One-sample T-test Over Multiple Columns with Multiple mu Values in R

user_3581430 0
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I have several datasets, each for a particular time point, and each containing several measures. For each of them, I want to conduct a one-sample t-test on each measure, so across all the columns. Each measure has a different mu value that I want to compare my results with. I have tried creating a function to do this so I only have to give it the name of the dataset as an argument. I have created a list of mu values. However, the function won't accept this and I get an error. Here is an example dataset:

t1 <- rnorm(20, 10, 1)
t2 <- rnorm(20, 10, 1)
t3 <- rnorm(20, 10, 1)
test_data <- data.frame(t1, t2, t3)

And the lists of mu values and variables:

muvals <- c(24, 51.8, 21.89)
varlist <- c(t1, t2, t3)

This is my attempt at the function:

onett <- function(tpoint) {
  t.test(tpoint$varlist, mu = muvals)
}

And the error message I get is: Error in t.test.default(tpoint$varlist, mu = muvals) : 'mu' must be a single number

Is there a way to get this function to work, or otherwise iterate through each column and the list of mu values?

Edit: Each mu value only applies to one column. So the first value for the first column, etc.

t-test statistics r
2 Answers

To iterate over every combination of each column and mu value and simply print out the results of all t-tests the purrr::cross2 function would give you a list of all column/mu combinations and purrr::map would loop over the tests:

library(purrr)

t1 <- rnorm(20, 10, 1)
t2 <- rnorm(20, 10, 1)
t3 <- rnorm(20, 10, 1)
test_data <- data.frame(t1, t2, t3)

onett <- function(data) {
  muvals <- c(24, 51.8, 21.89)
  map(cross2(data, muvals), ~ t.test(.x[[1]], mu = .x[[2]]))
}

onett(test_data)
#> Prints t-test results...

Edit #1

From your clarification of question, it looks like map2 would do the simultaneous iteration over two objects the same length. To make a function you'd pass the data to, I'd suggest something like the following:

library(purrr)
library(dplyr)
library(tidyr)

t1 <- rnorm(20, 10, 1)
t2 <- rnorm(20, 10, 1)
t3 <- rnorm(20, 10, 1)
test_data <- data.frame(t1, t2, t3)


# (Can work best to have `muvals` defined in function rather than environment)

onett <- function(data, muvals = c(24, 51.8, 21.89)) {
  map2(data, muvals, function(data, mu) t.test(data, mu = mu))
}

onett(test_data) %>% 
  map_dfr(broom::tidy)

#> # A tibble: 3 x 8
#>   estimate statistic  p.value parameter conf.low conf.high method    alternative
#>      <dbl>     <dbl>    <dbl>     <dbl>    <dbl>     <dbl> <chr>     <chr>      
#> 1    10.1      -50.4 1.07e-21        19     9.50      10.7 One Samp~ two.sided  
#> 2    10.3     -187.  1.65e-32        19     9.83      10.8 One Samp~ two.sided  
#> 3     9.99     -47.8 2.87e-21        19     9.47      10.5 One Samp~ two.sided

The function outputs the list of t-test results. You can used broom::tidy to extract all t statistics, p-values etc. (shown above), or incorporate that into the function, or tidy the output within the function to give what you need.

Created on 2021-12-04 by the reprex package (v2.0.1)

user_107440820

There may be shorter ways but here is a proposition to test all your samples with all your mu values: you store p-values into a data-frame.

You will find below a function where you can specify both your sample and your mu value ; then you could create a data-frame to store p-values.

# 1- Simulating samples into a data-frame
set.seed(1)
for(k in 1:3){ 
  assign(paste("t", k, sep=""), rnorm(20, 10, 1)) 
}
test_data <- data.frame(t1, t2, t3)

# 2- Choosing mu values to test
muvals <- c(24, 51.8, 21.89)

# 3- Creating function which depends on both your sample and your mu value
onett <- function(tPoint, muValue) {
  t.test(tPoint, mu=muValue)$p.value
}

# 4- Creating a data-frame for p-values storage with your mu values as row-names and your sample name as column-name
dfPvalues <- data.frame(matrix(NA, length(muvals), ncol(test_data)), row.names=muvals)
colnames(dfPvalues) <- colnames(test_data)

# 5- Filling the p-value data-frame through a loop
for(i in 1:nrow(dfPvalues)){
  for(j in 1:ncol(dfPvalues)){
    dfPvalues[i, j] <- onett(tPoint=test_data[,j], muValue=muvals[i])
  }
}
user_170497720
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