Open dnzmarcio opened 9 months ago
Hello,
Following the guidelines from the maintainers of R-package broom that strongly encourages to ask the maintainers of the model-parent package to implement new tidy methods for maintainability purposes, I implemented a tidy method for pim objects:
broom
tidy
pim
library(pim) data('FEVData') mod <- pim(FEV~ Smoke*Sex , data=FEVData) tidy(mod) # # A tibble: 3 × 5 # term estimate std.error statistic p.value # <chr> <dbl> <dbl> <dbl> <dbl> # 1 Smoke 1.01 0.137 7.40 1.40e-13 # 2 Sex 0.371 0.0975 3.80 1.44e- 4 # 3 Smoke:Sex 0.442 0.359 1.23 2.17e- 1
One of the main advantages of having a tidy method for pim objects is that multiple imputation can be performed using the R-package mice:
mice
library(mice) library(pim) data('FEVData') FEV_missing <- FEVData FEV_missing$Sex[sample(1:nrow(FEV_missing), 20)] <- NA FEV_missing$Smoke[sample(1:nrow(FEV_missing), 10)] <- NA FEV_imputed <- FEV_missing |> select(FEV, Smoke, Sex, Age, Height) |> droplevels() |> mice(m=5,maxit=50,meth='pmm',seed=500) fit <- with(FEV_imputed, pim(FEV~ Smoke*Sex)) summary(pool(fit)) # term estimate std.error statistic df p.value #1 Smoke 1.0359524 0.1403922 7.3789899 4471.828 1.891286e-13 #2 Sex 0.3587343 0.0985377 3.6405794 8706.559 2.736026e-04 #3 Smoke:Sex 0.3558264 0.3600426 0.9882898 1027.060 3.232435e-01
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Hello,
Following the guidelines from the maintainers of R-package
broomthat strongly encourages to ask the maintainers of the model-parent package to implement newtidymethods for maintainability purposes, I implemented atidymethod forpimobjects:One of the main advantages of having a
tidymethod forpimobjects is that multiple imputation can be performed using the R-packagemice: