cls <- read.table(header = TRUE, text = ' Name Sex Age Height Weight Alfred M 14 69 112.5 Alice F 13 56.5 84 Barbara F 13 65.3 98 Carol F 14 62.8 102.5 Henry M 14 63.5 102.5 James M 12 57.3 83 Jane F 12 59.8 84.5 Janet F 15 62.5 112.5 Jeffrey M 13 62.5 84 John M 12 59 99.5 Joyce F 11 51.3 50.5 Judy F 14 64.3 90 Louise F 12 56.3 77 Mary F 15 66.5 112 Philip M 16 72 150 Robert M 12 64.8 128 Ronald M 15 67 133 Thomas M 11 57.5 85 William M 15 66.5 112') hrdat <- read.table(header = TRUE, text = ' Region Eyes Hair Count 1 blue fair 23 1 blue dark 11 1 green medium 18 1 brown red 5 1 brown black 3 2 blue medium 44 2 green fair 50 2 green dark 23 2 brown medium 53 1 blue red 7 1 green fair 19 1 green dark 14 1 brown medium 41 2 blue fair 46 2 blue dark 40 2 green red 31 2 brown fair 56 2 brown dark 54 1 blue medium 24 1 green red 7 1 brown fair 34 1 brown dark 40 2 blue red 21 2 blue black 6 2 green medium 37 2 brown red 42 2 brown black 13 ') options("logr.output" = FALSE) options("procs.print" = FALSE) dev <- FALSE test_that("regplot1: regplot() object works as expected.", { res <- regplot() res expect_equal("regplot" %in% class(res), TRUE) expect_equal(res$stats, "default") expect_equal(res$panel, TRUE) expect_equal(res$type, c("diagnostics", "residuals", "fitplot")) res <- regplot(type = c("residuals", "fitplot"), stats = "default", panel = FALSE) res expect_equal("regplot" %in% class(res), TRUE) expect_equal(res$stats, "default") expect_equal(res$panel, FALSE) expect_equal(res$type, c("residuals", "fitplot")) }) # This plot gets split if there are multiple independent variables. test_that("regplot2: works for residuals", { # One independant variable res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "residuals")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Two independent variables res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "residuals")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) if (dev) { cls2 <- cls cls2$Sample1 <- round(runif(19, 50, 100)) cls2$Sample2 <- round(runif(19, 50, 100)) cls2$Sample3 <- round(runif(19, 50, 100)) cls2$Sample4 <- round(runif(19, 50, 100)) cls2$Sample5 <- round(runif(19, 50, 100)) cls2$Sample6 <- round(runif(19, 50, 100)) cls2$Sample7 <- round(runif(19, 50, 100)) cls2$Sample8 <- round(runif(19, 50, 100)) cls2$Sample9 <- round(runif(19, 50, 100)) cls2$Sample10 <- round(runif(19, 50, 100)) # Four independent variables res <- proc_reg(cls2, model = "Weight = Height Age Sample1 Sample2", output = report, plots = regplot(type = "residuals")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Seven independent variables res <- proc_reg(cls2, model = "Weight = Height Age Sample1 Sample2 Sample3 Sample4 Sample5 Sample6", output = report, plots = regplot(type = "residuals")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Twelve independent variables # Two charts mdl <- paste0("Weight = Height Age Sample1 Sample2 Sample3 Sample4 ", "Sample5 Sample6 Sample7 Sample8 Sample9 Sample10") res <- proc_reg(cls2, model = mdl, output = report, plots = regplot(type = "residuals")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) } }) # Good. test_that("regplot3: works for fitplot.", { # 95% Confidence res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "fitplot")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # 90% Confidence proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "fitplot"), options = v(alpha = .1)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Should produce no plot because of two independent variables res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "fitplot")) expect_equal(length(res), 4) # Custom statistics res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "fitplot", stats = c("nobs", "aic", "rsquare")), options = v(alpha = .1)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # No statistics res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "fitplot", stats = "none")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) }) test_that("regplot4: works for multiple plots.", { res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = c("diagnostics", "residuals", "fitplot"))) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) }) # Got these matching! test_that("regplot5: works for qqplot", { res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "qqplot") #, # stats = p ) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "qqplot") #, # stats = p ) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) res <- proc_reg(cls, model = "Height = Weight", output = report, plots = regplot(type = "qqplot") #, # stats = p ) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) res <- proc_reg(cls, model = "Height = Weight Age", output = report, plots = regplot(type = "qqplot") #, # stats = p ) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) }) # OK test_that("regplot6: works for rfplot plot", { res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "rfplot")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "rfplot")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) res <- proc_reg(cls, model = "Height = Weight", output = report, plots = regplot(type = "rfplot")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) res <- proc_reg(cls, model = "Height = Weight Age", output = report, plots = regplot(type = "rfplot")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) }) # Works test_that("regplot7: panel = FALSE works", { res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "diagnostics", panel = FALSE)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) expect_equal(length(res[[5]]) > 1, TRUE) }) # Good test_that("regplot8: works for cooksd plot", { res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "cooksd")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Labels res2 <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "cooksd", label = TRUE, id = "Name")) expect_equal(length(res2), 5) expect_equal("plot_spec" %in% class(res2[[5]][[1]]), TRUE) }) # Good test_that("regplot9: works for residualbypredicted", { res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "residualbypredicted")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "residualbypredicted")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) }) # Seems good. Worry about whether it is always +2 and -2 for boundary lines. # +2 and -2 appears to be standard deviation. It can be changed on the rstudent # function, but no obvious parameter on SAS chart. So probably OK. test_that("regplot10: works for rstudentbypredicted", { res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "rstudentbypredicted")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Labels for outliers - Works! res2 <- proc_reg(iris, model = Sepal.Length ~ Petal.Length, output = report, plots = regplot(type = "rstudentbypredicted", label = TRUE)) expect_equal(length(res2), 5) expect_equal("plot_spec" %in% class(res2[[5]][[1]]), TRUE) # Error check expect_error(proc_reg(iris, model = Sepal.Length ~ Petal.Length, output = report, plots = regplot(type = "rstudentbypredicted", label = TRUE, id = "fork"))) }) # Looks good. test_that("regplot11: works for rstudentbyleverage", { # Class data res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "rstudentbyleverage")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # More data points - Wow, great! res2 <- proc_reg(iris, model = Sepal.Length ~ Petal.Length, output = report, stats = p, plots = regplot(type = "rstudentbyleverage")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # More data res3 <- proc_reg(mtcars, model = mpg ~ disp, output = report, plots = regplot(type = "rstudentbyleverage")) expect_equal(length(res3), 5) expect_equal("plot_spec" %in% class(res3[[5]][[1]]), TRUE) # Two independent variables - Still works! res3 <- proc_reg(mtcars, model = mpg ~ hp + disp, output = report, plots = regplot(type = "rstudentbyleverage")) expect_equal(length(res3), 5) expect_equal("plot_spec" %in% class(res3[[5]][[1]]), TRUE) }) test_that("regplot12: works for rstudentbyleverage with labels and id", { # Assigned label - Awesome! res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "rstudentbyleverage", label = TRUE, id = "Name")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Default label res2 <- proc_reg(iris, model = Sepal.Length ~ Petal.Length, output = report, plots = regplot(type = "rstudentbyleverage", label = TRUE)) expect_equal(length(res2), 5) expect_equal("plot_spec" %in% class(res2[[5]][[1]]), TRUE) # Big labels cdt <- mtcars cdt$name <- rownames(mtcars) res3 <- proc_reg(cdt, model = mpg ~ disp, output = report, plots = regplot(type = "rstudentbyleverage", label = TRUE, id = "name")) expect_equal(length(res3), 5) expect_equal("plot_spec" %in% class(res3[[5]][[1]]), TRUE) # Two independent variables - Still works! res4 <- proc_reg(cdt, model = mpg ~ hp + disp, output = report, plots = regplot(type = "rstudentbyleverage", label = TRUE, id = "name")) expect_equal(length(res4), 5) expect_equal("plot_spec" %in% class(res4[[5]][[1]]), TRUE) }) # X and Y Scales have to be the same. # Margins also matter. Margins can change slope of line. test_that("regplot13: works for observedbypredicted", { # OK res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "observedbypredicted")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # OK