mcwr_genvars: Generate mcwr variables
In mcwr: Markov Chains with Rewards

Description Usage Arguments Details Value See Also

View source: R/mcwr.R

Generate (additional) transition probability or rewards variables

mcwr_genvars(
  data,
  timing = "",
  add = FALSE,
  replace = FALSE,
  nop = FALSE,
  nor = FALSE,
  order = FALSE
)

`data`	an mcwr data.frame
`timing`	a character or numeric scalar. It specifies how rewards are distributed to from and target states. It is required if option `nor` is not used. timespec can be one of ‘bop’, ‘mid’, and ‘eop’, which stands for ‘beginning-of-period’, ‘mid-period’, and ‘end-of period’, respectively. Alternatively, it can also be a number in the interval [0 1] that specifies the fraction of the interval that goes to the from-state. Values of 0, 0.5, and 1 correspond to ‘beginning-of-period’, ‘mid-period’, and ‘end-of-period’, respectively.
`add`	a boolean TRUE/FALSE. If TRUE, existing r-variables are left unchanged.
`replace`	add a boolean TRUE/FALSE. If TRUE, existing r-variables are replaced.
`nop`	a boolean TRUE/FALSE. If TRUE, not generate any p-variables. By default, all missing p-variables are generated. Since existing p-variables must satisfy the sums-to-unity condition, only p-variables that are (by implication) all-zero can be missing. `mcwr_expectancies()` will run whether such redundant variables exist or not.
`nor`	a boolean TRUE/FALSE. If TRUE, does not generate any r-variables.
`order`	a boolean TRUE/FALSE. If TRUE, orders variables alphabetically according to the column list 'age p* r*'.

The main purpose of this function is to generate rewards variables (r-variables). It examines existing p- and r-variables, determines the implied full set of states, and generates any missing variables that are missing from the data frame. It interacts flexibly with existing r-variables: You can leave them unchanged or have them replaced.

An effective way to create r-variables may be to generate a full set of r-variables using mcwr_genvars() and then edit them where necessary. This is illustrated under in the examples section of mcwr.

A data.frame, suitable for further processing by mcwr_*() functions.

Other mcwr: mcwr_check(), mcwr_exit(), mcwr_expectancies(), mcwr_switch()

mcwr documentation built on March 11, 2021, 1:06 a.m.