MIMFA: Handling Missing Individuals in MFA
In missRows: Handling Missing Individuals in Multi-Omics Data Integration

Description Usage Arguments Details Value Author(s) References See Also Examples

The MIMFA function estimates coordinates of individuals and variables on the MFA components by implementing a multiple imputation (MI) approach in order to deal with multiple tables in presence of missing individuals.

1	MIMFA(object, ncomp=2, M=NULL, estimeNC=FALSE, maxIter=500, tol=1e-10)

`object`	an object of class `MIDTList`.
`ncomp`	a number of components to include in MFA when `estimeNC=FALSE` (default to 2). If `estimeNC=TRUE`, then `ncomp` correspond to the maximum number of components to test.
`M`	integer, number of imputations. Default to `min(30, Mtotal)`, where `Mtotal` is the total number of possible imputations.
`estimeNC`	logical. If `TRUE` the number of MFA components for data imputation is estimated. Default is `FALSE`.
`maxIter`	integer, maximum number of iterations for the `imputeDataMFA` function.
`tol`	positive value, the threshold for assessing convergence in the `imputeDataMFA` algorithm.

According to the MI methodology, missing individuals are filled in by several sets of plausible values, resulting in M completed data. MFA is then applied to each completed data leading to M different configurations. Finally, the M configurations are combined using the STATIS method to yield one consensus solution.

If estimeNC=TRUE, the number of MFA components for data imputation is estimated using the generalized cross-validation approximation method. In this case, ncomp corresponds to the maximum number of components to test.

A MIDTList object containing additional slots for:

compromise
configurations
imputedIndv
MIparam

See MIDTList for description.

Ignacio González and Valentin Voillet

Voillet V., Besse P., Liaubet L., San Cristobal M., González I. (2016). Handling missing rows in multi-omics data integration: Multiple Imputation in Multiple Factor Analysis framework. BMC Bioinformatics, 17(40).

Lavit C., Escoufier Y., Sabatier R., Traissac P. (1994). The ACT (STATIS method). Computational Statistics & Data Analysis, 18(1), 97–119.

Josse J., Husson F. (2012). Selecting the number of components in PCA using cross-validation approximations. Computational Statistics and Data Analysis, 56, 1869–1879.

plotInd, plotVar, tuneM

#-- load data and create MIDTList object
data(NCI60)
midt <- MIDTList(NCI60$mae)
midt

#-- performs MIMFA
midt <- MIMFA(midt, ncomp=3, M=10)
midt

#-- estimates the number of MFA components for data imputation
#-- ncomp is chosen to being enough large
## Not run: 
midt <- MIMFA(midt, ncomp=50, M=10, estimeNC=TRUE)
midt
## End(Not run)