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Two little annoying stats detail

A very brief post at the end of the field season on two little “details” that are annoying me in paper/analysis that I see being done (sometimes) around me.

The first one concern mixed effect models where the models built in the contain a grouping factor (say month or season) that is fitted as both a fixed effect term and as a random effect term (on the right side of the | in lme4 model synthax). I don’t really understand why anyone would want to do this and instead of spending time writing equations let’s just make a simple simulation example and see what are the consequences of doing this:

#an example of a situation measuring plant biomass on four different month along a gradient of temperature
#the coefficient
#the simulated coefficient for Months are 0.5, 1.2 -0.9
#a simple lm
coef(m_fixed) #not too bad
## (Intercept)        temp      month2      month3      month4 
##   0.9567796   2.0654349   0.4307483   1.2649599  -0.8925088

#a lmm with month ONLY as random term

## (Intercept)        temp 
##    1.157095    2.063714


## $month
##   (Intercept)
## 1  -0.1916665
## 2   0.2197100
## 3   1.0131908
## 4  -1.0412343

VarCorr(m_rand) #the estimated sd for the month coeff

##  Groups   Name        Std.Dev.
##  month    (Intercept) 0.87720 
##  Residual             0.98016

sd(c(0,0.5,1.2,-0.9)) #the simulated one, not too bad!

## [1] 0.8831761

#now a lmm with month as both fixed and random term
m_fixedrand<-lmer(biom~temp+month+(1|month),data) fixef(m_fixedrand) ## (Intercept) temp month2 month3 month4 ## 0.9567796 2.0654349 0.4307483 1.2649599 -0.8925088 ranef(m_fixedrand) #very, VERY small ## $month ## (Intercept) ## 1 0.000000e+00 ## 2 1.118685e-15 ## 3 -9.588729e-16 ## 4 5.193895e-16 VarCorr(m_fixedrand) ## Groups Name Std.Dev. ## month (Intercept) 0.40397 ## Residual 0.98018 #how does it affect the estimation of the fixed effect coefficient? summary(m_fixed)$coefficients ## Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.9567796  0.2039313  4.691676 9.080522e-06
## temp         2.0654349  0.1048368 19.701440 2.549792e-35
## month2       0.4307483  0.2862849  1.504614 1.357408e-01
## month3       1.2649599  0.2772677  4.562233 1.511379e-05
## month4      -0.8925088  0.2789932 -3.199035 1.874375e-03


##               Estimate Std. Error    t value
## (Intercept)  0.9567796  0.4525224  2.1143256
## temp         2.0654349  0.1048368 19.7014396
## month2       0.4307483  0.6390118  0.6740851
## month3       1.2649599  0.6350232  1.9919901
## month4      -0.8925088  0.6357784 -1.4038048

#the numeric response is not affected but the standard error around the intercept and the month coefficient is doubled, this makes it less likely that a significant p-value will be given for these effect ie higher chance to infer that there is no month effect when there is some

#and what if we simulate data as is supposed by the model, ie a fixed effect of month and on top of it we add a random component
#an lmm model
fixef(m_fixedrand2) #weird coeff values for the fixed effect for month

## (Intercept)        temp      month2      month3      month4 
##   -2.064083    2.141428    1.644968    4.590429    3.064715

c(0,eff[3:5])+rnd.eff #if we look at the intervals between the intercept and the different levels we can realize that the fixed effect part of the model sucked in the added random part

## [1] -2.66714133 -1.26677658  1.47977624  0.02506236


##  Groups   Name        Std.Dev.
##  month    (Intercept) 0.74327 
##  Residual             0.93435

ranef(m_fixedrand2) #again very VERY small

## $month
##     (Intercept)
## 1  1.378195e-15
## 2  7.386264e-15
## 3 -2.118975e-14
## 4 -7.752347e-15

#so this is basically not working it does not make sense to have a grouping factor as both a fixed effect terms and a random term (ie on the right-hand side of the |)

Take-home message don’t put a grouping factor as both a fixed and random term effect in your mixed effect model. lmer is not able to separate between the fixed and random part of the effect (and I don’t know how it could be done) and basically gives everything to the fixed effect leaving very small random effects. The issue is abit pernicious because if you would only look at the standard deviation of the random term from the merMod summary output you could not have guessed that something is wrong. You need to actually look at the random effects to realize that they are incredibely small. So beware when building complex models with many fixed and random terms to always check the estimated random effects.

