mimi {mimi}

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

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)