fit: Fit a Function to Each Element of a List of Data
In jeksterslabds/jeksterslabRdata: Jekster's Lab Data Generation Utilities

Description Usage Arguments Details Value Author(s) Examples

View source: R/fit.R

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
)

`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.

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.

Returns a list of parameter estimates.

Ivan Jacob Agaloos Pesigan

xstar <- univ(n = 100, rFUN = rnorm, mean = 100, sd = sqrt(225), R = 100)
thetahatstar <- fit(Xstar = xstar, fitFUN = mean, rbind = TRUE)
mean(thetahatstar)
Xstar <- mvn(
  n = 100,
  Sigma = matrix(c(1, .5, .5, 1), nrow = 2),
  R = 100
)
foo <- function(X) {
  as.vector(cov(X))
}
thetahatstar <- fit(Xstar = Xstar, fitFUN = foo, rbind = TRUE)
thetahatstar <- thetahatstar[, 2]
mean(thetahatstar)