NEWS.md
scaled.dev() : scaled deviations between categories of a supplementary categorical variableggadd_supind() : bug fix when only one supplementary individual (thanks to Mathieu Ferry)barplot_contrib() : bar plot for contributionsdichotomixed() : dichotomizes the factor variables in a mixed format data frameggcloud_variables() and ggadd_supvars() : new options (“force” and “max.overlaps”) to adjust how text labels are repelled.dimdescr() : fixed column names in the results + junk categories are not displayed for speMCA resultsggadd_density() : fixed deprecated ggplot2 argumentsbcMCA() : bug fix when there are junk categoriestabcontrib() : new shortlabs option, to display short column labels (as suggested by @janhovden)planecontrib() : the elements of the resulting lists have been renamed. This fixes a bug in ggcloud_variables() and ggcloud_indiv() when points = “best” and axes are not c(1,2) (thanks to Amal Damien Tawfik)ggcloud_variables(), ggcloud_indiv(), plot.speMCA() and plot.csMCA().ggadd_supvars() : new option “excl”, to exclude some supplementary categories from the plot (as suggested by @janhovden)dimdescr() : new shortlabs option, to display short column labelsggaxis_variables() : new vlab argument, to choose whether to use variable names as prefixes (as suggested by @janhovden)ggadd_supvars() : vname argument has been renamed to vlab (for consistency with other functions)ggadd_supvars() : new arguments (points and min.cos2) to filter categories according to the squared cosine (as suggested by @janhovden)ggbootvalid_variables() and ggaxis_variables() when factor levels have special characters (thanks to Amal Damien Tawfik)ggadd_supvars() when factors have two levels (thanks to Amal Damien Tawfik)homog.test() (thanks to @Yusuke-Ono)ggaxis_variables() when var argument has two or more variable names (thanks to @janhovden)Please note that the 1.8 version of GDAtools was not published on CRAN. So, compared to the last version on CRAN, 2.0 version inherits the changes of 1.8 version.
descriptio package (available on CRAN or github) : wtable(), pem(), phi.table(), oddsratio.table(), catdesc(), condesc(), assoc.twocat(), assoc.twocont(), assoc.catcont(), assoc.yx(), darma(), ggassoc_chiasmogram(), ggassoc_assocplot(), ggassoc_bertin(), ggassoc_phiplot(), ggassoc_boxplot(), ggassoc_crosstab(), ggassoc_scatter(). Lastly, translate.logit() has moved to the (also new) translate.logit package (available on CRAN).gPCA() : Generalized Principal Component AnalysisbcMCA() : Between-class Multiple Correspondence AnalysisbcPCA() : Between-class Principal Component AnalysiswcMCA() : Within-class Multiple Correspondence AnalysiswcPCA() : Within-class Principal Component AnalysisPCAiv() : Principal Component Analysis with Instrumental VariablesMCAiv() : Multiple Correspondence Analysis with Instrumental VariablesPCAoiv() : Principal Component Analysis with Orthogonal Instrumental VariablesMCAoiv() : Multiple Correspondence Analysis with Orthogonal Instrumental VariablescoiPCA() : Coinertia analysis between two groups of numerical variablescoiMCA() : Coinertia analysis between two groups of categorical variablesDA() : Descriptive Discriminant AnalysisDAQ() : Descriptive Discriminant Analysis with Qualitative Variables (aka disqual)rvcoef() : RV coefficient between two groups of variablesplanecontrib() : For a given plane of a MCA, computes contributions et squared cosines of the active variables and categoriesand of the individualsggeta2_variables() : Plots the eta-squared of the active variables of a MCAquasindep() : Transforms a symmetrical contingency table so that it can be used for quasi-correspondence analysis, also called correspondence analysis of incomplete contingency tableggsmoothed_supvar() : Plots the density a supplementary variable in a MCA spacebootvalid_variables() : Bootstrap validation for active variables of a MCAbootvalid_supvars() : Bootstrap validation for supplementary variables of a MCAggbootvalid_variables() : Ellipses for bootstrap validation of active variables of a MCAggbootvalid_supvars() : Ellipses for bootstrap validation of supplementary variables of a MCAsupind() : replaces indsup(), which is softly deprecatedsupvar() : replaces varsup(), which is softly deprecatedsupvars() : replaces varsups(), which is softly deprecatednsCA() : Nonsymmetric Correspondence Analysistabcontrib() : the function has been rewritten to include contributions of deviations (thanks to @419kfj) and quality of representation.ggcloud_indiv(), ggcloud_variables(), ggadd_chulls(), ggadd_ellipses(), ggadd_kellipses() and ggadd_interaction().ggcloud_variables(), ggcloud_indiv() and plot.speMCA() can use contributions to the plane to select categories of individuals.speMCA() : new items are computed (squared cosines and total distances