update table 1 #636 [skip ci]

papers/JOSE/paper.Rmd

@@ -197,15 +197,9 @@ outliers
 
 Using the model-based outlier detection method, we identified a single outlier.
 
-All `check_outliers()` output objects possess a `plot()` method, meaning it is also possible to visualize the outliers using the generic `plot()` function on the resulting outlier object after loading the {see} package.
+Table 1 below summarizes which methods to use in which cases, and with what threshold. The recommended thresholds are the default thresholds.
 
-```{r model_fig, fig.cap = "Visual depiction of outliers based on Cook's distance (leverage and standardized residuals), based on the fitted model."}
-plot(outliers)
-```
-
-Table 1 below summarizes which methods to use in which cases, and with what threshold.
-
-```{r, echo=FALSE}
+```{r table1_prep, echo=FALSE}
 df <- data.frame(
   `Statistical Test` = c(
     "Supported regression model", 
@@ -216,13 +210,35 @@ df <- data.frame(
     "**Multivariate**: Minimum Covariance Determinant (MCD)", 
     "**Univariate**: robust *z* scores (MAD)"),
   `Recommended Threshold` = c(
-    "`qf(0.5, ncol(x), nrow(x) - ncol(x))` (or 0.7 for Pareto)", 
-    "`qchisq(p = 1 - 0.001, df = ncol(x))`", 
-    "`qnorm(p = 1 - 0.001 / 2)`, ~ 3.29")
+    "_qf(0.5, ncol(x), nrow(x) - ncol(x))_ (or 0.7 for Pareto)", 
+    "_qchisq(p = 1 - 0.001, df = ncol(x))_", 
+    "_qnorm(p = 1 - 0.001 / 2)_, ~ 3.29"),
+  `Function Usage` = c(
+    '_check_outliers(model, method = "cook")_', 
+    '_check_outliers(data, method = "mcd")_', 
+    '_check_outliers(data, method = "zscore_robust")_'),
+  check.names = FALSE
 )
-knitr::kable(
-  df, col.names = gsub("[.]", " ", names(df)), 
-  caption = "Summary of Statistical Outlier Detection Methods Recommendations.", longtable = TRUE)
+```
+
+### Table 1
+
+_Summary of Statistical Outlier Detection Methods Recommendations_
+
+```{r table1_print, echo=FALSE, message=FALSE}
+x <- flextable::flextable(df, cwidth = 1.25)
+x <- flextable::theme_apa(x)
+# x <- flextable::align(x, align = "left", part = "all")
+x <- flextable::font(x, fontname = "Latin Modern Roman", part = "all")
+x <- flextable::fontsize(x, size = 10, part = "all")
+ftExtra::colformat_md(x)
+
+```
+
+All `check_outliers()` output objects possess a `plot()` method, meaning it is also possible to visualize the outliers using the generic `plot()` function on the resulting outlier object after loading the {see} package (Figure 1).
+
+```{r model_fig, fig.cap = "Visual depiction of outliers based on Cook's distance (leverage and standardized residuals), based on the fitted model."}
+plot(outliers)
 ```
 
 ## Cook's Distance vs. MCD