Hello, I have many doubts regarding using mice for a dataset > 1000 elements and with 6% to 15% of nulls.
In my situation I try to create a mixed unsupervised model with FAMD or dude.mix but the examples in the different sources indicate the use of a logistic regression when there is a target variable
# source: https://amices.org/mice/
# multiple impute the missing values
imp <- mice(nhanes, maxit = 2, m = 2, seed = 1)
#>
#> iter imp variable
#> 1 1 bmi hyp chl
#> 1 2 bmi hyp chl
#> 2 1 bmi hyp chl
#> 2 2 bmi hyp chl
# fit complete-data model
fit <- with(imp, lm(chl ~ age + bmi))
# pool and summarize the results
summary(pool(fit))
#> term estimate std.error statistic df p.value
#> 1 (Intercept) 9.08 73.09 0.124 4.50 0.9065
#> 2 age 35.23 17.46 2.017 1.36 0.2377
#> 3 bmi 4.69 1.94 2.417 15.25 0.0286
Some articles indicate that using MICE to impute has better results than other methods.
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