How to fit a model I built to another data set and get residuals?

Question

I fitted a mixed model to Data A as follows:

model <- lme(Y~1+X1+X2+X3, random=~1|Class, method=\"ML\", data=A)

Next, I want to see how

Answer 1

You model:

model <- lme(Y~1+X1+X2+X3, random=~1|Class, method="ML", data=A)

2 predictions based on your model:

pred1=predict(model,newdata=A,type='response')
pred2=predict(model,newdata=B,type='response')

missed: A function that calculates the percent of false positives, with cut-off set to 0.5.
(predicted true but in reality those observations were not positive)

missed = function(values,prediction){sum(((prediction > 0.5)*1) != values)/length(values)}

missed(A,pred1)
missed(B,pred2)

Answer 2

The reason you are getting new coefficients in your second attempt with data=B is that the function lme returns a model fitted to your data set using the formula you provide, and stores that model in the variable model as you have selected.

To get more information about a model you can type summary(model_name). the nlme library includes a method called predict.lme which allows you to make predictions based on a fitted model. You can type predict(my_model) to get the predictions using the original data set, or type predict(my_model, some_other_data) as mentioned above to generate predictions using that model but with a different data set.

In your case to get the residuals you just need to subtract the predicted values from observed values. So use predict(my_model,some_other_data) - some_other_data$dependent_var, or in your case predict(model,B) - B$Y.