clusterMI
clusterMI is a R package to perform clustering with missing values. For achieving this goal, multiple imputation is used. The package offers various multiple imputation methods dedicated to clustered individuals (Audigier et al, 2021). In addition, it allows pooling results both in terms of partition and instability (Audigier and Niang, 2022). Among applications, such functionalities can be used to choose a number of clusters with missing values.
micemd
micemd is a R package dedicated to multiple imputation with two-level data.
Why using micemd?
Statistical analysis often requires allowance for a multilevel structure. For example, a two-level structure occurs when individual data from several studies are aggregated, as in individual participant data (IPD) meta-analysis: individuals are at the lowest level, and the studies at the higher level.
However, variables of each study are often incomplete (sporadically missing) and often differ between studies (leading to systematically missing variables), making challenging to analyse such data. micemd offers several solutions to overcome such issues.
micemd is an ad-don for the mice R package which performs multiple imputation using chained equations. Its additional functionnalities consist of:
imputation methods dedicated to:
tools for multiple imputation with mice:
Why using micemd?
Statistical analysis often requires allowance for a multilevel structure. For example, a two-level structure occurs when individual data from several studies are aggregated, as in individual participant data (IPD) meta-analysis: individuals are at the lowest level, and the studies at the higher level.
However, variables of each study are often incomplete (sporadically missing) and often differ between studies (leading to systematically missing variables), making challenging to analyse such data. micemd offers several solutions to overcome such issues.
micemd is an ad-don for the mice R package which performs multiple imputation using chained equations. Its additional functionnalities consist of:
imputation methods dedicated to:
- sporadically and systematically missing values
- continuous, binary or count variables
tools for multiple imputation with mice:
- parallel calculation
- choice of the number of imputed tables
- overimputation for model checking
missMDA
missMDA is a R package that allows one to:
How to use missMDA to apply a statistical method on an incomplete data set ?
- handle missing values in exploratory multivariate analysis such as principal component analysis (PCA), multiple correspondence analysis (MCA), factor analysis for mixed data (FAMD) and multiple factor analysis (MFA)
- impute missing values in:
- continuous data sets using the PCA model
- categorical data sets using MCA
- mixed data using FAMD
- generate multiple imputed data sets:
- for continuous data using the PCA model
- for categorical data using MCA
- visualize multiple imputation in PCA
How to use missMDA to apply a statistical method on an incomplete data set ?
CRAN Task View on missing data
All R packages dealing with missing data can be found here!
