GDAtools 1.7.2

Bug fixes

• bug fix with in medoids()

GDAtools 1.7.1

New functions

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

Bug fixes

• bug fix with vignettes
• bug fix with NA values in dichotom() (thanks to @juba)
• bug fix with dim option in dimdescr()

Changes in existing functions

• 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)

GDAtools 1.7

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 :

• dimdesc.MCA() : replaced by dimdescr()
• dimvtest() : use dimtypicality() instead

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

• csMCA(), speMCA() and translate.logit() : now work with tibbles
• ggcloud_variables() : now works when shapes=TRUE and there are many variables
• assoc.twocat() : bug fix for empty cells
• multiMCA() : bug fix with eigen values

GDAtools 1.6

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

• assoc.twocat() : bug fix with warning
• ggcloud_variables() : bug fix when prop not NULL.
• pem() : bug fix with NA values
• translate.logit() : results for multinomial models were instable

Changes in existing functions

• wtable() : 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 frame
• assoc.twocat() : better handling of NAs
• assoc.twocat() : faster computation
• assoc.catcont() : permutation p-values
• ggcloud_variables() : improved color management
• pem() : one can choose to sort rows and columns or not
• weights are allowed in functions phi.table(), pem(), assoc.twocat(), assoc.twocont(), assoc.catcont() and assoc.yx()

GDAtools 1.5

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

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

Changes in existing functions

• dimdesc.MCA(): now uses weights

Bug fixes

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

GDAtools 1.2

New functions

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

Changes in existing functions

• dimeta2(): now allows stMCA objects

GDAtools 1.1

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).