imputePCA
(Package: missMDA) :
Impute dataset with PCA
Impute the missing values of a dataset with the Principal Components Analysis model. Can be used as a preliminary step before performing a PCA on an completed dataset.
● Data Source:
CranContrib
● Keywords: models, multivariate
● Alias: imputePCA
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imputeMFA
(Package: missMDA) :
Impute dataset with variables structured into groups of variables (groups of continuous or categorical variables)
Impute the missing values of a dataset with Multiple Factor Analysis (MFA). The variables are structured a priori into groups of variables. The variables can be continuous or categorical but within a group the nature of the variables is the same. Can be used as a preliminary step before performing MFA on an incomplete dataset.
● Data Source:
CranContrib
● Keywords: models, multivariate
● Alias: imputeMFA
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estim_ncpPCA
(Package: missMDA) :
Estimate the number of dimensions for the Principal Component Analysis by cross-validation
Estimate the number of dimensions for the Principal Component Analysis by cross-validation
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: estim_ncpPCA
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plot.MIPCA
(Package: missMDA) :
Plot the graphs for the Multiple Imputation in PCA
From the multiple imputed datasets, the function plots graphs for the individuals, variables and dimensions for the Principal Component Analysis (PCA)
● Data Source:
CranContrib
● Keywords: dplot
● Alias: plot.MIPCA
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missMDA-package
(Package: missMDA) :
Handling missing values with/in multivariate data analysis (principal component methods)
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
● Data Source:
CranContrib
● Keywords: Factor analysis
● Alias: missMDA, missMDA-package
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imputeFAMD
(Package: missMDA) :
Impute mixed dataset
Impute the missing values of a mixed dataset (with continuous and categorical variables) using the principal component method "factorial analysis for mixed data" (FAMD). Can be used as a preliminary step before performing FAMD on an incomplete dataset.
● Data Source:
CranContrib
● Keywords: models, multivariate
● Alias: imputeFAMD
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MIPCA
(Package: missMDA) :
Multiple Imputation with PCA
MIPCA performs Multiple Imputation with a PCA model. Can be used as a preliminary step to perform Multiple Imputation in PCA.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: MIPCA
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prelim
(Package: missMDA) :
Converts a dataset imputed by MIMCA or MIPCA into a mids object
This function performs grouping and sorting operations on a multiply imputed dataset. It creates a mids object that is needed for input to with.mids, which allows analyse of the multiply imputed data set. The original incomplete data set needs to be available so that we know where the missing data are.
● Data Source:
CranContrib
● Keywords: multivariate,imputation,categorical,nominal
● Alias: prelim
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Overimpute
(Package: missMDA) :
Overimputation diagnostic plot
Assess the fit of the predictive distribution after performing multiple imputation with the function MIPCA.
● Data Source:
CranContrib
● Keywords:
● Alias: Overimpute
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estim_ncpFAMD
(Package: missMDA) :
Estimate the number of dimensions for the Factorial Analysis of Mixed Data by cross-validation
Estimate the number of dimensions for the Factorial Analysis of Mixed Data by cross-validation
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: estim_ncpFAMD
●
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