splines with time-varying covariates

[SoConfused]

Anyone?

[jlaake]

Sorry I was in the field and just now finding all of the emails I needed to address.

POPAN uses square PIMS whereas CJS uses triangular PIMS which is why you are getting the difference in the design data. My simple example only has a couple of age groups and if you marked all ages at each occasion you wouldn't have all of the gaps. But you can always compute whatever values you want from the model and in particular for additive models you can predict even if you don't have animals. Might not be a very good prediction but you can do it. But I think what you want to use is predict_real which allows you to modify the design data

Code: Select all

predict_real {RMark}   R Documentation
Compute estimates of real parameters with individual and design covariates
Description
Unlike covariate.predictions this function allows modification of design data covariates as well as individual covariates for the computation of the real parameters. It does this by modifying values in the design data, adding individual covariate values to the design data and then applying the model formula to the modified design data to construct a design matrix that is used with the beta estimates to construct the real parameter estimates.

Usage
predict_real(
  model,
  df,
  parameter,
  replicate = FALSE,
  beta = NULL,
  data = NULL,
  se = TRUE,
  vcv = FALSE
)

See the help file for my details and an example.