multipleOutcomes {multipleOutcomes}

Fitting Regression Models for Multiple Outcomes and Returning the Matrix of Covariance

Description

multipleOutcomes can fit different types of models for multiple outcomes simultaneously and return model parameters and variance-covariance matrix for further analysis.

Usage

multipleOutcomes(..., family, data, data_index = NULL, score_epsilon = 1e-06)

Arguments

...

formulas of models to be fitted, or moment functions for gmm.

family

a character vector of families to be used in the models. Currently only gaussian, binomial, coxph and gmm are supported. long for longitudinal data may be supported in the future. family can be of length 1 if all models are fitted in the same family; otherwise family should be specified for each of the models in ....

data

a data frame if all models are fitted on the same dataset; otherwise a list of data frames for fitting models in .... Note that a dataset can be used to fit multiple models, thus, length(data) is unnecessary to be equal to the number of models in .... The row names in a data frame are treated as sample IDs. Consequently, for any two records in different data frames that correspond to the same sample, their row names should be consistent.

data_index

NULL if data is a data frame; otherwise, a vector in integer specifying mapping a model in ... to a data frame in data (a list).

score_epsilon

whatever.

Value

It returns an object of class "multipleOutcomes", which is a list containing the following components:

coefficients	an unnamed vector of coefficients of all fitted models. Use id_map for variable mapping.
mcov	a unnamed matrix of covariance of coefficients. Use id_map for variable mapping.
id_map	a list mapping the elements in coefficients and mcov to variable names.
n_shared_sample_sizes	a matrix of shared sample sizes between datasets being used to fit the models.
call	the matched call.

Examples

## More examples can be found in the vignettes.
library(mvtnorm)
genData <- function(seed = NULL){

  set.seed(seed)
  n <- 400
  sigma <- matrix(c(1, .6, .6, 1), 2)
  x <- rmvnorm(n, sigma = sigma)
  gam <- c(.1, -.2)
  z <- rbinom(n, 1, plogis(1-1/(1+exp(-.5+x%*%gam+.1*rnorm(n)))))

  bet <- c(-.2,.2)
  #y <- rbinom(n, 1, plogis(1-1/(1+exp(-.5+x%*%bet + .2*z-.3*rnorm(n)))))
  y <- -.5+x%*%bet + .2*z-.3*rnorm(n)

  data.frame(y = y, z = z, x1 = x[, 1], x2 = x[, 2])

}

dat <- genData(123456)
dat1 <- head(dat,200)
dat2 <- tail(dat,200)
## fitting four models simultaneously.
fit <-
  multipleOutcomes(
    y ~ z + x1 - 1,
    z ~ x1 + x2,
    z ~ x1 - 1,
    y ~ x2,
    ## z can be fitted with a linear or logistic regression
    family = c('gaussian', 'binomial', 'gaussian','gaussian'),
    data = list(dat1, dat2),
    ## each dataset is used to fit two models
    data_index = c(1, 1, 2, 2)
  )

  ## unnamed coefficients of all model parameters
  coef(fit)

  ## named coefficients of a specific model
  coef(fit, 2)

  ## unnamed covariance matrix of all model parameters
  vcov(fit)

  ## named covariance matrix of a specific model
  vcov(fit, 1)

  ## summary of all parameter estimates
  summary(fit)

  ## summary of parameters in a specific model
  summary(fit, 4)