Basic differences between GTL & GenStat

Restrictions, Advanced Menus and Commands

The Teaching and Learning Edition of GenStat is a special version of GenStat for Windows, designed to cover the statistical analyses that are needed in schools or in a university undergraduate course. More advanced menus and commands are available in the full version of GenStat for Windows. Full details about GenStat here.

Data limits

Maximum number of values in a data structure: 2500. Maximum number of named data structures: 100. Maximum number of units in an analysis of variance, regression, REML or multivariate analysis: 500. Maximum number of parameters in a regression: 100. Maximum number of variables in a multivariate analysis: 100. Maximum number of factors in an ANOVA or REML analysis: 10. Maximum number of terms in a statistical model: 15.

Excluded Menus

The following details some of the advanced menus and tools that are only available in the full version of GenStat for Windows. General facilities Ability to run GenStat in batch mode Tools for creating custom menus Tools for creating and attaching procedure libraries Data manipulation Menus for subset, append and unit conversions. Spreadsheet Import/export data from databases using ODBC DDE links to other applications Graphics Plots of circular data Distributions Fit generalized extreme value and Pareto distributions Design Generate a factorial design in blocks Generate a fractional factorial design Generate a design efficient under ANCOVA Select design through sequence of questions and answers Analysis of Variance Unbalanced analysis of variance Multiple comparison testing Parallel analysis of variance Regression Regression trees Parallel Regression Hierarchical Generalized Linear Models Linear mixed models (REML) Random coefficient regression Spatial analysis Multivariate linear mixed models Meta-analysis of multiple experiments using REML Generalized linear mixed models Multivariate Procrustes rotation Generalized Procrustes Redundancy analysis Stepwise discriminant analysis Partial least squares Classification trees Repeated Measures Generalized Estimating Equations (GEE) Geostatistics Forming and modelling experimental variograms Kriging Survey Analysis Create and modify survey weights. Calibration weighting to perform calibration estimation of survey data. General survey analysis. Generalized linear models for survey data. Hot-deck imputation. Microarrays Generate one- and two-channel microarray designs Check two-channel microarray designs Graphical display of microarray data Normalize one- and two-channel microarray data Analysis of one- and two-channel microarray data False discovery rates Cluster probes or genes with microarray data QTL Linkage and Association Analysis Summary statistics for QTL data Graphical display of QTL data Preliminary single environment analysis Single trait linkage analysis Single environment multi-trait linkage analysis Single environment single trait association analysis Multi-environment single trait linkage analysis

