I have an ordinal variable, overall_tumor_grade, that can take on values of 1, 2, 3, or X if the measurement is indeterminable. There are some NAs that I want to impute using the mice package in R, but I know that the missing values cannot be "X" because their tumor sizes are greater than 0. I want to impute overall_tumor_grade but force mice to only choose from 1, 2, or 3.
Here is sample code for you to use:
df=data.frame(age=c(24,37,58,65,70,84), overall_tumor_grade=c(1,1,2,3,'X',NA), tumor_size=c(1.5,2.0,4.2,5.6,0,0.1)) imp=mice(df) na_index=which(is.na(df$overall_tumor_grade)) complete(imp)$overall_tumor_grade[na_index] #This can never be 'X'
Thank you for your help and please let me know if you need more information.
@longrob suggested I temporarily remove the patients with 'X' observations, impute, then add them back in to the full dataset. Would the imputation lose power by removing all of those observations? Along longrob's suggestion, here is the work-around I have right now using a sample dataframe that is closer to what I'm really working with (several columns with missing values):
df=data.frame(age=rnorm(mean=45,sd=10,25), overall_tumor_grade=factor(sample(c(1,2,3,'X'),25,replace=TRUE)), tumor_size=runif(25)*10) df[df$overall_tumor_grade=='X','tumor_size']=0 #Patients with Grade 'X' have tumorsz=0 df[sample(1:25,3),'age']=NA ##Setting some observations to NA df[sample(1:25,5),'overall_tumor_grade']=NA df[sample(1:25,1),'tumor_size']=NA ################ Imputation imp=mice(df,meth=c('pmm',"",'pmm')) #Suppress imputation of overall_tumor_grade dfimp=complete(imp) dfimp2=dfimp[dfimp$overall_tumor_grade!='X',] #I don't want to impute grade 'X' tumors #so I am trying to remove those observations here and then use droplevels(), but for #a reason I can't figure out, that statement is setting all rows to NA where grade='X'
Any suggestions on how I accomplish what @longrob suggested?