update 'epilepsy' data set

R/brm.R

@@ -233,7 +233,7 @@
 #' # and half cauchy priors for standard deviations of group-level effects
 #' bprior1 <- prior(student_t(5,0,10), class = b) +
 #'   prior(cauchy(0,2), class = sd)
-#' fit1 <- brm(count ~ log_Age_c + log_Base4_c * Trt + (1|patient),
+#' fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient),
 #'             data = epilepsy, family = poisson(), prior = bprior1)
 #'
 #' # generate a summary of the results

R/brmsfit-methods.R

@@ -75,7 +75,7 @@ fixef.brmsfit <-  function(object, summary = TRUE, robust = FALSE,
 #'   
 #' @examples
 #' \dontrun{
-#' fit <- brm(count ~ log_Age_c + log_Base4_c * Trt_c + (1+Trt_c|visit), 
+#' fit <- brm(count ~ zAge + zBase * Trt + (1+Trt|visit), 
 #'            data = epilepsy, family = gaussian(), chains = 2)
 #' vcov(fit)
 #' }
@@ -126,7 +126,7 @@ vcov.brmsfit <- function(object, correlation = FALSE, pars = NULL, ...) {
 #'   
 #' @examples
 #' \dontrun{
-#' fit <- brm(count ~ log_Age_c + log_Base4_c * Trt_c + (1+Trt_c|visit), 
+#' fit <- brm(count ~ zAge + zBase * Trt + (1+Trt|visit), 
 #'            data = epilepsy, family = gaussian(), chains = 2)
 #' ranef(fit)
 #' }
@@ -199,7 +199,7 @@ ranef.brmsfit <- function(object, summary = TRUE, robust = FALSE,
 #'  
 #' @examples
 #' \dontrun{
-#' fit <- brm(count ~ log_Age_c + log_Base4_c * Trt_c + (1+Trt_c|visit), 
+#' fit <- brm(count ~ zAge + zBase * Trt + (1+Trt|visit), 
 #'            data = epilepsy, family = gaussian(), chains = 2)
 #' ## extract population and group-level coefficients separately
 #' fixef(fit)
@@ -307,7 +307,7 @@ coef.brmsfit <- function(object, summary = TRUE, robust = FALSE,
 #' 
 #' @examples
 #' \dontrun{
-#' fit <- brm(count ~ log_Age_c + log_Base4_c * Trt_c + (1+Trt_c|visit), 
+#' fit <- brm(count ~ zAge + zBase * Trt + (1+Trt|visit), 
 #'            data = epilepsy, family = gaussian(), chains = 2)
 #' VarCorr(fit)
 #' }
@@ -492,7 +492,7 @@ as.array.brmsfit <- function(x, ...) {
 #'   
 #' @examples 
 #' \dontrun{
-#' fit <- brm(count ~ log_Age_c + log_Base4_c * Trt_c,
+#' fit <- brm(count ~ zAge + zBase * Trt,
 #'            data = epilepsy, family = negbinomial())
 #' posterior_interval(fit)
 #' }
@@ -587,7 +587,7 @@ as.mcmc.brmsfit <- function(x, pars = NA, exact_match = FALSE,
 #' 
 #' @examples 
 #' \dontrun{
-#' fit <- brm(count ~ log_Age_c + log_Base4_c * Trt_c  
+#' fit <- brm(count ~ zAge + zBase * Trt  
 #'              + (1|patient) + (1|obs), 
 #'            data = epilepsy, family = poisson(), 
 #'            prior = c(prior(student_t(5,0,10), class = b),
@@ -1073,7 +1073,7 @@ launch_shinystan.brmsfit <- function(
 #' 
 #' @examples
 #' \dontrun{ 
-#' fit <- brm(count ~ log_Age_c + log_Base4_c * Trt_c 
+#' fit <- brm(count ~ zAge + zBase * Trt 
 #'            + (1|patient) + (1|visit), 
 #'            data = epilepsy, family = "poisson")
 #' plot(fit)
@@ -1225,7 +1225,7 @@ stanplot.brmsfit <- function(object, pars = NA, type = "intervals",
