mi.lori: multiple imputation of count data using the lori model

In genevievelrobin/gammit: Low-Rank Interactions in Count Data with Covariates

Description

multiple imputation of count data using the lori model

Usage

mi.lori(Y, cov = NULL, lambda1 = NULL, lambda2 = NULL, M = 25,
  prob = 0.1, reff = T, ceff = T, rank.max = 10, 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
prob	[positive number in (0,1)] the proportion of entries to remove in bootstrap
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

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

genevievelrobin/gammit documentation built on May 3, 2019, 2:58 p.m.