version 0.5.0

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: jtools
 Type: Package
 Title: Analysis and Presentation of Social Scientific Data
-Version: 0.4.5
+Version: 0.5.0
 Authors@R: person(c("Jacob","A."), "Long", email = "long.1377@osu.edu",
   role = c("aut", "cre"))
 Description: This is a collection of tools that the author (Jacob) has written
@@ -20,8 +20,8 @@ Suggests: ggplot2, lmtest, sandwich, car, survey, weights,
 RoxygenNote: 6.0.1
 VignetteBuilder: knitr
 NeedsCompilation: no
-Packaged: 2017-05-24 14:06:59 UTC; Jacob
+Packaged: 2017-08-08 21:59:37 UTC; Jacob
 Author: Jacob A. Long [aut, cre]
 Maintainer: Jacob A. Long <long.1377@osu.edu>
 Repository: CRAN
-Date/Publication: 2017-05-24 23:07:03 UTC
+Date/Publication: 2017-08-08 22:13:41 UTC

MD5

@@ -1,54 +1,54 @@
-c9fcece25e2b7479d55cd370dd5580a2 *DESCRIPTION
+f7d42241b910e046c44a018641386251 *DESCRIPTION
 1955303bf9f5e947ff6a7734cd492159 *LICENSE
-7de443068b7ce01bff508f139733961f *NAMESPACE
-3927739bc7c8833476c5d02c2693e6ff *NEWS.md
+2072c6c3c887ef508042337b44a0637c *NAMESPACE
+69546a6a39f3d51a1b5af79a7c1d9467 *NEWS.md
 0d8d3fdde6bce163ad0af6badf1b1c44 *R/center_lm.R
-d0f891ea099510d75059f24ab4d09747 *R/gscale.R
-94a2dc341f2ac5dd3f62ff3409853064 *R/interact_plot.R
+68cfed138009ac5053072af97c5c3f7e *R/gscale.R
+36d1166a5da59b63aaf485a37b25ca8c *R/interact_plot.R
 96f2b84aba3fa57e99cd32b93e45f4b5 *R/internal.R
-2e4ae18edf89cbeca24db5ae1c7356bd *R/j_summary.R
-118bfef293e696e89e50a9d416d783d3 *R/johnson_neyman.R
-5869342260e4ed874efbf8a2f545fe76 *R/probe_interaction.R
+6b5b1af9e2c3a8ce65ee712f358eaaf0 *R/j_summary.R
+a44cfbb2c04929d93eb6ba4c5c4b5470 *R/johnson_neyman.R
+46bf802684f0190efa8e2c39ba93c26e *R/probe_interaction.R
 16c3050bbe7cf4d966112a90c810c1a9 *R/scale_lm.R
-3e6dad6a1cfcaf5971467d003f2e0d0b *R/simple_slopes.R
+e21d22d329ca6bff2ec2814cf372fab5 *R/simple_slopes.R
 90d0db9785b602c674b713286b471173 *R/svycor.R
 674eb20d69acd5a1d32457feae68b8c2 *R/svysd.R
 4478f432eebcc34bef702c7846042841 *R/theme_apa.R
-02c45b7d02ce712b416dd48a2698fb91 *R/wgttest.R
-01692350d280ac8e64cfc396c115d6b0 *README.md
+baa3ee1ff2d8a8c4f753b994356d3dea *R/wgttest.R
+afdfe062d7e007ef80e16a0680b05bba *README.md
 89f347f47be53aac47e3a1d787cf0bcc *build/vignette.rds
-63eb1bc6ef2d7d6c7bec8c7a55a35790 *inst/CITATION
+82b99fb1a2bee46fe3e4724cdd72e8f5 *inst/CITATION
 fcbdd288901706e698b39895a3ecd24e *inst/doc/interactions.R
 2f41db9b1761697eb61bfee4fd9679ab *inst/doc/interactions.Rmd
-f88e8ba89731ca477d0f62e5d0d4979f *inst/doc/interactions.html
