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I have a data frame A, which I split into a list of 100 data frames, each having 3 rows (In my real data each data frame has 500 rows). Here I show A with 2 elements of the list (row1-row3; row4-row6):

A <- data.frame(n = c(0, 1, 2, 0, 1, 2),
 prob = c(0.4, 0.5, 0.1, 0.4, 0.5, 0.1),
 count = c(24878, 33605, 12100 , 25899, 34777, 13765))

# This is the list:
nest <- split(A, rep(1:2, each = 3))

I want to apply the multinomial test to each of these data frames and extract the p-value of each test. So far I have done this:

library(EMT)

fun <- function(x)
 multinomial.test(x$count,
 prob=x$prob,
 useChisq = FALSE, MonteCarlo = TRUE,
 ntrial = 100, # n of withdrawals accomplished
 atOnce=100)


lapply(nest, fun)

However, I get:

 "Error in multinomial.test(x$counts_set, prob = x$norm_genome, useChisq = F, : 
 Observations have to be stored in a vector, e.g. 'observed <- c(5,2,1)'"

Does anyone have a smarter way of doing this?

The results of split are created with names 1, 2 and so on. That's why x$count in fun cannot access it. To make it simpler, you can combine your splitted elements using the list function and then use lapply:

n <- c(0,1,2,0,1,2)
prob <- c(0.4, 0.5, 0.1, 0.4, 0.5, 0.1)
count <- c(24878, 33605, 12100 , 25899, 34777, 13765)
A <- cbind.data.frame(n, prob, count)

nest = split(A,rep(1:2,each=3))

fun <- function(x)
 multinomial.test(x$count,
 prob=x$prob,
 useChisq = F, MonteCarlo = TRUE,
 ntrial = 100, # n of withdrawals accomplished
 atOnce=100)


# Create a list of splitted elements
new_list <- list(nest$`1`, nest$`2`)

lapply(new_list, fun)

A solution with dplyr.

A = data.frame(n = c(0,1,2,0,1,2),
 prob = c(0.4, 0.5, 0.1, 0.4, 0.5, 0.1),
 count = c(43, 42, 9, 74, 82, 9))

library(dplyr)
nest <- A %>%
 mutate(pattern = rep(1:2,each=3)) %>%
 group_by(pattern) %>%
 dplyr::summarize(mn_pvals = multinomial.test(count, prob)$p.value)
nest