问题
I have a data frame similar to the example below (which is a small extract of my actual data frame).
frequencies <- data.frame(sex=c("female", "female", "male", "male", "female", "female", "male", "male", "female", "female", "male", "male", "female", "female", "male", "male"),
ecotype=c("Crab", "Wave", "Crab", "Wave", "Crab", "Wave", "Crab", "Wave", "Crab", "Wave", "Crab", "Wave", "Crab", "Wave", "Crab", "Wave"),
contig_ID=c("Contig100169_2367", "Contig100169_2367", "Contig100169_2367", "Contig100169_2367", "Contig100169_2367", "Contig100169_2367", "Contig100169_2367", "Contig100169_2367",
"Contig100169_2481", "Contig100169_2481", "Contig100169_2481", "Contig100169_2481", "Contig100169_2481", "Contig100169_2481", "Contig100169_2481", "Contig100169_2481"),
allele=c("p", "p", "p", "p", "q", "q", "q", "q", "p", "p", "p", "p", "q", "q", "q", "q"),
frequency=c(157, 98, 140, 65, 29, 8, 26, 9, 182, 108, 147, 80, 46, 4, 49, 4))
I would like to do separate chi-square contingency tests for each combination of ‘contig_ID’ and ‘ecotype’, testing the association between ‘sex’ and ‘allele’. I would then like to summarise the results of these in a table that includes the p value for each combination of ‘contig_ID’ and ‘ecotype’. For instance, from the example table given, I would expect a results table of 4 p values like the example below.
results <- data.frame(ecotype=c("Crab", "Wave", "Crab", "Wave"),
contig_ID=c("Contig100169_2367", "Contig100169_2367", "Contig100169_2481", "Contig100169_2481"),
pvalue=c("pval", "pval", "pval", "pval"))
Alternatively, just adding a p value column to the original table would also work, with the p value for each combination just repeated in all the relevant rows.
I have been attempting to use functions such as lapply()
and summarise()
in combination with chisq.test()
to achieve this but have had no luck so far. I have also attempted to use a method similar to this: R chi squared test (3x2 contingency table) for each row in a table , but couldn't make this work either.
回答1:
We can group the contig_ID
and ecotype
columns and created a nested data frame with the data converted to a matrix as follows.
library(tidyverse)
frequencies2 <- frequencies %>%
group_by(contig_ID, ecotype) %>%
nest() %>%
mutate(M = map(data, function(dat){
dat2 <- dat %>% spread(sex, frequency)
M <- as.matrix(dat2[, -1])
row.names(M) <- dat2$allele
return(M)
}))
If we look at the first element of the M
column, we will find out that data from each group were converted to a matrix.
frequencies2$M[[1]]
# female male
# p 157 140
# q 29 26
From here, we can applied the chisq.test
to each matrix and pull out the p value. frequencies3
is the final output.
frequencies3 <- frequencies2 %>%
mutate(pvalue = map_dbl(M, ~chisq.test(.x)$p.value)) %>%
select(-data, -M) %>%
ungroup()
frequencies3
# # A tibble: 4 x 3
# contig_ID ecotype pvalue
# <fct> <fct> <dbl>
# 1 Contig100169_2367 Crab 1.00
# 2 Contig100169_2367 Wave 0.434
# 3 Contig100169_2481 Crab 0.284
# 4 Contig100169_2481 Wave 0.958
来源:https://stackoverflow.com/questions/49659103/using-r-apply-multiple-chi-square-contingency-table-tests-to-a-grouped-data-fra