Fit a Function to Each Element of a List of Data

Fits a function to each element of a list of data.

fit(
  Xstar,
  fitFUN,
  ...,
  par = FALSE,
  ncores = NULL,
  mc = TRUE,
  lb = FALSE,
  cl_eval = FALSE,
  cl_export = FALSE,
  cl_expr,
  cl_vars,
  rbind = TRUE
)

Arguments

Xstar	List. A list of length `R` with a data set of length `n` in each element of the list.
fitFUN	Function. Fit function to be applied to each element of `Xstar`.
...	Argument to pass to `fitFUN`.
par	Logical. If `TRUE`, use multiple cores. If `FALSE`, use `lapply()`.
ncores	Integer. Number of cores to use if `par = TRUE`. If unspecified, defaults to `detectCores() - 1`.
mc	Logical. If `TRUE`, use `parallel::mclapply()`. If `FALSE`, use `parallel::parLapply()` or `parallel::parLapplyLB()`. Ignored if `par = FALSE`.
lb	Logical. If `TRUE` use `parallel::parLapplyLB()`. If `FALSE`, use `parallel::parLapply()`. Ignored if `par = FALSE` and `mc = TRUE`.
cl_eval	Logical. Execute `parallel::clusterEvalQ()` using `cl_expr`. Ignored if `mc = TRUE`.
cl_export	Logical. Execute `parallel::clusterExport()` using `cl_vars`. Ignored if `mc = TRUE`.
cl_expr	Expression. Expression passed to `parallel::clusterEvalQ()` Ignored if `mc = TRUE`.
cl_vars	Character vector. Names of objects to pass to `parallel::clusterExport()` Ignored if `mc = TRUE`.
rbind	NULL or logical. If `rbind = NULL`, returns the list produced. If `TRUE`, uses `rbind()` to bind the rows of the list produced. If `FALSE`, uses `cbind()` to bind the columns of the list produced. Test that each element of the output list has the appropriate dimensions for binding before using this option.

Value

Returns a list of parameter estimates.

Details

The first argument of fitFUN should be data The output of fitFUN should be a vector.

Options for explicit parallelism are provided. See par and suceeding arguments.

Examples

xstar <- univ(n = 100, rFUN = rnorm, mean = 100, sd = sqrt(225), R = 100)
thetahatstar <- fit(Xstar = xstar, fitFUN = mean, rbind = TRUE)
mean(thetahatstar)
#> [1] 100.0171
Xstar <- mvn(
  n = 100,
  Sigma = matrix(c(1, .5, .5, 1), nrow = 2),
  R = 100
)
#> mu = NULL. mu is set to a vector of zeroes of length 2.
foo <- function(X) {
  as.vector(cov(X))
}
thetahatstar <- fit(Xstar = Xstar, fitFUN = foo, rbind = TRUE)
thetahatstar <- thetahatstar[, 2]
mean(thetahatstar)
#> [1] 0.5043345