Levene’s test is used to test if the variance between groups is comparable. The test can be used to compare the variances between two groups, but also between more than two groups.
mice functionThe lbp_orig as part of the miceafter package is a dataset with
missing values. So we first impute them with the mice
function. Than we use the mids2milist function to turn a
mids object with multiply imputed datasets, as a result of
using mice, into a milist object. Than we use
the with function to apply repeated analyses with the
levene_test function across the list of multiply imputed
datasets. Finally, we pool the results by using the
pool_levenetest function.
imp_data <- mice(lbp_orig, m=5, seed=3025, printFlag = FALSE)
imp_list <- mids2milist(imp_data)
ra <- with(data=imp_list,
expr = levene_test(Pain ~ factor(Satisfaction)))
res <- pool_levenetest(ra, method = "D1")
res
#> F_value df1 df2 P(>F) RIV
#> [1,] 0.9733556 2 39.1486 0.3867687 0.2869869
#> attr(,"class")
#> [1] "mipool"mice function in one
PipeThe lbp_orig as part of the miceafter package is a dataset with
missing values. So we first impute them with the mice
function. Than we use the mids2milist function to turn a
mids object with multiply imputed datasets, as a result of
using mice, into a milist object. Than we use
the with function to apply repeated analyses with the
levene_test function across the list of multiply imputed
datasets. Finally, we pool the results by using the
pool_levenetest function.
lbp_orig %>%
mice(m=5, seed=3025, printFlag = FALSE) %>%
mids2milist() %>%
with(expr = levene_test(Pain ~ factor(Satisfaction))) %>%
pool_levenetest(method = "D1")
#> F_value df1 df2 P(>F) RIV
#> [1,] 0.9733556 2 39.1486 0.3867687 0.2869869
#> attr(,"class")
#> [1] "mipool"The dataset lbpmilr as part of the miceafter package is
a long dataset that contains 10 multiply imputed datasets. The datasets
are distinguished by the Impnr variable. First we convert
the dataset into a milist object by the
df2milist function. Than we use the with
function to apply repeated analyses with the levene_test
function across the multiply imputed datasets. Finally, we pool the
results by using the pool_levenetest function. As pooling
method we use D1 (D2 is also possible).
lbpmilr %>%
df2milist(impvar = "Impnr") %>%
with(expr = levene_test(Pain ~ factor(Satisfaction))) %>%
pool_levenetest(method = "D1")
#> F_value df1 df2 P(>F) RIV
#> [1,] 1.014884 2 73.57617 0.3674612 0.3920127
#> attr(,"class")
#> [1] "mipool"