How to retrieve the data frame used in a GEE model fit? - r

Question

I have a longitudinal data frame with multiple rows per id.
> data("dietox")
> head(dietox, 5)
Pig Evit Cu Litter Start Weight Feed Time
1 4601 Evit000 Cu000 1 26.5 26.50000 NA 1
2 4601 Evit000 Cu000 1 26.5 27.59999 5.200005 2
3 4601 Evit000 Cu000 1 26.5 36.50000 17.600000 3
4 4601 Evit000 Cu000 1 26.5 40.29999 28.500000 4
5 4601 Evit000 Cu000 1 26.5 49.09998 45.200001 5
I am trying to fit a GEE model to predict Weight for each row of the data frame.
library(gee)
library(dplyr)
> model1 <- gee(Weight ~ Start + Feed, id=Pig, data=dietox, corstr="exchangeable")
> model1
GEE: GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
gee S-function, version 4.13 modified 98/01/27 (1998)
Model:
Link: Identity
Variance to Mean Relation: Gaussian
Correlation Structure: Exchangeable
Call:
gee(formula = Weight ~ Start + Feed, id = Pig, data = dietox,
corstr = "exchangeable")
Number of observations : 789
Maximum cluster size : 11
Coefficients:
(Intercept) Start Feed
5.1539561 0.9384232 0.4294209
I now want to be able to add a new column to the data frame- prediction, which contains the predicted weight value for each row of data. The idea is that I will then be able to compare the original Weight variable with the prediction variable at different points in the Time variable.
When I try do do this using mutate and predict functions, I get an error saying that the number of observations used in the model fit (789) is different from the number of observations in the original data frame (861).
> new_df <- dietox %>%
+ mutate(prediction = predict(model1))
Error: Column `prediction` must be length 861 (the number of rows) or one, not 789
My questions are:
1. How do I extract the data frame for the 789 observations that
were used in the model fit?
2. Why is the number of observations
used in the model fit different to the total number of observations
in the original data frame?

Answer 1

The 789 observations used in model fitting were the ones which were without NA. You had 72 observations as NA in Feed column
sum(is.na(dietox$Feed))
#[1] 72
and 789 + 72 gives you complete 861 observations. To get all the predicted values you could do
dietox$Prediction <- NA
dietox$Prediction[!is.na(dietox$Feed)] <- predict(model1)
head(dietox)
# Weight Feed Time Pig Evit Cu Litter Prediction
#1 26.50000 NA 1 4601 1 1 1 NA
#2 27.59999 5.200005 2 4601 1 1 1 31.43603
#3 36.50000 17.600000 3 4601 1 1 1 36.76708
#4 40.29999 28.500000 4 4601 1 1 1 41.45324
#5 49.09998 45.200001 5 4601 1 1 1 48.63296
#6 55.39999 56.900002 6 4601 1 1 1 53.66306
Also the values which were used in the model are present in model1$y.