fixed roxygen

R/detection_calculate.R

@@ -62,7 +62,7 @@
 #'                     cov_val = c(0,0), pcr_n = 3)
 #'
 #' # Calculate mu_critical at salinity 0.5 z-scores greater than the mean
-#' detection_calculate(fit.cov$model, mu = seq(from = 0.1, to = 1, by = 0.1),
+#' detection_calculate(fit_cov$model, mu = seq(from = 0.1, to = 1, by = 0.1),
 #'                     cov_val = c(0,0.5), pcr_n = 3)
 #'
 #' # Ex. 2: Calculating necessary effort for detection with multiple traditional

R/mu_critical.R

@@ -48,8 +48,8 @@
 #'
 #' # Fit a model including 'Filter_time' and 'Salinity' site-level covariates
 #' fit_cov <- joint_model(data = goby_data, cov = c('Filter_time','Salinity'),
-#'                       family = "poisson", p10_priors = c(1,20), q = FALSE,
-#'                       multicore = FALSE)
+#'                        family = "poisson", p10_priors = c(1,20), q = FALSE,
+#'                        multicore = FALSE)
 #'
 #' # Calculate mu_critical at the mean covariate values (covariates are
 #' # standardized, so mean = 0)

R/traditional_model.R

@@ -92,23 +92,23 @@
 #' @examples
 #' \donttest{
 #' # Load data
-#' data(greencrabData)
+#' data(green_crab_data)
 #'
 #' # Examine data in list
 #' # This function uses only traditional survey count data and optionally
 #' # the count type data
-#' names(greencrabData)
+#' names(green_crab_data)
 #'
 #' # Note that the surveyed sites (rows) should match in the data
-#' dim(greencrabData$count)[1]
-#' dim(greencrabData$count_type)[1]
+#' dim(green_crab_data$count)[1]
+#' dim(green_crab_data$count_type)[1]
 #'
 #' # Fit a model without estimating a gear scaling coefficient for traditional
 #' # survey gear types.
 #' # This model assumes all traditional survey methods have the same
 #' # catchability.
 #' # Count data is modeled using a poisson distribution.
-#' fit_no_q <- traditional_model(data = greencrabData, family = "poisson",
+#' fit_no_q <- traditional_model(data = green_crab_data, family = "poisson",
 #'                               q = FALSE, phi_priors = NULL,
 #'                               multicore = FALSE, verbose = TRUE)
 #'
@@ -118,11 +118,12 @@
 #' # This model does not assume all traditional survey methods have the same
 #' # catchability.
 #' # Count data is modeled using a negative binomial distribution.
-#' fit_q <- traditional_model(data = greencrabData, family = "negbin", q = TRUE,
-#'                            phi_priors = c(0.25,0.25), multicore = FALSE,
-#'                            initial_values = NULL, n_chain = 4,
-#'                            n_warmup = 500, n_iter = 3000, thin = 1,
-#'                            adapt_delta = 0.9, verbose = TRUE, seed = 123)
+#' fit_q <- traditional_model(data = green_crab_data, family = "negbin",
+#'                            q = TRUE, phi_priors = c(0.25,0.25),
+#'                            multicore = FALSE, initial_values = NULL,
+#'                            n_chain = 4, n_warmup = 500, n_iter = 3000,
+#'                            thin = 1, adapt_delta = 0.9, verbose = TRUE,
+#'                            seed = 123)
 #' }
 #'