finalized mixed vignette

afex.Rproj

@@ -14,3 +14,4 @@ LaTeX: pdfLaTeX
 
 BuildType: Package
 PackageInstallArgs: --no-multiarch --with-keep.source
+PackageCheckArgs: --no-tests

inst/doc/anova_posthoc.html

@@ -12,7 +12,7 @@
 
 <meta name="author" content="Henrik Singmann" />
 
-<meta name="date" content="2017-04-02" />
+<meta name="date" content="2017-04-04" />
 
 <title>ANOVA and Post-Hoc Contrasts: Reanalysis of Singmann and Klauer (2011)</title>
 
@@ -70,7 +70,7 @@
 
 <h1 class="title toc-ignore">ANOVA and Post-Hoc Contrasts: Reanalysis of Singmann and Klauer (2011)</h1>
 <h4 class="author"><em>Henrik Singmann</em></h4>
-<h4 class="date"><em>2017-04-02</em></h4>
+<h4 class="date"><em>2017-04-04</em></h4>
 
 
 
@@ -242,7 +242,7 @@ <h4 class="date"><em>2017-04-02</em></h4>
 <p>Alternatively, the <code>anova</code> method for <code>afex_aov</code> objects returns a <code>data.frame</code> of class <code>anova</code> that can be passed to, for example, <code>xtable</code> for nice formatting:</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(xtable::<span class="kw">xtable</span>(<span class="kw">anova</span>(a1), <span class="dt">digits =</span> <span class="kw">c</span>(<span class="kw">rep</span>(<span class="dv">2</span>, <span class="dv">5</span>), <span class="dv">3</span>, <span class="dv">4</span>)), <span class="dt">type =</span> <span class="st">&quot;html&quot;</span>)</code></pre></div>
 <!-- html table generated in R 3.3.3 by xtable 1.8-2 package -->
-<!-- Sun Apr  2 23:49:13 2017 -->
+<!-- Tue Apr  4 11:41:00 2017 -->
 <table border="1">
 <tr>
 <th>
@@ -468,12 +468,12 @@ <h4 class="date"><em>2017-04-02</em></h4>
 ## 
 ## Linear Hypotheses:
 ##              Estimate Std. Error t value Pr(&gt;|t|)    
-## MP - MT == 0   10.831      4.612   2.349 0.068842 .  
-## MP - AC == 0   18.100      4.612   3.925 0.000788 ***
+## MP - MT == 0   10.831      4.612   2.349 0.068913 .  
+## MP - AC == 0   18.100      4.612   3.925 0.000763 ***
 ## MP - DA == 0    4.556      4.612   0.988 0.325273    
-## MT - AC == 0    7.269      4.612   1.576 0.281747    
+## MT - AC == 0    7.269      4.612   1.576 0.281917    
 ## MT - DA == 0   -6.275      4.612  -1.361 0.296932    
-## AC - DA == 0  -13.544      4.612  -2.937 0.017788 *  
+## AC - DA == 0  -13.544      4.612  -2.937 0.017960 *  
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## (Adjusted p values reported -- free method)</code></pre>
@@ -713,8 +713,8 @@ <h4 class="date"><em>2017-04-02</em></h4>
 ## Linear Hypotheses:
 ##             Estimate Std. Error t value Pr(&gt;|t|)    
 ## diff_1 == 0    4.175      8.500   0.491 0.623874    
-## diff_2 == 0   34.925      8.500   4.109 0.000224 ***
-## diff_3 == 0  -23.600      8.500  -2.777 0.017382 *  
+## diff_2 == 0   34.925      8.500   4.109 0.000219 ***
+## diff_3 == 0  -23.600      8.500  -2.777 0.017300 *  
 ## diff_4 == 0   -8.100      8.500  -0.953 0.564739    
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

inst/doc/mixed_example_1.R

@@ -59,7 +59,6 @@ xyplot(mean ~ density:frequency|task+stimulus, agg_p, jitter.x = TRUE, pch = 20,
        }) + 
 bwplot(mean ~ density:frequency|task+stimulus, agg_p, pch="|", do.out = FALSE)
 
-
 ## ---- fig.width=7, fig.height=6---------------------------------------------------------
 agg_i <- fhch %>% group_by(item, task, stimulus, density, frequency) %>%
   summarise(mean = mean(log_rt)) %>%
@@ -73,7 +72,6 @@ xyplot(mean ~ density:frequency|task+stimulus, agg_i, jitter.x = TRUE, pch = 20,
        }) + 
 bwplot(mean ~ density:frequency|task+stimulus, agg_i, pch="|", do.out = FALSE)
 
-
 ## ---- eval = FALSE----------------------------------------------------------------------
 #  
 #  m1s <- mixed(log_rt ~ task*stimulus*density*frequency + (stimulus*density*frequency|id)+
@@ -120,7 +118,36 @@ res_lrt
 ## ---------------------------------------------------------------------------------------
 nice_lrt[[2]]
 
+## ---------------------------------------------------------------------------------------
+lsm.options(lmer.df = "asymptotic") # also possible: 'satterthwaite', 'kenward-roger'
+emm_i1 <- lsmeans(m2s, "frequency", by = c("stimulus", "task"))
+emm_i1
+
+## ---------------------------------------------------------------------------------------
+contrast(pairs(emm_i1), by = NULL, adjust = "holm")
+
+## ---------------------------------------------------------------------------------------
+emm_i1b <- summary(contrast(emm_i1, by = NULL))
+emm_i1b[,c("estimate", "SE")] <- exp(emm_i1b[,c("estimate", "SE")])
+emm_i1b
+
+## ---------------------------------------------------------------------------------------
+emm_i2 <- lsmeans(m2s, c("density", "frequency"), by = c("stimulus", "task"))
+con1 <- contrast(emm_i2, "trt.vs.ctrl1", by = c("frequency", "stimulus", "task")) # density
+con2 <- contrast(con1, "trt.vs.ctrl1", by = c("contrast", "stimulus", "task")) 
+test(con2, joint = TRUE, by = c("stimulus", "task"))
+
+## ---------------------------------------------------------------------------------------
+emm_i2
+# desired contrats:
+des_c <- list(
+  ll_hl = c(1, -1, 0, 0),
+  lh_hh = c(0, 0, 1, -1)
+  )
+contrast(contrast(emm_i2, des_c), by = NULL, adjust = "holm")
+
 ## ---- echo=FALSE, eval = FALSE----------------------------------------------------------
+#  ### OLD STUFF BELOW. PLEASE IGNORE.
 #  load("freeman_models.rda")
 #  load("../freeman_models_all.rda")
 #  m1lrt$restricted_models <- list(NULL)