test_that("plot.beaver_mcmc works against an S3 object of class beaver_mcmc_bma, produces an object with correct properties", { # nolint nb_monotone_incr <- readRDS(test_path("fixtures", "nb_monotone_incr.rds")) expect_failure(expect_s3_class(nb_monotone_incr, NA)) expect_s3_class(nb_monotone_incr, "data.frame") load(test_path("fixtures", "nb_bma_objects.Rdata")) expect_failure(expect_s3_class(nb_bma, NA)) expect_s3_class( nb_bma, c("beaver_mcmc_bma", "yodel_bma", "beaver_mcmc"), exact = TRUE ) #new_data---- #>reference_dose == NULL---- plot1 <- plot.beaver_mcmc( x = nb_bma, doses = attr(nb_bma, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr[1, ], ) expect_failure(expect_s3_class(plot1, NA)) expect_s3_class(plot1, "ggplot") #>reference_dose == [first dose], reference_type == "difference"---- plot1a <- plot.beaver_mcmc( x = nb_bma, doses = attr(nb_bma, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr[1, ], reference_dose = attr(nb_bma, "doses")[1], reference_type = "difference" ) expect_failure(expect_s3_class(plot1a, NA)) expect_s3_class(plot1a, "ggplot") #>reference_dose == [first dose], reference_type == "ratio"---- plot1b <- plot.beaver_mcmc( x = nb_bma, doses = attr(nb_bma, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr[1, ], reference_dose = attr(nb_bma, "doses")[1], reference_type = "ratio" ) expect_failure(expect_s3_class(plot1b, NA)) expect_s3_class(plot1b, "ggplot") #contrast---- #>reference_dose == NULL---- plot2 <- plot.beaver_mcmc( x = nb_bma, doses = attr(nb_bma, "doses"), prob = c(.025, .975), contrast = matrix(1, 1, 1) ) expect_failure(expect_s3_class(plot2, NA)) expect_s3_class(plot2, "ggplot") #>reference_dose == [first dose], reference_type == "difference"---- plot2a <- plot.beaver_mcmc( x = nb_bma, doses = attr(nb_bma, "doses"), prob = c(.025, .975), contrast = matrix(1, 1, 1), reference_dose = attr(nb_bma, "doses")[1], reference_type = "difference" ) expect_failure(expect_s3_class(plot2a, NA)) expect_s3_class(plot2a, "ggplot") #>reference_dose == [first dose], reference_type == "ratio"---- plot2b <- plot.beaver_mcmc( x = nb_bma, doses = attr(nb_bma, "doses"), prob = c(.025, .975), contrast = matrix(1, 1, 1), reference_dose = attr(nb_bma, "doses")[1], reference_type = "ratio" ) expect_failure(expect_s3_class(plot2b, NA)) expect_s3_class(plot2b, "ggplot") }) test_that("plot.beaver_mcmc works against an S3 object of class beaver_mcmc_bma, with covariates, produces an object with correct properties", { # nolint nb_monotone_incr_cov <- readRDS(test_path("fixtures", "nb_monotone_incr_cov.rds")) # nolint expect_failure(expect_s3_class(nb_monotone_incr_cov, NA)) expect_s3_class(nb_monotone_incr_cov, "data.frame") load(test_path("fixtures", "nb_bma_cov_objects.Rdata")) expect_failure(expect_s3_class(nb_bma_cov, NA)) expect_s3_class( nb_bma_cov, c("beaver_mcmc_bma", "yodel_bma", "beaver_mcmc"), exact = TRUE ) #new_data---- #>reference_dose == NULL---- plot1 <- plot.beaver_mcmc( x = nb_bma_cov, doses = attr(nb_bma_cov, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr_cov[1, ], ) expect_failure(expect_s3_class(plot1, NA)) expect_s3_class(plot1, "ggplot") #>reference_dose == [first dose], reference_type == "difference"---- plot1a <- plot.beaver_mcmc( x = nb_bma_cov, doses = attr(nb_bma_cov, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr_cov[1, ], reference_dose = attr(nb_bma_cov, "doses")[1], reference_type = "difference" ) expect_failure(expect_s3_class(plot1a, NA)) expect_s3_class(plot1a, "ggplot") #>reference_dose == [first dose], reference_type == "ratio"---- plot1b <- plot.beaver_mcmc( x = nb_bma_cov, doses = attr(nb_bma_cov, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr_cov[1, ], reference_dose = attr(nb_bma_cov, "doses")[1], reference_type = "ratio" ) expect_failure(expect_s3_class(plot1b, NA)) expect_s3_class(plot1b, "ggplot") #contrast---- #>reference_dose == NULL---- plot2 <- plot.beaver_mcmc( x = nb_bma_cov, doses = attr(nb_bma_cov, "doses"), prob = c(.025, .975), contrast = matrix(c(1, 40), 1, 2) ) expect_failure(expect_s3_class(plot2, NA)) expect_s3_class(plot2, "ggplot") #>reference_dose == [first dose], reference_type == "difference"---- plot2a <- plot.beaver_mcmc( x = nb_bma_cov, doses = attr(nb_bma_cov, "doses"), prob = c(.025, .975), contrast = matrix(c(1, 40), 1, 2), reference_dose = attr(nb_bma_cov, "doses")[1], reference_type = "difference" ) expect_failure(expect_s3_class(plot2a, NA)) expect_s3_class(plot2a, "ggplot") #>reference_dose == [first dose], reference_type == "ratio"---- plot2b <- plot.beaver_mcmc( x = nb_bma_cov, doses = attr(nb_bma_cov, "doses"), prob = c(.025, .975), contrast = matrix(c(1, 40), 1, 2), reference_dose = attr(nb_bma_cov, "doses")[1], reference_type = "ratio" ) expect_failure(expect_s3_class(plot2b, NA)) expect_s3_class(plot2b, "ggplot") }) test_that("plot.beaver_mcmc works against an S3 object of class beaver_mcmc_bma, with covariates & type == \"g-comp\", produces an object with correct properties", { # nolint nb_monotone_incr_cov <- readRDS(test_path("fixtures", "nb_monotone_incr_cov.rds")) # nolint expect_failure(expect_s3_class(nb_monotone_incr_cov, NA)) expect_s3_class(nb_monotone_incr_cov, "data.frame") load(test_path("fixtures", "nb_bma_cov_objects.Rdata")) expect_failure(expect_s3_class(nb_bma_cov, NA)) expect_s3_class( nb_bma_cov, c("beaver_mcmc_bma", "yodel_bma", "beaver_mcmc"), exact = TRUE ) #new_data---- #>reference_dose == NULL---- plot1 <- plot.beaver_mcmc( x = nb_bma_cov, doses = attr(nb_bma_cov, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr_cov, type = "g-comp" ) expect_failure(expect_s3_class(plot1, NA)) expect_s3_class(plot1, "ggplot") # nolint start # #>reference_dose == [first dose], reference_type == "difference"---- # # plot1a <- plot.beaver_mcmc( # x = nb_bma_cov, # doses = attr(nb_bma_cov, "doses"), # prob = c(.025, .975), # new_data = nb_monotone_incr_cov, # type = "g-comp", # reference_dose = attr(nb_bma_cov, "doses")[1], # reference_type = "difference" # ) # # expect_failure(expect_s3_class(plot1a, NA)) # expect_s3_class(plot1a, "ggplot") # # #>reference_dose == [first dose], reference_type == "ratio"---- # # plot1b <- plot.beaver_mcmc( # x = nb_bma_cov, # doses = attr(nb_bma_cov, "doses"), # prob = c(.025, .975), # new_data = nb_monotone_incr_cov, # type = "g-comp", # reference_dose = attr(nb_bma_cov, "doses")[1], # reference_type = "ratio" # ) # # expect_failure(expect_s3_class(plot1b, NA)) # expect_s3_class(plot1b, "ggplot") # # #contrast---- # # #>reference_dose == NULL---- # # plot2 <- plot.beaver_mcmc( # x = nb_bma_cov, # doses = attr(nb_bma_cov, "doses"), # prob = c(.025, .975), # contrast = matrix(c(1, 40), 1, 2), # type = "g-comp" # ) # # expect_failure(expect_s3_class(plot2, NA)) # expect_s3_class(plot2, "ggplot") # # #>reference_dose == [first dose], reference_type == "difference"---- # # plot2a <- plot.beaver_mcmc( # x = nb_bma_cov, # doses = attr(nb_bma_cov, "doses"), # prob = c(.025, .975), # contrast = matrix(c(1, 40), 1, 2), # type = "g-comp", # reference_dose = attr(nb_bma_cov, "doses")[1], # reference_type = "difference" # ) # # expect_failure(expect_s3_class(plot2a, NA)) # expect_s3_class(plot2a, "ggplot") # # #>reference_dose == [first dose], reference_type == "ratio"---- # # plot2b <- plot.beaver_mcmc( # x = nb_bma_cov, # doses = attr(nb_bma_cov, "doses"), # prob = c(.025, .975), # contrast = matrix(c(1, 40), 1, 2), # type = "g-comp", # reference_dose = attr(nb_bma_cov, "doses")[1], # reference_type = "ratio" # ) # # expect_failure(expect_s3_class(plot2b, NA)) # expect_s3_class(plot2b, "ggplot") # nolint end }) test_that("plot works against an S3 object of class beaver_mcmc_bma", { skip_on_cran() nb_monotone_incr <- readRDS(test_path("fixtures", "nb_monotone_incr.rds")) expect_failure(expect_s3_class(nb_monotone_incr, NA)) expect_s3_class(nb_monotone_incr, "data.frame") load(test_path("fixtures", "nb_bma_objects.Rdata")) expect_failure(expect_s3_class(nb_bma, NA)) expect_s3_class( nb_bma, c("beaver_mcmc_bma", "yodel_bma", "beaver_mcmc"), exact = TRUE ) #new_data---- #>reference_dose == NULL---- expect_no_error( plot( x = nb_bma, doses = attr(nb_bma, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr[1, ] ) ) #>reference_dose == [first dose], reference_type == "difference"---- expect_no_error( plot( x = nb_bma, doses = attr(nb_bma, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr[1, ], reference_dose = attr(nb_bma, "doses")[1], reference_type = "difference" ) ) #>reference_dose == [first dose], reference_type == "ratio"---- expect_no_error( plot( x = nb_bma, doses = attr(nb_bma, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr[1, ], reference_dose = attr(nb_bma, "doses")[1], reference_type = "ratio" ) ) #contrast---- #>reference_dose == NULL---- expect_no_error( plot( x = nb_bma, doses = attr(nb_bma, "doses"), prob = c(.025, .975), contrast = matrix(1, 1, 1) ) ) #>reference_dose == [first dose], reference_type == "difference"---- expect_no_error( plot( x = nb_bma, doses = attr(nb_bma, "doses"), prob = c(.025, .975), contrast = matrix(1, 1, 1), reference_dose = attr(nb_bma, "doses")[1], reference_type = "difference" ) ) #>reference_dose == [first dose], reference_type == "ratio"---- expect_no_error( plot( x = nb_bma, doses = attr(nb_bma, "doses"), prob = c(.025, .975), contrast = matrix(1, 1, 1), reference_dose = attr(nb_bma, "doses")[1], reference_type = "ratio" ) ) }) test_that("plot.beaver_mcmc works against an S3 object of class beaver_mcmc, produces an object with correct properties", { # nolint skip_on_cran() nb_monotone_incr <- readRDS(test_path("fixtures", "nb_monotone_incr.rds")) expect_failure(expect_s3_class(nb_monotone_incr, NA)) expect_s3_class(nb_monotone_incr, "data.frame") load(test_path("fixtures", "nb_indep_mcmc+_objects.Rdata")) expect_failure(expect_s3_class(nb_indep_model_samples_updatedattr, NA)) expect_s3_class(nb_indep_model_samples_updatedattr, "beaver_mcmc") expect_no_error( checkmate::assertSubset( c("covariate_names", "formula", "doses"), names(attributes(nb_indep_model_samples_updatedattr)) ) ) #new_data---- #>reference_dose == NULL---- plot1 <- plot.beaver_mcmc( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr[1, ] ) expect_failure(expect_s3_class(plot1, NA)) expect_s3_class(plot1, "ggplot") #>reference_dose == [first dose], reference_type == "difference"---- plot1a <- plot.beaver_mcmc( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr[1, ], reference_dose = attr(nb_indep_model_samples_updatedattr, "doses")[1], reference_type = "difference" ) expect_failure(expect_s3_class(plot1a, NA)) expect_s3_class(plot1a, "ggplot") #>reference_dose == [first dose], reference_type == "ratio"---- plot1b <- plot.beaver_mcmc( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr[1, ], reference_dose = attr(nb_indep_model_samples_updatedattr, "doses")[1], reference_type = "ratio" ) expect_failure(expect_s3_class(plot1b, NA)) expect_s3_class(plot1b, "ggplot") #contrast---- #>reference_dose == NULL---- plot2 <- plot.beaver_mcmc( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), contrast = matrix(1, 1, 1) ) expect_failure(expect_s3_class(plot2, NA)) expect_s3_class(plot2, "ggplot") #>reference_dose == [first dose], reference_type == "difference"---- plot2a <- plot.beaver_mcmc( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), contrast = matrix(1, 1, 1), reference_dose = attr(nb_indep_model_samples_updatedattr, "doses")[1], reference_type = "difference" ) expect_failure(expect_s3_class(plot2a, NA)) expect_s3_class(plot2a, "ggplot") #>reference_dose == [first dose], reference_type == "ratio"---- plot2b <- plot.beaver_mcmc( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), contrast = matrix(1, 1, 1), reference_dose = attr(nb_indep_model_samples_updatedattr, "doses")[1], reference_type = "ratio" ) expect_failure(expect_s3_class(plot2b, NA)) expect_s3_class(plot2b, "ggplot") }) test_that("plot.beaver_mcmc works against an S3 object of class beaver_mcmc, with covariates, produces an object with correct properties", { # nolint skip_on_cran() nb_monotone_incr_cov <- readRDS(test_path("fixtures", "nb_monotone_incr_cov.rds")) # nolint expect_failure(expect_s3_class(nb_monotone_incr_cov, NA)) expect_s3_class(nb_monotone_incr_cov, "data.frame") load(test_path("fixtures", "nb_emax_cov_mcmc+_objects.Rdata")) expect_failure(expect_s3_class(nb_emax_model_cov_samples_updatedattr, NA)) expect_s3_class(nb_emax_model_cov_samples_updatedattr, "beaver_mcmc") expect_no_error( checkmate::assertSubset( c("covariate_names", "formula", "doses"), names(attributes(nb_emax_model_cov_samples_updatedattr)) ) ) #new_data---- #>reference_dose == NULL---- plot1 <- plot.beaver_mcmc( x = nb_emax_model_cov_samples_updatedattr, doses = attr(nb_emax_model_cov_samples_updatedattr, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr_cov[1, ] ) expect_failure(expect_s3_class(plot1, NA)) expect_s3_class(plot1, "ggplot") #>reference_dose == [first dose], reference_type == "difference"---- plot1a <- plot.beaver_mcmc( x = nb_emax_model_cov_samples_updatedattr, doses = attr(nb_emax_model_cov_samples_updatedattr, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr_cov[1, ], reference_dose = attr(nb_emax_model_cov_samples_updatedattr, "doses")[1], reference_type = "difference" ) expect_failure(expect_s3_class(plot1a, NA)) expect_s3_class(plot1a, "ggplot") #>reference_dose == [first dose], reference_type == "ratio"---- plot1b <- plot.beaver_mcmc( x = nb_emax_model_cov_samples_updatedattr, doses = attr(nb_emax_model_cov_samples_updatedattr, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr_cov[1, ], reference_dose = attr(nb_emax_model_cov_samples_updatedattr, "doses")[1], reference_type = "ratio" ) expect_failure(expect_s3_class(plot1b, NA)) expect_s3_class(plot1b, "ggplot") #contrast---- #>reference_dose == NULL---- plot2 <- plot.beaver_mcmc( x = nb_emax_model_cov_samples_updatedattr, doses = attr(nb_emax_model_cov_samples_updatedattr, "doses"), prob = c(.025, .975), contrast = matrix(c(1, 40), 1, 2) ) expect_failure(expect_s3_class(plot2, NA)) expect_s3_class(plot2, "ggplot") #>reference_dose == [first dose], reference_type == "difference"---- plot2a <- plot.beaver_mcmc( x = nb_emax_model_cov_samples_updatedattr, doses = attr(nb_emax_model_cov_samples_updatedattr, "doses"), prob = c(.025, .975), contrast = matrix(c(1, 40), 1, 2), reference_dose = attr(nb_emax_model_cov_samples_updatedattr, "doses")[1], reference_type = "difference" ) expect_failure(expect_s3_class(plot2a, NA)) expect_s3_class(plot2a, "ggplot") #>reference_dose == [first dose], reference_type == "ratio"---- plot2b <- plot.beaver_mcmc( x = nb_emax_model_cov_samples_updatedattr, doses = attr(nb_emax_model_cov_samples_updatedattr, "doses"), prob = c(.025, .975), contrast = matrix(c(1, 40), 1, 2), reference_dose = attr(nb_emax_model_cov_samples_updatedattr, "doses")[1], reference_type = "ratio" ) expect_failure(expect_s3_class(plot2b, NA)) expect_s3_class(plot2b, "ggplot") }) test_that("plot.beaver_mcmc works against an S3 object of class beaver_mcmc, with covariates & type == \"g-comp\", produces an object with correct properties", { # nolint skip_on_cran() nb_monotone_incr_cov <- readRDS(test_path("fixtures", "nb_monotone_incr_cov.rds")) # nolint expect_failure(expect_s3_class(nb_monotone_incr_cov, NA)) expect_s3_class(nb_monotone_incr_cov, "data.frame") load(test_path("fixtures", "nb_emax_cov_mcmc+_objects.Rdata")) expect_failure(expect_s3_class(nb_emax_model_cov_samples_updatedattr, NA)) expect_s3_class(nb_emax_model_cov_samples_updatedattr, "beaver_mcmc") expect_no_error( checkmate::assertSubset( c("covariate_names", "formula", "doses"), names(attributes(nb_emax_model_cov_samples_updatedattr)) ) ) #new_data---- #>reference_dose == NULL---- plot1 <- plot.beaver_mcmc( x = nb_emax_model_cov_samples_updatedattr, doses = attr(nb_emax_model_cov_samples_updatedattr, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr_cov, type = "g-comp" ) expect_failure(expect_s3_class(plot1, NA)) expect_s3_class(plot1, "ggplot") # nolint start # #>reference_dose == [first dose], reference_type == "difference"---- # # plot1a <- plot.beaver_mcmc( # x = nb_emax_model_cov_samples_updatedattr, # doses = attr(nb_emax_model_cov_samples_updatedattr, "doses"), # prob = c(.025, .975), # new_data = nb_monotone_incr_cov, # type = "g-comp", # reference_dose = attr(nb_emax_model_cov_samples_updatedattr, "doses")[1], # reference_type = "difference" # ) # # expect_failure(expect_s3_class(plot1a, NA)) # expect_s3_class(plot1a, "ggplot") # # #>reference_dose == [first dose], reference_type == "ratio"---- # # plot1b <- plot.beaver_mcmc( # x = nb_emax_model_cov_samples_updatedattr, # doses = attr(nb_emax_model_cov_samples_updatedattr, "doses"), # prob = c(.025, .975), # new_data = nb_monotone_incr_cov, # type = "g-comp", # reference_dose = attr(nb_emax_model_cov_samples_updatedattr, "doses")[1], # reference_type = "ratio" # ) # # expect_failure(expect_s3_class(plot1b, NA)) # expect_s3_class(plot1b, "ggplot") # # #contrast---- # # #>reference_dose == NULL---- # # plot2 <- plot.beaver_mcmc( # x = nb_emax_model_cov_samples_updatedattr, # doses = attr(nb_emax_model_cov_samples_updatedattr, "doses"), # prob = c(.025, .975), # data = nb_monotone_incr_cov, # contrast = matrix(c(1, 40), 1, 2), # type = "g-comp" # ) # # expect_failure(expect_s3_class(plot2, NA)) # expect_s3_class(plot2, "ggplot") # # #>reference_dose == [first dose], reference_type == "difference"---- # # plot2a <- plot.beaver_mcmc( # x = nb_emax_model_cov_samples_updatedattr, # doses = attr(nb_emax_model_cov_samples_updatedattr, "doses"), # prob = c(.025, .975), # contrast = matrix(c(1, 40), 1, 2), # type = "g-comp", # reference_dose = attr(nb_emax_model_cov_samples_updatedattr, "doses")[1], # reference_type = "difference" # ) # # expect_failure(expect_s3_class(plot2a, NA)) # expect_s3_class(plot2a, "ggplot") # # #>reference_dose == [first dose], reference_type == "ratio"---- # # plot2b <- plot.beaver_mcmc( # x = nb_emax_model_cov_samples_updatedattr, # doses = attr(nb_emax_model_cov_samples_updatedattr, "doses"), # prob = c(.025, .975), # contrast = matrix(c(1, 40), 