DP generate 10 plots for the analysis of 2-level factorial designs, uses DEXPLOTSUB.DP (this is an early version of DEX10STEPANALYSIS.DP)
DEXPLOTHISTORY.DP this is a no-op macro which contains a list of some of the more interesting cases used by DEXPLOT.DP
DEXPLOTINTERROGATE.DP carry out DEX 10-step analysis for a single data set
DEXSCAT1.DP generate a multiplot of main effects scatter plots
DEXSCAT2.DP generate a multiplot of main effects and 2-term interaction scatter plots
DEXSSCOR.DP design of experiment supersaturated correlation, what main effects are partially confounded with other main effects
DEXSTAT1.DP generate a multiplot of main effects statistics plots
DEXSTAT2.DP generate a multiplot of main effects and 2-term interaction effects statistics plots
DEXSTAT3.DP generate a multiplot of main effects and 2-term and 3-term interaction effects statistics plots
DEXSTAT4.DP generate a multiplot of statistic plots for a user-specified statistic (main effects and 2-term interactions)
DEXYP64.DP define the plot character depending on whether n ≥ 64 or n < 64
K2_ANALYSIS.DP perform a k = 2 factor analysis where both factors are discrete
REVERT_TO_ORIGINAL_UNITS.DP revert from -1/+1 coding back to original units
RIGHTMARGINFACTORSANDLEVELS.DP on an existing sorted response plot, write factors and levels in right margin
SIMPLEX.DP generate a mixture experiment simplex triangle (display the grid and the superimposed data)
SIMPLEX1.DP generate a mixture experiment simplex triangle with dotted "axis lines" running through the centroid and with labels at the corner
SIMPLEX2.DP generate a mixture experiment simplex triangle and superimpose design points
SIMPLEX3.DP generate a mixture experiment simplex triangular grid (display the grid and the region, but not the data)
SIMPLEXD.DP generate a mixture experiment simplex triangular grid (display grid and display data)
SIMPLEXR.DP generate a mixture experiment simplex triangular grid
TESTORTH.DP test to see if a design matrix is orthogonal
YATESGEN.DP generate a ranked list of effects for a general 2-level design, uses SDIGIT.DP
Dataplot  / Dataplot Macros ]

Dataplot Menu Macros
Menu Macros
CONNDOTS.DP connect dots via cross-hair
DEXCUBE.DP generate a 2**3 data cube
DEXPARET.DP generate a DEX Pareto diagram
DEXSQUAR.DP generate a 2**2 data square
INVMAT.DP invert a matrix
ISHIKAWA.DP generate an Ishikawa diagram
ISHIKAW2.DP generate an Ishikawa diagram
MULTTEXT.DP position text via cross-hair
NORMHIST.DP generate a histogram with an overlaid normal density
PARETO.DP generate a Pareto plot
PIECHART.DP generate labelled pie chart
PLOTFUNC.DP plot a function
PLOTSIN.DP plot sin function
PLOT1VAR.DP read and plot 1 variable
PLOT2VAR.DP read and plot 2 variables
RANDSAMP.DP generate a stratified random sample
SIMPMETH.DP compute a simplex solution (linear programming)
SORT.DP sort alpha list via priorities
SUM.DP sum a list of numbers
TTEST.DP perform a t test on 2 variables
WORDCHAH.DP generate a centered horizontal wordchart
WORDCHAV.DP generate a centered vertical wordchart
3DPLOT.DP generate a 3dplot with cube frame
Dataplot  / Dataplot Macros ]

Plot Identification, Plot Control and Annotation Dataplot Macros
Plot Identification, Plot Control and Annotation Macros
ANNOTATE.DP write out a header (and up to 4 sub-headers), the specified date, a page count and a trailer on a plot
ANNOTATE_BOXPLOT_SUBPLOT.DP used by the step 5 (block plots) of the Dataplot 10-step macros to annotate the plots
COLOR.DP change color to all plot components
DARKBLUE.DP set all colors to dark blue
LABELXX.DP put vertical and horizontal axis labels on a multiplot
LABELYX.DP put vertical and horizontal axis labels on a multiplot
LOGO.DP write DATAPLOT logo in lower right corner (variation 1)
LOGO2.DP write DATAPLOT logo in lower right corner (variation 2)
LOGO3.DP write DATAPLOT logo in lower right corner (variation 3)
LOGO3B.DP write DATAPLOT logo in lower right corner (variation 4)
LOGO4.DP write DATAPLOT logo in lower right corner (variation 5)
MARK.DP write name of DATAPLOT macro in lower right corner
MARK2.DP write name of DATAPLOT macro in lower right corner
MARK3.DP write the contents of the string MARK in the lower right corner of the plot
MARK4.DP similar to MARK3.DP
MARK5.DP similar to MARK3.DP, but print larger size
SHADEBOX.DP draw a shaded box
SETCOLOR.DP setup colors and page size for Postscript printer
SIZE2.DP change all plot related sizes to 2, uses SIZE_ORIG.DP
JJFCOLOR.DP define Filliben color settings (set to blue) COLOR.DP
JJFCOLORBLACK.DP define Filliben color settings (set to black) COLOR.DP
WORDPLOT.DP simplify the creation of encapsulated Postscript files for inclusion into a Word document
Dataplot  / Dataplot Macros ]

