mimi: main function: low-rank models to analyze and impute mixed...

In mimi: Main Effects and Interactions in Mixed and Incomplete Data

Description

main function: low-rank models to analyze and impute mixed and incomplete data frames with numeric, binary and discrete variables, and missing values

Usage

mimi(y, model = c("low-rank", "multilevel", "covariates"), x = NULL,
  groups = NULL, var.type = c("gaussian", "binomial", "poisson"),
  lambda1, lambda2, algo = c("mcgd", "bcgd"), maxit = 100,
  alpha0 = NULL, theta0 = NULL, thresh = 1e-05, trace.it = F,
  max.rank = NULL)

Arguments

y	nxp matrix of observations
model	either one of "groups", "covariates" or "low-rank", indicating which model should be fitted
x	(np)xN matrix of covariates (optional)
groups	factor of length n indicating groups (optional)
var.type	vector of length p indicating the data types of the columns of y (gaussian, binomial or poisson)
lambda1	positive number regularization parameter for nuclear norm penalty
lambda2	positive number regularization parameter for l1 norm penalty
algo	type of algorithm to use, either one of "bcgd" (small dimensions, gaussian and binomial variables) or "mcgd" (large dimensions, poisson variables)
maxit	integer maximum number of iterations
alpha0	vector of length N: initial value of regression parameter (optional)
theta0	matrix of size nxp: initial value of interactions (optional)
thresh	positive number, convergence criterion
trace.it	boolean indicating whether convergence information should be printed
max.rank	integer, maximum rank of interaction matrix theta

Value

A list with the following elements

alpha	vector of main effects
theta	interaction matrix

Examples

n = 6; p = 2
y1 <- matrix(rnorm(mean = 0, n * p), nrow = n)
y2 <- matrix(rnorm(mean = 0, n * p), nrow = n)
y3 <- matrix(rnorm(mean = 2, n * p), nrow = n)
y <- cbind(matrix(rnorm(mean = c(y1), n * p), nrow = n),
           matrix(rbinom(n * p, prob = c(exp(y2)/(1+exp(y2))), size = 1), nrow = n),
           matrix(rpois(n * p, lambda = c(exp(y3))), nrow = n))
var.type <- c(rep("gaussian", p), rep("binomial", p), rep("poisson", p))
idx_NA <- sample(1:(3 * n * p), size = round(0.01 * 3 * n * p))
y[idx_NA] <- NA
res <- mimi(y, model = "low-rank", var.type = var.type, lambda1 = 1, maxit=5)

mimi documentation built on May 2, 2019, 10:59 a.m.