How to use apply() on an array of matrices in R? - r

Question

I am trying to use apply() on an array of matrices.
Here is an example:
data(UCBAdmissions)
fisher.test(UCBAdmissions[,,1]) #This works great
apply(UCBAdmissions, c(1,2,3), fisher.test) #This fails

Answer 1

The UCBAdmissions data has six contingency table data within Dept part, namely: "A", "B", "C" , "D", "E" , and "F".
dimnames(UCBAdmissions)
#$Admit
#[1] "Admitted" "Rejected"
#$Gender
#[1] "Male" "Female"
#$Dept
#[1] "A" "B" "C" "D" "E" "F"
You can apply fisher.test to each of these six tables. It is not clear to me from your code apply(UCBAdmissions, c(1,2,3), fisher.test) to which part of the six tables you want to apply fisher.test.
If you want to apply fisher.test to the first three of the six tables, namely "A", "B", and "C", you need to subset the UCBAdmissions data first, and then set the dimension to 3.
apply(UCBAdmissions[,,1:3], 3, fisher.test)
# $A
#
# Fisher's Exact Test for Count Data
#
# data: array(newX[, i], d.call, dn.call)
# p-value = 1.669e-05
# alternative hypothesis: true odds ratio is not equal to 1
# 95 percent confidence interval:
# 0.1970420 0.5920417
# sample estimates:
# odds ratio
# 0.3495628
#
#
# $B
#
# Fisher's Exact Test for Count Data
#
# data: array(newX[, i], d.call, dn.call)
# p-value = 0.6771
# alternative hypothesis: true odds ratio is not equal to 1
# 95 percent confidence interval:
# 0.2944986 2.0040231
# sample estimates:
# odds ratio
# 0.8028124
#
#
# $C
#
# Fisher's Exact Test for Count Data
#
# data: array(newX[, i], d.call, dn.call)
# p-value = 0.3866
# alternative hypothesis: true odds ratio is not equal to 1
# 95 percent confidence interval:
# 0.8452173 1.5162918
# sample estimates:
# odds ratio
# 1.1329
Another option is to replace 3 with the dimension name:
apply(UCBAdmissions[,,1:3], "Dept", fisher.test)
This will give exactly the same result as the previous code.
In another case, if you want to apply fisher.test to contingency tables between Admit and Dept for "A", "B", "C", grouped by Gender, you can use:
apply(UCBAdmissions[,,1:3], "Gender", fisher.test)
# $Male
#
# Fisher's Exact Test for Count Data
#
# data: array(newX[, i], d.call, dn.call)
# p-value = 7.217e-16
# alternative hypothesis: two.sided
#
#
# $Female
#
# Fisher's Exact Test for Count Data
#
# data: array(newX[, i], d.call, dn.call)
# p-value < 2.2e-16
# alternative hypothesis: two.sided
To show the part being tested more clearly, I reshape the data and then filter it so that I have male only students in depts A, B, and C. Then, I apply fisher.test to the data
DF <- UCBAdmissions %>%
as.data.frame %>%
filter(Gender == "Male",
Dept == "A" | Dept == "B" | Dept == "C") %>%
pivot_wider(-Gender, names_from = Admit, values_from = Freq)
DF
# # A tibble: 3 x 3
# Dept Admitted Rejected
# <fct> <dbl> <dbl>
# 1 A 512 313
# 2 B 353 207
# 3 C 120 205
fisher.test(DF[1:3, 2:3])
#
# Fisher's Exact Test for Count Data
#
# data: DF[1:3, 2:3]
# p-value = 7.217e-16
# alternative hypothesis: two.sided
The result is exactly the same as the one resulted from apply(UCBAdmissions[,,1:3], "Gender", fisher.test) for Male group.

Answer 2

Something like this:
Personally I do it this way:
First make a list UCB_list
then bind list elements to dataframe with rbindlist from data.table
finally, use lapply indicating the column y=df$Gender you want to iterate through:
library(data.table)
UCB_list <- list(UCBAdmissions)
df <- rbindlist(lapply(UCB_list, data.frame))
lapply(df, fisher.test, y = df$Gender)
> lapply(df, fisher.test, y = df$Gender)
$Admit
Fisher's Exact Test for Count Data
data: X[[i]] and df$Gender
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.1537975 6.5020580
sample estimates:
odds ratio
1
$Gender
Fisher's Exact Test for Count Data
data: X[[i]] and df$Gender
p-value = 7.396e-07
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
16.56459 Inf
sample estimates:
odds ratio
Inf
$Dept
Fisher's Exact Test for Count Data
data: X[[i]] and df$Gender
p-value = 1
alternative hypothesis: two.sided
$Freq
Fisher's Exact Test for Count Data
data: X[[i]] and df$Gender
p-value = 0.4783
alternative hypothesis: two.sided