y |
a vector of possible y values in order (numeric, character, factor)
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times |
vector of measurement times
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initial |
initial value of y (baseline state; numeric, character, factr)
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absorb |
vector of absorbing states, a subset of y. The default is no absorbing states. (numeric, character, factor)
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intercepts |
vector of intercepts in the proportional odds model, with length one less than the length of y
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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.
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... |
additional arguments to pass to g such as covariate settings
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matrix with rows corresponding to times and columns corresponding to states, with values equal to exact state occupancy probabilities