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Y |

### A

`ABIVARIATE`

produces graphs and statistics for bivariate analysis of variance.

`ABOXCOX`

estimates the power λ in a Box-Cox transformation, that maximizes the partial log-likelihood in `ANOVA`

.

`ACANONICAL`

determines the orthogonal decomposition of the sample space for a design, using an analysis of the canonical relationships between the projectors derived from two or more model formulae.

`ACDISPLAY`

provides further output from an analysis by `ACANONICAL`

.

`ACHECK`

checks assumptions for an `ANOVA`

analysis.

`ACKEEP`

saves information from an analysis by `ACANONICAL`

.

`ACONFIDENCE`

calculates simultaneous confidence intervals for `ANOVA`

means.

`ADETECTION`

calculates the minimum size of effect or contrast detectable in an analysis of variance.

`ADPOLYNOMIAL`

plots single-factor polynomial contrasts fitted by `ANOVA`

.

`ADSPREADSHEET`

puts the data and plan of an experimental design into Genstat spreadsheets.

`AEFFICIENCY`

calculates efficiency factors for experimental designs.

`AFALPHA`

generates alpha designs.

`AFAUGMENTED`

forms an augmented design.

`AFCARRYOVER`

forms factors to represent carry-over effects in cross-over trials.

`AFCOVARIATES`

defines covariates from a model formula for `ANOVA`

.

`AFCYCLIC`

generates block and treatment factors for cyclic designs.

`AFDISCREPANCY`

calculates the discrepancy of a design.

`AFFYMETRIX`

estimates expression values for Affymetrix slides.

`AFIELDRESIDUALS`

display residuals in field layout.

`AFLABELS`

forms a variate of unit labels for a design.

`AFMEANS`

forms tables of means classified by `ANOVA`

treatment factors.

`AFNONLINEAR`

forms D-optimal designs to estimate the parameters of a nonlinear or generalized linear model.

`AFORMS`

prints data forms for an experimental design.

`AFPREP`

searches for an efficient partially-replicated design.

`AFUNITS`

forms a factor to index the units of the final stratum of a design.

`AGALPHA`

forms alpha designs by standard generators for up to 100 treatments.

`AGBIB`

generates balanced incomplete block designs.

`AGBOXBEHNKEN`

generates Box-Behnken designs.

`AGCENTRALCOMPOSITE`

generates central composite designs.

`AGCROSSOVERLATIN`

generates Latin squares balanced for carry-over effects.

`AGCYCLIC`

generates cyclic designs from standard generators.

`AGDESIGN`

generates generally balanced designs.

`AGFACTORIAL`

generates minimum aberration block or fractional factorial designs.

`AGFRACTION`

generates fractional factorial designs.

`AGHIERARCHICAL`

generates orthogonal hierarchical designs.

`AGLATIN`

generates mutually orthogonal Latin squares.

`AGLOOP`

generates loop designs e.g. for time-course microarray experiments.

`AGMAINEFFECT`

generates designs to estimate main effects of two-level factors.

`AGNATURALBLOCK`

forms 1- and 2-dimensional designs with blocks of natural size.

`AGNEIGHBOUR`

generates neighbour-balanced designs.

`AGNONORTHOGONALDESIGN`

generates non-orthogonal multi-stratum designs.

`AGQLATIN`

generates complete and quasi-complete Latin squares.

`AGRAPH`

plots tables of means from `ANOVA`

.

`AGREFERENCE`

generates reference-level designs e.g. for microarray experiments.

`AGSEMILATIN`

generates semi-Latin squares.

`AGSPACEFILLINGDESIGN`

generates space filling designs.

`AGSQLATTICE`

generates square lattice designs.

`AKAIKEHISTOGRAM`

prints histograms with improved definition of groups.

`AKEY`

generates values for treatment factors using the design key method.

`ALIAS`

finds out information about aliased model terms in analysis of variance.

`ALIGNCURVE`

forms an optimal warping to align an observed series of observations with a standard series.

`ALLDIFFERENCES`

shows all pairwise differences of values in a variate or table.

`ALLPAIRWISE`

performs a range of all pairwise multiple comparison tests.

`AMCOMPARISON`

performs pairwise multiple comparison tests for `ANOVA`

means.

`AMDUNNETT`

forms Dunnett’s simultaneous confidence interval around a control.

`AMERGE`

merges extra units into an experimental design.

`AMMI`

allows exploratory analysis of genotype × environment interactions.

`AMTDISPLAY`

displays further output for a multi-tiered design analysed by `AMTIER`

.

`AMTIER`

analyses a multi-tiered design with up to 3 structures.

`AMTKEEP`

saves information from the analysis of a multitiered design by `AMTIER`

.

`ANTMVESTIMATE`

estimates missing values in repeated measurements.

`ANTORDER`

assesses order of ante-dependence for repeated measures data.

`ANTTEST`

calculates overall tests based on a specified order of ante-dependence.

`AN1ADVICE`

aims to give useful advice if a design that is thought to be balanced fails to be analysed by `ANOVA`

.

`AONEWAY`

performs one-way analysis of variance.

`AOVANYHOW`

performs analysis of variance using `ANOVA`

, regression or `REML`

as appropriate.

`AOVDISPLAY`

provides further output from an analysis by `AOVANYHOW`

.

`APERMTEST`

does random permutation tests for analysis-of-variance tables.

`APLOT`

plots residuals from an `ANOVA`

analysis.

`APOLYNOMIAL`

forms equations for single-factor polynomial contrasts fitted by `ANOVA`

.

`APOWER`

calculates the power (probability of detection) for terms in an aov.

`APPEND`

appends a list of vectors of the same type.

`APRODUCT`

forms a new experimental design from the product of two designs.

`ARANDOMIZE`

randomizes and prints an experimental design.

`AREPMEASURES`

produces an analysis of variance for repeated measurements.

`ARESULTSUMMARY`

provides a summary of results from an `ANOVA`

analysis.

`ARETRIEVE`

retrieves an `ANOVA`

save structure from an external file.

`ASAMPLESIZE`

finds the replication to detect a treatment effect or contrast.

`ASCREEN`

performs screening tests for designs with orthogonal block structure.

`ASPREADSHEET`

saves results from an analysis of variance in a spreadsheet.

`ASTATUS`

provides information about the settings of `ANOVA`

models and variates.

`ASTORE`

stores an `ANOVA`

save structure in an external file.

`ASWEEP`

performs sweeps for model terms in an analysis of variance.

`AUDISPLAY`

produces further output for an unbalanced design (after `AUNBALANCED`

).

`AUGRAPH`

plots tables of means from `AUNBALANCED`

.

`AUKEEP`

saves output from analysis of an unbalanced design (by `AUNBALANCED`

).

`AUNBALANCED`

performs analysis of variance for unbalanced designs.

`AUMCOMPARISON`

performs pairwise multiple comparison tests for means from an unbalanced analysis of variance, performed previously by `AUNBALANCED`

.

