When I am running multinom()
, say Y ~ X1 + X2 + X3
, if for one particular row X1
is NA
(i.e. missing), but Y
,
Here is a simple example (from ?multinom
from the nnet
package) to explore the different na.action
:
> library(nnet)
> library(MASS)
> example(birthwt)
> (bwt.mu <- multinom(low ~ ., bwt))
Intentionally create a NA
value:
> bwt[1,"age"]<-NA # Intentionally create NA value
> nrow(bwt)
[1] 189
Test the 4 different na.action
:
> predict(multinom(low ~ ., bwt, na.action=na.exclude)) # Note length is 189
# weights: 12 (11 variable)
initial value 130.311670
iter 10 value 97.622035
final value 97.359978
converged
[1] <NA> 0 0 0 0 0 0 0 0 0 0 0 1 1 0
[16] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
....
> predict(multinom(low ~ ., bwt, na.action=na.omit)) # Note length is 188
# weights: 12 (11 variable)
initial value 130.311670
iter 10 value 97.622035
final value 97.359978
converged
[1] 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0
[38] 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0
.....
> predict(multinom(low ~ ., bwt, na.action=na.fail)) # Generates error
Error in na.fail.default(list(low = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
missing values in object
> predict(multinom(low ~ ., bwt, na.action=na.pass)) # Generates error
Error in qr.default(X) : NA/NaN/Inf in foreign function call (arg 1)
So na.exclude
generates a NA
in the prediction while na.omit
omits it entirely. na.pass
and na.fail
will not create the model.
If na.action
is not specified, this shows the default:
> getOption("na.action")
[1] "na.omit"
You can specify the behaviour
- na.omit and na.exclude: returns the object with observations removed if they contain any missing values; differences between omitting and excluding NAs can be seen in some prediction and residual functions
- na.pass: returns the object unchanged
- na.fail: returns the object only if it contains no missing values
http://www.ats.ucla.edu/stat/r/faq/missing.htm