1, 2), # type = "g-comp", # reference_dose = attr(nb_emax_model_cov_samples_updatedattr, "doses")[1], # reference_type = "ratio" # ) # # expect_failure(expect_s3_class(plot2b, NA)) # expect_s3_class(plot2b, "ggplot") # nolint end }) test_that("plot works against an S3 object of class beaver_mcmc", { skip_on_cran() nb_monotone_incr <- readRDS(test_path("fixtures", "nb_monotone_incr.rds")) expect_failure(expect_s3_class(nb_monotone_incr, NA)) expect_s3_class(nb_monotone_incr, "data.frame") load(test_path("fixtures", "nb_indep_mcmc+_objects.Rdata")) expect_failure(expect_s3_class(nb_indep_model_samples_updatedattr, NA)) expect_s3_class(nb_indep_model_samples_updatedattr, "beaver_mcmc") expect_no_error( checkmate::assertSubset( c("covariate_names", "formula", "doses"), names(attributes(nb_indep_model_samples_updatedattr)) ) ) #new_data---- #>reference_dose == NULL---- expect_no_error( plot( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr[1, ] ) ) #>reference_dose == [first dose], reference_type == "difference"---- expect_no_error( plot( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr[1, ], reference_dose = attr(nb_indep_model_samples_updatedattr, "doses")[1], reference_type = "difference" ) ) #>reference_dose == [first dose], reference_type == "ratio"---- expect_no_error( plot( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), new_data = nb_monotone_incr[1, ], reference_dose = attr(nb_indep_model_samples_updatedattr, "doses")[1], reference_type = "ratio" ) ) #contrast---- #>reference_dose == NULL---- expect_no_error( plot( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), contrast = matrix(1, 1, 1) ) ) #>reference_dose == [first dose], reference_type == "difference"---- expect_no_error( plot( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), contrast = matrix(1, 1, 1), reference_dose = attr(nb_indep_model_samples_updatedattr, "doses")[1], reference_type = "difference" ) ) #>reference_dose == [first dose], reference_type == "ratio"---- expect_no_error( plot( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), contrast = matrix(1, 1, 1), reference_dose = attr(nb_indep_model_samples_updatedattr, "doses")[1], reference_type = "ratio" ) ) }) test_that("plot.beaver_mcmc throws an error when warranted", { nb_monotone_incr <- readRDS(test_path("fixtures", "nb_monotone_incr.rds")) expect_failure(expect_s3_class(nb_monotone_incr, NA)) expect_s3_class(nb_monotone_incr, "data.frame") nb_monotone_incr_new <- readRDS(test_path("fixtures", "nb_monotone_incr_new.rds")) # nolint expect_failure(expect_s3_class(nb_monotone_incr_new, NA)) expect_s3_class(nb_monotone_incr_new, "data.frame") load(test_path("fixtures", "nb_indep_mcmc+_objects.Rdata")) expect_failure(expect_s3_class(nb_indep_model_samples_updatedattr, NA)) expect_s3_class(nb_indep_model_samples_updatedattr, "beaver_mcmc") expect_no_error( checkmate::assertSubset( c("covariate_names", "formula", "doses"), names(attributes(nb_indep_model_samples_updatedattr)) ) ) expect_error( plot.beaver_mcmc( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025), data = nb_monotone_incr, contrast = matrix(1, 1, 1) ), "\"prob\" must have length 2." ) expect_error( plot.beaver_mcmc( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), data = nb_monotone_incr, new_data = nb_monotone_incr ), "\"new_data\" must have only one row for plotting." ) expect_error( plot.beaver_mcmc( x = nb_indep_model_samples_updatedattr, doses = attr(nb_indep_model_samples_updatedattr, "doses"), prob = c(.025, .975), data = nb_monotone_incr, contrast = matrix(c(1, 1), 2, 1) ), "\"contrast\" must have only one row for plotting." ) })