Package 'mimi'

Excerpt of the 2016 Public Use American Census Survey (Alabama only)

Description

A dataset containing answers of 24614 Alabama households to 20 questions

Usage

acs2016

Format

survey A data frame with 24614 rows and 20 columns:

NP

Number of persons in household

ACCESS

Access to the internet. 1 yes 0 no.

AGS

Sales of agriculture products ($, yearly)

BATH

Bathtub or shower. 0 yes 1 no.

BDSP

Number of bedrooms in household.

BROADBND

Cellular data plan for a smartphone or other mobile device

1 yes 2 no

COMPOTHX

Other computer equipment. 1 yes 2 no

CONP

Condo fee ($, monthly)

ELEP

Electricity ($, monthly)

FS

Food Stamps. 0 no 1 yes

FULP

Fuel cost ($, yearly)

GASP

Gas ($, monthly)

MHP

Mobile home costs

$, yearly

REFR

Refrigerator, 1 yes, 2 no.

RMSP

Number of rooms in household

RWAT

Hot and cold running water. 1 yes 2 no

SATELLITE

Satellite internet service. 1 yes 2 no.

WATP

Water ($, yearly)

FFINCP

Family income allocation flag (past 12 months) 0 No 1 yes.

Source

https://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t

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construct covariate matrix (predictor matrix) in the right format for input to the mimi or cv.mimi functions from tables of attributes about the rows or columns of data frames.

Description

construct covariate matrix (predictor matrix) in the right format for input to the mimi or cv.mimi functions from tables of attributes about the rows or columns of data frames.

Usage

covmat(n, p, R = NULL, C = NULL, E = NULL, center = T)

Arguments

n	number of rows
p	number ofcolumns
R	nxK1 matrix of row covariates
C	nxK2 matrix of column covariates
E	(n+p)xK3 matrix of row-column covariates
center	boolean indicating whether the returned covariate matrix should be centered (for identifiability)

Value

the joint product of R and C column-binded with E, a (np)x(K1+K2+K3) matrix in order row1col1,row2col1,...,rowncol1, row1col2, row2col2,...,rowncolp

Examples

R <- matrix(rnorm(10), 5)
C <- matrix(rnorm(9), 3)
covs <- covmat(5,3,R,C)

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selection of the regularization parameters (lambda1 and lambda2) of the mimi function by cross-validation

Description

selection of the regularization parameters (lambda1 and lambda2) of the mimi function by cross-validation

Usage

cv.mimi(y, model = c("low-rank", "covariates"), var.type, x = NULL,
  groups = NULL, N = 5, algo = c("mcgd", "bcgd"), thresh = 1e-05,
  maxit = 100, max.rank = NULL, trace.it = F, parallel = F,
  len = 15)

Arguments

y	[matrix, data.frame] incomplete and mixed data frame (nxp)
model	either one of "groups", "covariates" or "low-rank", indicating which model should be fitted
var.type	vector of length p indicating types of y columns (gaussian, binomial, poisson)
x	[matrix, data.frame] covariate matrix (npxq)
groups	factor of length n indicating groups (optional)
N	[integer] number of cross-validation folds
algo	type of algorithm to use, either one of "bcgd" (small dimensions, gaussian and binomial variables) or "mcgd" (large dimensions, poisson variables)
thresh	[positive number] convergence threshold, default is 1e-5
maxit	[integer] maximum number of iterations, default is 100
max.rank	[integer] maximum rank of interaction matrix, default is 2
trace.it	[boolean] whether information about convergence should be printed
parallel	[boolean] whether the N-fold cross-validation should be parallelized, default value is TRUE
len	[integer] the size of the grid

Value

A list with the following elements

lambda1	regularization parameter estimated by cross-validation for nuclear norm penalty (interaction matrix)
lambda2	regularization parameter estimated by cross-validation for l1 norm penalty (main effects)
errors	a table containing the prediction errors for all pairs of parameters

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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)

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