Fitting Dataplot Programs
Fitting
HOTELL.DP compute Hotelling simultaneous confidence limits for a linear fit
HOTELL2.DP compute Hotelling simultaneous confidence limits for the predicted values from a linear fit
IRLS.DP generate an iteratively re-weighted least squares analysis (10 weight functions supported)
LAD.DP perform a least absolute deviations or a Lp fit (for 1<=p<=2) via iteratively re-weighted least squares
SDPRED.DP compute linear fit confidence limits for observations
Dataplot  / Dataplot Macros ]

One Factor Dataplot Programs
One Factor
BARTLETT.DP perform Bartlett's test for equal variances (this is now a built-in Dataplot command)
COMPARATIVE_K1_ANALYSIS.DP assess equivalence (and drift) across the k=1 single factor (usually discrete)
FTESTLOC.DP perform an F-test for a one-way shift in location built-in Dataplot command)
LEVENE.DP perform Levene's test for equal variances (this is now a built-in Dataplot command)
KW.DP perform a Kruskal-Wallis 1-way ANOVA (this is now a built-in Dataplot command)
WILCOXRS.DP perform a Wilcoxon rank sum test (also known as Mann-Whitney U) (this is now a built-in Dataplot command)
WILCOXSR.DP perform a Wilcoxon signed rank sum test (this is now a built-in Dataplot command)
Dataplot  / Dataplot Macros ]

Multi-Factor Dataplot Programs
Multi-Factor
ANOVA.DP carry out augmented ANOVA analysis, uses PAIRCOMP.DP and INTCHAR.DP
ANOVASUB.DP carry out ANOVA-like analysis, called by ANOVAALL.DP
BLOCKPLOT_4PLOT.DP generate 4 block plots: 1.unsorted, 2. sort by mean, 3. sort by range (height of block) and 4. sort residuals by rannge, uses BLOCKPLOT_HORIZONTAL_AXIS_SORTED.DP
BLOCKPLOT_STATS_AND_LEGEND.DP compute block plot statistics and write them out in the legend area
E691BPH1.DP generate a box plot of h-consistency statistics (laboratories within materials) for an ASTM E691 analysis
E691BPH2.DP generate a box plot of h-consistency statistics (materials within laboratories) for an ASTM E691 analysis
E691BPK1.DP generate a box plot of k-consistency statistics (laboratories within materials) for an ASTM E691 analysis
E691BPK2.DP generate a box plot of k-consistency statistics (materials within laboratories) for an ASTM E691 analysis
E691CVPL.DP generate a Cochran variance plot for (laboratories within materials) for an ASTM E691 analysis
E691DOH1.DP generate a dot plot of h-consistency statistic for (laboratories within materials) for an ASTM E691 analysis
E691DOH2.DP generate a dot plot of h-consistency statistic for (materials within laboratories) for an ASTM E691 analysis
E691DOK1.DP generate a dot plot of k-consistency statistic for (laboratories within materials) for an ASTM E691 analysis
E691DOK2.DP generate a dot plot of k-consistency statistic for (materials within laboratories) for an ASTM E691 analysis
E691PLO1.DP generate a plot of lab means and lab standard deviations for each material for an ASTM E691 analysis
E691PLO2.DP generate some preliminary graphs for an ASTM E691 analysis
  1. plot of means for each material
  2. plot of standard deviation for each material
  3. plot of repeatability standard deviation for each material
  4. plot of reproducibility standard deviation for each material
E691PLO3.DP plot of h-consistency statistic for laboratories within materials for an ASTM E691 analysis
E691PLO4.DP plot of k-consistency statistic for laboratories within materials for an ASTM E691 analysis
E691PLO5.DP plot of h-consistency statistic for materials within laboratories for an ASTM E691 analysis
E691PLO6.DP plot of k-consistency statistic for materials within laboratories for an ASTM E691 analysis
E691PLO7.DP plot of repeatability standard deviation and reproducibility standard deviation versus average for an ASTM E691 analysis
GET_ANOVA_FCDF.DP after a 1-way ANOVA, retrieve cdf and p-value info from dpst1f.dat file
Dataplot  / Dataplot Macros ]