+7f2a8c57448319cb1d7b84b779bd6fde *inst/doc/interactions.html
 5ac7c50306d285c394b6cb9bad675dbf *inst/doc/j_summ.R
 ae5b9b8537f8cfce37eb8a2331f2a332 *inst/doc/j_summ.Rmd
-6e9a97f2b3f1adb1eb31e9b98c2c00a0 *inst/doc/j_summ.html
+57cce60a2096303c9ce9ba61675422fe *inst/doc/j_summ.html
 8bdc85384181cceaddb1f0d5703e2037 *inst/doc/svycor.R
 2bf323910af42bded5d29b3a0961bb4e *inst/doc/svycor.Rmd
-76b158285ff5315d50517892d7652f72 *inst/doc/svycor.html
+0d638c4858fdffa8a4703b688db83092 *inst/doc/svycor.html
 08b9872b5883d00d731cfe646557d67e *man/center_lm.Rd
 5ac5cf87cef6823f260aff1e478b61ee *man/gscale.Rd
-e65e20b205ecd7c0f164bcdc1b366505 *man/interact_plot.Rd
-c523e3ef1d3227442e687b51ac07b5ec *man/j_summ.Rd
+6e9e02d44125e13fe6acff175df7349c *man/interact_plot.Rd
+b6652ac9ac271efe725fcffd7f1abc1f *man/j_summ.Rd
 2f3a3f72b72039c6d45fa725f19c33ec *man/johnson_neyman.Rd
 0beb508256c1dd988c97e1bb1de26fcf *man/probe_interaction.Rd
 9d60ea693e899f7d6bd4b9bd77998ed3 *man/scale_lm.Rd
 1d247beb07308d904e88c0df35014899 *man/sim_slopes.Rd
 d9224e4b96cc45a06581ebc651411c09 *man/svycor.Rd
 f3aed175c95b46239be0a0ecf9f52b9c *man/svysd.Rd
 7f094bf0752b1a18dbfbd80bd6b0c7e6 *man/theme_apa.Rd
-688f78b760b2cd9c0f191330ed67ffc5 *man/wgttest.Rd
+c5b5b63f050f71339c633c555b396fd1 *man/wgttest.Rd
 f4c841e89ad33e3419091a8a673b5262 *tests/testthat.R
 92d6b291b8b229a01e518e2dc4f9eac0 *tests/testthat/test-gscale.R
 318dca7c813ba6de19af1cf3c873b28e *tests/testthat/test-jsumm.R
 0473bf2cdc972e5688e468949900b2ac *tests/testthat/test-misc.R
-1b51b852a9deea4f7338db49d01c6cf8 *tools/README-interact_plot_continuous-1.png
-353770aa04351af1b7e2cc933ca14445 *tools/README-interact_plot_factor-1.png
-76c8f1c0dbd321a385f329bb1b76c1b8 *tools/README-j-n-plot-1.png
-15a147836f40aab8cf7432832a2f98c2 *tools/README-probe_interaction_ex-1.png
-511ddd70a874d9dad6a69fd90fe6565f *tools/README-theme_apa_ex-1.png
-b63a015aaa96f290191f93943e82b6d6 *tools/README-theme_apa_facet_ex-1.png
+d71fb1ba48a93074b47a4404af631028 *tools/README-interact_plot_continuous-1.png
+c86140e1275d84d9263e15646d51fada *tools/README-interact_plot_factor-1.png
+12a3ea0a69f70362278cf807f8228e4f *tools/README-j-n-plot-1.png
+41605fb6f617fa0fa1bbc9a5d7badca6 *tools/README-probe_interaction_ex-1.png
+3a0bf9c65d0a2f0992472b4521368a77 *tools/README-theme_apa_ex-1.png
+fe48fb20e05fadff4f95453459531258 *tools/README-theme_apa_facet_ex-1.png
 2f41db9b1761697eb61bfee4fd9679ab *vignettes/interactions.Rmd
 ae5b9b8537f8cfce37eb8a2331f2a332 *vignettes/j_summ.Rmd
 2bf323910af42bded5d29b3a0961bb4e *vignettes/svycor.Rmd

