Using metafoR and ggplot together:part 2

In the last post I introduced you to the basics of meta-analysis using metafor together with ggplot2. If you haven’t seen that post, I suggest you go back and read it since this post will be hard to follow otherwise.

In this post I will concentrate on trying to explain differences between different sites/studies.

Going back to our previous example, we saw that logging tended to have a negative effect on species richness.

Luckily my completely made up data had information on the intensity of logging (volume of wood removed per hectare) and the method used (conventional or reduced impact).

We can make a model looking at the effects of logging intensity and method on species richness. First we can start with our most complex model

Model1<-rma.uni(yi,vi,mods=~Intensity*Method,method="ML",data=ROM)
summary(Model1)

This spits out the following:

Mixed-Effects Model (k = 30; tau^2 estimator: ML)

  logLik  deviance       AIC       BIC
-15.8430  121.7556   41.6860   48.6920

tau^2 (estimated amount of residual heterogeneity):     0.1576 (SE = 0.0436)
tau (square root of estimated tau^2 value):             0.3970
I^2 (residual heterogeneity / unaccounted variability): 96.52%
H^2 (unaccounted variability / sampling variability):   28.74

Test for Residual Heterogeneity:
QE(df = 26) = 1151.1656, p-val &amp;lt; .0001

Test of Moderators (coefficient(s) 2,3,4):
QM(df = 3) = 38.2110, p-val &amp;lt; .0001

Model Results:

                     estimate      se     zval    pval    ci.lb    ci.ub
intrcpt                0.1430  0.3566   0.4011  0.6883  -0.5558   0.8419
Intensity             -0.0281  0.0099  -2.8549  0.0043  -0.0474  -0.0088  **
MethodRIL              0.1715  0.4210   0.4074  0.6837  -0.6536   0.9966
Intensity:MethodRIL    0.0106  0.0138   0.7638  0.4450  -0.0166   0.0377

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Looking at our residual plots, they seem to conform to our assumptions of constant variance.

plot(fitted.rma(Model1),residuals.rma(Model1))
qqnorm.rma.uni(Model1)

Our models suggests that the more wood is extracted from a forest the greater the negative effect on species richness, but that seems to be little evidence about the effect of method or the the interaction between intensity and method.

So, lets get rid of the interaction and see what happens:

Model2<-rma.uni(yi,vi,mods=~Intensity+Method,method="ML",data=ROM)
summary(Model2)

 

Mixed-Effects Model (k = 30; tau^2 estimator: ML)

  logLik  deviance       AIC       BIC
-16.1323  122.3341   40.2646   45.8694

tau^2 (estimated amount of residual heterogeneity):     0.1606 (SE = 0.0443)
tau (square root of estimated tau^2 value):             0.4008
I^2 (residual heterogeneity / unaccounted variability): 96.75%
H^2 (unaccounted variability / sampling variability):   30.80

Test for Residual Heterogeneity:
QE(df = 27) = 1151.4310, p-val &amp;lt; .0001

Test of Moderators (coefficient(s) 2,3):
QM(df = 2) = 36.9762, p-val &amp;lt; .0001

Model Results:

           estimate      se     zval    pval    ci.lb    ci.ub
intrcpt     -0.0416  0.2648  -0.1570  0.8753  -0.5606   0.4774
Intensity   -0.0228  0.0070  -3.2617  0.0011  -0.0364  -0.0091  **
MethodRIL    0.4630  0.1803   2.5681  0.0102   0.1096   0.8163   *

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Now both intensity and method are statistically significant, but method less so. Let’s take it out:

Model3<-rma.uni(yi,vi,mods=~Intensity,method="ML",data=ROM)
summary(Model3)

 

Mixed-Effects Model (k = 30; tau^2 estimator: ML)

  logLik  deviance       AIC       BIC
-19.1526  128.3747   44.3052   48.5088

tau^2 (estimated amount of residual heterogeneity):     0.1962 (SE = 0.0536)
tau (square root of estimated tau^2 value):             0.4429
I^2 (residual heterogeneity / unaccounted variability): 97.45%
H^2 (unaccounted variability / sampling variability):   39.27

Test for Residual Heterogeneity:
QE(df = 28) = 1258.6907, p-val &amp;lt; .0001

Test of Moderators (coefficient(s) 2):
QM(df = 1) = 25.2163, p-val &amp;lt; .0001

Model Results:

           estimate      se     zval    pval    ci.lb    ci.ub
intrcpt      0.4678  0.1927   2.4276  0.0152   0.0901   0.8455    *
Intensity   -0.0324  0.0065  -5.0216  &amp;lt;.0001  -0.0451  -0.0198  ***

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Unsurprisingly, intensity is now more significant. However, we have taken out method which may have some value in explaining differences. The tool I use most for model selection is AIC. This basically aims to get the best fitting model with the least explanatory variables. This is why I did the last step of removing method. It could be that the effect of method is significant but adds relatively little explanatory power to our model.

