multipleOutcomes {multipleOutcomes}

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

Description

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

Usage

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

Arguments

...

formulas of models to be fitted.

family

a character vector of families to be used in the models. Currently only gaussian and binomial are supported. cox for time-to-event data with potential censoring and 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)