NAMESPACE

@@ -14,6 +14,7 @@ S3method(print,probe_interaction)
 S3method(print,sim_slopes)
 S3method(print,svycor)
 S3method(print,svysd)
+S3method(print,wgttest)
 export(center_lm)
 export(gscale)
 export(interact_plot)
@@ -39,7 +40,9 @@ importFrom(stats,formula)
 importFrom(stats,getCall)
 importFrom(stats,lm)
 importFrom(stats,logLik)
+importFrom(stats,median)
 importFrom(stats,model.frame)
+importFrom(stats,model.offset)
 importFrom(stats,na.pass)
 importFrom(stats,pf)
 importFrom(stats,predict)

NEWS.md

@@ -1,3 +1,38 @@
+## jtools 0.5.0
+
+More goodies for users of interact_plot:
+
+* Added support for models with a weights parameter in interact_plot. It would 
+work previously, but didn't use a weighted mean or SD in calculating values of 
+the moderator(s) and for mean-centering other predictors. Now it does.
+* Added support for two-level factor predictors in interact_plot. Previously, 
+factor variables had to be a moderator. 
+* When predictor in interact_plot has only two unique values (e.g., dummy 
+variables that have numeric class), by default only those two values have
+tick marks on the x-axis. Users may use the pred.labels argument to specify
+labels for those ticks.
+* Offsets are now supported (especially useful for Poisson GLMs), but only
+if specified via the offset argument rather than included in the model formula.
+You can (and should) specify the offset used for the plot using the set.offset
+argument. By default it is 1 so that the y-axis represents a proportion. 
+
+Other feature changes:
+
+* sim_slopes now supports weights (from the weights argument rather than a
+svyglm model). Previously it used unweighted mean and standard deviation for
+non-survey models with weights.
+* Improved printing features of wgttest
+
+Bug fixes:
+
+* R 3.4 introduced a change that caused warning messages when return objects
+are created in a certain way. This was first addressed in jtools 0.4.5, but
+a few instances slipped through the cracks. Thanks to Kim Henry for pointing
+out one such instance.
+* When sim_slopes called johnson_neyman while the robust argument was set to 
+TRUE, the robust.type argument was not being passed (causing the default of 
+"HC3" to be used). Now it is passing that argument correctly.
+
 ## jtools 0.4.5
 
 * Added better support for plotting nonlinear interactions with interact_plot,

R/gscale.R

@@ -252,9 +252,14 @@ if (is.numeric(x)) {
     # Loop through the variable names
     for (i in x) {
 
+      # Dealing with two-level factors
+      if (length(unique(d[,i])) == 2 && is.factor(d[,i])) {
+        d[,i] <- as.numeric(d[,i]) - 1
+      } # Now it will follow the logic of any numeric binary var
+
       # Now just calling the rescale function, basically
       # for binary cases
-      if (length(unique(d[,i]))==2 && is.numeric(d[,i])) {
+      if (length(unique(d[,i])) == 2 && is.numeric(d[,i])) {
 
         if (binary.inputs=="0/1") {
 
@@ -357,17 +362,22 @@ if (is.numeric(x)) {
         }
       }
 
-      if (!is.numeric(d[,i]) || all(is.na(d[,i])) || skip == TRUE) {
-        # just skip over non-numeric variables
+      if ((!is.numeric(d[,i]) & length(unique(d[,i])) != 2) || all(is.na(d[,i])) || skip == TRUE) {
+        # just skip over non-numeric variables except binary factors
         # columns that are all NA will still show up as numeric
         if (all(is.na(d[,i]))) {
           message <- paste("All values of", names(d)[i], "were NA. Skipping...\n")
           warning(message)
         }
       } else {
 
+      # Dealing with two-level factors
+      if (length(unique(d[,i])) == 2 && is.factor(d[,i])) {
+        d[,i] <- as.numeric(d[,i]) - 1
+      } # Now it will follow the logic of any numeric binary var
+
       # for binary cases
-      if (length(unique(d[,i]))==2) {
+      if (length(unique(d[,i])) == 2) {
 
         if (binary.inputs=="0/1") {
 
@@ -449,7 +459,7 @@ if (is.numeric(x)) {
     # Just calling rescale
     for (i in x) {
 
-      if (!is.numeric(d[,i]) || all(is.na(d[,i]))) {
+      if ((!is.numeric(d[,i]) & length(unique(d[,i])) != 2) || all(is.na(d[,i]))) {
         # just skip over non-numeric variables
         # columns that are all NA will still show up as numeric
         if (all(is.na(d[,i]))) {
@@ -458,6 +468,11 @@ if (is.numeric(x)) {
         }
       } else {
 
+      # Dealing with two-level factors
+      if (length(unique(d[,i])) == 2 && is.factor(d[,i])) {
+        d[,i] <- as.numeric(d[,i]) - 1
+      } # Now it will follow the logic of any numeric binary var
+
       # for binary cases
       if (length(unique(d[,i])) == 2) {
 
@@ -547,7 +562,7 @@ if (is.numeric(x)) {
         # I need its actual name for the svymean() commands
         i <- names(d)[i]
 
-        if (!is.numeric(d[,i]) || all(is.na(d[,i]))) {
+        if ((!is.numeric(d[,i]) & length(unique(d[,i])) != 2) || all(is.na(d[,i]))) {
           # just skip over non-numeric variables
           # columns that are all NA will still show up as numeric
           if (all(is.na(d[,i]))) {
@@ -556,8 +571,14 @@ if (is.numeric(x)) {
           }
         } else {
 
+          # Dealing with two-level factors
+          if (length(unique(d[,i])) == 2 && is.factor(d[,i])) {
+            d[,i] <- as.numeric(d[,i]) - 1
+            design$variables <- d
+          } # Now it will follow the logic of any numeric binary var
+
           # for binary cases
-          if (length(unique(d[,i]))==2) {
+          if (length(unique(d[,i])) == 2) {
 
             if (binary.inputs=="0/1") {