intMarkovOrd function

intMarkovOrd

Compute Parameters for Proportional Odds Markov Model

intMarkovOrd(
  y,
  times,
  initial,
  absorb = NULL,
  intercepts,
  extra = NULL,
  g,
  target,
  t,
  ftarget = NULL,
  onlycrit = FALSE,
  constraints = NULL,
  printsop = FALSE,
  ...
)

Arguments

• 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.
• 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 initial guesses for the intercepts
• extra: an optional vector of intial guesses for other parameters passed to g such as regression coefficients for previous states and for general time trends. Name the elements of extra for more informative output.
• 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.
• target: vector of target state occupancy probabilities at time t. If extra is specified, target must be a matrix where row names are character versions of t and columns represent occupancy probabilities corresponding to values of y at the time given in the row.
• t: target times. Can have more than one element only if extra is given.
• ftarget: an optional function defining constraints that relate to transition probabilities. The function returns a penalty which is a sum of absolute differences in probabilities from target probabilities over possibly multiple targets. The ftarget function must have two arguments: intercepts and extra.
• onlycrit: set to TRUE to only return the achieved objective criterion and not print anything
• constraints: a function of two arguments: the vector of current intercept values and the vector of extra parameters, returning TRUE if that vector meets the constrains and FALSE otherwise
• printsop: set to TRUE to print solved-for state occupancy probabilities for groups 1 and 2 and log odds ratios corresponding to them
• ...: optional arguments to pass to stats::nlm(). If this is specified, the arguments that intMarkovOrd normally sends to nlm are not used.

Returns

list containing two vectors named intercepts and extra unless oncrit=TRUE in which case the best achieved sum of absolute errors is returned

Details

Given a vector intercepts of initial guesses at the intercepts in a Markov proportional odds model, and a vector extra if there are other parameters, solves for the intercepts and extra vectors that yields a set of occupancy probabilities at time t that equal, as closely as possible, a vector of target values.

See Also

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

Author(s)

Frank Harrell