`AUPREDICT`

forms predictions from an unbalanced design (after `AUNBALANCED`

).

`AUSPREADSHEET`

saves results from an analysis of an unbalanced design (by `AUNBALANCED`

) in a spreadsheet.

`AU2RDA`

saves results from an unbalanced analysis of variance, by `AUNBALANCED`

, in R data frames.

`AYPARALLEL`

does the same analysis of variance for several y-variates, and collates the output.

`A2DISPLAY`

provides further output following an analysis of variance by `A2WAY`

.

`A2KEEP`

copies information from an `A2WAY`

analysis into Genstat data structures.

`A2PLOT`

plots effects from two-level designs with robust s.e. estimates.

`A2RDA`

saves results from an analysis of variance in R data frames.

`A2RESULTSUMMARY`

provides a summary of results from an analysis by `A2WAY`

.

`A2WAY`

performs analysis of variance of a balanced or unbalanced design with up to two treatment factors.

### B

`BACKTRANSFORM`

calculates back-transformed means with approximate standard errors and confidence intervals.

`BAFFYMETRIX`

estimates expression values from an Affymetrix CED and CDF file.

`BANK`

calculates the optimum aspect ratio for a graph.

`BASELINE`

estimates a baseline for a series of numbers whose minimum value is drifting.

`BBINOMIAL`

estimates the parameters of the beta binomial distribution.

`BCDISPLAY`

displays a classification tree.

`BCFDISPLAY`

displays information about a random classification forest.

`BCFIDENTIFY`

identifies specimens using a random classification forest.

`BCFOREST`

constructs a random classification forest.

`BCIDENTIFY`

identifies specimens using a classification tree.

`BCKEEP`

saves information from a classification tree.

`BCLASSIFICATION`

constructs a classification tree.

`BCONSTRUCT`

constructs a tree.

`BCVALUES`

forms values for nodes of a classification tree.

`BGIMPORT`

imports MCMC output in CODA format produced by WinBUGS or OpenBUGS.

`BGPLOT`

produces plots for output and diagnostics from MCMC simulations.

`BGRAPH`

plots a tree.

`BGXGENSTAT`

runs WinBUGS or OpenBUGS from Genstat in batch mode using scripts.

`BIPLOT`

produces a biplot from a set of variates.

`BJESTIMATE`

fits an ARIMA model, with forecast and residual checks.

`BJFORECAST`

plots forecasts of a time series using a previously fitted ARIMA.

`BJIDENTIFY`

displays time series statistics useful for ARIMA model selection.

`BKDISPLAY`

displays an identification key.

`BKEY`

constructs an identification key.

`BKIDENTIFY`

identifies specimens using a key.

`BKKEEP`

saves information from an identification key.

`BLANDALTMAN`

produces Bland-Altman plots to assess the agreement between two variates.

`BNTEST`

calculates one- and two-sample binomial tests.

`BOOTSTRAP`

produces bootstrapped estimates, standard errors and distributions.

`BOXPLOT`

draws box-and-whisker diagrams or schematic plots.

`BPRINT`

displays a tree.

`BPRUNE`

prunes a tree using minimal cost complexity.

`BRDISPLAY`

displays a regression key.

`BREGRESSION`

constructs a regression tree.

`BRFDISPLAY`

displays information about a random regression forest.

`BRFOREST`

constructs a random regression forest.

`BRFPREDICT`

makes predictions using a random regression forest.

`BRKEEP`

saves information from a regression tree.

`BRPREDICT`

makes predictions using a regression tree.

`BRVALUES`

forms values for nodes of a regression tree.

### C

`CABIPLOT`

plots results from correspondence analysis or multiple correspondence analysis.

`CANCORRELATION`

does canonical correlation analysis.

`CASSOCIATION`

calculates measures of association for circular data.

`CATRENDTEST`

calculates the Cochran-Armitage chi-square test for trend.

`CCA`

performs canonical correspondence analysis.

`CCOMPARE`

tests whether samples from circular distributions have a common mean direction or have identical distributions.

`CDESCRIBE`

calculates summary statistics and tests of circular data.

`CDNBLOCKDESIGN`

constructs a block design using CycDesigN.

`CDNPREP`

constructs a multi-location partially-replicated design using CycDesigN.

`CDNROWCOLUMNDESIGN`

constructs a row-column design using CycDesigN.

`CENSOR`

pre-processes censored data before analysis by `ANOVA`

.

`CHECKARGUMENT`

checks the arguments of a procedure.

`CHIPERMTEST`

performs a random permutation test for a two-dimensional contingency table.

`CHISQUARE`

calculates chi-square statistics for one- and two-way tables.

`CINTERACTION`

clusters rows and columns of a two-way interaction table.

`CLASSIFY`

obtains a starting classification for non-hierarchical clustering.

`CMHTEST`

performs the Cochran-Mantel-Haenszel test.

`CONCORD`

is a synonym for `KCONCORDANCE`

.

`CONFIDENCE`

calculates simultaneous confidence intervals.

`CONVEXHULL`

finds the points of a single or a full peel of convex hulls.

`CORANALYSIS`

does correspondence analysis, or reciprocal averaging.

`CORRESP`

is a synonym for `CORANALYSIS`

.

`COVDESIGN`

produces experimental designs efficient under analysis of covariance.

`CRBIPLOT`

plots correlation or distance biplots after `RDA`

, or ranking biplots after `CCA`

.

`CRTRIPLOT`

plots ordination biplots or triplots after `CCA`

or `RDA`

.

`CSPRO`

reads a data set from a CSPro survey data file and dictionary, and loads it into Genstat or puts it into a spreadsheet file.

`CUMDISTRIBUTION`

fits frequency distributions to accumulated counts.

`CVAPLOT`

plots the mean and unit scores from a canonical variates analysis.

`CVASCORES`

calculates scores for individual units in canonical variates analysis.

### D

`DAYLENGTH`

calculates daylengths at a given period of the year.

`DARROW`

adds arrows to an existing plot.

`DBARCHART`

produces bar charts for one or two-way tables.

`DBCOMMAND`

runs an SQL command on an ODBC database.

`DBEXPORT`

updates an ODBC database table using data from Genstat.

`DBIMPORT`

loads data into Genstat from an ODBC database.

`DBINFORMATION`

loads information on the tables and columns in an ODBC database.

`DBIPLOT`

plots a biplot from an analysis by `PCP`

, `CVA`

or `PCO`

.

`DCIRCULAR`

plots circular data.

`DCLUSTERLABELS`

labels clusters in a single-page dendrogram plotted by `DDENDROGRAM`

.

`DCOLOURS`

forms a band of graduated colours for graphics.

`DCOMPOSITIONAL`

plots 3-part compositional data within a barycentric triangle.

`DCORRELATION`

plots a correlation matrix.

