R: Extract original data from 'msm' objects.

model.frame.msm {msm} | R Documentation

## Extract original data from `msm` objects.

### Description

Extract the data from a multi-state model fitted with `msm`.

### Usage

    ## S3 method for class 'msm'
    model.frame(formula, agg=FALSE, ...)
    ## S3 method for class 'msm'
    model.matrix(object, model="intens", state=1, ...)

### Arguments

`formula` | A fitted multi-state model object, as returned by `msm`.
`agg` | Return the model frame in the efficient aggregated form used to calculate the likelihood internally for non-hidden Markov models. This has one row for each unique combination of from-state, to-state, time lag, covariate value and observation type. The variable named `"(nocc)"` counts how many observations of that combination there are in the original data.
`object` | A fitted multi-state model object, as returned by `msm`.
`model` | `"intens"` to return the design matrix for covariates on intensities, `"misc"` for misclassification probabilities, `"hmm"` for a general hidden Markov model, and `"inits"` for initial state probabilities in hidden Markov models.
`state` | State corresponding to the required covariate design matrix in a hidden Markov model.
`...` | Further arguments (not used).

### Value

`model.frame` returns a data frame with all the original variables used for the model fit, with any missing data removed (see `na.action` in `msm`). The state, time, subject, `obstype` and `obstrue` variables are named `"(state)"`, `"(time)"`, `"(subject)"`, `"(obstype)"` and `"(obstrue)"` respectively (note the brackets). A variable called `"(obs)"` is the observation number from the original data before any missing data were dropped. The variable `"(pcomb)"` is used for computing the likelihood for hidden Markov models, and identifies which distinct time difference, `obstype` and covariate values (thus which distinct interval transition probability matrix) each observation corresponds to.

The model frame object has some other useful attributes, including `"usernames"` giving the user's original names for these variables (used for model refitting, e.g. in bootstrapping or cross validation) and `"covnames"` identifying which ones are covariates.

`model.matrix` returns a design matrix for a part of the model that includes covariates. The required part is indicated by the `"model"` argument.

For time-inhomogeneous models fitted with `"pci"`, these datasets will have imputed observations at each time change point, indicated where the variable `"(pci.imp)"` in the model frame is 1. The model matrix for intensities will have factor contrasts for the `timeperiod` covariate.

### Author(s)

C. H. Jackson chris.jackson@mrc-bsu.cam.ac.uk

### See Also

`msm`, `model.frame`, `model.matrix`.

[Package msm version 1.6.7 Index]