docs: Added coef function to readme, updated sample size a bit and tr…

…ied to change the performance vignette to run faster

NAMESPACE

@@ -30,4 +30,6 @@ importFrom(stats,gaussian)
 importFrom(stats,is.empty.model)
 importFrom(stats,model.matrix)
 importFrom(stats,model.response)
+importFrom(stats,rnorm)
 importFrom(stats,setNames)
+importFrom(stats,time)

R/mcmcglm-package.R

@@ -12,6 +12,8 @@
 #' @importFrom stats is.empty.model
 #' @importFrom stats model.matrix
 #' @importFrom stats model.response
+#' @importFrom stats rnorm
 #' @importFrom stats setNames
+#' @importFrom stats time
 ## usethis namespace: end
 NULL

README.Rmd

@@ -41,7 +41,7 @@ withr::local_seed(42)
 
 We first simulate some data from a linear model to use for showcasing the use of `mcmcglm` for a gaussian family. 
 ```{r, label = data-generation}
-n <- 100
+n <- 1000
 x1 <- rnorm (n)
 x2 <- rbinom (n, 1, .5)
 b0 <- 1
@@ -75,6 +75,11 @@ summarising the call of the function with averages of the samples of each parame
 
 ### Investigating results
 
+The averages shown in the print method of the object can be retrieved with the generic `coef` like so:
+```{r, label = gaussian-coef}
+coef(norm)
+```
+
 The full data set of samples can be accessed with the `samples` function:
 ```{r, label = gaussian-samples}
 head(samples(norm))

README.md

@@ -35,7 +35,7 @@ We first simulate some data from a linear model to use for showcasing
 the use of `mcmcglm` for a gaussian family.
 
 ``` r
-n <- 100
+n <- 1000
 x1 <- rnorm (n)
 x2 <- rbinom (n, 1, .5)
 b0 <- 1
@@ -77,26 +77,35 @@ norm
 #> 
 #> Average of parameter samples:
 #>   (Intercept)       X1       X2
-#> 1    1.081188 1.563513 1.733437
+#> 1    1.011134 1.490459 2.026047
 ```
 
 summarising the call of the function with averages of the samples of
 each parameter in the GLM model.
 
 ### Investigating results
 
+The averages shown in the print method of the object can be retrieved
+with the generic `coef` like so:
+
+``` r
+coef(norm)
+#>   (Intercept)       X1       X2
+#> 1    1.011134 1.490459 2.026047
+```
+
 The full data set of samples can be accessed with the `samples`
 function:
 
 ``` r
 head(samples(norm))
-#>   (Intercept)         X1         X2 iteration burnin
-#> 1   -1.096156 0.04905045 -1.1984959         0   TRUE
-#> 2    5.150198 2.27939313 -2.0547715         1   TRUE
-#> 3    1.171198 2.10736753 -1.4556810         2   TRUE
-#> 4    2.201093 1.02283189  0.1304377         3   TRUE
-#> 5    1.761626 1.99163723  0.3076245         4   TRUE
-#> 6    1.715483 1.29845999  1.5699513         5   TRUE
+#>   (Intercept)           X1          X2 iteration burnin
+#> 1   0.6173367 -0.004541141 -0.09125636         0   TRUE
+#> 2   2.6508146  0.281295470  0.68343132         1   TRUE
+#> 3   0.8240996  0.324627659  2.30889073         2   TRUE
+#> 4   0.8170086  1.028326905  2.20351455         3   TRUE
+#> 5   0.8777350  1.592074284  2.16115289         4   TRUE
+#> 6   0.9092187  1.442872350  2.02913214         5   TRUE
 ```
 
 A trace plot can be seen with the function `trace_plot`:

vignettes/performance.Rmd

@@ -26,11 +26,10 @@ withr::local_seed(42)
 ### Using default slice function
 ```{r run-compare-nvars}
 res <- compare_eta_comptime_across_nvars(
-  n_vars = c(2, seq(from = 500, to = 1000, by = 500)),
-  n_samples = 10,
-  burnin = 1,
-  w = 0.5,
-  parallelise = TRUE)
+  n_vars = c(2, seq(from = 20, to = 200, by = 20)),
+  n_samples = 1,
+  burnin = 0,
+  w = 0.5)
 ```
 
 ```{r show-compare}