`DCOVARIOGRAM`

plots 2-dimensional auto- and cross-variograms.

`DDEEXPORT`

sends data or commands to a Dynamic Data Exchange server.

`DDEIMPORT`

gets data from a Dynamic Data Exchange (DDE) server.

`DDENDROGRAM`

draws dendrograms with control over structure and style.

`DDESIGN`

plots the plan of an experimental design.

`DECIMALS`

sets the number of decimals for a structure, using its round-off.

`DEMC`

performs Bayesian computing using the Differential Evolution Markov Chain algorithm.

`DERRORBAR`

adds error bars to a graph.

`DESCRIBE`

saves and/or prints summary statistics for variates.

`DESIGN`

helps to select and generate effective experimental designs.

`DFOURIER`

performs a harmonic analysis of a univariate time series.

`DFRTEXT`

adds text to a graphics frame.

`DFUNCTION`

plots a function.

`DHELP`

provides information about Genstat graphics.

`DHSCATTERGRAM`

plots an h-scattergram.

`DIALLEL`

analyses full and half diallel tables with parents.

`DILUTION`

calculates Most Probable Numbers from dilution series data.

`DIRECTORY`

prints or saves a list of files with names matching a specified mask.

`DISCRIMINATE`

performs discriminant analysis.

`DKALMAN`

plots results from an analysis by `KALMAN`

.

`DKEY`

adds a key to a graph.

`DKSTPLOT`

produces diagnostic plots for space-time clustering.

`DMADENSITY`

plots the empirical CDF or PDF (kernel smoothed) by groups.

`DMASS`

plots discrete data like mass spectra, discrete probability functions.

`DMSCATTER`

produces a scatter-plot matrix for one or two sets of variables.

`DMST`

gives a high resolution plot of an ordination with minimum spanning tree.

`DOTPLOT`

produces a dot-plot using line-printer or high-resolution graphics.

`DOTHISTOGRAM`

plots dot histograms.

`DPARALLEL`

displays multivariate data using parallel coordinates.

`DPOLYGON`

draws polygons using high-resolution graphics.

`DPROBABILITY`

plots probability distributions, and estimates their parameters.

`DPSPECTRALPLOT`

calculates an estimate of the spectrum of a spatial point pattern.

`DPTMAP`

draws maps for spatial point patterns using high-resolution graphics.

`DPTREAD`

adds points interactively to a spatial point pattern.

`DQMAP`

displays a genetic map.

`DQMKSCORES`

plots a grid of marker scores for genotypes and indicates missing data.

`DQMQTLSCAN`

plots the results of a genome-wide scan for QTL effects in multi-environment trials.

`DQRECOMBINATIONS`

plots a matrix of recombination frequencies between markers.

`DQSQTLSCAN`

plots the results of a genome-wide scan for QTL effects in single-environment trials.

`DREFERENCELINE`

adds reference lines to a graph.

`DRESIDUALS`

plots residuals.

`DREPMEASURES`

plots profiles and differences of profiles for repeated measures data.

`DRPOLYGON`

reads a polygon interactively from the current graphics device.

`DSCATTER`

produces a scatter-plot matrix using high-resolution graphics.

`DSPIDERWEB`

displays spider-web and star plots.

`DSTTEST`

plots power and significance for t-tests, including equivalence tests.

`DTABLE`

plots tables.

`DTEXT`

adds text to a graph.

`DTIMEPLOT`

produces horizontal bars displaying a continuous time record.

`DVARIOGRAM`

plots fitted models to an experimental variogram.

`DXDENSITY`

produces one-dimensional density (or violin) plots.

`DXYDENSITY`

produces density plots for large data sets.

`DXYGRAPH`

draws two-dimensional graphs with marginal distribution plots alongside the y- and x-axes.

`DYPOLAR`

produces polar plots.

### E

`ECABUNDANCEPLOT`

produces rank/abundance, *ABC* and *k*-dominance plots.

`ECACCUMULATION`

plots species accumulation curves for samples or individuals.

`ECANOSIM`

performs an analysis of similarities (ANOSIM).

`ECDIVERSITY`

calculates measures of diversity with jackknife or bootstrap estimates.

`ECFIT`

fits models to species abundance data.

`ECNICHE`

generates relative abundance of species for niche-based models.

`ECNPESTIMATE`

calculates nonparametric estimates of species richness.

`ECRAREFACTION`

calculates individual or sample-based rarefaction.

`EDDUNNETT`

calculates equivalent deviates for Dunnett’s simultaneous confidence interval around a control.

`EDFTEST`

performs empirical-distribution-function goodness-of-fit tests.

`EXAMPLE`

obtains and runs a Genstat example program.

`EXPORT`

outputs data structures in foreign file formats, including Excel, Quattro, dBase, SPlus, Gauss, MatLab and Instat, or as plain or comma-delimited text.

`EXTRABINOMIAL`

fits the models of Williams (1982) to overdispersed proportions.

### F

`FACAMEND`

permutes the levels and labels of a factor.

`FACCOMBINATIONS`

forms a factor to indicate observations with identical combinations of values of a set of variates, texts or factors.

`FACDIVIDE`

represents a factor by factorial combinations of a set of factors.

`FACEXCLUDEUNUSED`

redefines the levels and labels of a factor to exclude those that are unused.

`FACLEVSTANDARDIZE`

standardizes the levels or labels of a list of factors.

`FACMERGE`

merges levels of factors.

`FACPRODUCT`

forms a factor with a level for every combination of other factors.

`FACSORT`

sorts the levels of a factor according to an index vector.

`FACUNIQUE`

redefines a factor so that its levels and labels are unique.

`FALIASTERMS`

forms information about aliased model terms in analysis of variance.

`FBASICCONTRASTS`

breaks a model term down into its basic contrasts.

`FBETWEENGROUPVECTORS`

forms variates and classifying factors containing within-group summaries to use e.g. in a between-group analysis.

`FCOMPLEMENT`

forms the complement of an incomplete block design.

`FCONTRASTS`

modifies a model formula to contain contrasts of factors.

`FCORRELATION`

forms the correlation matrix for a list of variates.

`FDESIGNFILE`

forms a backing-store file of information for `AGDESIGN`

.

`FDIALLEL`

forms the components of a diallel model for `REML`

or regression.

`FDISTINCTFACTORS`

checks sets of factors to remove any that define duplicate classifications.

`FDRBONFERRONI`

estimates false discovery rates by a Bonferroni-type procedure.

`FDRMIXTURE`

estimates false discovery rates using mixture distributions.

`FEXACT2X2`

does Fisher’s exact test for 2×2 tables.

`FFRAME`

forms multiple windows in a plot-matrix for high-resolution graphics.

`FFREERESPONSEFACTOR`

forms multiple-response factors from free-response data.

`FHADAMARDMATRIX`

forms Hadamard matrices.

`FHAT`

calculates an estimate of the F nearest-neighbour distribution function.