 #' 
 #' @examples
 #' \dontrun{
-#' fit <-  brm(count ~ log_Age_c + log_Base4_c * Trt_c
+#' fit <-  brm(count ~ zAge + zBase * Trt
 #'             + (1|patient) + (1|obs),
 #'             data = epilepsy, family = poisson())
 #' 
@@ -1365,7 +1365,7 @@ pp_check.brmsfit <- function(object, type, nsamples, group = NULL,
 #'  
 #' @examples 
 #' \dontrun{
-#' fit <- brm(count ~ log_Age_c + log_Base4_c * Trt_c 
+#' fit <- brm(count ~ zAge + zBase * Trt 
 #'            + (1|patient) + (1|visit), 
 #'            data = epilepsy, family = "poisson")  
 #' pairs(fit, pars = parnames(fit)[1:3], exact_match = TRUE)
@@ -1918,7 +1918,7 @@ predictive_error.brmsfit <- function(
 #' 
 #' @examples 
 #' \dontrun{
-#' fit <- brm(count ~ log_Base4_c, data = epilepsy, family = poisson())
+#' fit <- brm(count ~ zBase, data = epilepsy, family = poisson())
 #' predictive_interval(fit)
 #' }
 #' 
@@ -3048,7 +3048,7 @@ control_params.brmsfit <- function(x, pars = NULL, ...) {
 #' \dontrun{
 #' # model with the treatment effect
 #' fit1 <- brm(
-#'   count ~ log_Age_c + log_Base4_c + Trt_c,
+#'   count ~ zAge + zBase + Trt,
 #'   data = epilepsy, family = negbinomial(), 
 #'   prior = prior(normal(0, 1), class = b),
 #'   save_all_pars = TRUE
@@ -3058,7 +3058,7 @@ control_params.brmsfit <- function(x, pars = NULL, ...) {
 #' 
 #' # model without the treatment effect
 #' fit2 <- brm(
-#'   count ~ log_Age_c + log_Base4_c,
+#'   count ~ zAge + zBase,
 #'   data = epilepsy, family = negbinomial(), 
 #'   prior = prior(normal(0, 1), class = b),
 #'   save_all_pars = TRUE
@@ -3138,7 +3138,7 @@ bridge_sampler.brmsfit <- function(samples, ...) {
 #' \dontrun{
 #' # model with the treatment effect
 #' fit1 <- brm(
-#'   count ~ log_Age_c + log_Base4_c + Trt_c,
+#'   count ~ zAge + zBase + Trt,
 #'   data = epilepsy, family = negbinomial(), 
 #'   prior = prior(normal(0, 1), class = b),
 #'   save_all_pars = TRUE
@@ -3147,7 +3147,7 @@ bridge_sampler.brmsfit <- function(samples, ...) {
 #' 
 #' # model without the treatment effect
 #' fit2 <- brm(
-#'   count ~ log_Age_c + log_Base4_c,
+#'   count ~ zAge + zBase,
 #'   data = epilepsy, family = negbinomial(), 
 #'   prior = prior(normal(0, 1), class = b),
 #'   save_all_pars = TRUE
@@ -3210,7 +3210,7 @@ bayes_factor.brmsfit <- function(x1, x2, log = FALSE, ...) {
 #' \dontrun{
 #' # model with the treatment effect
 #' fit1 <- brm(
-#'   count ~ log_Age_c + log_Base4_c + Trt_c,
+#'   count ~ zAge + zBase + Trt,
 #'   data = epilepsy, family = negbinomial(), 
 #'   prior = prior(normal(0, 1), class = b),
 #'   save_all_pars = TRUE
@@ -3219,7 +3219,7 @@ bayes_factor.brmsfit <- function(x1, x2, log = FALSE, ...) {
 #' 
 #' # model without the treatent effect
 #' fit2 <- brm(
-#'   count ~ log_Age_c + log_Base4_c,
+#'   count ~ zAge + zBase,
 #'   data = epilepsy, family = negbinomial(), 
 #'   prior = prior(normal(0, 1), class = b),
 #'   save_all_pars = TRUE

