MFnestBoot
MFnestBoot.RdMFnest using bootstrapping
Arguments
- x
output from MFhBoot
- which.factor
one or more grouping variable(s) of interest. This can be any of the core or nest variables from the data set. A MF value will be calculated for each level of the variable(s) specified. Default is "All", to sum over entire tree.
- alpha
Passed to
emp_hpdto calculate eq tailed upper and high lower of mitigated fraction
Value
A list with the following elements:
mfnest_details: The MF and summary statistics as calculated for each bootstrap event. Variables as in MFnest output.mfnest_summary: Statistical summary of bootstrapped MF with each unique level of a core or nest variable passed towhich.factoras a row. Other variables include:median: Median of MFs from all of the bootstrap events.etlower: Lower value of equal tailed range.etupper: Upper value of equal tailed range.hdlower: Lower value of the highest posterior density range.hdupper: Upper value of the highest posterior density range.mf.obs: MF calculated from data using MFh.
Examples
set.seed(76153)
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))
a[a$tx == "vac", "lung"] <- rnorm(24, 5, 1.3)
a[a$tx == "con", "lung"] <- rnorm(24, 7, 1.3)
a
#> room pen litter tx lung
#> 1 Room W Pen A Litter 11 vac 5.633023
#> 2 Room W Pen A Litter 11 vac 4.618143
#> 3 Room W Pen A Litter 11 con 9.196909
#> 4 Room W Pen A Litter 11 con 7.277479
#> 5 Room W Pen A Litter 12 vac 3.760431
#> 6 Room W Pen A Litter 12 vac 3.856939
#> 7 Room W Pen A Litter 12 con 6.307358
#> 8 Room W Pen A Litter 12 con 3.521230
#> 9 Room W Pen B Litter 13 vac 4.356031
#> 10 Room W Pen B Litter 13 vac 6.098324
#> 11 Room W Pen B Litter 13 con 7.866934
#> 12 Room W Pen B Litter 13 con 8.339948
#> 13 Room W Pen B Litter 14 vac 5.389661
#> 14 Room W Pen B Litter 14 vac 5.799638
#> 15 Room W Pen B Litter 14 con 8.275256
#> 16 Room W Pen B Litter 14 con 7.952039
#> 17 Room W Pen C Litter 15 vac 4.855498
#> 18 Room W Pen C Litter 15 vac 5.657725
#> 19 Room W Pen C Litter 15 con 7.658824
#> 20 Room W Pen C Litter 15 con 8.517004
#> 21 Room W Pen C Litter 16 vac 4.169034
#> 22 Room W Pen C Litter 16 vac 4.837650
#> 23 Room W Pen C Litter 16 con 5.822156
#> 24 Room W Pen C Litter 16 con 7.718757
#> 25 Room Z Pen D Litter 17 vac 3.595465
#> 26 Room Z Pen D Litter 17 vac 4.921762
#> 27 Room Z Pen D Litter 17 con 5.222559
#> 28 Room Z Pen D Litter 17 con 5.928738
#> 29 Room Z Pen D Litter 18 vac 7.622600
#> 30 Room Z Pen D Litter 18 vac 5.036121
#> 31 Room Z Pen D Litter 18 con 8.620428
#> 32 Room Z Pen D Litter 18 con 6.265424
#> 33 Room Z Pen E Litter 19 vac 3.787388
#> 34 Room Z Pen E Litter 19 vac 5.380694
#> 35 Room Z Pen E Litter 19 con 9.454640
#> 36 Room Z Pen E Litter 19 con 6.505600
#> 37 Room Z Pen E Litter 20 vac 5.300123
#> 38 Room Z Pen E Litter 20 vac 4.417709
#> 39 Room Z Pen E Litter 20 con 7.211453
#> 40 Room Z Pen E Litter 20 con 6.351508
#> 41 Room Z Pen F Litter 21 vac 4.690320
#> 42 Room Z Pen F Litter 21 vac 6.035818
#> 43 Room Z Pen F Litter 21 con 6.643916
#> 44 Room Z Pen F Litter 21 con 6.995050
#> 45 Room Z Pen F Litter 22 vac 4.896764
#> 46 Room Z Pen F Litter 22 vac 5.371713
#> 47 Room Z Pen F Litter 22 con 6.773614
#> 48 Room Z Pen F Litter 22 con 7.772144
formula <- lung ~ tx + room / pen / litter
nboot <- 10000
boot.cluster <- TRUE
boot.unit <- TRUE
which.factors <- c("All", "room", "pen", "litter")
#################
test1 <- MFhBoot(formula, a,
nboot = 10000,
boot.cluster = TRUE, boot.unit = TRUE, seed = 12345)
MFnestBoot(test1, c("All", "litter"))
#> Complete separation observed for variable(s): litter
#> $mfnest_details
#> # A tibble: 87,760 × 8
#> # Groups: variable, level [13]
#> variable level bootID U N1N2 con_N vac_N MF
#> <chr> <chr> <int> <dbl> <int> <int> <int> <dbl>
#> 1 All All 1 47 48 24 24 0.958
#> 2 All All 2 40 48 24 24 0.667
#> 3 All All 3 44 48 24 24 0.833
#> 4 All All 4 46 48 24 24 0.917
#> 5 All All 5 44 48 24 24 0.833
#> 6 All All 6 44 48 24 24 0.833
#> 7 All All 7 46 48 24 24 0.917
#> 8 All All 8 44 48 24 24 0.833
#> 9 All All 9 48 48 24 24 1
#> 10 All All 10 48 48 24 24 1
#> # ℹ 87,750 more rows
#>
#> $mfnest_summary
#> # A tibble: 13 × 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
#> 2 litter Litter 11 1 0 1 0 1 1
#> 3 litter Litter 12 1 0 1 0 1 0
#> 4 litter Litter 13 1 0 1 0 1 1
#> 5 litter Litter 14 1 0 1 0 1 1
#> 6 litter Litter 15 1 0 1 0 1 1
#> 7 litter Litter 16 1 0 1 0 1 1
#> 8 litter Litter 17 1 0 1 0 1 1
#> 9 litter Litter 18 1 0 1 0 1 0.5
#> 10 litter Litter 19 1 0 1 0 1 1
#> 11 litter Litter 20 1 0 1 0 1 1
#> 12 litter Litter 21 1 0 1 0 1 1
#> 13 litter Litter 22 1 0 1 0 1 1
#>
#> $seed
#> [1] 12345
#>
if (FALSE) { # \dontrun{
system.time(test2 <- MFnestBoot(test1, which.factors))
test2
system.time(test3 <- MFnestBoot(test1, which.factors[1]))
test3
system.time(test4 <- MFnestBoot(test1, which.factors[2]))
test4
system.time(test5 <- MFnestBoot(test1, which.factors[2:3]))
test5
system.time(test6 <- MFnestBoot(test1, which.factors[2:4]))
test6
} # }