explor {explor}R Documentation

Interface for analysis results exploration

Description

This function launches a shiny app in a web browser in order to do interactive visualisation and exploration of an analysis results.

Usage

explor(obj)

## S3 method for class 'CA'
explor(obj)

## S3 method for class 'textmodel_ca'
explor(obj)

## S3 method for class 'coa'
explor(obj)

## S3 method for class 'MCA'
explor(obj)

## S3 method for class 'speMCA'
explor(obj)

## S3 method for class 'mca'
explor(obj)

## S3 method for class 'acm'
explor(obj)

## S3 method for class 'PCA'
explor(obj)

## S3 method for class 'princomp'
explor(obj)

## S3 method for class 'prcomp'
explor(obj)

## S3 method for class 'pca'
explor(obj)

Arguments

obj

object containing analysis results

Details

If you want to display supplementary individuals or variables and you're using the dudi.coa function, you can add the coordinates of suprow and/or supcol to as supr and/or supr elements added to your dudi.coa result (See example).

If you want to display supplementary individuals or variables and you're using the dudi.acm function, you can add the coordinates of suprow and/or supcol to as supi and/or supv elements added to your dudi.acm result (See example).

If you want to display supplementary individuals or variables and you're using the dudi.pca function, you can add the coordinates of suprow and/or supcol to as supi and/or supv elements added to your dudi.pca result (See example).

Value

The function launches a shiny app in the system web browser.

Examples

## Not run: 

require(FactoMineR)

## FactoMineR::MCA exploration
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)
explor(mca)

## FactoMineR::PCA exploration
data(decathlon)
d <- decathlon[,1:12]
pca <- PCA(d, quanti.sup = 11:12, graph = FALSE)
explor(pca)

## End(Not run)
## Not run: 

library(ade4)

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$supr <- suprow(coa, row_sup)
coa$supc <- supcol(coa, col_sup)
explor(coa)

## End(Not run)
## Not run: 

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)
acm$supv <- supcol(acm, dudi.acm(var_sup, scannf = FALSE, nf = 5)$tab)
colw <- acm$cw*ncol(d)
X <- acm.disjonctif(ind_sup)
X <- data.frame(t(t(X)/colw) - 1)
acm$supi <- suprow(acm, X)
explor(acm)

## End(Not run)
## Not run: 

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
supv <- supcol(pca, dudi.pca(sup_var, scale = TRUE, scannf = FALSE)$tab)
pca$supv <- supv
explor(pca)

## End(Not run)

[Package explor version 0.3.9 Index]