R MICE impute new observations - r

Question

When I use the mice package to impute data I have the following issue:
I can't seem to find a way to replace NA values of new observations, given that I already have imputed the missing data in the training set.
Example 1
I have trained an algorithm with data from data frame with 10 features and 1000 observations.
How can I predict a new observation using this algorithm (with missing data)?
Example 2
Supose we have a data frame with NA values:
V1 V2 V3 R1
1 2 NA 1
1.4 -1 0 0
1.2 NA 0 1
1.6 NA 1 1
1.2 3 1 0
I impute the missing values using the mice package:
imp <- mice(df, m = 2, maxit = 100, meth = 'pmmm', seed = 12345)
The object df now has 2 dataframes with imputed values.
(dfImp1)
V1 V2 V3 R1
1 2 0.5 1
1.4 -1 0 0
1.2 1.5 0 1
1.6 1.5 1 1
1.2 3 1 0
Now with this data frame, I can train an algorithm:
modl <- glm(R1~., (dfImp1), family = binomial)
I want to predict the response of a new observation, e.g:
obs1 <- data.frame(V1 = 1, V2 = 1.4, V3 = NA)
How do I impute the missing data a of new individual observation?

Answer 1

It seems that mice package has not a built-in solution but we can write one.
The idea is to:
(1) use the same mice algorithm to fill NA in dataset used to train GLM and the new observation(s);
(2) predict only the new observation without NA.
I'm going to use iris as a data example.
library(R6)
library(mice)
# Binary output to use Binomial
df <- iris %>% filter(Species != "virginica")
# The new observation
new_data <- tail(df, 1)
# the dataset used to train the model
df <- head(df,-1)
# Now, let insert some NAs
insert_nas <- function(x) {
set.seed(123)
len <- length(x)
n <- sample(1:floor(0.2*len), 1)
i <- sample(1:len, n)
x[i] <- NA
x
}
df$Sepal.Length <- insert_nas(df$Sepal.Length)
df$Petal.Width <- insert_nas(df$Petal.Width)
new_data$Sepal.Width = NA
summary(df)
In fit method we apply mice to fill NAs, fit a GLM model and store it to be used in predict method.
In predict method we (1) add the new_observation to the dataset (with NAs), (2) replace NA again using mice, (3) get back the row(s) of the new observation(s) without NA and then (4) apply GLM to predict this new observation.
# R6 Class Generator
GLMWithMice <- R6Class("GLMWithMice", list(
model = NULL,
df = NULL,
fitted = FALSE,
initialize = function(df) {
self$df <- df
},
fit = function(formula = "Species~.", family = binomial) {
imp <- mice(self$df, m = 2, maxit = 100, meth = 'pmm', seed = 12345, print=FALSE)
df_cleaned <- complete(imp,1)
self$model <- glm(formula, df_cleaned, family = family, maxit = 100)
self$fitted <- TRUE
return(cat("\n model fitted!"))
},
predict = function(new_data, type = "response"){
n_rows <- nrow(self$df)
df_new <- bind_rows(self$df, new_data)
imp <- mice(df_new, m = 2, maxit = 100, meth = 'pmm', seed = 12345, print=FALSE)
df_cleaned <- complete(imp,1)
new_data_cleaned <- tail(df_cleaned, nrow(df_new) - n_rows)
return(predict(self$model,new_data_cleaned, type = type))
}
)
)
#Let's create a new instance of "GLMWithMice" class
model <- GLMWithMice$new(df = df)
class(model)
model$fit(formula = Species~., family = binomial)
model$predict(new_data = new_data)