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| Original file line number | Diff line number | Diff line change |
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| --- | ||
| title: "Working with Posteriors" | ||
| output: | ||
| rmarkdown::html_vignette: | ||
| toc: true | ||
| toc_depth: 3 | ||
| params: | ||
| EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true") | ||
| vignette: > | ||
| %\VignetteIndexEntry{Working with Posteriors} | ||
| %\VignetteEngine{knitr::rmarkdown} | ||
| %\VignetteEncoding{UTF-8} | ||
| --- | ||
|
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| ```{r child="children/settings-knitr.Rmd"} | ||
| ``` | ||
|
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| ## Summary | ||
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| We can easily customise the summary statistics reported by `$summary()` and `$print()`. | ||
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| ```{r} | ||
| fit <- cmdstanr::cmdstanr_example("schools", method = "sample") | ||
| fit$summary() | ||
| ``` | ||
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| By default all variables are summaries with the follow functions: | ||
| ```{r} | ||
| posterior::default_summary_measures() | ||
| ``` | ||
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| To change the variables summarised, we use the variables argument | ||
| ```{r} | ||
| fit$summary(variables = c("mu", "tau")) | ||
| ``` | ||
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| We can additionally change which functions are used | ||
| ```{r} | ||
| fit$summary(variables = c("mu", "tau"), mean, sd) | ||
| ``` | ||
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| To summarise all variables with non-default functions, it is necessary to set explicitly set the variables argument, either to `NULL` or the full vector of variable names. | ||
| ```{r} | ||
| fit$metadata()$model_params | ||
| fit$summary(variables = NULL, "mean", "median") | ||
| ``` | ||
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| Summary functions can be specified by character string, function, or using a formula (or anything else supported by [rlang::as_function]). If these arguments are named, those names will be used in the tibble output. If the summary results are named they will take precedence. | ||
| ```{r} | ||
| my_sd <- function(x) c(My_SD = sd(x)) | ||
| fit$summary( | ||
| c("mu", "tau"), | ||
| MEAN = mean, | ||
| "median", | ||
| my_sd, | ||
| ~quantile(.x, probs = c(0.1, 0.9)), | ||
| Minimum = \(x) min(x) | ||
| ) | ||
| ``` | ||
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| Arguments to all summary functions can also be specified with `.args`. | ||
| ```{r} | ||
| fit$summary(c("mu", "tau"), quantile, .args = list(probs = c(0.025, .05, .95, .975))) | ||
| ``` | ||
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| The summary functions are applied to the array of sample values, with dimension `iter_sampling`x`chains`. | ||
| ```{r} | ||
| fit$summary(variables = NULL, dim, colMeans) | ||
| ``` | ||
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| For this reason users may have unexpected results if they use [stats::var()] directly, as it will return a covariance matrix. An alternative is the [distributional::variance] function. | ||
| ```{r} | ||
| fit$summary(c("mu", "tau"), distributional::variance, ~var(as.vector(.x))) | ||
| ``` | ||
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| Summary functions need not be numeric, but these won't work with `$print()`. | ||
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| ```{r} | ||
| strict_pos <- function(x) if (all(x > 0)) "yes" else "no" | ||
| fit$summary(variables = NULL, "Strictly Positive" = strict_pos) | ||
| # fit$print(variables = NULL, "Strictly Positive" = strict_pos) | ||
| ``` | ||
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| For more information, see [posterior::summarise_draws()], which is is called by `$summary()`. |