From 5d9b23e5bf03e8f52dcc01c7d937d14f86339dc5 Mon Sep 17 00:00:00 2001 From: Jacob Long Date: Fri, 8 Feb 2019 19:13:44 -0500 Subject: [PATCH] Minor documentation updates --- .gitattributes | 0 NEWS.md | 3 ++- README.Rmd | 22 +++++++++++++++++++++- man/figures/unnamed-chunk-10-1.png | Bin 0 -> 163180 bytes man/figures/unnamed-chunk-9-1.png | Bin 0 -> 155882 bytes 5 files changed, 23 insertions(+), 2 deletions(-) delete mode 100644 .gitattributes create mode 100644 man/figures/unnamed-chunk-10-1.png create mode 100644 man/figures/unnamed-chunk-9-1.png diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index e69de29b..00000000 diff --git a/NEWS.md b/NEWS.md index c9b86cc4..05802984 100644 --- a/NEWS.md +++ b/NEWS.md @@ -6,7 +6,8 @@ To reduce the complexity of this package and help people understand what they are getting, I have removed all functions that directly analyze -interaction/moderation effects and put them into a new package, `interactions`. +interaction/moderation effects and put them into a new package, +[`interactions`](https://interactions.jacob-long.com). There are still some functions in `jtools` that support `interactions`, but some users may find that everything they ever used `jtools` for has now moved to `interactions`. The following functions have moved to `interactions`: diff --git a/README.Rmd b/README.Rmd index e551e868..8e6a9c7e 100644 --- a/README.Rmd +++ b/README.Rmd @@ -176,11 +176,31 @@ These show the 95% interval width of a normal distribution for each estimate. arguments like `robust` and `scale`. This enables a wider range of models that have support from the `broom` package but not for `summ`. +### Plotting model predictions (`effect_plot`) + +Sometimes the best way to understand your model is to look at the predictions +it generates. Rather than look at coefficients, `effect_plot` lets you plot +predictions across values of a predictor variable alongside the observed data. + +```{r} +effect_plot(fit_c, pred = hp, interval = TRUE, plot.points = TRUE) +``` + +And a new feature in version `2.0.0` lets you plot *partial residuals* +instead of the raw observed data, allowing you to assess model quality after +accounting for effects of control variables. + +```{r} +effect_plot(fit_c, pred = hp, interval = TRUE, partial.residuals = TRUE) +``` + +Categorical predictors, polynomial terms, (G)LM(M)s, weighted data, and much +more are supported. + ### Other stuff There are several other things that might interest you. -* `effect_plot`: Plot predicted lines from regression models * `gscale`: Scale and/or mean-center data, including `svydesign` objects * `scale_mod` and `center_mod`: Re-fit models with scaled and/or mean-centered data diff --git a/man/figures/unnamed-chunk-10-1.png b/man/figures/unnamed-chunk-10-1.png new file 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