updates to documentation

DESCRIPTION

@@ -29,6 +29,6 @@ Authors@R: c(person(given="Henrik", family="Singmann", role=c("aut", "cre"),
          A.", family="Lawrence", role=c("ctb")), person(given="Ulf",
          family="Mertens", role=c("ctb")), person(given="Jonathan", family="Love",
          role=c("ctb")) )
-Version: 0.17-7
-Date: 2017-04-07
+Version: 0.17-8
+Date: 2017-04-11
 RoxygenNote: 6.0.1

R/afex-package.R

@@ -3,8 +3,8 @@
 #' \tabular{ll}{
 #' Package: \tab afex\cr
 #' Type: \tab Package\cr
-#' Version: \tab 0.17-7\cr
-#' Date: \tab 2017-04-07\cr
+#' Version: \tab 0.17-8\cr
+#' Date: \tab 2017-04-11\cr
 #' Depends: \tab R (>= 3.1.0), lme4 (>= 1.1-8), lsmeans (>= 2.17)\cr
 #' Encoding: \tab UTF-8\cr
 #' License: \tab GPL (>=2)\cr

dev.R

@@ -37,7 +37,7 @@ R.libs <- "./packages/library"
 closeAllConnections()
 roxy.package(
 	pck.source.dir = pkg.src.dir,
-	pck.version = "0.17-7",
+	pck.version = "0.17-8",
 	pck.description = data.frame(
 		Package = "afex",
 		Type = "Package",

examples/examples.aov_car.R

@@ -152,13 +152,12 @@ obk_anova <- aov_car(value ~ treatment * gender + Error(id/(phase*hour)),
 # in contrast to aov you do not need the within-subject factors outside Error()
 
 str(obk_anova, 1, give.attr = FALSE)
-## List of 6
-##  $ anova_table:Classes 'anova' and 'data.frame':  15 obs. of  6 variables:
-##  $ aov        :List of 5
-##  $ Anova      :List of 14
-##  $ lm         :List of 13
-##  $ data       :List of 3
-##  $ information:List of 5
+# List of 5
+#  $ anova_table:Classes ‘anova’ and 'data.frame':	15 obs. of  6 variables:
+#  $ aov        :List of 5
+#  $ Anova      :List of 14
+#  $ lm         :List of 13
+#  $ data       :List of 3
 
 obk_anova$Anova
 ## Type II Repeated Measures MANOVA Tests: Pillai test statistic

examples/examples.mixed.R

@@ -167,6 +167,11 @@ data(lexdec, package = "languageR")
 m1 <- mixed(RT ~ Correct + Trial + PrevType * meanWeight + 
     Frequency + NativeLanguage * Length + (1|Subject) + (1|Word), data = lexdec)
 m1
+# Mixed Model Anova Table (Type 3 tests, KR-method)
+# 
+# Model: RT ~ Correct + Trial + PrevType * meanWeight + Frequency + NativeLanguage * 
+# Model:     Length + (1 | Subject) + (1 | Word)
+# Data: lexdec
 #                  Effect         df         F p.value
 # 1               Correct 1, 1627.73   8.15 **    .004
 # 2                 Trial 1, 1592.43   7.57 **    .006
@@ -177,6 +182,8 @@ m1
 # 7                Length   1, 75.83   8.70 **    .004
 # 8   PrevType:meanWeight 1, 1601.18    6.18 *     .01
 # 9 NativeLanguage:Length 1, 1555.49 14.24 ***   .0002
+# ---
+# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
 
 # Fitting a GLMM using parametric bootstrap:
 require("mlmRev") # for the data, see ?Contraception