From 17266257d5fd4128e3fde491c89e4e9e14891c54 Mon Sep 17 00:00:00 2001 From: rempsyc Date: Wed, 4 Oct 2023 16:58:12 -0400 Subject: [PATCH] update table 1 #636 [skip ci] --- papers/JOSE/paper.Rmd | 44 +- papers/JOSE/paper.log | 822 +++++++++++++++--- papers/JOSE/paper.md | 84 +- papers/JOSE/paper.pdf | Bin 202771 -> 214432 bytes .../paper_files/figure-latex/model_fig-1.pdf | Bin 8479 -> 8483 bytes vignettes/check_outliers.Rmd | 2 +- 6 files changed, 815 insertions(+), 137 deletions(-) diff --git a/papers/JOSE/paper.Rmd b/papers/JOSE/paper.Rmd index c72fefc15..5e8409178 100644 --- a/papers/JOSE/paper.Rmd +++ b/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 diff --git a/papers/JOSE/paper.log b/papers/JOSE/paper.log index 33daaa7f8..231a93157 100644 --- a/papers/JOSE/paper.log +++ b/papers/JOSE/paper.log @@ -1,4 +1,4 @@ -This is XeTeX, Version 3.141592653-2.6-0.999995 (TeX Live 2023) (preloaded format=xelatex 2023.9.14) 4 OCT 2023 15:06 +This is XeTeX, Version 3.141592653-2.6-0.999995 (TeX Live 2023) (preloaded format=xelatex 2023.10.4) 4 OCT 2023 16:57 entering extended mode restricted \write18 enabled. %&-line parsing enabled. @@ -384,7 +384,7 @@ Package uniquecounter Info: New unique counter `rerunfilecheck' on input line 28 Package hyperref Info: Option `colorlinks' set `true' on input line 12. 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File: D:/Rpackages/rticles/rmarkdown/templates/joss/resources/JOSE-logo.png Graphic file (type bmp) @@ -1113,6 +1713,8 @@ Package fancyhdr Warning: \headheight is too small (62.59596pt): (fancyhdr) \addtolength{\topmargin}{-1.71957pt}. [5] +File: paper_files/figure-latex/model_fig-1.pdf Graphic file (type pdf) + File: D:/Rpackages/rticles/rmarkdown/templates/joss/resources/JOSE-logo.png Graphic file (type bmp) @@ -1143,17 +1745,17 @@ Package fancyhdr Warning: \headheight is too small (62.59596pt): (fancyhdr) \addtolength{\topmargin}{-1.71957pt}. [8] -Underfull \hbox (badness 1584) in paragraph at lines 968--974 +Underfull \hbox (badness 1584) in paragraph at lines 995--1001 []\TU/lmr/m/n/10 Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psy- [] -Underfull \hbox (badness 3049) in paragraph at lines 968--974 +Underfull \hbox (badness 3049) in paragraph at lines 995--1001 \TU/lmr/m/n/10 chology: Undisclosed flexibility in data collection and analysis allows pre- [] -Underfull \hbox (badness 3735) in paragraph at lines 968--974 +Underfull \hbox (badness 3735) in paragraph at lines 995--1001 \TU/lmr/m/n/10 senting anything as significant. \TU/lmr/m/it/10 Psychological Science\TU/lmr/m/n/10 , \TU/lmr/m/it/10 22\TU/lmr/m/n/10 (11), 1359–1366. [] @@ -1172,18 +1774,18 @@ LaTeX2e <2023-06-01> patch level 1 L3 programming layer <2023-08-29> *********** Package rerunfilecheck Info: File `paper.out' has not changed. -(rerunfilecheck) Checksum: EEBE57815200557F8EDD73A1BF6F9118;2043. +(rerunfilecheck) Checksum: 18F584A1BC96404D165BE4F0A067B822;2146. 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\multicolumn{1}{>{\centering}m{\dimexpr 1.25in+0\tabcolsep}}{\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{check\_outliers(data,}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{\ }}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{method}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{\ }}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{=}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{\ }}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{“}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern 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Roman}{\ }}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{scores}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\ }}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{(MAD)}}}} & \multicolumn{1}{>{\centering}m{\dimexpr 1.25in+0\tabcolsep}}{\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{qnorm(p}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{\ }}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{=}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{\ }}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{1}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{\ }}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{-}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{\ }}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{0.001}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{\ }}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{/}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{\ }}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{2)}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{,}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\ }}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textasciitilde }}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\ }}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{3.29}}}} & \multicolumn{1}{>{\centering}m{\dimexpr 1.25in+0\tabcolsep}}{\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{check\_outliers(data,}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{\ }}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{method}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{\ }}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{=}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{\ }}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{“}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{zscore\_robust}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{”}}}}\textcolor[HTML]{000000}{\fontsize{10}{20}\selectfont{\global\setmainfont{Latin Modern Roman}{\textit{)}}}}} \\ + +\ascline{0.75pt}{000000}{1-4} + + + +\end{longtable} + + + +\arrayrulecolor[HTML]{000000} + +\global\setlength{\arrayrulewidth}{\Oldarrayrulewidth} + +\global\setlength{\tabcolsep}{\Oldtabcolsep} + +\renewcommand*{\arraystretch}{1} +``` + +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 @@ -246,17 +317,6 @@ plot(outliers) \includegraphics[width=1\linewidth]{paper_files/figure-latex/model_fig-1} \caption{Visual depiction of outliers based on Cook's distance (leverage and standardized residuals), based on the fitted model.}\label{fig:model_fig} \end{figure} -Table 1 below summarizes which methods to use in which cases, and with what threshold. - - -Table: Summary of Statistical Outlier Detection Methods Recommendations. - -|Statistical Test |Diagnosis Method |Recommended Threshold | -|:------------------------------------------------------------|:------------------------------------------------------|:---------------------------------------------------------| -|Supported regression model |**Model-based**: Cook (or Pareto for Bayesian models) |`qf(0.5, ncol(x), nrow(x) - ncol(x))` (or 0.7 for Pareto) | -|Structural Equation Modeling (or other unsupported model) |**Multivariate**: Minimum Covariance Determinant (MCD) |`qchisq(p = 1 - 0.001, df = ncol(x))` | -|Simple test with few variables (*t* test, correlation, etc.) |**Univariate**: robust *z* scores (MAD) |`qnorm(p = 1 - 0.001 / 2)`, ~ 3.29 | - ## Cook's Distance vs. MCD @leys2018outliers report a preference for the MCD method over Cook's distance. This is because Cook's distance removes one observation at a time and checks its corresponding influence on the model each time [@cook1977detection], and flags any observation that has a large influence. 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zLn3~!wQ~HL`gtGKryM=)YF9t?@0*{uw>=8i{cUuL{q%#t$<-g50=L$Fe73sw_wl!n zSFf-8@#x*h=Qp~&>?d@u6zaM_9 zaCmlj{GHtTzn=uJ{XCeoHhxd#*Iy6cM*V$p|LOkoeg2at>k68)ni`lHm`*+?Ck|n4 zZWXlU1v4!sAD5GZvR=u}U^JZEEN@kBZl+)W0t$HwTwsQQiHV^hhM1|TF@~6#sR5># zr3r?Zxe2B^a|=_jI&Wl)EsQV~S{h;)Xl`zd;TUss3**V{3U+Y@<|dYwE@n=Smd?f| zE>7ldu9hwaj%H5IE-p?6mX=O-3N{3l#46a?aTS*&7L`;KrKWLN7#J9Hsj9mAyKw;k DB_CBg diff --git a/vignettes/check_outliers.Rmd b/vignettes/check_outliers.Rmd index e700cfb16..cd3ff33e0 100644 --- a/vignettes/check_outliers.Rmd +++ b/vignettes/check_outliers.Rmd @@ -166,7 +166,7 @@ plot(outliers) Using the model-based outlier detection method, we identified a single outlier. -Table 1 below summarizes which methods to use in which cases, and with what threshold. +Table 1 below summarizes which methods to use in which cases, and with what threshold. The recommended thresholds are the default thresholds. ```{r, echo=FALSE} df <- data.frame(