Let’s test this by calculating their AICs.

AIC(Model1)
AIC(Model2)
AIC(Model3)

 

[1] 41.68604
> AIC(Model2)
[1] 40.26459
> AIC(Model3)
[1] 44.30516

We choose the model with the lowest AIC, in this case Model2.

To calculate the fit of our model, we can compare it to our intercept only model (the original meta-analysis from part 1), but we have to set method=”ML” since REML is a poor estimator of deviance.

ROM.ma2&amp;lt;-rma.uni(yi,vi,method=&amp;quot;ML&amp;quot;,data=ROM)
summary(Model2)
summary(ROM.ma2)

Using the two Tau squared statistics we can calculate the proportion of deviance not explained by the intercept only model using the sum:
1-\frac{tau^{2}_{Model2}} {tau^{2}_{ROM.ma2}}

in.plot<-ggplot(data=ROM,aes(x=Intensity,y=yi,size=1/vi,colour=Method))+geom_point(data=ROM,shape=16)
in.plot2<-in.plot+geom_line(data=ROM,aes(x=Intensity,y=preds,size=1))
in.plot3<-in.plot2+ylab("log response ratio")+xlab("Logging intensity (m-3 ha-1)")
in.plot3+theme(legend.position="none")

Very pretty.
Intens_RR

Circle size is relative to weight for each study, since we weight the most precise studies most heavily. Blue points and line refer to reduced impact logging sites, while red refers to conventionally logged sites.

We can also transform it all to proportions for more intuitive interpretations:

theme_set(theme_bw(base_size=10))
in.plot<-ggplot(data=ROM,aes(x=Intensity,y=exp(yi)-1,size=1/vi,colour=Method))+geom_point(data=ROM,shape=16)
in.plot2<-in.plot+geom_line(data=ROM,aes(x=Intensity,y=exp(preds)-1),size=1)
in.plot3<-in.plot2+ylab("Proportional change\nin species richness")+xlab("Logging intensity (m-3 ha-1)")
in.plot3+theme(legend.position="none")

Intens_Prop
The graphs show that, for my made up data, reduced impact logging has a less negative effect than conventional logging on species richness when carried out at similar intensities but that increasing intensity leads to reductions in species richness for both conventional and reduced impact methods.

I think that wraps it up for now. I hope you’ve found this useful. As ever any comments or questions are welcome.
I will be back to writing non-stats posts again soon!

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11 thoughts on “Using metafoR and ggplot together:part 2

  1. Sorry, Philip! I mean when I run code ” in.plot2<-in.plot+geom_line(data=ROM,aes(x=Intensity,y=preds,size=1)) ", R shows "Error in eval(expr, envir, enclos) : object 'preds' not found".

  2. Hi Philip, I would like to know what you called preds? It would be the prediction of metafor (predict)? If so, how did you get to enter it in ggplot? when I run it appears the following error …. Error: Aesthetics must be length Either one, or the same length of the dataProblems: Preds $ pred
    Thank you

  3. Hi Philip, Thanks for sharing this – I’m having some trouble, however, as the lines on my graph are jagged, rather than one straight line. What could I be doing wrong? I used the fitted function in metafor to get the values which I used for ‘preds’. I also tried using the stat_smooth function instead of geom_line, but then I get lines with different gradients for each level of the categorical factor. Thanks again.

    1. Hi Vicky. Jagged lines could be as a result of not saving at a high enough resolution, but it could also be due to relative lack of x values over which to predict. To fix your problem with geom_smooth() you can use geom_smooth(aes, (groups=NULL)) and it should only fit a single line. However geom_smooth is not the same as a model fit, for this you really need to use predictions combined with geom_line. Does that help?

      1. Hi Philip,
        Thank you for replying so quickly. It sounds like the problem is a lack of x values, as you suggest (the x-axis is the density of a crop species in an intercrop design relative to its density when grown as a monocrop, and all the individual points happen to take one of only 6 values). geom_smooth() didn’t work unless I changed the the method to “lm” – same when I used stat_smooth(), and the resulting plot was the same too, each group has a single line but they have different gradients. The jagged lines from using geom_line were not parallel either.
        The models I need to present have several categorical moderators, and sometimes a continuous moderator as well. I had thought (after reading your blogpost) to make a series of figures all with relative density on the x-axis but with a different categorical moderator shown by the lines in each figure. Could the effect of the categorical moderators I didn’t include in the plot (but which are part of the model that the fitted values come from) be causing the lines to not be parallel?
        Thank you again for your help, and apologies for the extra questions!

      2. Without looking at your data it is difficult to say exactly what’s going with your lines. It could well be that the extra moderators are screwing things up. I will think about it and get back to you tomorrow if I get some time.

        Phil

      3. Thank you; I appreciate it’s difficult to identify the issue without more information. I tried running some models with only one categorical moderator and one continuous moderator included, and using the fitted values from those models worked perfectly, so I suspect that it is something to do with there being more than one categorical moderator.

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