Identify ranks for use when evaluating MF for nested hierarchy.
MFh.RdIdentify ranks for use when evaluating MF for nested hierarchy.
Usage
MFh(formula, data, compare = c("con", "vac"))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 vgrouping 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 whichcompare[2]is compared.
Value
A mfhierdata object, which is a list of three items.
coreTblA dplyr::tibble with one row for each unique core level showing values for:con_n&vac_n: counts of observations for each treatment level in the core level.con_medResp&vac_medResp: median of theycontinuous response for each treatment level.n1n2: product of the counts,con_n\(\times\)vac_n.w: Wilcoxon statisticu: Mann-Whitney statistic
data: A dplyr::tibble of the restructured input data used for calculations.compare: The compare variables as input by user.formula: The formula as input by user.
Note
Core variable is the variable corresponding to the lowest nodes of the hierarchial tree. Nest variables are those above the core.
See also
MFnest for calculation of MF for nest, core and all variables. mfhierdata for returned object. MFClusHier for a wrapper.
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)
aCore <- MFh(lung ~ tx + room / pen / litter, a)
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
# A tibble: 12 x 10
# room pen litter con_medResp con_n w vac_medResp vac_n n1n2
# <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 Room W Pen A Litter 11 8.24 2 7 5.13 2 4
# 2 Room W Pen A Litter 12 4.91 2 5 3.81 2 4
# 3 Room W Pen B Litter 13 8.10 2 7 5.23 2 4
# 4 Room W Pen B Litter 14 8.11 2 7 5.59 2 4
# 5 Room W Pen C Litter 15 8.09 2 7 5.26 2 4
# 6 Room W Pen C Litter 16 6.77 2 7 4.50 2 4
# 7 Room Z Pen D Litter 17 5.58 2 7 4.26 2 4
# 8 Room Z Pen D Litter 18 7.44 2 6 6.33 2 4
# 9 Room Z Pen E Litter 19 7.98 2 7 4.58 2 4
# 10 Room Z Pen E Litter 20 6.78 2 7 4.86 2 4
# 11 Room Z Pen F Litter 21 6.82 2 7 5.36 2 4
# 12 Room Z Pen F Litter 22 7.27 2 7 5.13 2 4