Plot random effects from lmer (lme4 package) using qqmath or dotplot: How to make it look fancy?

Question

The qqmath function makes great caterpillar plots of random effects using the output from the lmer package. That is, qqmath is great at plotting the intercepts from a hierarchical model with their errors around the point estimate. An example of the lmer and qqmath functions are below using the built-in data in the lme4 package called Dyestuff. The code will produce the hierarchical model and a nice plot using the ggmath function. ``` library("lme4") data(package = "lme4") # Dyestuff # a balanced one-way classiï¬cation of Yield # from samples produced from six Batches summary(Dyestuff) # Batch is an example of a random effect # Fit 1-way random effects linear model fit1 <- lmer(Yield ~ 1 + (1|Batch), Dyestuff) summary(fit1) coef(fit1) #intercept for each level in Batch # qqplot of the random effects with their variances qqmath(ranef(fit1, postVar = TRUE), strip = FALSE)$Batch ``` The last line of code produces a really nice plot of each intercept with the error around each estimate. Bu…

Accepted Answer

One possibility is to use library `ggplot2` to draw similar graph and then you can adjust appearance of your plot.

First, `ranef` object is saved as `randoms`. Then variances of intercepts are saved in object `qq`.

```
randoms<-ranef(fit1, postVar = TRUE)
qq <- attr(ranef(fit1, postVar = TRUE)[[1]], "postVar")
```

Object `rand.interc` contains just random intercepts with level names.

```
rand.interc<-randoms$Batch
```

All objects put in one data frame. For error intervals `sd.interc` is calculated as 2 times square root of variance.

```
df<-data.frame(Intercepts=randoms$Batch[,1],
              sd.interc=2*sqrt(qq[,,1:length(qq)]),
              lev.names=rownames(rand.interc))
```

If you need that intercepts are ordered in plot according to value then `lev.names` should be reordered. This line can be skipped if intercepts should be ordered by level names.

```
df$lev.names<-factor(df$lev.names,levels=df$lev.names[order(df$Intercepts)])
```

This code produces plot. Now points will differ by `shape` according to factor levels.

```
library(ggplot2)
p <- ggplot(df,aes(lev.names,Intercepts,shape=lev.names))

#Added horizontal line at y=0, error bars to points and points with size two
p <- p + geom_hline(yintercept=0) +geom_errorbar(aes(ymin=Intercepts-sd.interc, ymax=Intercepts+sd.interc), width=0,color="black") + geom_point(aes(size=2))

#Removed legends and with scale_shape_manual point shapes set to 1 and 16
p <- p + guides(size=FALSE,shape=FALSE) + scale_shape_manual(values=c(1,1,1,16,16,16))

#Changed appearance of plot (black and white theme) and x and y axis labels
p <- p + theme_bw() + xlab("Levels") + ylab("")

#Final adjustments of plot
p <- p + theme(axis.text.x=element_text(size=rel(1.2)),
               axis.title.x=element_text(size=rel(1.3)),
               axis.text.y=element_text(size=rel(1.2)),
               panel.grid.minor=element_blank(),
               panel.grid.major.x=element_blank())

#To put levels on y axis you just need to use coord_flip()
p <- p+ coord_flip()
print(p)
```

Plot random effects from lmer (lme4 package) using qqmath or dotplot: How to make it look fancy?

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