# mi.lori: The mi.lori performs M multiple imputations using the lori... In lori: Imputation of High-Dimensional Count Data using Side Information

## Description

The mi.lori performs M multiple imputations using the lori method. Multiple imputation allows to produce estimates of missing values, as well as intervals of variability. The classical procedure is to perform M multiple imputations using the mi.lori method, and to aggregate them using the pool.lori method.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```mi.lori( Y, cov = NULL, lambda1 = NULL, lambda2 = NULL, M = 25, intercept = T, reff = T, ceff = T, rank.max = 5, algo = c("alt", "mcgd"), thresh = 1e-05, maxit = 1000, trace.it = F ) ```

## Arguments

 `Y` [matrix, data.frame] count table (nxp). `cov` [matrix, data.frame] design matrix (np*q) in order row1xcol1,row2xcol2,..,rownxcol1,row1xcol2,row2xcol2,...,...,rownxcolp `lambda1` [positive number] the regularization parameter for the interaction matrix. `lambda2` [positive number] the regularization parameter for the covariate effects. `M` [integer] the number of multiple imputations to perform `intercept` [boolean] whether an intercept should be fitted, default value is FALSE `reff` [boolean] whether row effects should be fitted, default value is TRUE `ceff` [boolean] whether column effects should be fitted, default value is TRUE `rank.max` [integer] maximum rank of interaction matrix (smaller than min(n-1,p-1)) `algo` type of algorithm to use, either one of "mcgd" (mixed coordinate gradient descent, adapted to large dimensions) or "alt" (alternating minimization, adapted to small dimensions) `thresh` [positive number] convergence tolerance of algorithm, by default `1e-6`. `maxit` [integer] maximum allowed number of iterations. `trace.it` [boolean] whether convergence information should be printed

## Value

 `mi.imputed` a list of length M containing the imputed count tables `mi.alpha` a (Mxn) matrix containing in rows the estimated row effects (one row corresponds to one single imputation) `mi.beta` a (Mxp) matrix containing in rows the estimated column effects (one row corresponds to one single imputation) `mi.epsilon` a (Mxq) matrix containing in rows the estimated effects of covariates (one row corresponds to one single imputation) `mi.theta` a list of length M containing the estimated interaction matrices `mi.mu` a list of length M containing the estimated Poisson means `mi.y` list of bootstrapped count tables used fot multiple imputation `Y` original incomplete count table

## Examples

 ```1 2 3``` ```X <- matrix(rnorm(50), 25) Y <- matrix(rpois(25, 1:25), 5) res <- mi.lori(Y, X, 10, 10, 2) ```

lori documentation built on Dec. 16, 2020, 5:08 p.m.