## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, # merges source code and its printed output into a single block comment = ">" # Adds > to every line of printed output; visually distinguishing it from the code that generated it ) ## ----setup-------------------------------------------------------------------- library(birp) ## ----echo=FALSE--------------------------------------------------------------- # Create example data exampleData <- data.frame( location = rep(c("site1", "site2", "site3"), each = 5), timepoint = rep(2020:2024, times = 3), counts = c(28, 12, 26, 48, 20, 21, 22, 76, 22, 100, 65, 32, 60, 19, 42), effort = c(2,1,2,3,1,1,1,4,1,4,5,2,5,1,2), CI_group = rep("Group_1", times = 15) ) ## ----echo=FALSE--------------------------------------------------------------- print(exampleData) ## ----eval=FALSE--------------------------------------------------------------- # # Create example data # exampleData <- data.frame( # location = rep(c("site1", "site2", "site3"), each = 5), # timepoint = rep(2020:2024, times = 3), # counts = c(28, 12, 26, 48, 20, 21, 22, 76, 22, 100, 65, 32, 60, 19, 42), # effort = c(2,1,2,3,1,1,1,4,1,4,5,2,5,1,2), # CI_group = rep("Group_1", times = 15) # ) ## ----------------------------------------------------------------------------- exampleBirp <- birp_data_from_data_frame(exampleData) print(exampleBirp) ## ----echo=FALSE--------------------------------------------------------------- est <- birp(exampleBirp, verbose=FALSE) ## ----eval=FALSE--------------------------------------------------------------- # est <- birp(exampleBirp) ## ----------------------------------------------------------------------------- print(est) ## ----echo=FALSE--------------------------------------------------------------- cat("Posterior mean of gamma: ", est$post_gamma$posterior_mean, "\n") cat("Posterior probability of increasing trend P(gamma > 0): ", est$post_gamma$prob_positive, "\n") ## ----fig.width=6, fig.height=4------------------------------------------------ plot(est) ## ----fig.width=6, fig.height=4------------------------------------------------ plot(est, col="deeppink", legend=NA, ylab = "Density of posterior estimates") ## ----------------------------------------------------------------------------- est <- birp(exampleBirp, verbose = FALSE, timesOfChange = 2023) print(est) ## ----echo=FALSE--------------------------------------------------------------- cat("Posterior probability of increasing trend P(gamma > 0): ", est$post_gamma$prob_positive, "\n") ## ----echo=TRUE---------------------------------------------------------------- rate_design <- matrix(c("Group_1", 1,2), nrow = 1) print(rate_design) step_design <- matrix(c("Group_1", 1,0), nrow = 1) print(step_design) ## ----echo=TRUE---------------------------------------------------------------- est <- birp(exampleBirp, verbose = FALSE, timesOfChange = 2023, rate_design = rate_design, step_design = step_design, change = "both") print(est) ## ----------------------------------------------------------------------------- exampleBACI <- data.frame( location = rep(c("ctrl1", "ctrl2", "ctrl3", "int1", "int2", "int3"), each = 10), timepoint = rep(c(2015, 2016, 2018:2024, 2026), times = 6), counts = c( 80, 76, 72, 68, 65, 62, 59, 56, 53, 50, 60, 57, 54, 51, 49, 46, 44, 42, 40, 38, 70, 66, 63, 60, 57, 54, 51, 49, 46, 44, 80, 76, 72, 68, 65, 80, 95, 113, 135, 160, 60, 57, 54, 51, 49, 60, 72, 86, 103, 123, 70, 66, 63, 60, 57, 70, 84, 100, 120, 143 ), effort = rep(3, times = 60), CI_group = rep(c("Control", "Intervention"), each = 30) ) birpBACI <- birp_data_from_data_frame(exampleBACI) ## ----------------------------------------------------------------------------- rate_design_CI <- matrix(c( "Control", 1, "Intervention", 2 ), nrow = 2, byrow = TRUE) estCI <- birp(birpBACI, change = "rate", rate_design = rate_design_CI, verbose = FALSE) print(estCI) ## ----------------------------------------------------------------------------- rate_design_BACI <- matrix(c( "Control", 1, 1, "Intervention", 1, 2 ), nrow = 2, byrow = TRUE) est_BACI <- birp(birpBACI, change = "rate", rate_design = rate_design_BACI, timesOfChange = 2020, verbose = FALSE) print(est_BACI) ## ----------------------------------------------------------------------------- plot(est_BACI) ## ----fig.width=6, fig.height=4------------------------------------------------ plot(est_BACI) ## ----fig.width=6, fig.height=4------------------------------------------------ plot_epoch_pair(est_BACI, col="navy") ## ----------------------------------------------------------------------------- # Access the path to the example data provided with the package pathToFiles <- system.file("extdata", package = "birp") # Read in both files data <- birp_data_from_file(filenames = c( file.path(pathToFiles, "cameraTrapData.csv"), file.path(pathToFiles, "trackData.csv") )) ## ----eval=FALSE--------------------------------------------------------------- # data <- birp_data_from_file(filenames = c( # "path/to/your/file/cameraTrapData.csv", # "path/to/your/file/trackData.csv" # )) ## ----------------------------------------------------------------------------- estMultiMethod <- birp(data, verbose=FALSE) print(estMultiMethod) ## ----------------------------------------------------------------------------- exampleData <- data.frame( location = rep(c("site1", "site2", "site3"), each = 5), timepoint = rep(2020:2024, times = 3), counts = c(28, 12, 26, 48, 20, 21, 22, 76, 22, 100, 65, 32, 60, 19, 42), effort = c(2,1,2,3,1,1,1,4,1,4,5,2,5,1,2), CI_group = rep("Group_1", times = 15) ) exampleBirp <- birp_data_from_data_frame(exampleData) fit_nb <- birp(data = exampleBirp, negativeBinomial = TRUE, verbose = FALSE) print(fit_nb) ## ----------------------------------------------------------------------------- exampleBirp <- birp_data_from_data_frame(exampleData) est <- birp(exampleBirp, negativeBinomial = TRUE, verbose=FALSE) res_assess <- assess_NB(est, numRep = 100, verbose=FALSE) ## ----------------------------------------------------------------------------- exampleBirp <- birp_data_from_data_frame(exampleData) fit_stoch <- birp(data = exampleBirp, stochastic = TRUE, verbose = FALSE) ## ----------------------------------------------------------------------------- detectionExample_data <- data.frame( location = rep(c("site1", "site2"), each = 5), timepoint = rep(2015:2019, times = 2), counts = sample(10:100, 10, replace = TRUE), effort = sample(1:5, 10, replace = TRUE), CI_group = rep("Group_1", 10), covDetection_1 = runif(10, 0, 1) # random values between 0 and 1 ) ## ----------------------------------------------------------------------------- detectionBirp <- birp_data_from_data_frame(detectionExample_data) ## ----fig.width=6, fig.height=4------------------------------------------------ detectionEst <- birp(detectionBirp, assumeTrueDetectionProbability=FALSE, verbose = FALSE) ## ----fig.width=6, fig.height=4------------------------------------------------ plot(detectionEst) ## ----------------------------------------------------------------------------- simData <- simulate_birp(gamma = c(-0.03, 0.03), timepoints = 2000:2020, timesOfChange = 2010, verbose = FALSE) ## ----------------------------------------------------------------------------- est <- birp(simData, verbose=FALSE, timesOfChange = 2010) print(est)