# missMDA's tutorials

You can see the section on missing values to better learn more on the handling of missinge values.

You will find here some tutorials.

## Steps to perform PCA with missing values?

1. estimate the number of dimensions used in the reconstruction formula with the estim_ncpPCA function
2. impute the data set with the impute.PCA function using the number of dimensions previously calculated (by default, 2 dimensions are chosen)
3. perform the PCA on the completed data set using the PCA function of the FactoMineR package

Example ```library(missMDA) data(orange) nb = estim_ncpPCA(orange,ncp.max=5) res.comp = imputePCA(orange,ncp=2) res.pca = PCA(res.comp\$completeObs) ```

## Steps to perform MCA with missing values?

1. estimate the number of dimensions used in the reconstruction formula with the estim_ncpMCA function
2. impute the data set with the impute.MCA function using the number of dimensions previously calculated (by default, 2 dimensions are chosen); this step impute the disjuntive matrix used in MCA
3. perform the MCA on the completed disjunctive matrix using the MCA function of the FactoMineR package, and the tab.disj argument

Example ```library(missMDA) data(vnf) nb = estim_ncpMCA(vnf,ncp.max=5) tab.disj = imputeMCA(vnf, ncp=4)\$tab.disj res.mca = MCA(vnf,tab.disj=tab.disj) ```

## Steps to generate multiple imputed data sets (with continuous variables)

1. estimate the number of dimensions used in the reconstruction formula with the estim_ncpPCA function
2. generate the imputed data sets with the MIPCA function using the number of dimensions previously calculated (by default, 2 dimensions are chosen)
3. visualize the imputed data sets with the plot.MIPCA function

Example ```library(missMDA) data(orange) nb = estim_ncpPCA(orange,ncp.max=5) resMI = MIPCA(orange,ncp=2) plot(resMI) ```