Package: missMDA 1.21

Francois Husson

missMDA: Handling Missing Values with Multivariate Data Analysis

Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.

Authors:Francois Husson [aut, cre], Julie Josse [aut]

missMDA_1.21.tar.gz
missMDA_1.21.zip(r-4.7)missMDA_1.21.zip(r-4.6)missMDA_1.21.zip(r-4.5)
missMDA_1.21.tgz(r-4.6-any)missMDA_1.21.tgz(r-4.5-any)
missMDA_1.21.tar.gz(r-4.7-any)missMDA_1.21.tar.gz(r-4.6-any)
missMDA_1.21.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
missMDA/json (API)

# Install 'missMDA' in R:

install.packages('missMDA', repos = c('https://husson.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/husson/missmda/issues

Datasets:

gene - Gene expression
geno - Genotype-environment data set with missing values
orange - Sensory description of 12 orange juices by 8 attributes.
ozone - Daily measurements of meteorological variables and ozone concentration
snorena - Characterization of people who snore
TitanicNA - Categorical data set with missing values: Survival of passengers on the Titanic
vnf - Questionnaire done by 1232 individuals who answered 14 questions

On CRAN:

8.76 score 3 stars 13 packages 672 scripts 7.3k downloads 68 mentions 18 exports 134 dependencies

Last updated from:38d25a85ed. Checks:7 WARNING, 1 ERROR, 1 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	WARNING	182
source / vignettes	ERROR	229
linux-release-x86_64	WARNING	190
macos-release-arm64	WARNING	119
macos-oldrel-arm64	WARNING	122
windows-devel	WARNING	174
windows-release	WARNING	132
windows-oldrel	WARNING	147
wasm-release	OK	145

Exports:estim_ncpFAMD estim_ncpMCA estim_ncpMultilevel estim_ncpPCA imputeCA imputeFAMD imputeMCA imputeMFA imputeMultilevel imputePCA MIFAMD MIMCA MIPCA Overimpute plot.MIFAMD plot.MIMCA plot.MIPCA prelim

Dependencies:abind backports base64enc bit bit64 boot broom bslib cachem car carData cli clipr cluster codetools colorspace cowplot cpp11 crayon crosstalk Deriv digest doBy doParallel dplyr DT ellipse emmeans estimability evaluate FactoMineR farver fastmap flashClust fontawesome forcats foreach forecast Formula fracdiff fs generics ggplot2 ggrepel glmnet glue gtable haven highr hms htmltools htmlwidgets irlba isoband iterators jomo jquerylib jsonlite knitr labeling later lattice lazyeval leaps lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels memoise mgcv mice microbenchmark mime minqa mitml modelr multcompView mvtnorm nlme nloptr nnet numDeriv ordinal otel pan pbkrtest pillar pkgconfig prettyunits progress promises purrr quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack readr reformulas rlang rmarkdown rpart S7 sass scales scatterplot3d shape SparseM stringi stringr survival tibble tidyr tidyselect timeDate tinytex tzdb ucminf urca utf8 vctrs viridisLite vroom withr xfun yaml zoo

Vignettes and/or manual for this package failed to build, some documents may be unavailable. Please inspect thebuild logs for more information.

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Handling missing values with/in multivariate data analysis (principal component methods)	missMDA-package missMDA
Estimate the number of dimensions for the Factorial Analysis of Mixed Data by cross-validation	estim_ncpFAMD
Estimate the number of dimensions for the Multiple Correspondence Analysis by cross-validation	estim_ncpMCA
Estimate the number of dimensions for the Multilevel PCA, multlevel MCA or Multilevel FAMD by cross-validation	estim_ncpMultilevel
Estimate the number of dimensions for the Principal Component Analysis by cross-validation	estim_ncpPCA
Gene expression	gene
Genotype-environment data set with missing values	geno
Impute contingency table	imputeCA
Impute mixed dataset	imputeFAMD
Impute categorical dataset	imputeMCA
Impute dataset with variables structured into groups of variables (groups of continuous or categorical variables)	imputeMFA
Impute a multilevel mixed dataset	imputeMultilevel
Impute dataset with PCA	imputePCA
Multiple Imputation with FAMD	MIFAMD
Multiple Imputation with MCA	MIMCA
Multiple Imputation with PCA	MIPCA
Sensory description of 12 orange juices by 8 attributes.	orange
Overimputation diagnostic plot	Overimpute
Daily measurements of meteorological variables and ozone concentration	ozone
Plot the graphs for the Multiple Imputation in FAMD	plot.MIFAMD
Plot the graphs for the Multiple Imputation in MCA	plot.MIMCA
Plot the graphs for the Multiple Imputation in PCA	plot.MIPCA
Converts a dataset imputed by MIMCA, MIPCA or MIFAMD into a mids object	prelim
Characterization of people who snore	snorena
Categorical data set with missing values: Survival of passengers on the Titanic	TitanicNA
Questionnaire done by 1232 individuals who answered 14 questions	vnf