`FIELLER`

calculates effective doses or relative potencies.

`FILEREAD`

reads data from a file.

`FITINDIVIDUALLY`

fits regression models one term at a time.

`FITMULTINOMIAL`

fits generalized linear models with multinomial distribution.

`FITNONNEGATIVE`

is a synonym for `RNONNEGATIVE`

.

`FITPARALLEL`

is a synonym for `RPARALLEL`

.

`FITSCHNUTE`

is a synonym for `RSCHNUTE`

.

`FMEGAENVIRONMENTS`

forms mega-environments based on winning genotypes from an AMMI-2 model.

`FMFACTORS`

forms a pointer of factors representing a multiple-response.

`FNCORRELATION`

calculates correlations from variances and covariances, together with their variances and covariances.

`FNLINEAR`

estimates linear functions of random variables, and calculates their variances and covariances.

`FNPOWER`

estimates products of powers of two random variables, and calculates their variances and covariances.

`FOCCURRENCES`

counts how often each pair of treatments occurs in the same block.

`FPARETOSET`

forms the Pareto optimal set of non-dominated groups.

`FPROJECTIONMATRIX`

forms a projection matrix for a set of model terms.

`FREGULAR`

expands vectors onto a regular two-dimensional grid.

`FRESTRICTEDSET`

forms vectors with the restricted subset of a list of vectors.

`FRIEDMAN`

performs Friedman’s non-parametric analysis of variance.

`FROWCANONICALMATRIX`

puts a matrix into row canonical, or reduced row echelon, form.

`FRTPRODUCTDESIGNMATRIX`

forms summation, or relationship, matrices for model terms.

`FSPREADSHEET`

creates a Genstat Spreadsheet file (GSH) from specified data structures.

`FSTRING`

forms a single string from a list of strings in a text.

`FTEXT`

forms a text structure from a variate.

`FUNIQUEVALUES`

redefines a variate or text so that its values are unique.

`FVCOVARIANCE`

forms the variance-covariance matrix for a list of variates.

`FVSTRING`

forms a string listing the identifiers of a set of data structures.

`FZERO`

gives the F function expectation under complete spatial randomness.

`F2DRESIDUALVARIOGRAM`

calculates and plots a 2-dimensional variogram from a 2-dimensional array of residuals.

### G

`GALOIS`

forms addition and multiplication tables for a Galois finite field.

`GBGRIDCONVERSION`

converts GB grid references to or from latitudes and longitudes or to or from UTM coordinates.

`GEE`

fits models to longitudinal data by generalized estimating equations.

`GENPROCRUSTES`

performs a generalized Procrustes analysis.

`GESTABILITY`

calculates stability coefficients for genotype-by-environment data.

`GETNAME`

forms the name of a structure according to its `IPRINT`

attribute.

`GETRGB`

gets the RGB values of the standard graphics colours.

`GGEBIPLOT`

plots displays to assess genotype+genotype-by-environment variation.

`GHAT`

calculates an estimate of the G nearest-neighbour distribution function.

`GINVERSE`

calculates the generalized inverse of a matrix.

`GLM`

analyses non-standard generalized linear models.

`GLMM`

fits a generalized linear mixed model.

`GPREDICTION`

produces genomic predictions (breeding values) using phenotypic and molecular marker information.

`GRANDOM`

generates pseudo-random numbers from probability distributions.

`GRCSR`

generates completely spatially random points in a polygon.

`GREJECTIONSAMPLE`

generates random samples using rejection sampling.

`GRIBIMPORT`

reads data from a GRIB2 meteorological data file, and loads it or converts it to a spreadsheet file.

`GRLABEL`

randomly labels two or more spatial point patterns.

`GRMNOMIAL`

generates multinomial pseudo-random numbers.

`GRMULTINORMAL`

generates multivariate normal pseudo-random numbers.

`GRTHIN`

randomly thins a spatial point pattern.

`GRTORSHIFT`

performs a random toroidal shift on a spatial point pattern.

`GSTATISTIC`

calculates the gamma statistic of agreement for ordinal data.

`G2AEXPORT`

forms a dbase file to transfer `ANOVA`

output to Agronomix Generation II.

`G2AFACTORS`

redefines block and treatment variables as factors.

`G2VEXPORT`

forms a dbase file to transfer `REML`

output to Agronomix Generation II.

### H

`HANOVA`

does hierarchical analysis of variance or covariance for unbalanced data.

`HBOOSTRAP`

performs bootstrap analyses to assess the reliability of clusters from hierarchical cluster analysis.

`HCOMPAREGROUPINGS`

compares groupings generated, for example, from cluster analyses.

`HEATUNITS`

calculates accumulated heat units of a temperature dependent process.

`HFAMALGAMATIONS`

forms an amalgamations matrix from a minimum spanning tree.

`HFCLUSTERS`

forms a set of clusters from an amalgamations matrix.

`HPCLUSTERS`

prints a set of clusters.

`HGANALYSE`

analyses data using a hierarchical or double hierarchical generalized linear model.

`HGDISPLAY`

displays results from a hierarchical or double hierarchical generalized linear model.

`HGDRANDOMMODEL`

defines the random model in a hierarchical generalized linear model for the dispersion model of a double hierarchical generalized linear model.

`HGFIXEDMODEL`

defines the fixed model for a hierarchical or double hierarchical generalized linear model.

`HGFTEST`

calculates likelihood tests for fixed terms in a hierarchical generalized linear model

`HGGRAPH`

draws a graph to display the fit of an HGLM or DHGLM analysis.

`HGKEEP`

saves information from a hierarchical or double hierarchical generalized linear model analysis.

`HGNONLINEAR`

defines nonlinear parameters for the fixed model of a hierarchical generalized linear model.

`HGPLOT`

produces model-checking plots for a hierarchical or double hierarchical generalized linear model.

`HGPREDICT`

forms predictions from a hierarchical or double hierarchical generalized linear model.

`HGRANDOMMODEL`

defines the random model for a hierarchical or double hierarchical generalized linear model.

`HGRTEST`

calculates likelihood tests for random terms in a hierarchical generalized linear model.

`HGSTATUS`

displays the current HGLM model definitions.

`HGWALD`

prints or saves Wald tests for fixed terms in an HGLM.

### I

`IDENTIFY`

identifies an unknown specimen from a defined set of objects.

`IFUNCTION`

estimates implicit and/or explicit functions of parameters.

`IMPORT`

reads data from a foreign file format and loads it or converts it to a spreadsheet file.

`INSIDE`

determines whether points lie within a specified polygon.

### J

`JACKKNIFE`

produces Jackknife estimates and standard errors.

`JOIN`

joins or merges two sets of vectors together, based on classifying keys.

### K

`KALMAN`

calculates estimates from the Kalman filter.

`KAPLANMEIER`

calculates the Kaplan-Meier estimate of the survivor function.