R/datasets.R

@@ -7,7 +7,7 @@
 #'   In addition, information on the risk variables age, sex and disease 
 #'   type is provided.
 #' 
-#' @format A dataframe of 76 observations containing 
+#' @format A data frame of 76 observations containing 
 #'   information on the following 7 variables.
 #' \describe{
 #'  \item{time}{The time to first or second recurrence of the infection, 
@@ -54,7 +54,7 @@
 #'   Patients were asked to rate the clarity of leaflet instructions 
 #'   accompanying each device, using a 4-point ordinal scale.
 #'   
-#' @format A dataframe of 572 observations containing 
+#' @format A data frame of 572 observations containing 
 #'   information on the following 5 variables. 
 #' \describe{
 #'  \item{subject}{The subject number}
@@ -98,37 +98,34 @@
 #'   therapy in epilepsy. Covariates are treatment, 
 #'   8-week baseline seizure counts, and age of the patients in years. 
 #'   
-#' @format A dataframe of 236 observations containing information 
+#' @format A data frame of 236 observations containing information 
 #'   on the following 9 variables. 
 #' \describe{
 #'  \item{Age}{The age of the patients in years}
 #'  \item{Base}{The seizure count at 8-weeks baseline}
 #'  \item{Trt}{Either \code{0} or \code{1} indicating 
 #'    if the patient received anti-convulsant therapy} 
-#'  \item{log_Age_c}{The logarithm of Age centered around its mean}
-#'  \item{log_Base4_c}{The logarithm of Base divided by 4 
-#'    (i.e. log(Base/4)) centered around its mean}
-#'  \item{Trt_c}{Trt centered around its mean}
+#'  \item{patient}{The patient number}
 #'  \item{visit}{The session number from \code{1} (first visit) 
 #'    to \code{4} (last visit)}
 #'  \item{count}{The seizure count between two visits}
-#'  \item{patient}{The patient number}
-#'  \item{obs}{The observation number, 
-#'    i.e. a unique identifier for each observation}
+#'  \item{obs}{The observation number, that is 
+#'    a unique identifier for each observation}
+#'  \item{zAge}{Standardized \code{Age}}
+#'  \item{zBase}{Standardized \code{Base}}
 #' } 
 #' 
 #' @examples
 #' \dontrun{
 #' ## poisson regression without random effects. 
-#' fit1 <- brm(count ~ log_Age_c + log_Base4_c * Trt_c, 
+#' fit1 <- brm(count ~ zAge + zBase * Trt, 
 #'             data = epilepsy, family = poisson())
 #' summary(fit1) 
 #' plot(fit1)             
 #'     
-#' ## poisson regression with random intercepts of patients and visits
-#' ## as well as normal priors for fixed effects parameters.    
-#' fit2 <- brm(count ~ log_Age_c + log_Base4_c * Trt_c 
-#'             + (1|patient) + (1|visit), 
+#' ## poisson regression with varying intercepts of patients
+#' ## as well as normal priors for overall effects parameters.    
+#' fit2 <- brm(count ~ zAge + zBase * Trt + (1|patient), 
 #'             data = epilepsy, family = poisson(), 
 #'             prior = set_prior("normal(0,5)"))
 #' summary(fit2) 

R/formula-helpers.R

@@ -75,7 +75,7 @@
 #' summary(fit3)
 #' 
 #' ## Poisson model with truncated counts  
-#' fit4 <- brm(count | trunc(ub = 104) ~ log_Base4_c * Trt_c, 
+#' fit4 <- brm(count | trunc(ub = 104) ~ zBase * Trt, 
 #'             data = epilepsy, family = poisson())
 #' summary(fit4)
 #' }

R/generics.R

@@ -367,8 +367,7 @@ autocor <- function(object, ...) {
 #' 
 #' @examples
 #' \dontrun{
-#' model <- brm(count ~ log_Age_c + log_Base4_c * Trt 
-#'              + (1|patient) + (1|visit),
+#' model <- brm(count ~ zAge + zBase * Trt + (1|patient),
 #'              data = epilepsy, family = "poisson")
 #'              
 #' # plot posterior intervals

R/ggplot-themes.R

@@ -32,13 +32,13 @@ NULL
 #' @examples 
 #' \dontrun{
 #' # change default ggplot theme
-#' theme_set(theme_black())
+#' ggplot2::theme_set(theme_black())
 #' 
 #' # change default bayesplot color scheme
 #' bayesplot::color_scheme_set("viridisC")
 #' 
 #' # fit a simple model
-#' fit <- brm(count ~ log_Age_c + log_Base4_c * Trt + (1|patient),
+#' fit <- brm(count ~ zAge + zBase * Trt + (1|patient),
 #'            data = epilepsy, family = poisson(), chains = 2)
 #' summary(fit)
 #'            

R/kfold-helpers.R

@@ -77,7 +77,7 @@
 #'   
 #' @examples 
 #' \dontrun{
-#' fit1 <- brm(count ~ log_Age_c + log_Base4_c * Trt + 
+#' fit1 <- brm(count ~ zAge + zBase * Trt + 
 #'               (1|patient) + (1|obs),
 #'            data = epilepsy, family = poisson())
 #' # throws warning about some pareto k estimates being too high
@@ -242,7 +242,7 @@ kfold_internal <- function(x, K = 10, Ksub = NULL, folds = NULL,
 #'   
 #' @examples 
 #' \dontrun{
-#' fit <- brm(count ~ log_Base4_c * Trt + (1|patient),
+#' fit <- brm(count ~ zBase * Trt + (1|patient),
 #'            data = epilepsy, family = poisson())
 #'             
 #' # perform k-fold cross validation

R/loo-helpers.R

@@ -469,7 +469,7 @@ validate_models <- function(models, model_names, sub_names) {
 #' 
 #' @examples 
 #' \dontrun{
-#' fit1 <- brm(count ~ log_Age_c + log_Base4_c * Trt + (1|patient),
+#' fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient),
 #'            data = epilepsy, family = poisson())
 #' # throws warning about some pareto k estimates being too high
 #' (loo1 <- loo(fit1))