for individuals, total distances for categories)ijunk(): Shiny app to select interactively the junk categories before a specific MCA.quadrant(): Computes the quadrant of active individuals in a given space of a MCA.oddsratio.table(): Computes the odds ratio for every cell in a contingency table.ggassoc_chiasmogram(): Plots the chiasmogram of a crosstabulation, using ggplot2.ggassoc_assocplot(): Association plot of a crosstabulation, using ggplot2.ggassoc_bertin(): Bertin plot of a crosstabulation, using ggplot2.ahc.plots(): Various plots of Ascending Hierarchical Clustering.dist.chi2(): Computes chi-squared distance.ggaxis_variables(): Plots variables on a single axis of a MCA.varsups(): Computes statistics for categorical supplementary variables.ggadd_supvars(): Adds categorical supplementary variables to a cloud of variables.speMCA(), csMCA() and getindexcat() when empty levels or non-factor vectors in the dataindsup() : supdata can now be a tibbleassoc.yx() : integers are now allowed for y; empty levels are dropped in xwtable() : empty cells are replaced by 0.speMCA() and csMCA() : junk categories can now be specified as a character vectorcsMCA() : results can now be used with explor packagetabcontrib() : new “best” option (thanks to @419kfj)assoc.twocat() : standardized (i.e. Pearson) residuals, adjusted standardized residuals, odds ratios, PEM and Goodman-Kruskal tau are computed. The object is reorganized into several sublists. “gather” data frame has columns for margins frequencies and percentages.ggassoc_crosstab() : rewriting with several new options (size, measure, limit, palette and direction) and no more dependency to GGally packageggassoc_phiplot(), ggassoc_assocplot() and ggassoc_crosstab() : now allow faceting. The measure of local association can be any one computed by assoc.twocat()
ggadd_interaction() : geom_line replaced by geom_path (thanks to @419kfj)ggadd_chulls() : new “prop” option to allow peeling of the hullmedoids()
angles.csa(): Computes the cosines similarities and angles between the dimensions of a CSA and those of a MCA.dichotom() (thanks to @juba)dimdescr()
assoc.twocat() : PEM are no longer computed.ggadd_supvar() : for shapes, a value of 0 is mapped to a size of 0 and new shapesize option (as suggested by @osturnus)ggadd_density(): adds a density layer to the cloud of individuals for a category of a supplementary variableggadd_corr(): adds a heatmap of under/over-representation of a supplementary variable to a cloud of individualsggadd_kellipses() : adds concentration ellipses to a cloud of individuals, using ggplotggadd_chulls() : adds convex hulls to a cloud of individuals, using ggplotggassoc_crosstab() : plots counts and associations of a crosstabulation, using ggplotggassoc_phiplot() : bar plot of phi measures of association of a crosstabulation, using ggplotggassoc_boxplot() : displays of boxplot and combines it with a violin plot, using ggplotggassoc_scatter() : scatter plot with a smoothing line, using ggplotdimdescr() : works with condesc() instead of FactoMineR::condes() and takes row weights into account.dimtypicality() : computes typicality tests for supplementary variablesggadd_attractions() : adds attractions between categories (via segments) to a cloud of variablesggadd_supind() : adds supplementary individuals to a cloud of individuals, using ggplotflip.mca() : flips the coordinates of the individuals and the categories on one or more dimensions of a MCAdimdesc.MCA() : replaced by dimdescr()
dimvtest() : use dimtypicality() insteadggcloud_indiv() : the density of points can be represented as an additional layer through contours or hexagon binscatdesc() and condesc() : allow weightscatdesc() and condesc() : new nperm and distrib optionscatdesc() and condesc() : new robust optionassoc.twocont(), assoc.twocat() and assoc.catcont() : nperm option is set to NULL by defaultdarma() : nperm is set to 100 by defaultggcloud_variables() and ggcloud_indiv() : a few changes in the theme (grids are removed, etc.)ggcloud_indiv() and ggadd_ellipses() : new size optionggcloud_variables() : new min.ctr option to filter categories according to their contribution (for objects of class MCA, speMCA and csMCA)ggcloud_variables() : new max.pval option to filter categories according to the p-value derived from their test-value (for objects of class stMCA and multiMCA)ggcloud_variables() : prop argument can take values “vtest1” and “vtest2”ggcloud_variables() : for shapes and colors, variables are used in their order of appearance in the data instead of alphabetical orderggcloud_variables() : new face argument to use font face to identify the most contributing categorieshomog.test() : gives the p-values in addition to the test statisticsdimeta2() : l argument renamed to vars and n argument