`KAPPA`

calculates a kappa coefficient of agreement for nominally scaled data.

`KCONCORDANCE`

calculates Kendall’s Coefficient of Concordance.

`KCROSSVALIDATION`

computes cross validation statistics for punctual kriging.

`KCSRENVELOPES`

simulates K function bounds under complete spatial randomness.

`KERNELDENSITY`

uses kernel density estimation to estimate a sample density.

`KHAT`

calculates an estimate of the K function.

`KLABENVELOPES`

gives bounds for K function differences under random labelling.

`KNEARESTNEIGHBOURS`

classifies items or predicts their responses by examining their *k* nearest neighbours.

`KOLMOG2`

performs a Kolmogorov-Smirnoff two-sample test.

`KRUSKAL`

carries out a Kruskal-Wallis one-way analysis of variance.

`KSED`

calculates the standard error for K function differences under random labelling.

`KSTHAT`

calculates an estimate of the K function in space, time and space-time.

`KSTMCTEST`

performs a Monte-Carlo test for space-time interaction.

`KSTSE`

calculates the standard error for the space-time K function.

`KTAU`

calculates Kendall’s rank correlation coefficient τ.

`KTORENVELOPES`

gives bounds for the bivariate K function under independence.

`K12HAT`

calculates an estimate of the bivariate K function.

### L

`LCONCORDANCE`

calculates Lin’s concordance correlation coefficient.

`LIBEXAMPLE`

accesses examples and source code of library procedures.

`LIBFILENAME`

supplies the names of information files for library procedures.

`LIBHELP`

provides help information about library procedures.

`LIBSOURCE`

obtains the source code of a Genstat procedure.

`LIBVERSION`

provides the name of the current Genstat Procedure Library.

`LINDEPENDENCE`

finds the linear relations associated with matrix singularities.

`LORENZ`

plots the Lorenz curve and calculates the Gini and asymmetry coefficients.

`LRIDGE`

does logistic ridge regression.

`LRVSCREE`

prints a scree diagram and/or a difference table of latent roots.

`LSIPLOT`

plots least significant intervals, saved from `SEDLSI`

.

`LSPLINE`

calculates design matrices to fit a natural polynomial or trigonometric L-spline as a linear mixed model.

`LVARMODEL`

analyses a field trial using the Linear Variance Neighbour model.

### M

`MAANOVA`

does analysis of variance for a single-channel microarray design.

`MABGCORRECT`

performs background correction of Affymetrix slides.

`MACALCULATE`

corrects and transforms two-colour microarray differential expressions.

`MADESIGN`

assesses the efficiency of a two-colour microarray design.

`MAEBAYES`

modifies t-values by an empirical Bayes method.

`MAESTIMATE`

estimates treatment effects from a two-colour microarray design.

`MAHISTOGRAM`

plots histograms of microarray data.

`MANNWHITNEY`

performs a Mann-Whitney U test.

`MANOVA`

performs multivariate analysis of variance and covariance.

`MANTEL`

assesses the association between similarity matrices.

`MAPCLUSTER`

clusters probes or genes with microarray data.

`MAPLOT`

produces two-dimensional plots of microarray data.

`MAREGRESSION`

does regressions for single-channel microarray data.

`MARMA`

calculates Affymetrix expression values.

`MAROBUSTMEANS`

does a robust means analysis for Affymetrix slides.

`MASCLUSTER`

clusters microarray slides.

`MASHADE`

produces shade plots to display spatial variation of microarray data.

`MAVDIFFERENCE`

applies the average difference algorithm to Affymetrix data.

`MAVOLCANO`

produces volcano plots of microarray data.

`MA2CLUSTER`

performs a two-way clustering of microarray data by probes (or genes) and slides.

`MCNEMAR`

performs McNemar’s test for the significance of changes.

`MCOMPARISON`

performs pairwise multiple comparison tests within a table of means.

`MCORANALYSIS`

does multiple correspondence analysis.

`MCROSSPECTRUM`

performs a spectral analysis of a multiple time series.

`MC1PSTATIONARY`

gives the stationary probabilities for a 1st-order Markov chain.

`MEDIANTETRAD`

gives robust identification of multiple outliers in 2-way tables.

`META`

combines estimates from individual trials.

`MICHAELISMENTEN`

fits the Michaelis-Menten equation for substrate concentration versus time data.

`MINFIELDWIDTH`

calculates minimum field widths for printing data structures.

`MINIMIZE`

finds the minimum of a function calculated by a procedure.

`MIN1DIMENSION`

finds the minimum of a function in one dimension.

`MMPREDICT`

predicts the Michaelis-Menten curve for a particular set of parameter values.

`MNORMALIZE`

normalizes two-colour microarray data.

`MOVINGAVERAGE`

calculates and plots the moving average of a time series.

`MPOLISH`

performs a median polish of two-way data.

`MPOWER`

forms integer powers of a square matrix.

`MTABULATE`

forms tables classified by multiple-response factors.

`MULTMISSING`

estimates missing values for units in a multivariate data set.

`MSEKERNEL2D`

estimates the mean square error for a kernel smoothing.

`MVAOD`

does an analysis of distance of multivariate data.

`MVARIOGRAM`

fits models to an experimental variogram.

`MVFILL`

replaces missing values in a vector with the previous non-missing value.

### N

`NCONVERT`

converts integers between base 10 and other bases.

`NCSPLINE`

calculates natural cubic spline basis functions (for use e.g. in `REML`

).

`NLAR1`

fits curves with an AR1 or a power-distance correlation model.

`NLCONTRASTS`

fits nonlinear contrasts to quantitative factors in `ANOVA`

.

`NORMTEST`

performs tests of univariate and/or multivariate Normality.

`NOTICE`

provides news and other information about Genstat.

### O

`OPLS`

performs orthogonal partial least squares regression.

`ORTHPOLYNOMIAL`

calculates orthogonal polynomials.

### P

`PAIRTEST`

performs t-tests for pairwise differences.

`PARTIALCORRELATIONS`

calculates partial correlations for a list of variates.

`PCOPROCRUSTES`

performs a multiple Procrustes analysis.

`PDESIGN`

prints or stores treatment combinations tabulated by the block factors.

`PDUPLICATE`

duplicates a pointer, with all its components.

`PEAKFINDER`

finds the locations of peaks in an observed series.

`PENSPLINE`

calculates design matrices to fit a penalized spline as a linear mixed model.

`PERCENT`

expresses the body of a table as percentages of one of its margins.

`PERIODTEST`

gives periodogram-based tests for white noise in time series.

`PERMUTE`

forms all possible permutations of the integers 1…*n*.

`PFACLEVELS`

prints levels and labels of factors.

`PLINK`

prints a link to a graphics file into an HTML file.

`PLS`

fits a partial least squares regression model.

`PNTEST`

calculates one- and two-sample Poisson tests.

`POSSEMIDEFINITE`

calculates a positive semi-definite approximation of a non-positive semi-definite symmetric matrix.

