MFClusHier

Calculate mitigated fraction directly from hierarchial nested data. Combines MFh and MFnest into a single function.

Usage

MFClusHier(formula, data, compare = c("con", "vac"), which.factor = "All")

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.
which.factor: one or more variable(s) of interest. This can be any of the core or nest variables from the data set. If none or NULL is specified, MF will be calculated for the whole tree.

Value

A list with the following elements:

MFh: as output from MFh.
MFnest: as output from MFnest.

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)
thismf <- MFClusHier(lung ~ tx + room / pen / litter, a)
thismf$MFnest
#> # A tibble: 1 × 9
#>   variable level    MF  N1N2     U con_N vac_N con_medResp vac_medResp
#>   <fct>    <chr> <dbl> <dbl> <dbl> <dbl> <dbl>       <dbl>       <dbl>
#> 1 All      All   0.875    48    45    24    24        7.24        4.91
aCore <- thismf$MFh
aCore
#> # A tibble: 12 × 10
#>    room   pen   litter    con_medResp con_n     w vac_medResp vac_n  n1n2     u
#>    <chr>  <chr> <chr>           <dbl> <dbl> <dbl>       <dbl> <dbl> <dbl> <dbl>
#>  1 Room W Pen A Litter 11        8.24     2     7        5.13     2     4     4
#>  2 Room W Pen A Litter 12        4.91     2     5        3.81     2     4     2
#>  3 Room W Pen B Litter 13        8.10     2     7        5.23     2     4     4
#>  4 Room W Pen B Litter 14        8.11     2     7        5.59     2     4     4
#>  5 Room W Pen C Litter 15        8.09     2     7        5.26     2     4     4
#>  6 Room W Pen C Litter 16        6.77     2     7        4.50     2     4     4
#>  7 Room Z Pen D Litter 17        5.58     2     7        4.26     2     4     4
#>  8 Room Z Pen D Litter 18        7.44     2     6        6.33     2     4     3
#>  9 Room Z Pen E Litter 19        7.98     2     7        4.58     2     4     4
#> 10 Room Z Pen E Litter 20        6.78     2     7        4.86     2     4     4
#> 11 Room Z Pen F Litter 21        6.82     2     7        5.36     2     4     4
#> 12 Room Z Pen F Litter 22        7.27     2     7        5.13     2     4     4
aCore$data
#> # A tibble: 48 × 5
#>    room   pen   litter    tgroup  resp
#>    <chr>  <chr> <chr>     <chr>  <dbl>
#>  1 Room W Pen A Litter 11 vac     5.63
#>  2 Room W Pen A Litter 11 vac     4.62
#>  3 Room W Pen A Litter 11 con     9.20
#>  4 Room W Pen A Litter 11 con     7.28
#>  5 Room W Pen A Litter 12 vac     3.76
#>  6 Room W Pen A Litter 12 vac     3.86
#>  7 Room W Pen A Litter 12 con     6.31
#>  8 Room W Pen A Litter 12 con     3.52
#>  9 Room W Pen B Litter 13 vac     4.36
#> 10 Room W Pen B Litter 13 vac     6.10
#> # ℹ 38 more rows
aCore$formula
#> lung ~ tx + room/pen/litter
#> <environment: 0x559bc69a4b98>
aCore$compare
#> [1] "con" "vac"