Little's missing completely at random (MCAR) test error with naniar

[NurM]

I want to perform Little's MCAR Test to evaluate missing data pattern with a dataset of 36 Variables (metric, ordinal, nominal) and 106 observations.

I get the following error applying little MCAR test in R with Naniar package:

mcar_test(myData)

Error: Problem with mutate() column d2.

i d2 = purrr::pmap_dbl(...). x system is computationally singular:
reciprocal condition number = 9.76219e-17

i The error occurred in
group 1: miss_pattern = 1. Run rlang::last_error() to see where the
error occurred.

In addition: Warning message: In
norm::prelim.norm(data) : NAs introduced by coercion to integer range

When I exclude certain variables (A,B,C, D, E) it works.

Here's my code for the relevant Variables:

#packages (tidyverse, broom, robustbase, readxl, ggThemeAssist, knitr, MASS, rmarkdown, devtools, naniar)


#A (ordinal variabel) myData <- myData %>% mutate(A = factor(A, ordered = TRUE, 
levels = c(0, 1, 2, 3, 4, 5, 6), labels = c("no Attempt", "1th" , "2nd", "3rd", 
"4th", "5th", "6th and more")))
myData$A[myData$A==-999] <-NA 

#B (metric variabel, time variabel in hours)
myData$B <- as.factor(myData$B)
myData$B[myData$B==-999] <-NA

#C (metric variabel, time variabel in hours)
myData$C <- as.factor(myData$C)
myData$C[myData$C==-999] <-NA

#D (ordinal variabel)
myData <- myData %>%
mutate(D = factor(D, ordered = TRUE, levels = c(0, 1, 2, 3, 4), labels = c("0", 
"1" , "2a", "2b", "3")))
myData$D[myData$D==-999] <-NA

#E (kategorial variabel)
myData <- myData %>%
mutate(E = factor(E, levels = 0:1, labels = c("no", "yes")))
myData$E[myData$E==-999] <-NA

dput(mydata[1:10, 1:5])

structure(list(A = structure(c(1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,1L, 2L), .Label = 
c("no Attempt", "1th", "2nd", "3rd", "4th", 
"5th", "6th and more"), class = c("ordered", "factor")), B = structure(c(36L, 
35L, 42L, 38L, 20L, 17L, 40L, 27L, 44L, 39L), .Label = c("-999", 
"0.07", "0.09", "0.11", "0.16", "0.18", "0.21", "0.22", "0.3", 
"0.32", "0.33", "0.38", "0.39", "0.4", "0.41", "0.43", "0.44", 
"0.47", "0.5", "0.51", "0.55", "0.59", "1", "1.12", "1.13", "1.15", 
"1.18", "1.28", "1.35", "1.37", "1.39", "1.45", "1.49", "1.55", 
"1.57", "2.02", "2.03", "2.24", "2.26", "2.33", "2.41", "3.28", 
"3.54", "6.25", "6.32", "23.3"), class = "factor"), C = structure(c(NA, 
19L, 53L, NA, NA, 42L, NA, 5L, 13L, 10L), .Label = c("-999", 
"0.15", "0.48", "0.54", "1", "1.1", "1.13", "1.15", "1.16", "1.2", 
"1.25", "1.28", "1.3", "1.39", "1.41", "1.43", "1.45", "1.52", 
"1.8", "2", "2.3", "2.35", "2.45", "2.48", "2.5", "3", "3.15", 
"3.28", "3.29", "3.53", "3.55", "4", "4.02", "4.09", "4.2", "4.31", 
"4.47", "5", "5.14", "5.47", "6", "6.3", "6.45", "7.2", "7.21", 
"7.56", "7.58", "10", "10.05", "10.11", "11.14", "11.51", "12", 
"12.13", "15", "16.14", "19.32", "22.11", "24", "25.52", "32.3", 
"42", "48", "53", "70", "72", "96"), class = "factor"), D = 
structure(c(5L, 
3L, 3L, 5L, 5L, 3L, 3L, 4L, 3L, 5L), .Label = c("0", "1", "2a", 
"2b", "3"), class = c("ordered", "factor")), E = structure(c(2L, 
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L), .Label = c("no", "yes"), class = 
"factor")), 
row.names = c(NA, 
-10L), class = c("tbl_df", "tbl", "data.frame"))

Any advice?

M.