Excluded commands

ABIVARIATE produces graphs and statistics for bivariate analysis of variance. AFALPHA generates alpha designs. 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. AFFYMETRIX estimates expression values for Affymetrix slides. AFMINABERRATION forms minimum aberration factorial or fractional-factorial designs. AFORMS prints data forms for an experimental design. AFRESPONSESURFACE uses the BLKL algorithm to construct designs for estimating response surfaces. 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. 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. AGQLATIN generates complete and quasi-complete Latin squares. AGREFERENCE generates reference-level designs e.g. for microarray experiments AGSEMILATIN generates semi-Latin squares. AKAIKEHISTOGRAM prints histograms with improved definition of groups. ALIAS finds out information about aliased model terms in analysis of variance. ALLDIFFERENCES shows all pairwise differences of values in a variate or table. ALLPAIRWISE performs a range of all pairwise multiple comparison tests. 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. 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. APRODUCT forms a new experimental design from the product of two designs. ASCREEN performs screening tests for designs with orthogonal block structure ASWEEP performs sweeps for model terms in an analysis of variance. AYPARALLEL does the same analysis of variance for several y-variates, and collates the output. BASSESS assesses potential splits for regression and classification trees. 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. BCLASSIFICATION constructs a classification tree. BCONSTRUCT constructs a tree. BCUT cuts a tree at a defined node, discarding nodes and information below it. BCVALUES forms values for nodes of a classification tree. BGRAPH plots a tree. BGROW adds new branches to a node of a tree. BIDENTIFY identifies specimens using a tree. BJOIN extends a tree by joining another tree to a terminal node. BKDISPLAY displays an identification key. BKEY constructs an identification key. BKIDENTIFY identifies specimens using a key. BPRINT displays a tree. BPRUNE prunes a tree using minimal cost complexity. BRDISPLAY displays a regression key. BREAK suspends execution of the statements in the current channel or control structure and takes subsequent statements from the channel specified. BREGRESSION constructs a regression tree. BRPREDICT makes predictions using a regression tree. BRVALUES forms values for nodes of a regression tree. CASSOCIATION calculates measures of association for circular data. CATALOGUE displays the contents of a backing-store file. 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. CENSOR pre-processes censored data before analysis by ANOVA. CINTERACTION clusters rows and columns of a two-way interaction table. COKRIGE calculates kriged estimates using a model fitted to the sample variograms and cross variograms of a set of variates. COVDESIGN produces experimental designs efficient under analysis of covariance. CUMDISTRIBUTION fits frequency distributions to accumulated counts. DBARCHART produces bar charts for one or two-way tables. DCIRCULAR plots circular data. DCOVARIOGRAM plots 2-dimensional auto- and cross-variograms. DEBUG puts an implicit BREAK statement after the current statement and after every NSTATEMENTS subsequent statements, until an ENDDEBUG is reached. DECLARE declares one or more customized data structures. DEMC performs Bayesian computing using the Differential Evolution Markov Chain algorithm. DESIGN helps to select and generate effective experimental designs. DHSCATTERGRAM plots an h-scattergram. DIALLEL analyses full and half diallel tables with parents. DILUTION calculates Most Probable Numbers from dilution series data. DKSTPLOT produces diagnostic plots for space-time clustering. DMADENSITY plots the empirical CDF or PDF (kernel smoothed) by groups. DPOLYGON draws polygons using high-resolution graphics. 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. DQSQTLSCAN plots the results of a genome-wide scan for QTL effects in single-environment trials. DREAD reads the locations of points from an interactive graphical device. DRPOLYGON reads a polygon interactively from the current graphics device. DVARIOGRAM plots fitted models to an experimental variogram. ENDDEBUG cancels a DEBUG statement. EXTRABINOMIAL fits the models of Williams (1982) to overdispersed proportions. FBASICCONTRASTS breaks a model term down into its basic contrasts. FCOMPLEMENT forms the complement of an incomplete block design. FCONTRASTS modifies a model formula to contain contrasts of factors. FCOVARIOGRAM forms a covariogram structure containing auto-variograms of individual variates and cross-variograms for pairs from a list of variates. FDIALLEL forms the components of a diallel model for REML or regression. FDRBONFERRONI estimates false discovery rates by a Bonferroni-type procedure. FHADAMARDMATRIX forms Hadamard matrices. FHAT calculates an estimate of the F nearest-neighbour distribution function. FKEY forms design keys for multi-stratum experimental designs, allowing for confounded and aliased treatments. FOCCURRENCES counts how often each pair of treatments occurs in the same block. FPROJECTIONMATRIX forms a projection matrix for a set of model terms. FPSEUDOFACTORS determines patterns of confounding and aliasing from design keys, and extends the treatment model to incorporate the necessary pseudo-factors. FVARIOGRAM forms experimental variograms. FZERO gives the F function expectation under complete spatial randomness. GEE fits models to longitudinal data by generalized estimating equations. GENPROCRUSTES performs a generalized Procrustes analysis. GHAT calculates an estimate of the G nearest-neighbour distribution function. GLM analyses non-standard generalized linear models. GLMM fits a generalized linear mixed model. GRCSR generates completely spatially random points in a polygon. GRLABEL randomly labels two or more spatial point patterns. GRTHIN randomly thins a spatial point pattern. GRTORSHIFT performs a random toroidal shift on a spatial point pattern. HANOVA does hierarchical analysis of variance or covariance for unbalanced data. 