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 ggplotconc.ellipses(): additional optionstranslate.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 axesdimdesc.MCA(): now uses weightsdimdesc.MCA(): problem of compatibility next to a
FactoMineR updatedimvtest(): computes test-values for supplementary
variablesdimeta2(): 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).