How much missing data is too much? Multiple Imputation (MICE) R If the imputation method is poor (i e , it predicts missing values in a biased manner), then it doesn't matter if only 5% or 10% of your data are missing - it will still yield biased results (though, perhaps tolerably so) The more missing data you have, the more you are relying on your imputation algorithm to be valid
Multiple imputation and modelling using penalised splines I ran multiple imputation in R using mice Only one categorical variable had missingness and I specified the imputation model to imputate it using polyreg After imputation, I run the Cox model bel
missing data - Test set imputation - Cross Validated As far as the second point - people developing predictive models rarely think how missing data occurs in application You need to have methods for missing values to render useful predictions - this is a "so called package deal" It seems hard to make a case that you can observe the future "test" set in batch and re-develop an imputation model
Rubins rule from scratch for multiple imputations I have multiple set of imputations generated from multiple instances of random forest (such that the predictors are all the variables except the one column to impute) I was referred to Rubin's rul
How should I determine what imputation method to use? What imputation method should I use here and, more generally, how should I determine what imputation method to use for a given data set? I've referenced this answer but I'm not sure what to do from it
normalization - Should data be normalized before or after imputation of . . . 9 I am working on a metabolomics data set of 81 samples x 407 variables with ~17% missing data I would like to compare a number of imputation methods to see which is best for my data Is there a general rule for the order of pre-treating a data set? Should I impute first and normalize after or normalize first?
How do you choose the imputation technique? - Cross Validated I read the scikit-learn Imputation of Missing Values and Impute Missing Values Before Building an Estimator tutorials and a blog post on Stop Wasting Useful Information When Imputing Missing Values
Creating a Pooled Data Set From Multiple Imputation Output in SPSS I used the built-in Multiple Imputation script and made 10 imputed datasets for each year that I've been able to perform all of my regression analysis just fine on, since the GLM process runs on all the individual imputed sets as well as a pooled set that contains the imputed sets
Does this imputation with mice() make sense? - Cross Validated I am currently working on my first R project using medical data I wanted to use MICE imputation for a few variables, and I had a doubt If, for example, variable BMI had zero missing values, then