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. IFUNCTION estimates implicit and/or explicit functions of parameters. IRREDUNDANT forms irredundant test sets for the efficient identification of a set of objects. KCROSSVALIDATION computes cross validation statistics for punctual kriging. KCSRENVELOPES simulates K function bounds under complete spatial randomness. KHAT calculates an estimate of the K function. KLABENVELOPES gives bounds for K function differences under random labelling. KRIGE calculates kriged estimates using a model fitted to the sample variogram. 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. KTORENVELOPES gives bounds for the bivariate K function under independence. K12HAT calculates an estimate of the bivariate K function. LVARMODEL analyses a field trial using the Linear Variance Neighbour model. 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. 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. MCORANALYSIS does multiple correspondence analysis. MCOVARIOGRAM fits models to sets of variograms and cross variograms. MC1PSTATIONARY gives the stationary probabilities for a 1st-order Markov chain. MERGE copies subfiles from backing-store files into a single file. MINIMIZE finds the minimum of a function calculated by a procedure. MNORMALIZE normalizes two-colour microarray data. MPOLISH performs a median polish of two-way data. MSEKERNEL2D estimates the mean square error for a kernel smoothing. MVARIOGRAM fits models to an experimental variogram. NCSPLINE calculates natural cubic spline basis functions (for use e.g. in REML) NLCONTRASTS fits nonlinear contrasts to quantitative factors in ANOVA. NNDISPLAY displays output from a multi-layer perceptron neural network fitted by NNFIT. NNFIT fits a multi-layer perceptron neural network. NNPREDICT forms predictions from a multi-layer perceptron neural network fitted by NNFIT. PCOPROCRUSTES performs a multiple Procrustes analysis. PERIODTEST gives periodogram-based tests for white noise in time series. PREWHITEN filters a time series before spectral analysis. 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. QASSOCIATION performs marker-trait association analysis in a genetically diverse population. QCANDIDATES selects QTLs on the basis of a test statistic profile along the genome. QDESCRIBE calculates descriptive statistics of molecular markers. 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. QIBDPROBABILITIES reads molecular marker data and calculates IBD probabilities. QIMPORT imports genotypic and phenotypic data for QTL analysis. QLDDECAY estimates linkage disequilibrium (LD) decay along a chromosome. QMBACKSELECT performs a QTL backward selection for loci in multi-environment trials. QMESTIMATE calculates QTL effects in multi-environment trials. QMKDIAGNOSTICS generates descriptive statistics and diagnostic plots of molecular marker data. QMQTLSCAN performs a genome-wide scan for QTL effects (Simple and Composite Interval Mapping) in multi-environment trials. QMVAF calculates percentage variance accounted for by QTL effects in a multi-environment analysis. QMVREPLACE replaces missing marker scores with one of the scores of the most similar genotypes. QNORMALIZE performs quantile normalization. QSBACKSELECT performs a QTL backward selection for loci in single-environment trials. QSESTIMATE calculates QTL effects in single-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. RCIRCULAR does circular regression of mean direction for an angular response. RDA performs redundancy analysis. REPPERIODOGRAM gives periodogram-based analyses for replicated time series. RIDGE produces ridge regression and principal component regression analyses. RJOINT does modified joint regression analysis for variety-by-environment data. RMGLM fits a model where different units follow different generalized linear models. 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. ROTATE does a Procrustes rotation of one configuration of points to fit another. RPARALLEL carries out analysis of parallelism for nonlinear functions. RPROPORTIONAL fits the proportional hazards model to survival data as a GLM. 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. RSCHNUTE fits a general 4 parameter growth model to a non-decreasing Y-variate. 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. SAGRAPES produces statistics and graphs for checking sensory panel performance. SETOPTION sets or modifies defaults of options of GenStat directives or procedures. SETPARAMETER sets or modifies defaults of parameters of GenStat directives or procedures. SIMPLEX searches for the minimum of a function using the Nelder-Mead algorithm. SKEWSYMMETRY provides an analysis of skew-symmetry for an asymmetric matrix. 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. SPLINE calculates a set of basis functions for M-, B- or I-splines. STORE to store structures in a subfile of a backing-store file. STRUCTURE defines a compound data structure. 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. 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. THINPLATE calculates the basis functions for thin-plate splines. TREE declares a tree, & initializes it to have a single node known as the root. TUKEYBIWEIGHT estimates means using the Tukey biweight algorithm. VGESELECT selects the best variance-covariance model for a set of environments. VPEDIGREE generates an inverse relationship matrix for use when fitting animal or plant breeding models by REML. VRESIDUAL defines the residual term for a REML model. WADLEY fits models for Wadley’s problem, allowing alternative links and errors. 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.