MFClusBootHier

Combines MFhBoot and MFnestBoot into a single function.

Usage

MFClusBootHier(
  formula,
  data,
  compare = c("con", "vac"),
  nboot = 10000,
  boot.unit = TRUE,
  boot.cluster = TRUE,
  which.factor = "All",
  alpha = 0.05,
  seed = sample(1:1e+05, 1)
)

Arguments

formula: Formula of the form y ~ x + a/b/c, where y is a continuous response, x is a factor with two levels of treatment, and a/b/c are grouping variables corresponding to the clusters. Nesting is assumed to be in order, left to right, highest to lowest. So a single level of "a" will contain multiple levels of "b" and a single level of "b" will contain multiple levels of "c".
data: a data.frame or tibble with the variables specified in formula. Additional variables will be ignored.
compare: Text vector stating the factor levels - compare[1] is the control or reference group to which compare[2] is compared.
nboot: number of bootstrapping events
boot.unit: Boolean whether to sample observations from within those of the same core.
boot.cluster: Boolean whether to sample which cores are present. If TRUE, some trees have all the cores while others only have a subset.
which.factor: Which variables to include in the mitigated fraction summation. Default is "All", to sum over entire tree.
alpha: Passed to emp_hpd to calculate high tailed upper and high tailed lower of mitigated fraction.
seed: Passed to MFhBoot to to initialize random number generator for reproducibility.

Value

A list with the following elements:

MFhBoot: as output from MFhBoot.
MFnestBoot: as output from MFnestBoot.

Note

Core variable is the variable corresponding to the lowest nodes of the hierarchical tree. Nest variables are those above the core. All refers to a summary of the entire tree.

Author

MF-package

Examples

a <- data.frame(
  room = paste("Room", rep(c("W", "Z"), each = 24)),
  pen = paste("Pen", rep(LETTERS[1:6], each = 8)),
  litter = paste("Litter", rep(11:22, each = 4)),
  tx = rep(rep(c("vac", "con"), each = 2), 12))
set.seed(76153)
a$lung[a$tx == "vac"] <- rnorm(24, 5, 1.3)
a$lung[a$tx == "con"] <- rnorm(24, 7, 1.3)
thismf1 <- MFClusBootHier(lung ~ tx + room / pen / litter, a, nboot = 10000,
                       boot.cluster = TRUE, boot.unit = TRUE, seed = 12345)
thismf1
#> # A tibble: 1 × 8
#>   variable level median etlower etupper hdlower hdupper mf.obs
#>   <fct>    <chr>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>  <dbl>
#> 1 All      All    0.917   0.625       1   0.667       1  0.875