GDAtools 1.7.1 Unreleased

New functions

  • angles.csa(): Computes the cosines similarities and angles between the dimensions of a CSA and those of a MCA.

Bug fixes

Changes in existing functions

GDAtools 1.7 2021-05-31

New functions

  • ggadd_density(): adds a density layer to the cloud of individuals for a category of a supplementary variable
  • ggadd_corr(): adds a heatmap of under/over-representation of a supplementary variable to a cloud of individuals
  • ggadd_kellipses() : adds concentration ellipses to a cloud of individuals, using ggplot
  • ggadd_chulls() : adds convex hulls to a cloud of individuals, using ggplot
  • ggassoc_crosstab() : plots counts and associations of a crosstabulation, using ggplot
  • ggassoc_phiplot() : bar plot of phi measures of association of a crosstabulation, using ggplot
  • ggassoc_boxplot() : displays of boxplot and combines it with a violin plot, using ggplot
  • ggassoc_scatter() : scatter plot with a smoothing line, using ggplot
  • dimdescr() : works with condesc() instead of FactoMineR::condes() and takes row weights into account.
  • dimtypicality() : computes typicality tests for supplementary variables
  • ggadd_attractions() : adds attractions between categories (via segments) to a cloud of variables
  • ggadd_supind() : adds supplementary individuals to a cloud of individuals, using ggplot
  • flip.mca() : flips the coordinates of the individuals and the categories on one or more dimensions of a MCA

Removed functions :

Changes in existing functions

  • ggcloud_indiv() : the density of points can be represented as an additional layer through contours or hexagon bins
  • catdesc() and condesc() : allow weights
  • catdesc() and condesc() : new nperm and distrib options
  • catdesc() and condesc() : new robust option
  • assoc.twocont(), assoc.twocat() and assoc.catcont() : nperm option is set to NULL by default
  • darma() : nperm is set to 100 by default
  • ggcloud_variables() and ggcloud_indiv() : a few changes in the theme (grids are removed, etc.)
  • ggcloud_indiv() and ggadd_ellipses() : new size option
  • ggcloud_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 order
  • ggcloud_variables() : new face argument to use font face to identify the most contributing categories
  • homog.test() : gives the p-values in addition to the test statistics
  • dimeta2() : l argument renamed to vars and n argument removed
  • varsup() : also computes typicality tests and correlation coefficients
  • conc.ellipse() : several kinds of inertia ellipses can be plotted thanks to the kappa option
  • ggadd_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 rates
  • homog.test() : new dim argument
  • modif.rate() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA
  • ggcloud_variables() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA
  • ggcloud_indiv() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA
  • ggadd_supvar() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA
  • ggadd_interaction() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA
  • dimeta2() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA
  • dimcontrib() : compatibility with objects of class MCA, speMCA and csMCA
  • tabcontrib() : compatibility with objects of class MCA, speMCA and csMCA
  • homog.test() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA
  • varsup() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA
  • ggadd_chulls() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA
  • ggadd_corr() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA
  • ggadd_density() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA
  • ggadd_ellipses() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA
  • ggadd_kellipses() : compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA

Bug fixes

GDAtools 1.6 2021-03-28

New functions

  • phi.table() : computes phi coefficient for every cells of a contingency table
  • assoc.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 variables
  • darma() : computes bivariate association measures between a response and predictor variables, displaying results in a form looking like the summary of a regression model analysis.

Bug fixes

Changes in existing functions

GDAtools 1.5 2020-05-17

New functions

  • assoc.twocat(): measures the association between two categorical variables
  • assoc.catcont(): measures the association between a categorical variable and a continuous variable
  • catdesc(): measures the association between a categorical variable and some continuous and/or categorical variables
  • condesc(): measures the association between a continuous variable and some continuous and/or categorical variables
  • ggcloud_indiv(): cloud of individuals, using ggplot
  • ggcloud_variables(): cloud of variables, using ggplot
  • ggadd_supvar(): adds a supplementary variable to a cloud of variables, using ggplot
  • ggadd_interaction(): adds the interaction between two variables to a cloud of variables, using ggplot
  • ggadd_ellipses(): adds confidence ellipses to a cloud of individuals, using ggplot

Changes in existing functions

  • conc.ellipses(): additional options

GDAtools 1.4 2017-03-07

New functions

  • translate.logit(): translates logit models coefficients into percentages
  • tabcontrib(): displays the categories contributing most to MCA dimensions

Changes in existing functions

  • varsup(): with csMCA, the length of variable argument can be equal to the size of the cloud or the subcloud
  • textvarsup(): with csMCA, the length of variable argument can be equal to the size of the cloud or the subcloud
  • conc.ellipse(): with csMCA, the length of variable argument can be equal to the size of the cloud or the subcloud
  • plot.multiMCA(): threshold argument, aimed at selecting the categories most associated to axes
  • plot.stMCA(): threshold argument, aimed at selecting the categories most associated to axes

GDAtools 1.3 2014-09-06

Changes in existing functions

  • dimdesc.MCA(): now uses weights

Bug fixes

  • dimdesc.MCA(): problem of compatibility next to a FactoMineR update

GDAtools 1.2 Unreleased

New functions

  • dimvtest(): computes test-values for supplementary variables

Changes in existing functions

GDAtools 1.1 2014-04-20

New functions

  • wtable(): works as table() but allows weights and shows NAs as default
  • prop.wtable(): works as prop.table() but allows weights and shows NAs as default

Changes in existing functions

  • multiMCA(): RV computation is now an option, with FALSE as default, which makes the function execute faster

Bug fixes

  • textvarsup(): there was an error with the supplementary variable labels when resmca was of class csMCA.

Error fixes

  • textvarsup(): plots supplementary variables on the cloud of categories (and not the cloud of individuals as it was mentioned in help).