Hi. I am looking for a way to perform multiple imputation using nonlinear predictors and then analyze data considering these predictors as nonlinear terms. I came across aregImpute after some research and it's been very handy so thanks a lot for it.
Besides evaluating nonlinear effects, I want to assess the marginal effects of a continuous variable on the probability of a dichotomous outcome. While it's quite straightforward to obtain the model predictions using fit.mult.impute with fitter = lrm or fitter = glm, I am having a hard time estimating the slopes and contrasts. I had done it previously with the complete-case analysis through applying marginaleffects's functions to a mgcv::gam() model with a binomial link. Now I am trying to re-do it with multiply imputed data since we have ~20% missing outcome values and a few missing covariates too.
When I call class(model) using the fit.mult.impute object, I get
Is it possible to extract the glm/lrm part of the object with a view to making it compatible with marginaleffects? Or would you recommend any other way for estimating such contrasts?
Hi. I am looking for a way to perform multiple imputation using nonlinear predictors and then analyze data considering these predictors as nonlinear terms. I came across
aregImpute
after some research and it's been very handy so thanks a lot for it.Besides evaluating nonlinear effects, I want to assess the marginal effects of a continuous variable on the probability of a dichotomous outcome. While it's quite straightforward to obtain the model predictions using
fit.mult.impute
withfitter = lrm
orfitter = glm
, I am having a hard time estimating the slopes and contrasts. I had done it previously with the complete-case analysis through applyingmarginaleffects
's functions to amgcv::gam()
model with a binomial link. Now I am trying to re-do it with multiply imputed data since we have ~20% missing outcome values and a few missing covariates too.When I call class(model) using the
fit.mult.impute
object, I getIs it possible to extract the glm/lrm part of the object with a view to making it compatible with marginaleffects? Or would you recommend any other way for estimating such contrasts?
Thanks in advance!