Create a list containing predicted transition probabilities at discrete times.
Since the transition probabilities have presumably
already been predicted based on covariate values, no input data is required for
simulation. The class can be instantiated from either an `array`

, a `data.table`

,
a `data.frame`

, or a `tpmatrix`

.

tparams_transprobs(object, ...) # S3 method for array tparams_transprobs( object, tpmatrix_id = NULL, times = NULL, grp_id = NULL, patient_wt = NULL, ... ) # S3 method for data.table tparams_transprobs(object, ...) # S3 method for data.frame tparams_transprobs(object, ...) # S3 method for tpmatrix tparams_transprobs(object, tpmatrix_id, ...)

object | An object of the appropriate class. |
---|---|

... | Further arguments passed to or from other methods. Currently unused. |

tpmatrix_id | An object of class |

times | An optional numeric vector of distinct times to pass to time_intervals representing time intervals indexed by the 4th dimension of the array. May either be the start or the end of intervals. This argument is not required if there is only one time interval. |

grp_id | An optional numeric vector of integers denoting the subgroups. Must be the same length as the 3rd dimension of the array. |

patient_wt | An optional numeric vector denoting the weight to apply to each patient within a subgroup. Must be the same length as the 3rd dimension of the array. |

An object of class `tparams_transprobs`

,
which is a list containing `value`

and relevant ID attributes. The element `value`

is an
array of predicted transition probability matrices from the probability
distribution of the underlying statistical model. Each matrix in
`value`

is a prediction for a `sample`

, `strategy_id`

,
`patient_id`

, and optionally `time_id`

combination.

The format of `object`

depends on its class:

- array
Either a 3D or a 6D array is possible.

If a 3D array, then each slice is a square transition probability matrix. In this case

`tpmatrix_id`

is required and each matrix slice corresponds to the same numbered row in`tpmatrix_id`

. The number of matrix slices must equal the number of rows in`tpmatrix_id`

.If a 6D array, then the dimensions of the array should be indexed as follows: 1st (

`sample`

), 2nd (`strategy_id`

), 3rd (`patient_id`

), 4th (`time_id`

), 5th (rows of transition matrix), and 6th (columns of transition matrix). In other words, an index of`[s, k, i, t]`

represents the transition matrix for the`s`

th sample,`k`

th treatment strategy,`i`

th patient, and`t`

th time interval.

- data.table
Must contain the following:

ID columns for the parameter sample (

`sample`

), treatment strategy (`strategy_id`

), and patient (`patient_id`

). If the number of time intervals is greater than 1 it must also contain the column`time_start`

denoting the starting time of a time interval. A column`patient_wt`

may also be used to denote the weight to apply to each patient.Columns for each element of the transition probability matrix. They should be prefixed with "prob_" and ordered rowwise. For example, the following columns would be used for a 2x2 transition probability matrix:

`prob_1`

(1st row, 1st column),`prob_2`

(1st row, 2nd column),`prob_3`

(2nd row, 1st column), and`prob_4`

(2nd row, 2nd column).

- data.frame
Same as

`data.table`

.- tpmatrix
An object of class

`tpmatrix`

.

A `tparams_transprobs`

object is also instantiated when creating a
cohort discrete time state transition model using `define_model()`

.