`PPAIR`

displays results of t-tests for pairwise differences in compact diagrams.

`PRCORRELATION`

calculates probabilities for product moment correlations.

`PRDOUBLEPOISSON`

calculates the probability density for the double Poisson distribution.

`PREWHITEN`

filters a time series before spectral analysis.

`PRIMEPOWER`

decomposes a positive integer into its constituent prime powers.

`PRKTAU`

calculates probabilities for Kendall’s rank correlation coefficient τ.

`PRMANNWHITNEYU`

calculates probabilities for the Mann-Whitney U statistic.

`PROBITANALYSIS`

fits probit models allowing for natural mortality and immunity.

`PRSPEARMAN`

calculates probabilities for Spearman’s rank correlation statistic.

`PRWILCOXON`

calculates probabilities for the Wilcoxon signed-rank statistic.

`PSPLINE`

calculates design matrices to fit a P-spline as a linear mixed model.

`PTAREAPOLYGON`

calculates the area of a polygon.

`PTBOX`

generates a bounding or surrounding box for a spatial point pattern.

`PTCLOSEPOLYGON`

closes open polygons.

`PTDESCRIBE`

gives summary and second order statistics for a point process.

`PTGRID`

generates a grid of points in a polygon.

`PTINTENSITY`

calculates the overall density for a spatial point pattern.

`PTKERNEL2D`

performs kernel smoothing of a spatial point pattern.

`PTK3D`

performs kernel smoothing of space-time data.

`PTREMOVE`

removes points interactively from a spatial point pattern.

`PTROTATE`

rotates a point pattern.

`PTSINPOLYGON`

returns points inside or outside a polygon.

### Q

`QBESTGENOTYPES`

sorts individuals of a segregating population by their genetic similarity with a target genotype, using the identity by descent (IBD) information at QTL positions.

`QCANDIDATES`

selects QTLs on the basis of a test statistic profile along the genome.

`QCOCHRAN`

performs Cochran’s *Q* test for differences between related-samples.

`QDESCRIBE`

calculates descriptive statistics of molecular markers.

`QDISCRIMINATE`

performs quadratic discrimination between groups i.e. allowing for different variance-covariance matrices.

`QEIGENANALYSIS`

uses principal components analysis and the Tracy-Widom statistic to find the number of significant principal components to represent a set of variables.

`QEXPORT`

exports genotypic data for QTL analysis.

`QFACTOR`

allows the user to decide to convert texts or variates to factors.

`QFLAPJACK`

creates a Flapjack project file from genotypic and phenotypic data.

`QGSELECT`

obtains a representative selection of genotypes by means of genetic distance sampling or genetic distance optimization.

`QIBDPROBABILITIES`

reads molecular marker data and calculates IBD probabilities.

`QIMPORT`

imports genotypic and phenotypic data for QTL analysis.

`QKINSHIPMATRIX`

forms a kinship matrix from molecular markers.

`QLDDECAY`

estimates linkage disequilibrium (LD) decay along a chromosome.

`QLINKAGEGROUPS`

forms linkage groups using marker data from experimental populations.

`QLIST`

gets the user to select a response interactively from a list.

`QMAP`

constructs genetic linkage maps using marker data from experimental populations.

`QMASSOCIATION`

performs multi-environment marker-trait association analysis in a genetically diverse population using bi-allelic and multi-allelic markers.

`QMATCH`

matches different data structures to be used in QTL estimation.

`QMBACKSELECT`

performs a QTL backward selection for loci in multi-environment trials or multiple populations.

`QMESTIMATE`

calculates QTL effects in multi-environment trials or multiple populations.

`QMKDIAGNOSTICS`

generates descriptive statistics and diagnostic plots of molecular marker data.

`QMKRECODE`

recodes marker scores into separate alleles.

`QMKSELECT`

obtains a representative selection of markers by means of genetic distance sampling or genetic distance optimization.

`QMQTLSCAN`

performs a genome-wide scan for QTL effects (Simple and Composite Interval Mapping) in multi-environment trials or multiple populations.

`QMTBACKSELECT`

performs a QTL backward selection for loci in multi-trait trials.

`QMTESTIMATE`

calculates QTL effects in multi-trait trials.

`QMTQTLSCAN`

performs a genome-wide scan for QTL effects (Simple and Composite Interval Mapping) in multi-trait trials.

`QMVAF`

calculates percentage variance accounted for by QTL effects in a multi-environment analysis.

`QMVESTIMATE`

replaces missing molecular marker scores using conditional genotypic probabilities.

`QMVREPLACE`

replaces missing marker scores with the mode scores of the most similar genotypes.

`QNORMALIZE`

performs quantile normalization.

`QRECOMBINATIONS`

calculates the expected numbers of recombinations and the recombination frequencies between markers.

`QREPORT`

creates an HTML report from QTL linkage or association analysis results.

`QSASSOCIATION`

performs marker-trait association analysis in a genetically diverse population using bi-allelic and multi-allelic markers.

`QSBACKSELECT`

performs a QTL backward selection for loci in single-environment trials.

`QSELECTIONINDEX`

calculates (molecular) selection indexes by using phenotypic information and/or molecular scores of multiple traits.

`QSESTIMATE`

calculates QTL effects in single-environment trials.

`QSIMULATE`

simulates marker data and QTL effects for single and multiple environment trials.

`QSQTLSCAN`

performs a genome-wide scan for QTL effects (Simple and Composite Interval Mapping) in single-environment trials.

`QTHRESHOLD`

calculates a threshold to identify a significant QTL.

`QUANTILE`

calculates quantiles of the values in a variate.

`QUESTION`

obtains a response using a Genstat menu.

### R

`RADIALSPLINE`

calculates design matrices to fit a radial-spline surface as a linear mixed model.

`RANK`

produces ranks, from the values in a variate, allowing for ties.

`RAR1`

fits regressions with an AR1 or a power-distance correlation model.

`RBRADLEYTERRY`

fits the Bradley-Terry model for paired-comparison preference tests.

`RCATENELSON`

performs a Cate-Nelson graphical analysis of bivariate data.

`RCHECK`

checks the fit of a linear or generalized linear regression.

`RCIRCULAR`

does circular regression of mean direction for an angular response.

`RCOMPARISONS`

calculates comparison contrasts amongst regression means.

`RDA`

performs redundancy analysis.

`RDESTIMATES`

plots one- or two-way tables of regression estimates.

`REPPERIODOGRAM`

gives periodogram-based analyses for replicated time series.

`RFINLAYWILKINSON`

performs Finlay and Wilkinson’s joint regression analysis of genotype-by-environment data.

`RGRAPH`

draws a graph to display the fit of a regression model.

`RIDGE`

produces ridge regression and principal component regression analyses.

`RJOINT`

does modified joint regression analysis for variety-by-environment data.

