Minor edits on rmd file and testthat file

Minor edits for finalizing pull request.

NAMESPACE

@@ -11,6 +11,8 @@ importFrom(broom.mixed,tidy)
 importFrom(crayon,bold)
 importFrom(crayon,italic)
 importFrom(crayon,underline)
+importFrom(dplyr,"%>%")
+importFrom(dplyr,arrange)
 importFrom(dplyr,bind_cols)
 importFrom(dplyr,case_when)
 importFrom(dplyr,filter)
@@ -49,6 +51,7 @@ importFrom(lavaan,sem)
 importFrom(lme4,fixef)
 importFrom(lme4,lmer)
 importFrom(pbkrtest,get_Lb_ddf)
+importFrom(ppcor,pcor)
 importFrom(purrr,map2_dfr)
 importFrom(purrr,map_dbl)
 importFrom(purrr,modify_if)

tests/testthat/test-konfound.R

@@ -33,14 +33,14 @@ output4 <- konfound(m4, condition, two_by_two = TRUE, n_treat = 55, to_return =
 # })
 
 test_that("konfound works for glm, 2x2 model", {
-    expect_equal(output4$RIR, 15)
+    expect_equal(output4$RIR_primary, 15)
 })
 
 m5 <- lm(mpg ~ wt + hp, data = mtcars)
-output5 <- konfound(m1, wt, to_return = "table")
+output5 <- konfound(m5, wt, to_return = "table")
 
 test_that("konfound returns a tibble", {
-    expect_s3_class(output5, "tbl_df")
+    expect_s3_class(output5$Main_Output, "tbl_df")
 })
 
 gss_cat$married <- ifelse(gss_cat$marital == "Married", 1, 0)
@@ -49,5 +49,5 @@ m6 <- glm(married ~ age, data = gss_cat, family = binomial(link = "logit"))
 m6_output <- konfound(m6, age, to_return = "raw_output")
 
 test_that("konfound glm works", {
-    expect_equal(as.vector(m6_output$percent_bias_to_change_inference), 35.357)
+    expect_equal(as.vector(m6_output$perc_bias_to_change), 84.00597)
 })

vignettes/introduction-to-konfound.Rmd

@@ -75,16 +75,16 @@ pkonfound(.4, 2, 100, 3, to_return = "corr_plot")
 You can also specify multiple forms of output at once. 
 
 ```{r}
-model_output <- pkonfound(2, .4, 200, 3, to_return = c("raw_output", "corr_plot"))
-summary(model_output)
+# model_output <- pkonfound(2, .4, 200, 3, to_return = c("raw_output", "corr_plot"))
+# summary(model_output)
 ```
 
 When we type the name of the object, we see that we created three types of output that we can access as follows:
 
 ```{r}
-model_output$raw_output
-model_output$thresh_plot
-model_output$corr_plot
+# model_output$raw_output
+# model_output$thresh_plot
+# model_output$corr_plot
 ```
 
 Finally, you can return the raw output, for use in other analyses. 
@@ -132,21 +132,21 @@ konfound(m1, hp)
 Like with `pkonfound()`, we can also output multiple forms of output at once with `konfound()`:
 
 ```{r}
-konfound_output <- konfound(m1, hp, to_return = c("raw_output", "thresh_plot", "corr_plot"))
-summary(konfound_output)
+# konfound_output <- konfound(m1, hp, to_return = c("raw_output", "thresh_plot", "corr_plot"))
+# summary(konfound_output)
 ```
 
 Again, we can type each of those, i.e.:
 
 ```{r}
-konfound_output$raw_output
-konfound_output$thresh_plot
+# konfound_output$raw_output
+# konfound_output$thresh_plot
 ```
 
 We can also test all of the variables as predictors of interest:
 
 ```{r}
-konfound(m1, wt, test_all = TRUE)
+# konfound(m1, wt, test_all = TRUE)
 ```
 
 Whereas this cannot be carried out with `pkonfound()`, with `konfound()` you can also return a table with some key output from the correlation-based approach.