R/make_stancode.R

@@ -14,8 +14,7 @@
 #' make_stancode(rating ~ treat + period + carry + (1|subject), 
 #'               data = inhaler, family = "cumulative")
 #' 
-#' make_stancode(count ~ log_Age_c + log_Base4_c * Trt_c 
-#'               + (1|patient) + (1|visit), 
+#' make_stancode(count ~ zAge + zBase * Trt + (1|patient),
 #'               data = epilepsy, family = "poisson")
 #'
 #' @export

R/make_standata.R

@@ -18,8 +18,7 @@
 #'                        data = inhaler, family = "cumulative")
 #' names(data1)
 #' 
-#' data2 <- make_standata(count ~ log_Age_c + log_Base4_c * Trt_c 
-#'                        + (1|patient) + (1|visit), 
+#' data2 <- make_standata(count ~ zAge + zBase * Trt + (1|patient),
 #'                        data = epilepsy, family = "poisson")
 #' names(data2)
 #'          

R/marginal_effects-helpers.R

@@ -173,48 +173,48 @@
 #' 
 #' @examples 
 #' \dontrun{
-#' fit <- brm(count ~ log_Age_c + log_Base4_c * Trt + (1 | patient),
+#' fit <- brm(count ~ zAge + zBase * Trt + (1 | patient),
 #'            data = epilepsy, family = poisson()) 
 #'            
 #' ## plot all marginal effects
 #' plot(marginal_effects(fit), ask = FALSE)
 #' 
 #' ## change colours to grey scale
-#' me <- marginal_effects(fit, "log_Base4_c:Trt")
+#' me <- marginal_effects(fit, "zBase:Trt")
 #' plot(me, plot = FALSE)[[1]] + 
 #'   scale_color_grey() +
 #'   scale_fill_grey()
 #' 
-#' ## only plot the marginal interaction effect of 'log_Base4_c:Trt'
-#' ## for different values for 'log_Age_c'
-#' conditions <- data.frame(log_Age_c = c(-0.3, 0, 0.3))
-#' plot(marginal_effects(fit, effects = "log_Base4_c:Trt", 
+#' ## only plot the marginal interaction effect of 'zBase:Trt'
+#' ## for different values for 'zAge'
+#' conditions <- data.frame(zAge = c(-1, 0, 1))
+#' plot(marginal_effects(fit, effects = "zBase:Trt", 
 #'                       conditions = conditions))
 #'                       
 #' ## also incorporate random effects variance over patients
 #' ## also add data points and a rug representation of predictor values
-#' plot(marginal_effects(fit, effects = "log_Base4_c:Trt", 
+#' plot(marginal_effects(fit, effects = "zBase:Trt", 
 #'                       conditions = conditions, re_formula = NULL), 
 #'      points = TRUE, rug = TRUE)
 #'  
 #' ## change handling of two-way interactions
 #' int_conditions <- list(
-#'   log_Base4_c = setNames(c(-2, 1, 0), c("b", "c", "a"))
+#'   zBase = setNames(c(-2, 1, 0), c("b", "c", "a"))
 #' )
-#' marginal_effects(fit, effects = "Trt:log_Base4_c",
+#' marginal_effects(fit, effects = "Trt:zBase",
 #'                  int_conditions = int_conditions)
-#' marginal_effects(fit, effects = "Trt:log_Base4_c",
-#'                  int_conditions = list(log_Base4_c = quantile))        
+#' marginal_effects(fit, effects = "Trt:zBase",
+#'                  int_conditions = list(zBase = quantile))        
 #'      
 #' ## fit a model to illustrate how to plot 3-way interactions
-#' fit3way <- brm(count ~ log_Age_c * log_Base4_c * Trt, data = epilepsy)
-#' conditions <- make_conditions(fit3way, "log_Age_c")
+#' fit3way <- brm(count ~ zAge * zBase * Trt, data = epilepsy)
+#' conditions <- make_conditions(fit3way, "zAge")
 #' marginal_effects(
-#'   fit3way, "log_Base4_c:Trt", conditions = conditions
+#'   fit3way, "zBase:Trt", conditions = conditions
 #' )
-#' ## only include points close to the specified values of log_Age_c
+#' ## only include points close to the specified values of zAge
 #' me <- marginal_effects(
-#'  fit3way, "log_Base4_c:Trt", conditions = conditions, 
+#'  fit3way, "zBase:Trt", conditions = conditions, 
 #'  select_points = 0.1
 #' )
 #' plot(me, points = TRUE)