Transition intensity matrix from an msmbayes model

Transition intensity matrix from an msmbayes model

Usage

qmatrix(draws, new_data = NULL, X = NULL, drop = TRUE, type = "posterior")

Arguments

draws

Object returned by msmbayes.

new_data

Data frame with covariate values to predict for

X

Lower-level alternative to specifying new_data, for developer use only. X is a numeric matrix formed from column-binding the covariate design matrices for each transition in turn.

drop

Only used if there are no covariates supplied in new_data. Then if drop=TRUE this returns a nstates x nstates matrix, or if drop=FALSE this returns a 3D array with first dimension ncovs=1.

type

"posterior" to return rvar objects containing posterior samples.

"mode" to return posterior modes (only applicable if model was fitted with posterior mode optimisation).

Value

An array or matrix of rvar objects or numbers, representing the transition intensity matrix for each new prediction data point

See also

qdf returns the same information in a tidy data frame format

Examples

qmatrix(infsim_model)
#> rvar<4000>[2,2] mean ± sd:
#>      [,1]          [,2]         
#> [1,] -0.74 ± 0.34   0.74 ± 0.34 
#> [2,]  4.26 ± 1.98  -4.26 ± 1.98 
summary(qmatrix(infsim_model))
#> # A tibble: 4 × 10
#>   variable         mean median    sd   mad     q5    q95  rhat ess_bulk ess_tail
#>   <chr>           <dbl>  <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl>    <dbl>    <dbl>
#> 1 qmatrix(infsi… -0.740 -0.662 0.344 0.283 -1.41  -0.324 1.000    3891.    4019.
#> 2 qmatrix(infsi…  4.26   3.88  1.98  1.67   1.84   7.97  1.000    3893.    3960.
#> 3 qmatrix(infsi…  0.740  0.662 0.344 0.283  0.324  1.41  1.000    3891.    4019.
#> 4 qmatrix(infsi… -4.26  -3.88  1.98  1.67  -7.97  -1.84  1.000    3893.    3960.
summary(qmatrix(infsim_model), median, ~quantile(.x, c(0.025, 0.975)))
#> # A tibble: 4 × 4
#>   variable                   median `2.5%` `97.5%`
#>   <chr>                       <dbl>  <dbl>   <dbl>
#> 1 qmatrix(infsim_model)[1,1] -0.662 -1.62   -0.278
#> 2 qmatrix(infsim_model)[2,1]  3.88   1.61    9.08 
#> 3 qmatrix(infsim_model)[1,2]  0.662  0.278   1.62 
#> 4 qmatrix(infsim_model)[2,2] -3.88  -9.08   -1.61