Univariate Dataplot Programs
Univariate
4PLOT.DP generate the 4-plot, but augment it with various statistics
4PLOT_EXTRACT.DP extract required and optional arguments for the 4PLOT.DP macro
4PLOT_NOARGS.DP generate the 4-plot, but augment it with various statistics (this version does not use arguments)
7STEP_UNIVARIATE_ANALYSIS.DP carry out a 7-step univariate analysis
GRUBB.DP perform Grubb's test for outliers
GRUBBS_CDF_AND_CRITICAL_VALUES.DP compute a Grubbs test cdf value and critical values and print a text message to the pre-existing plot
UNIVARIATE_UNCERTAINTY.DP carry out a univariate uncertainty analysis (this version can optionally accept arguments)
UNIVARIATE_UNCERTAINTY_EXTRACT.DP extract requited and optional arguments for the UNIVARIATE_UNCERTAINTY.DP macro
TOLERANCE.DP perform a normal tolerance limits analysis
Dataplot  / Dataplot Macros ]

Interlaboratory/SRM Dataplot Programs
Interlaboratory/SRM
CONSENSUS_VALUE_PLOT.DP generate a consensus value plot, uses CONSENSUS_VALUE_PLOT_EXTRACT.DP, COMPUTELABSTATS.DP, GETANOVAFCDF.DP, GETALLCVESTIMATES.DP and NOUR81JUSTIFICATION.DP
GET1CVESTIMATE.DP for a fixed dataset with lab means and a fixed estimator method and an input weights vector, normalized the weights vector and re-compute the CV estimator
GET1K2UNCCV.DP for many WAOS and WAO2 CV estimators, compute the k = 2 expanded uncertainty
GET1WACVWEIGHTSAND3STATISTICS.DP for one of five weighted average CV estimators, compute the CV weights vector, the consensus mean estimate, the k = 2 expanded uncertainty and the relative k = 2 expanded uncertainty
GET1WACVWEIGHTSAND3STATISTICS.DP for one of five weighted average CV estimators, compute the CV weights vector, the consensus mean estimate, the k = 2 expanded uncertainty and the relative k = 2 expanded uncertainty
GETALLCVESTIMATES.DP compute 14 consensus value estimators
SRM_9STEP_ANALYSIS.DP perform a battery of univariate tests for an SRM analysis, uses
Dataplot  / Dataplot Macros ]

Quality Control Dataplot Programs
One Factor
AOQSS.DP generate an Average Outgoing Quality (AOQ) curve for a single sample plan
ARL_C.DP generate an Average Run Length (ARL) curve for a C (proportions) control chart
ARL_P.DP generate an Average Run Length (ARL) curve for a P (counts) control chart
ARL_XBAR.DP generate an Average Run Length (ARL) curve for a XBAR control chart
ATISS.DP generate an Average Total Inspection (ATI) curve for a single sample plan
OC_C.DP generate an Operating Characteristic (OC) curve for a C (proportions) control chart
OC_P.DP generate an Operating Characteristic (OC) curve for a P (counts) control chart
OC_SSA.DP generate an Operating Characteristic (OC) curve for a single sample plan (hypergeometric)
OC_SSB.DP generate an Operating Characteristic (OC) curve for a single sample plan (binomial)
OC_XBAR.DP generate an Operating Characteristic (OC) curve for a XBAR control chart
Dataplot  / Dataplot Macros ]

Multivariate Analysis Dataplot Programs
Multivariate Analysis
BARTCOVA.DP perform Bartlett's test for equal covariance matrices
CANNONIC.DP perform a cannonical correlation
CLASSIFICATION_ANALYSIS.DP carry out a classification analysis (graphical approach), uses DEFAULT_PLOT.DP, FEATURE_STATS.DP, LEGEND_CATEGORIES.DP, RIGHT_MARGIN_SORTED_FEATURES.DP, U_CONCLUSIONS.DP and Z_CONCLUSIONS.DP, also see the CLASSIFICATION SCATTER PLOT
FISHCLAS.DP classify new points based on a prior Fisher's discriminant analysis
FISHDISC.DP perform a Fisher's discriminant analysis
Dataplot  / Dataplot Macros ]

Distributional and Reliability Analysis Dataplot Programs
Distributional and Reliability Analysis
COMP2EXP.DP compare two exponential distributions
NORMAL_PPCC_AND_CRITICAL_VALUES.DP compute a normal ppcc value along with critical values
NORPPCC_05.DP compute the 5% point of the normal ppcc
POWEREST.DP fit a power law model with MTBF confidence bounds
PPCC_CRITICAL_VALUES.DP generate the 1% and 5% critical values for the ppcc statistic for a normal distribution
PPCCPLOT.DP generate a Tukey-Lambda probability plot with shape parameter 1.5
Dataplot  / Dataplot Macros ]

Time Series
Time Series Analysis
DDS.DP carry out a Data Dependent System (DDS) analysis
DDS2SUB.DP subprogram called by TESTDDS.DP
Dataplot  / Dataplot Macros ]

Date created: 06/05/2001
Last updated: 03/17/2023

Please email comments on this WWW page to alan.heckert@nist.gov.