Summarise basic parameter estimates from an msmbayes model

Summarise basic parameter estimates from an msmbayes model

Usage

# S3 method for class 'msmbayes'
summary(object, pars = NULL, ...)

Arguments

object

Object returned by msmbayes.

pars

Character string indicating the parameters to include in the summary. This can include:

q: transition intensities. In semi-Markov models specified with pastates these refer to the intensities of transition between the latent phases.

logq: log transition intensities

time: inverse transition intensities (mean time to event without competing risks)

mst: mean sojourn times

shape, scale: shape and/or scale for Weibull/Gamma phase-type approximations

logshape,logscale corresponding log shape or scale

pnext, logoddsnext next-state probabilites (or log odds) in phase-type approximation models

hr: hazard ratios on transition intensities, including effects on scale parameters in phase-type approximation models.

loghr: log hazard ratios

taf,logtaf: effects on scale parameters in semi-Markov phase-type approximations.

rrnext,logrrnext: effects on competing risk transition probabilities in semi-Markov phase-type approximations.

e: misclassification probabilities

This defaults to whichever of c("q","mst","hr","shape","scale","e") are included in the model.

...

Further arguments passed to both qdf, hr, loghr and edf.

Value

A data frame with one row for each basic model parameter, plus rows for the mean sojourn times. The posterior distribution for the parameter is encoded in the column posterior, which has the rvar data type defined by the posterior package. This distribution can be summarised in any way by calling summary again on the data frame (see the examples).

Transition intensities, or transformations of transition intensities, are those for covariate values of zero.

Remaining parameters (in non-HMMs) are log hazard ratios for covariate effects.

The columns prior and prior_string summarise the corresponding prior distribution in two different ways. prior is a quasi-random sample from the prior in the rvar data type, and is printed as mean and standard deviation. This sample can then be used to produce any summary or plot of the prior. The string prior_string is a summary of this sample, showing the median and 95% equal tailed credible interval.

See also

qdf, hr, loghr, posterior::summarise_draws

Examples

summary(infsim_model)
#> # A tibble: 4 × 7
#>   name   from    to    posterior  mode prior_string                      prior
#>   <chr> <int> <int>   <rvar[1d]> <dbl> <chr>                        <rvar[1d]>
#> 1 q         1     2  0.74 ± 0.34 0.666 " 0.14 ( 0.0027,   6.7)"   0.92 ± 3.5  
#> 2 q         2     1  4.26 ± 1.98 3.86  " 0.14 ( 0.0027,   6.7)"   0.92 ± 3.5  
#> 3 mst       1    NA  1.65 ± 0.78 1.50  "7.4 (0.15, 369)"         50.07 ± 192.1
#> 4 mst       2    NA  0.29 ± 0.13 0.259 "7.4 (0.15, 369)"         50.07 ± 192.1
summary(summary(infsim_model))
#>   name from to      mode           prior_string         prior      mean
#> 1    q    1  2 0.6661191  0.14 ( 0.0027,   6.7)  0.92 ± 3.5   0.7396011
#> 2    q    2  1 3.8606438  0.14 ( 0.0027,   6.7)  0.92 ± 3.5   4.2635807
#> 3  mst    1 NA 1.5012331        7.4 (0.15, 369) 50.07 ± 192.1 1.6488597
#> 4  mst    2 NA 0.2590242        7.4 (0.15, 369) 50.07 ± 192.1 0.2854084
#>      median        sd       mad        q5       q95      rhat ess_bulk ess_tail
#> 1 0.6623418 0.3435138 0.2831664 0.3238362 1.4070959 0.9998919 3891.378 4018.977
#> 2 3.8764643 1.9757256 1.6720749 1.8415761 7.9724115 0.9999342 3893.001 3960.449
#> 3 1.5097945 0.7772463 0.6521936 0.7106836 3.0879814 0.9998919 3891.378 4018.977
#> 4 0.2579671 0.1328940 0.1114707 0.1254326 0.5430131 0.9999786 3893.001 3960.449
summary(summary(infsim_model), median, ~quantile(.x, 0.025, 0.975))
#>   name from to      mode           prior_string         prior    median
#> 1    q    1  2 0.6661191  0.14 ( 0.0027,   6.7)  0.92 ± 3.5   0.6623418
#> 2    q    2  1 3.8606438  0.14 ( 0.0027,   6.7)  0.92 ± 3.5   3.8764643
#> 3  mst    1 NA 1.5012331        7.4 (0.15, 369) 50.07 ± 192.1 1.5097945
#> 4  mst    2 NA 0.2590242        7.4 (0.15, 369) 50.07 ± 192.1 0.2579671
#>        2.5%
#> 1 0.2775348
#> 2 1.6053720
#> 3 0.6177746
#> 4 0.1101494