Computes Little's test

Little’s chi-squared test takes into account possible patterns of missingness across all variables in the dataset. Rejection of the null hypothesis of this test would provide sufficient evidence to indicate that the data are (globally) not MCAR. Please note that compared to smdi_hotelling, this function tests for MCAR globally across all missing covariates.

#' #' Important: don't include variables like ID variables, ZIP codes, dates, etc.

Usage

smdi_little(data = NULL)

Arguments

data

dataframe or tibble object with partially observed/missing variables

Value

returns a little object with statistics on little's test globally.

Details

CAVE: Hotelling's and Little's show high susceptibility with large sample sizes and it is recommended to always interpret the results along with the other diagnostics.

References

Little RJA. A Test of Missing Completely at Random for Multivariate Data with Missing Values. J Am Stat Assoc. 1988;83(404):1198-1202.

See also

mcar_test

Examples

library(smdi)
library(dplyr)

smdi_data %>%
 smdi_little()
#> $statistic
#> [1] 811.5801
#> 
#> $df
#> [1] 93
#> 
#> $p.value
#> [1] 0
#> 
#> $missing.patterns
#> [1] 8
#> 
#> attr(,"class")
#> [1] "little"
#> attr(,"row.names")
#> [1] 1