`RLASSO`

performs lasso using iteratively reweighted least-squares.

`RLFUNCTIONAL`

fits a linear functional relationship model.

`RLIFETABLE`

calculates the life-table estimate of the survivor function.

`RMGLM`

fits a model where different units follow different generalized linear models.

`RMULTIVARIATE`

performs multivariate linear regression with accumulated tests.

`RNEGBINOMIAL`

fits a negative binomial generalized linear model estimating the aggregation parameter.

`RNONNEGATIVE`

fits a generalized linear model with nonnegativity constraints.

`ROBSSPM`

forms robust estimates of sum-of-squares-and-products matrices.

`RPAIR`

gives t-tests for all pairwise differences of means from a regression or generalized linear model.

`RPARALLEL`

carries out analysis of parallelism for nonlinear functions.

`RPERMTEST`

does random permutation tests for regression or generalized-linear-model analyses.

`RPHCHANGE`

modifies a proportional hazards model fitted by RPHFIT.

`RPHDISPLAY`

prints output for a proportional hazards model fitted by RPHFIT.

`RPHFIT`

fits the proportional hazards model to survival data as a generalized linear model.

`RPHKEEP`

saves information from a proportional hazards model fitted by RPHFIT.

`RPHVECTORS`

forms vectors for fitting proportional hazards data as a generalized linear model.

`RPOWER`

calculates the power (probability of detection) for regression models.

`RPROPORTIONAL`

fits the proportional hazards model to survival data as a generalized linear model.

`RQLINEAR`

fits and plots quantile regressions for linear models.

`RQNONLINEAR`

fits and plots quantile regressions for nonlinear models.

`RQSMOOTH`

fits and plots quantile regressions for loess or spline models.

`RQUADRATIC`

fits a quadratic surface and estimates its stationary point.

`RRETRIEVE`

retrieves a regression save structure from an external file.

`RSCHNUTE`

fits a general 4 parameter growth model to a non-decreasing Y-variate.

`RSCREEN`

performs screening tests for generalized or multivariate linear models.

`RSEARCH`

helps search through models for a regression or generalized linear model.

`RSPREADSHEET`

puts results from a regression, generalized linear or nonlinear model into Genstat spreadsheets.

`RSTEST`

compares groups of right-censored survival data by nonparametric tests.

`RSTORE`

stores a regression save structure in an external file.

`RSURVIVAL`

models survival times of exponential, Weibull, extreme-value, log-logistic or lognormal distributions.

`RTCOMPARISONS`

calculates comparison contrasts within a multi-way table of means.

`RUGPLOT`

draws “rugplots” to display the distribution of one or more samples.

`RUNTEST`

performs a test of randomness of a sequence of observations.

`RWALD`

calculates Wald and F tests for dropping terms from a regression.

`RXGENSTAT`

submits a set of commands externally to R and reads the output.

`RYPARALLEL`

fits the same regression model to several response variates, and collates the output.

`R0INFLATED`

fits zero-inflated regression models to count data with excess zeros.

`R0KEEP`

saves information from a zero-inflated regression model for count data with excess zeros fitted by `R0INFLATED`

.

`R2LINES`

fits two-straight-line (broken-stick) models to data.

### S

`SAGRAPES`

produces statistics and graphs for checking sensory panel performance.

`SAMPLE`

samples from a set of units, possibly stratified by factors.

`SBNTEST`

calculates the sample size for binomial tests.

`SCORRELATION`

calculates the sample size to detect specified correlations.

`SDISCRIMINATE`

selects the best set of variates to discriminate between groups.

`SEDLSI`

calculates least significant intervals.

`SED2ESE`

calculates effective standard errors that give good approximate sed’s.

`SETDEVICE`

opens a graphical file and specifies the device number on basis of its extension.

`SETNAME`

sets the identifier of a data structure to be one specified in a text.

`SIGNTEST`

performs a one or two sample sign test.

`SIMPLEX`

searches for the minimum of a function using the Nelder-Mead algorithm.

`SKEWSYMMETRY`

provides an analysis of skew-symmetry for an asymmetric matrix.

`SLCONCORDANCE`

calculates the sample size for Lin’s concordance coefficient.

`SMANNWHITNEY`

calculates sample sizes for the Mann-Whitney test.

`SMCNEMAR`

calculates sample sizes for McNemar’s test.

`SMOOTHSPECTRUM`

forms smoothed spectrum estimates for univariate time series.

`SOM`

declares a self-organizing map.

`SOMADJUST`

performs adjustments to the weights of a self-organizing map.

`SOMDESCRIBE`

summarizes values of variables at nodes of a self-organizing map.

`SOMESTIMATE`

estimates the weights for self-organizing maps.

`SOMIDENTIFY`

allocates samples to nodes of a self-organizing map.

`SOMPREDICT`

makes predictions using a self-organizing map.

`SPCAPABILITY`

calculates capability statistics.

`SPCCHART`

plots c or u charts representing numbers of defective items.

`SPCOMBINE`

combines spreadsheet and data files, without reading them into Genstat.

`SPCUSUM`

prints CUSUM tables for controlling a process mean.

`SPEARMAN`

calculates Spearman’s rank correlation coefficient.

`SPEWMA`

plots exponentially weighted moving-average control charts.

`SPLINE`

calculates a set of basis functions for M-, B- or I-splines.

`SPNTEST`

calculates the sample size for a Poisson test.

`SPPCHART`

plots p or np charts for binomial testing for defective items.

`SPRECISION`

calculates the sample size to obtain a specified precision.

`SPSHEWHART`

plots control charts for mean and standard deviation or range.

`SSIGNTEST`

calculates the sample size for a sign test.

`STACK`

combines several data sets by “stacking” the corresponding vectors.

`STANDARDIZE`

standardizes columns of a data matrix to have mean zero and variance one.

`STEEL`

performs Steel’s many-one rank test.

`STEM`

produces a simple stem-and-leaf chart.

`STTEST`

calculates the sample size for t-tests (including equivalence tests).

`SUBSET`

forms vectors containing subsets of the values in other vectors.

`SVBOOT`

bootstraps data from random surveys.

`SVCALIBRATE`

performs generalized calibration of survey data.

`SVGLM`

fits generalized linear models to survey data.

`SVHOTDECK`

performs hot-deck and model-based imputation for survey data.

`SVMERGE`

merges strata prior to survey analysis.

`SVMFIT`

fits a support vector machine.

`SVMPREDICT`

forms the predictions using a support vector machine.

`SVREWEIGHT`

modifies survey weights, adjusting other weights to ensure that their overall sum remains unchanged.

`SVSAMPLE`

constructs stratified random samples.

`SVSTRATIFIED`

analyses stratified random surveys by expansion or ratio raising.

`SVTABULATE`

tabulates data from random surveys, including multistage surveys and surveys with unequal probabilities of selection.

`SVWEIGHT`

forms survey weights.

