A function that runs a MANOVA and returns a summary statistics. For pillai you get
just the pillai score and for manova_p you get the p-value. It works great
within a tidyverse pipeline, though the function itself uses only base R.
pillai(...)
manova_p(...)Arguments that may also be passed to manova(). Typically a formula and a dataframe.
The pillai score from the MANOVA test.
This function is best run when you've got your data properly subsetted so that
only two vowels' data are included. If you want to calculate the pillai score
for multiple speakers, you can do so by grouping the data beforehand (using
dplyr::group_by()).
If you want to incorporate this function within a tidyverse pipeline, it's best to do so within `dplyr::summarize()` since we are boiling lot of data down to just one number. See the examples below.
Note that because it's just a MANOVA under the hood, you can incorporate whatever variables you want within the model. However, currently, only the pillai score of the first independent variable will be returned. So if you want to include vowel and duration in the model, for example, but you're mostly interested in the vowel, be sure to put it first in the formula immediately after the tilde.
NAs won't crash the function, but observations with any NAs will be excluded from analysis.
suppressPackageStartupMessages(library(tidyverse))
options(dplyr.summarise.inform = FALSE)
# Look at the low back merger in one speaker.
one_speaker <- joeysvowels::midpoints
one_speaker %>%
filter(vowel %in% c("LOT", "THOUGHT")) %>%
summarize(pillai = pillai(cbind(F1, F2) ~ vowel))
#> # A tibble: 1 × 1
#> pillai
#> <dbl>
#> 1 0.444
# A non-tidyverse method
pillai(cbind(F1, F2) ~ vowel, data = subset(one_speaker, vowel %in% c("LOT", "THOUGHT")))
#> [1] 0.4437837
# Look at the low back merger in many speakers
many_speakers <- joeysvowels::idahoans
many_speakers %>%
filter(vowel %in% c("AA", "AO")) %>%
group_by(speaker) %>%
summarize(pillai = pillai(cbind(F1, F2) ~ vowel))
#> # A tibble: 10 × 2
#> speaker pillai
#> <fct> <dbl>
#> 1 01 0.592
#> 2 02 0.500
#> 3 03 0.763
#> 4 04 0.663
#> 5 05 0.557
#> 6 06 0.136
#> 7 07 0.412
#> 8 08 0.310
#> 9 09 0.173
#> 10 10 0.267
# Other variables can be included, but the pillai of only the first independent variable is returned
one_speaker %>%
filter(vowel %in% c("LOT", "THOUGHT")) %>%
mutate(dur = end - start) %>%
summarize(pillai = pillai(cbind(F1, F2, F3, F4) ~ vowel + dur))
#> # A tibble: 1 × 1
#> pillai
#> <dbl>
#> 1 0.554
# Observations with NAs are excluded from the analysis
one_speaker[8,]$F1 <- NA
one_speaker %>%
filter(vowel %in% c("LOT", "THOUGHT")) %>%
summarize(pillai = pillai(cbind(F1, F2) ~ vowel))
#> # A tibble: 1 × 1
#> pillai
#> <dbl>
#> 1 0.441