State Occupancy Probabilities for First-Order Markov Ordinal Model

`soprobMarkovOrd(y, times, initial, absorb = NULL, intercepts, g, ...)`

matrix with rows corresponding to times and columns corresponding to states, with values equal to exact state occupancy probabilities

- y
a vector of possible y values in order (numeric, character, factor)

- times
vector of measurement times

- initial
initial value of

`y`

(baseline state; numeric, character, factr)- absorb
vector of absorbing states, a subset of

`y`

. The default is no absorbing states. (numeric, character, factor)- intercepts
vector of intercepts in the proportional odds model, with length one less than the length of

`y`

- g
a user-specified function of three or more arguments which in order are

`yprev`

- the value of`y`

at the previous time, the current time`t`

, the`gap`

between the previous time and the current time, an optional (usually named) covariate vector`X`

, and optional arguments such as a regression coefficient value to simulate from. The function needs to allow`yprev`

to be a vector and`yprev`

must not include any absorbing states. The`g`

function returns the linear predictor for the proportional odds model aside from`intercepts`

. The returned value must be a matrix with row names taken from`yprev`

. If the model is a proportional odds model, the returned value must be one column. If it is a partial proportional odds model, the value must have one column for each distinct value of the response variable Y after the first one, with the levels of Y used as optional column names. So columns correspond to`intercepts`

. The different columns are used for`y`

-specific contributions to the linear predictor (aside from`intercepts`

) for a partial or constrained partial proportional odds model. Parameters for partial proportional odds effects may be included in the ... arguments.- ...
additional arguments to pass to

`g`

such as covariate settings

Frank Harrell