### T

`TABINSERT`

inserts the contents of a sub-table into a table.

`TABMODE`

forms summary tables of modes of values.

`TABSORT`

sorts tables so their margins are in ascending or descending order.

`TABTABLE`

opens a tabbed-table spreadsheet in the Genstat client.

`TALLY`

forms a simple tally table of the distinct values in a vector.

`TCOMBINE`

combines several tables into a single table.

`TENSORSPLINE`

calculates design matrices to fit a tensor-spline surface as a linear mixed model.

`THINPLATE`

calculates the basis functions for thin-plate splines.

`TRELLIS`

does a trellis plot.

`TTEST`

performs a one- or two-sample t-test.

`TUKEYBIWEIGHT`

estimates means using the Tukey biweight algorithm.

`TVARMA`

fits a vector autoregressive moving average (VARMA) model.

`TVFORECAST`

forecasts future values from a vector autoregressive moving average (VARMA) model.

`TVGRAPH`

plots a vector autoregressive moving average (VARMA) model.

`TXPAD`

pads strings of a text structure with extra characters so that their lengths are equal.

`TXPROGRESSION`

forms a text containing a progression of strings.

`TXSPLIT`

splits a text into individual texts, at positions on each line marked by separator character(s).

`T%CONTROL`

expresses tables as percentages of control cells.

### U

`UNSTACK`

splits vectors into individual vectors according to levels of a factor.

`UTMCONVERSION`

converts between geographical latitude and longitude coordinates and UTM eastings and northings.

### V

`VABLOCKDESIGN`

analyses an incomplete-block design by `REML`

, allowing automatic selection of random and spatial covariance models.

`VAIC`

calculates the Akaike and Schwarz (Bayesian) information coefficients for `REML`

.

`VALINEBYTESTER`

provides combinabilities and deviances for a line-by-tester trial analysed by `VABLOCKDESIGN`

or `VAROWCOLUMNDESIGN`

.

`VAMETA`

performs a `REML`

meta analysis of a series of trials.

`VAOPTIONS`

defines options for the fitting of models by `VARANDOM`

and associated procedures.

`VARANDOM`

finds the best `REML`

random model from a set of models defined by `VFMODEL`

.

`VARECOVER`

recovers when `REML`

, is unable to fit a model, by simplifying the random model.

`VAROWCOLUMNDESIGN`

analyses a row-and-column design by `REML`

, with automatic selection of the best random and spatial covariance model.

`VASDISPLAY`

displays further output from an analysis by `VASERIES`

.

`VASERIES`

analyses a series of trials with incomplete-block or row-and-column designs by `REML`

, automatically selecting the best random models.

`VASKEEP`

copies information from an analysis by `VASERIES`

into Genstat data structures.

`VAYPARALLEL`

does the same `REML`

analysis for several y-variates, and collates the output.

`VBOOTSTRAP`

performs a parametric bootstrap of the fixed effects in a `REML`

analysis.

`VCHECK`

checks standardized residuals from a `REML`

analysis.

`VCRITICAL`

uses a parametric bootstrap to estimate critical values for a fixed term in a `REML`

analysis.

`VDEFFECTS`

plots one- or two-way tables of effects estimated in a `REML`

analysis.

`VDFIELDRESIDUALS`

display residuals from a `REML`

analysis in field layout.

`VEQUATE`

equates values across a set of data structures.

`VFIXEDTESTS`

saves fixed tests from a `REML`

analysis.

`VFMODEL`

forms a model-definition structure for a `REML`

analysis.

`VFPEDIGREE`

checks and prepares pedigree information from several factors, for use by `VPEDIGREE`

and `REML`

.

`VFRESIDUALS`

obtains residuals, fitted values and their standard errors from a `REML`

analysis.

`VFSTRUCTURE`

adds a covariance-structure definition to a `REML`

model-definition structure.

`VFUNCTION`

calculates functions of variance components from a `REML`

analysis.

`VGESELECT`

selects the best variance-covariance model for a set of environments.

`VGRAPH`

plots tables of means from `REML`

.

`VHERITABILITY`

calculates generalized heritability for a random term in a `REML`

analysis.

`VHOMOGENEITY`

tests homogeneity of variances and variance-covariance matrices.

`VINTERPOLATE`

performs linear & inverse linear interpolation between variates.

`VLINEBYTESTER`

analyses a line-by-tester trial by `REML`

.

`VLSD`

prints approximate least significant differences for `REML`

means.

`VMATRIX`

copies values and row/column labels from a matrix to variates or texts.

`VMCOMPARISON`

performs pairwise comparisons between `REML`

means.

`VMODEL`

specifies the model for a `REML`

analysis using a model-definition structure defined by `VFMODEL`

.

`VNEARESTNEIGHBOUR`

analyses a field trial using nearest neighbour analysis.

`VORTHPOLYNOMIAL`

calculates orthogonal polynomials over time for repeated measures.

`VPLOT`

plots residuals from a `REML`

analysis.

`VPOWER`

uses a parametric bootstrap to estimate the power (probability of detection) for terms in a `REML`

analysis.

`VRACCUMULATE`

forms a summary accumulating the results of a sequence of `REML`

random models.

`VRCHECK`

checks effects of a random term in a `REML`

analysis.

`VREGRESS`

performs regression across variates.

`VRMETA`

forms the random model for a `REML`

meta analysis.

`VRPERMTEST`

performs permutation tests for random terms in `REML`

analysis.

`VSAMPLESIZE`

estimates the replication to detect a fixed term or contrast in a `REML`

analysis, using parametric bootstrap.

`VSCREEN`

performs screening tests for fixed terms in a `REML`

analysis.

`VSOM`

analyses a simple `REML`

variance components model for outliers using a variance shift outlier model.

`VSPECTRALCHECK`

forms the spectral components from the canonical components of a multitiered design, and constrains any negative spectral components to zero.

`VSPREADSHEET`

saves results from a `REML`

analysis in a spreadsheet.

`VTABLE`

forms a variate and set of classifying factors from a table.

`VTCOMPARISONS`

calculates comparison contrasts within a multi-way table of predicted means from a `REML`

analysis.

`VUVCOVARIANCE`

forms the unit-by-unit variance-covariance matrix for specified variance components in a `REML`

model.

### W

`WADLEY`

fits models for Wadley’s problem, allowing alternative links and errors.

`WILCOXON`

performs a Wilcoxon Matched-Pairs (Signed-Rank) test.

`WINDROSE`

plots rose diagrams of circular data like wind speeds.

`WSTATISTIC`

calculates the Shapiro-Wilk test for Normality.

### X

`XOCATEGORIES`

performs analyses of categorical data from cross-over trials.

`XOEFFICIENCY`

calculates efficiency of estimating effects in cross-over designs.

`XOPOWER`

estimates the power of contrasts in cross-over designs.

### Y

`YTRANSFORM`

estimates the parameter lambda of a single parameter transformation.