Error in na.fail.default: missing values in object - but no missing values

Question

I am trying to run a lme model with these data:

tot_nochc=runif(10,1,15)
cor_partner=factor(c(1,1,0,1,0,0,0,0,1,0))
age=runif(10,18,75)
agecu=age^3
day=factor(c(1,2,2,3,3,NA,NA,4,4,4))
dt=as.data.frame(cbind(tot_nochc,cor_partner,agecu,day))
attach(dt)

corpart.lme.1=lme(tot_nochc~cor_partner+agecu+cor_partner *agecu, 
                  random = ~cor_partner+agecu+cor_partner *agecu |day, 
                  na.exclude(day))

I get this error code:

Error in na.fail.default(list(cor_partner = c(1L, 1L, 2L, 1L, 1L, 1L, : missing values in object

I am aware there are similar questions in the forum. However, in my case:

• cor_partner has no missing values;
• the whole object is coded as a factor (at least from what the Global Environment shows).

I could exclude those NA values with an na.action, but I'd rather know why the function is reading missing values - to understand exactly what is happening to my data.

Answer 1

tl;dr you have to use na.exclude() (or whatever) on the whole data frame at once, so that the remaining observations stay matched up across variables ...

set.seed(101)
tot_nochc=runif(10,1,15)
cor_partner=factor(c(1,1,0,1,0,0,0,0,1,0))
age=runif(10,18,75)
agecu=age^3
day=factor(c(1,2,2,3,3,NA,NA,4,4,4))
## use data.frame() -- *DON'T* cbind() first
dt=data.frame(tot_nochc,cor_partner,agecu,day)
## DON'T attach(dt) ...

Now try:

library(nlme)
corpart.lme.1=lme(tot_nochc~cor_partner+agecu+cor_partner *agecu, 
              random = ~cor_partner+agecu+cor_partner *agecu |day, 
              data=dt,
              na.action=na.exclude)

We get convergence errors and warnings, but I think that's now because we're using a tiny made-up data set without enough information in it and not because of any inherent problem with the code.

Answer 2

randomForest package has a na.roughfix function that "imputes Missing Values by median/mode"

You can use it as follows

fit_rf<-randomForest(store~.,
        data=store_train,
        importance=TRUE,
        prOximity=TRUE,
        na.action=na.roughfix)

Answer 3

if your data contain Na or missing values you can use this it will pass the data exactly the same as it is in datasets.

rf<-randomForest(target~.,data=train, na.action = na.roughfix)