res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "observedbypredicted")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # OK res <- proc_reg(cls, model = "Age = Weight", output = report, plots = regplot(type = "observedbypredicted")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # OK res <- proc_reg(cls, model = "Age = Height", output = report, plots = regplot(type = "observedbypredicted")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) }) # Bins will not match. Using Sturges algoritm instead. test_that("regplot14: works for residualhistogram", { # res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "residualhistogram")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "residualhistogram")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) res <- proc_reg(cls, model = "Height = Weight", output = report, plots = regplot(type = "residualhistogram")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) res <- proc_reg(cls, model = "Height = Weight Age", output = report, plots = regplot(type = "residualhistogram")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) }) test_that("regplot15: passing type strings works.", { # Pass quoted string type res <- proc_reg(cls, model = "Weight = Height", output = report, plots = "residualhistogram") expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Quoted string res <- proc_reg(cls, model = "Weight = Height", output = report, plots = "residualhistogram") expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # v function res <- proc_reg(cls, model = "Weight = Height", output = report, plots = v(fitplot, residuals, residualhistogram)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Vector of strings res <- proc_reg(cls, model = "Weight = Height", output = report, plots = c("fitplot", "residuals", "residualhistogram")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) }) # This is amazing test_that("regplot16: works for diagnostics", { # One variable res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "diagnostics")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Two independent variables res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "diagnostics")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Control stats res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "diagnostics", stats = c("default", "aic"))) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # No stats res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "diagnostics", stats = "none")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Labels no ID res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "diagnostics", label = TRUE)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Labels with ID res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "diagnostics", label = TRUE, id = "Name")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) }) test_that("regplot17: edge cases.", { # New data res <- proc_reg(iris, model = "Sepal.Length = Petal.Length", output = report, plots = regplot(type = c("diagnostics", "residualhistogram", "cooksd"), label = TRUE)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # New data res <- proc_reg(iris, model = "Sepal.Length = Petal.Length", output = report, plots = regplot(type = c("diagnostics", "residualhistogram", "residuals"), label = TRUE, panel = FALSE)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Multiple models res <- proc_reg(iris, model = c("Sepal.Length = Petal.Length", "Sepal.Length = Sepal.Width", "Sepal.Length = Petal.Width"), output = report, plots = "diagnostics", titles = "Iris Regression Statistics") expect_equal(length(res), 3) # 3 Models expect_equal("plot_spec" %in% class(res[[1]][[5]][[1]]), TRUE) }) test_that("regplot18: statistics work as expected.", { # ADJRSQ, AIC, BIC, COEFFVAR, CP, DEFAULT, DEPMEAN, EDF, GMSEP, JP, # MSE, NOBS, NPARM, PC, RSQUARE, SBC, SP, SSE res <- proc_reg(cls, model = Weight ~ Height, output = report, plots = regplot(type = "diagnostics", stats = c("nobs", "edf", "rsquare", "adjrsq", "nparm", "coeffvar", "mse", "depmean"))) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) res <- proc_reg(cls, model = Weight ~ Height, output = report, plots = regplot(type = "diagnostics", stats = c("nobs", "aic", "sse"))) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) res <- proc_reg(cls, model = Weight ~ Height, output = report, plots = regplot(type = "fitplot", stats = c("nobs", "aic", "sse"))) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # fit <- lm(Weight ~ Age , cls) # # avl <- extractAIC(fit) # # avl # # bvl <- extractAIC(fit, k = log(nrow(cls))) # # bvl # # bvl2 <- BIC(fit) # Can't get BIC to work # # bvl3 <- extractAIC(fit, log(nrow(cls))) }) # Works test_that("regplot19: works for residualboxplot", { res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "residualboxplot")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "residualboxplot")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) }) # Works test_that("regplot20: works for TRUE and 'all'", { # plots = TRUE res <- proc_reg(cls, model = "Weight = Height", output = report, plots = TRUE) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # All res <- proc_reg(cls, model = "Weight = Height", output = report, plots = 'all') expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # All with label and id res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot('all', label = TRUE, id = "Name")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) }) test_that("regplot21: works for dffits", { # Basic res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "dffits")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Labels res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "dffits", label = TRUE, id = "Name")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # No ID variable res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "dffits", label = TRUE)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # New data - Awesome res <- proc_reg(iris, model = "Sepal.Length = Petal.Length", output = report, plots = regplot(type = "dffits", label = TRUE)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) }) test_that("regplot22: works for dfbetas", { # Basic res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "dfbetas")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # Labels res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "dfbetas", label = TRUE, id = "Name")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # No ID variable res <- proc_reg(cls, model = "Weight = Height", output = report, plots = regplot(type = "dfbetas", label = TRUE)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # New data - Awesome res <- proc_reg(iris, model = "Sepal.Length = Petal.Length", output = report, plots = regplot(type = "dfbetas", label = TRUE)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) }) test_that("regplot23: dfbetas multiple dependent variables", { cls2 <- cls cls$Sample1 <- runif(nrow(cls2), 50, 100) cls$Sample2 <- runif(nrow(cls2), 50, 100) cls$Sample3 <- runif(nrow(cls2), 50, 100) cls$Sample4 <- runif(nrow(cls2), 50, 100) cls$Sample5 <- runif(nrow(cls2), 50, 100) cls$Sample6 <- runif(nrow(cls2), 50, 100) cls$Sample7 <- runif(nrow(cls2), 50, 100) cls$Sample8 <- runif(nrow(cls2), 50, 100) cls$Sample9 <- runif(nrow(cls2), 50, 100) # 4 variables res <- proc_reg(cls, model = "Weight = Height Age Sample1", output = report, plots = regplot(type = "dfbetas")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # 6 variables res <- proc_reg(cls, model = "Weight = Height Sample1 Sample2 Sample3 Sample4", output = report, plots = regplot(type = "dfbetas")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # 9 variables res <- proc_reg(cls, model = "Weight = Height Sample1 Sample2 Sample3 Sample4 Sample5 Sample6 Sample7", output = report, plots = regplot(type = "dfbetas")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) expect_equal("plot_spec" %in% class(res[[5]][[2]]), TRUE) # 3 variables - panel = FALSE res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "dfbetas", panel = FALSE)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) expect_equal("plot_spec" %in% class(res[[5]][[2]]), TRUE) expect_equal("plot_spec" %in% class(res[[5]][[3]]), TRUE) # 3 variables - panel = FALSE and labels res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "dfbetas", panel = FALSE, label = TRUE)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) expect_equal("plot_spec" %in% class(res[[5]][[2]]), TRUE) expect_equal("plot_spec" %in% class(res[[5]][[3]]), TRUE) # 3 variables - panel = FALSE, labels, and id res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "dfbetas", panel = FALSE, label = TRUE, id = "Name")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) expect_equal("plot_spec" %in% class(res[[5]][[2]]), TRUE) expect_equal("plot_spec" %in% class(res[[5]][[3]]), TRUE) # 3 variables - panel = TRUE, labels res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "dfbetas", panel = TRUE, label = TRUE)) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) # 3 variables - panel = TRUE, labels, and id res <- proc_reg(cls, model = "Weight = Height Age", output = report, plots = regplot(type = "dfbetas", panel = TRUE, label = TRUE, id = "Name")) expect_equal(length(res), 5) expect_equal("plot_spec" %in% class(res[[5]][[1]]), TRUE) })