From f84f0d8d524d624358d568d2efa6a5c7561e73b0 Mon Sep 17 00:00:00 2001 From: singmann Date: Thu, 25 May 2017 14:38:25 +0200 Subject: [PATCH] now with black figures. --- inst/doc/afex_anova_example.html | 22 +++++++++++----------- inst/doc/afex_mixed_example.html | 6 +++--- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/inst/doc/afex_anova_example.html b/inst/doc/afex_anova_example.html index dd157c8..c244997 100644 --- a/inst/doc/afex_anova_example.html +++ b/inst/doc/afex_anova_example.html @@ -246,7 +246,7 @@

2017-05-25

Alternatively, the anova method for afex_aov objects returns a data.frame of class anova that can be passed to, for example, xtable for nice formatting:

print(xtable::xtable(anova(a1), digits = c(rep(2, 5), 3, 4)), type = "html")
- +
@@ -472,12 +472,12 @@

2017-05-25

## ## Linear Hypotheses: ## Estimate Std. Error t value Pr(>|t|) -## MP - MT == 0 10.831 4.612 2.349 0.068859 . -## MP - AC == 0 18.100 4.612 3.925 0.000777 *** +## MP - MT == 0 10.831 4.612 2.349 0.068834 . +## MP - AC == 0 18.100 4.612 3.925 0.000885 *** ## MP - DA == 0 4.556 4.612 0.988 0.325273 ## MT - AC == 0 7.269 4.612 1.576 0.281590 ## MT - DA == 0 -6.275 4.612 -1.361 0.296932 -## AC - DA == 0 -13.544 4.612 -2.937 0.017800 * +## AC - DA == 0 -13.544 4.612 -2.937 0.017607 * ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## (Adjusted p values reported -- free method) @@ -610,7 +610,7 @@

2017-05-25

Plotting

Function lsmip from package lsmeans can be used for plotting the data directly from an afex_aov object. As said initially, we are interested in the three-way interaction of instruction with inference, plausibility, and instruction. A plot of this interaction could be the following:

lsmip(a1, instruction ~ inference|plausibility)
-

+

@@ -649,7 +649,7 @@

2017-05-25

at = 1:4, labels = c("pl:v", "im:v", "pl:i", "im:i") )))
-

+

We see the critical predicted cross-over interaction in the left of those two graphs. For valid but implausible problems (im:v) deductive responses are larger than probabilistic responses. The opposite is true for invalid but plausible problems (pl:i). We now tests these differences at each of the four x-axis ticks in each plot using custom contrasts (diff_1 to diff_4). Furthermore, we test for a validity effect and plausibility effect in both conditions.

(m4 <- lsmeans(a2, ~instruction+plausibility+validity|what))
## what = affirmation:
@@ -718,10 +718,10 @@ 
2017-05-25

 ## 
 ## Linear Hypotheses:
 ##             Estimate Std. Error t value Pr(>|t|)    
-## diff_1 == 0    4.175      8.500   0.491  0.62387    
-## diff_2 == 0   34.925      8.500   4.109  0.00023 ***
-## diff_3 == 0  -23.600      8.500  -2.777  0.01733 *  
-## diff_4 == 0   -8.100      8.500  -0.953  0.56474    
+## diff_1 == 0    4.175      8.500   0.491 0.623874    
+## diff_2 == 0   34.925      8.500   4.109 0.000263 ***
+## diff_3 == 0  -23.600      8.500  -2.777 0.017272 *  
+## diff_4 == 0   -8.100      8.500  -0.953 0.564739    
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## (Adjusted p values reported -- free method)
@@ -733,7 +733,7 @@ 
2017-05-25

 ## 
 ## Linear Hypotheses:
 ##             Estimate Std. Error t value Pr(>|t|)  
-## diff_1 == 0  -22.425      8.500  -2.638   0.0331 *
+## diff_1 == 0  -22.425      8.500  -2.638   0.0332 *
 ## diff_2 == 0   -2.700      8.500  -0.318   0.9554  
 ## diff_3 == 0   -0.925      8.500  -0.109   0.9554  
 ## diff_4 == 0   -3.650      8.500  -0.429   0.9554  
diff --git a/inst/doc/afex_mixed_example.html b/inst/doc/afex_mixed_example.html
index f250c0d..a0a9f36 100644
--- a/inst/doc/afex_mixed_example.html
+++ b/inst/doc/afex_mixed_example.html
@@ -164,7 +164,7 @@ 
2017-05-25

 
fhch_long <- fhch %>% gather("rt_type", "rt", rt, log_rt)
 histogram(~rt|rt_type, fhch_long, breaks = "Scott", type = "density",
           scale = list(x = list(relation = "free")))

-

+

 
 

 
Descriptive Analysis

@@ -181,7 +181,7 @@ 
2017-05-25

          panel.points(tmp$x, tmp$y, pch = 13, cex =1.5)
        }) + 
 bwplot(mean ~ density:frequency|task+stimulus, agg_p, pch="|", do.out = FALSE)
-

+

Now we plot the same data but aggregated across items:

agg_i <- fhch %>% group_by(item, task, stimulus, density, frequency) %>%
   summarise(mean = mean(log_rt)) %>%
@@ -194,7 +194,7 @@ 
2017-05-25

          panel.points(tmp$x, tmp$y, pch = 13, cex =1.5)
        }) + 
 bwplot(mean ~ density:frequency|task+stimulus, agg_i, pch="|", do.out = FALSE)
-

+

These two plots show a very similar pattern and suggest several things:

  • Responses to nonwords appear slower than responses to words, at least for the naming task.