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example.R
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example.R
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## FactoMineR examples --------------------------------------------------------------
## MCA 1
library(questionr)
library(FactoMineR)
library(dplyr)
library(explor)
data(hdv2003)
d <- hdv2003 %>%
select(sexe, qualif, relig, cuisine, bricol, cinema, sport, age, freres.soeurs)
acm <- MCA(d, quali.sup = 6:7, ind.sup = 1:50, quanti.sup = 8:9, graph = FALSE)
explor(acm)
d <- hdv2003 %>%
select(sexe, nivetud, qualif, clso, relig, cuisine, bricol)
acm <- MCA(d, graph = FALSE)
detach(package:explor, unload=TRUE); library(explor)
explor(acm)
## MCA 2
library(FactoMineR)
library(explor)
data(hobbies)
mca <- MCA(hobbies[1:1000,c(1:8,21:23)],quali.sup = 9:10, quanti.sup = 11, ind.sup = 1:100, graph = FALSE)
detach(package:explor, unload=TRUE); library(explor)
explor(mca)
## PCA
library(FactoMineR)
library(explor)
data(decathlon)
d <- decathlon[,1:12]
pca <- PCA(d, quanti.sup = 11:12, ind.sup = 1:4, graph = FALSE, scale.unit = TRUE)
detach(package:explor, unload=TRUE); library(explor)
explor(pca)
## PCA with quali.sup
library(FactoMineR)
library(explor)
data(decathlon)
d <- decathlon
d$sexe <- sample(c("Homme", "Femme"), 41, replace = TRUE)
pca <- PCA(d, quanti.sup = 11:12, quali.sup = 13:14, ind.sup = 1:4, graph = FALSE, scale.unit = TRUE)
detach(package:explor, unload=TRUE); library(explor)
explor(pca)
## CA
library(FactoMineR)
library(explor)
library(questionr)
data(children)
res.ca <- CA(children, row.sup = 15:18, col.sup = 6:8, graph = FALSE)
detach(package:explor, unload=TRUE); library(explor)
explor(res.ca)
## Ade4 examples --------------------------------------------------------------
## PCA
library(ade4)
data(deug)
d <- deug$tab
sup_var <- d[-(1:10), 8:9]
sup_ind <- d[1:10, -(8:9)]
pca <- dudi.pca(d[-(1:10), -(8:9)], scale = TRUE, scannf = FALSE, nf = 5)
supi <- suprow(pca, sup_ind)
pca$supi <- supi$lisup
supv <- supcol(pca, dudi.pca(sup_var, scale = TRUE, scannf = FALSE)$tab)
pca$supv <- supv$cosup
detach(package:explor, unload=TRUE); library(explor)
explor(pca)
library(ade4)
data(deug)
pca <- dudi.pca(deug$tab, scale = TRUE, scannf = FALSE, nf = 5)
detach(package:explor, unload=TRUE); library(explor)
explor(pca)
## MCA
library(explor)
library(ade4)
data(banque)
d <- banque[-(1:100),-(19:21)]
ind_sup <- banque[1:100, -(19:21)]
var_sup <- banque[-(1:100),19:21]
acm <- dudi.acm(d, scannf = FALSE, nf = 5)
## Supplementary variables
acm$supv <- supcol(acm, dudi.acm(var_sup, scannf = FALSE, nf = 5)$tab)$cosup
## Supplementary individuals
acm$supi <- suprow(acm, ind_sup)$lisup
detach(package:explor, unload=TRUE); library(explor)
explor(acm)
## CA
library(ade4)
library(explor)
data(bordeaux)
tab <- bordeaux
row_sup <- tab[5,-4]
col_sup <- tab[-5,4]
coa <- dudi.coa(tab[-5,-4], nf = 5, scannf = FALSE)
coa$supr <- suprow(coa, row_sup)$lisup
coa$supc <- supcol(coa, col_sup)$cosup
detach(package:explor, unload=TRUE); library(explor)
explor(coa)
data(bordeaux)
coa <- dudi.coa(bordeaux, nf = 5, scannf = FALSE)
detach(package:explor, unload=TRUE); library(explor)
explor(coa)
## GDAtools examples ----------------------------------------
## speMCA
library(explor)
library(GDAtools)
data(Music)
mca <- speMCA(Music[,1:5],excl=c(3,6,9,12,15))
explor(mca)
## speMCA with indsup
library(explor)
library(GDAtools)
data(Music)
mca <- speMCA(Music[3:nrow(Music),1:5],excl=c(3,6,9,12,15))
mca$supi <- indsup(mca,Music[1:2,1:5])
explor(mca)
## MASS examples ---------------------------------------------
## mca
library(MASS)
library(explor)
tmp <- farms[4:20, 2:4]
mca <- MASS::mca(tmp, nf = 11)
supi_df <- farms[1:3, 2:4]
supi <- predict(mca, supi_df, type="row")
rownames(supi) <- rownames(supi_df)
mca$supi <- supi
mca$supv <- predict(mca, farms[4:20, 1, drop=FALSE], type="factor")
detach(package:explor, unload=TRUE); library(explor)
explor(mca)
## Base examples ---------------------------------------------
# princomp
tmp <- USArrests
pca <- princomp(tmp, cor = FALSE)
detach(package:explor, unload=TRUE); library(explor)
explor(pca)
tmp <- USArrests[6:50,]
pca <- princomp(tmp, cor = TRUE)
pca$supi <- predict(pca, USArrests[1:5,])
detach(package:explor, unload=TRUE); library(explor)
explor(pca)
# prcomp
tmp <- USArrests
pca <- prcomp(tmp, scale. = FALSE)
detach(package:explor, unload=TRUE); library(explor)
explor(pca)
tmp <- USArrests[6:50,]
pca <- prcomp(tmp, scale. = TRUE)
pca$supi <- predict(pca, USArrests[1:5,])
detach(package:explor, unload=TRUE); library(explor)
explor(pca)