simMarkovOrd function

simMarkovOrd

Simulate Ordinal Markov Process

simMarkovOrd(
  n = 1,
  y,
  times,
  initial,
  X = NULL,
  absorb = NULL,
  intercepts,
  g,
  carry = FALSE,
  rdsample = NULL,
  ...
)

Arguments

• n: number of subjects to simulate
• y: vector of possible y values in order (numeric, character, factor)
• times: vector of measurement times
• initial: initial value of y (baseline state; numeric, character, or factor matching y). If length 1 this value is used for all subjects, otherwise it is a vector of length n.
• X: an optional vector of matrix of baseline covariate values passed to g. If a vector, X represents a set of single values for all the covariates and those values are used for every subject. Otherwise X is a matrix with rows corresponding to subjects and columns corresponding to covariates which g must know how to handle. g only sees one row of X at a time.
• absorb: vector of absorbing states, a subset of y (numeric, character, or factor matching y). The default is no absorbing states. Observations are truncated when an absorbing state is simulated.
• intercepts: vector of intercepts in the proportional odds model. There must be one fewer of these 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.
• carry: set to TRUE to carry absorbing state forward after it is first hit; the default is to end records for the subject once the absorbing state is hit
• rdsample: an optional function to do response-dependent sampling. It is a function of these arguments, which are vectors that stop at any absorbing state: times (ascending measurement times for one subject), y (vector of ordinal outcomes at these times for one subject. The function returns NULL if no observations are to be dropped, returns the vector of new times to sample.
• ...: additional arguments to pass to g such as a regresson coefficient

Returns

data frame with one row per subject per time, and columns id, time, yprev, y, values in ...

Details

Simulates longitudinal data for subjects following a first-order Markov process under a proportional odds model. Optionally, response-dependent sampling can be done, e.g., if a subject hits a specified state at time t, measurements are removed for times t+1, t+3, t+5, ... This is applicable when for example a study of hospitalized patients samples every day, Y=1 denotes patient discharge to home, and sampling is less frequent outside the hospital. This example assumes that arriving home is not an absorbing state, i.e., a patient could return to the hospital.

See Also

https://hbiostat.org/R/Hmisc/markov/

Author(s)

Frank Harrell