removedvarsup() : also computes typicality tests and correlation coefficientsconc.ellipse() : several kinds of inertia ellipses can be plotted thanks to the kappa optionggadd_ellipses() : level is set to 0.05 by default, which corresponds to conventional confidence ellipses. Option ‘points’ to choose to color the points or not.modif.rate() : computes raw and modified rateshomog.test() : new dim argumentmodif.rate() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCAggcloud_variables() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCAggcloud_indiv() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCAggadd_supvar() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCAggadd_interaction() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCAdimeta2() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCAdimcontrib() : compatibility with objects of class MCA, speMCA and csMCAtabcontrib() : compatibility with objects of class MCA, speMCA and csMCAhomog.test() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCAvarsup() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCAggadd_chulls() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCAggadd_corr() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCAggadd_density() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCAggadd_ellipses() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCAggadd_kellipses() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCAcsMCA(), speMCA() and translate.logit() : now work with tibblesggcloud_variables() : now works when shapes=TRUE and there are many variablesassoc.twocat() : bug fix for empty cellsmultiMCA() : bug fix with eigen valuesphi.table() : computes phi coefficient for every cells of a contingency tableassoc.twocont() : measures the association between two continuous variables with Pearson, Spearman and Kendall correlations and a permutation test.assoc.yx() : computes bivariate association measures between a response and predictor variablesdarma() : computes bivariate association measures between a response and predictor variables, displaying results in a form looking like the summary of a regression model analysis.assoc.twocat() : bug fix with warningggcloud_variables() : bug fix when prop not NULL.pem() : bug fix with NA valuestranslate.logit() : results for multinomial models were instablewtable() : can compute percentages (prop.wtable() is removed)assoc.twocat() : Cramer’s V instead of V-squared, permutation p-values, Pearson residuals, percentage of maximum deviation from independence, summary data frameassoc.twocat() : better handling of NAsassoc.twocat() : faster computationassoc.catcont() : permutation p-valuesggcloud_variables() : improved color managementpem() : one can choose to sort rows and columns or notphi.table(), pem(), assoc.twocat(), assoc.twocont(), assoc.catcont() and assoc.yx()
assoc.twocat(): measures the association between two categorical variablesassoc.catcont(): measures the association between a categorical variable and a continuous variablecatdesc(): measures the association between a categorical variable and some continuous and/or categorical variablescondesc(): measures the association between a continuous variable and some continuous and/or categorical variablesggcloud_indiv(): cloud of individuals, using ggplotggcloud_variables(): cloud of variables, using ggplotggadd_supvar(): adds a supplementary variable to a cloud of variables, using ggplotggadd_interaction(): adds the interaction between two variables to a cloud of variables, using ggplotggadd_ellipses(): adds confidence ellipses to a cloud of individuals, using ggplottranslate.logit(): translates logit models coefficients into percentagestabcontrib(): displays the categories contributing most to MCA dimensionsvarsup(): with csMCA, the length of variable argument can be equal to the size of the cloud or the subcloudtextvarsup(): with csMCA, the length of variable argument can be equal to the size of the cloud or the subcloudconc.ellipse(): with csMCA, the length of variable argument can be equal to the size of the cloud or the subcloudplot.multiMCA(): threshold argument, aimed at selecting the categories most associated to axesplot.stMCA(): threshold argument, aimed at selecting the categories most associated to axesdimeta2(): now allows stMCA objectswtable(): works as table() but allows weights and shows NAs as defaultprop.wtable(): works as prop.table() but allows weights and shows NAs as defaultmultiMCA(): RV computation is now an option, with FALSE as default, which makes the function execute fastertextvarsup(): there was an error with the supplementary variable labels when resmca was of class csMCA.textvarsup(): plots supplementary variables on the cloud of categories (and not the cloud of individuals as it was mentioned in help).