The second issue is maybe a bit older but I saw it appear in a recent paper (which is a cool one excpet for this stats detail). After fitting a model with several predictors one wants to plot their effects on the response, some people use partial residuals plot to do this (wiki). The issue with these plots is that when two variables have a high covariance the partial residual plot will tend to be over-optimistic concerning the effect of variable X on Y (ie the plot will look much nice than it should be). Again let’s do a little simulation on this:

#say we measure plant biomass in relation with measured temperature and number of sunny hours say per week
#the variance-covariance matrix between temperature and sunny hours
#simulate some data

#partial residual plot of sun
plot(data$sun,sun_res,xlab="Number of sunny hours",ylab="Partial residuals of Sun")


#plot of sun effect while controlling for temp
plot(biom~sun,data,xlab="Number of sunny hours",ylab="Plant biomass")


#same stuff for temp
plot(biom~temp,data,xlab="Temperature",ylab="Plant biomass")


The first graph is a partial residual plot, from this graph alone we would be tempted to say that the number of hour with sun has a large influence on the biomass. This conclusion is biased by the fact that the number of sunny hours covary with temperature and temperature has a large influence on plant biomass. So who is more important temperature or sun? The way to resolve this is to plot the actual observation and to add a fitted regression line from a new dataset (sun_new in the example) where one variable is allowed to vary while all others are fixed to their means. This way we see how an increase in the number of sunny hour at an average temperature affect the biomass (the second figure). The final graph is then showing the effect of temperature while controlling for the effect of the number of sunny hours.

Happy modelling!

Count data: To Log or Not To Log

Count data are widely collected in ecology, for example when one count the number of birds or the number of flowers. These data follow naturally a Poisson or negative binomial distribution and are therefore sometime tricky to fit with standard LMs. A traditional approach has been to log-transform such data and then fit LMs to the transformed data. Recently a paper advocated against the use of such transformation since it led to high bias in the estimated coefficients. More recently another paper argued that log-transformation of count data followed by LM led to lower type I error rate (ie saying that an effect is significant when it is not) than GLMs. What should we do then?

Using a slightly changed version of the code published in the Ives 2015 paper let’s explore the impact of using these different modelling strategies on the rejection of the null hypothesis and the bias of the estimated coefficients.

#load the libraries

#download and load the functions that will be used

Following the paper from Ives and code therein I simulated some predictor (x) and a response (y) that follow a negative binomial distribution and is linearly related to x.
In the first case I look at the impact of varying the sample size on the rejection of the null hypothesis and the bias in the estimated coefficient between y and x.

######univariate NB case############
#range over n
output<-compute.stats(NRep=500,n.range = c(10,20,40,80,160,320,640,1280))
ggplot(output,aes(x=n,y=Reject,color=Model))+geom_path(size=2)+scale_x_log10()+labs(x="Sample Size (log-scale)",y="Proportion of Rejected H0")+base_theme
ggplot(output,aes(x=n,y=Bias,color=Model))+geom_path(size=2)+scale_x_log10()+labs(x="Sample Size (log-scale)",y="Bias in the slope coefficient")+base_theme

Figure 1

For this simulation round the coefficient of the slope (b1) was set to 0 (no effect of x on y), and only the sample size varied. The top panel show the average proportion of time that the p-value of the slope coefficient was lower than 0.05 (H0:b1=0 rejected). We see that for low sample size (<40) the Negative Binomial model has higher proportion of rejected H0 (type I error rate) but this difference between the model disappear as we reached sample size bigger than 100. The bottom panel show the bias (estimated value – true value) in the estimated coefficient. For very low sample size (n=10), Log001, Negative Binomial and Quasipoisson have higher bias than Log1 and LogHalf. For larger sample size the difference between the GLM team (NB and QuasiP) and the LM one (Log1 and LogHalf) gradually decrease and both teams converged to a bias around 0 for larger sample size. Only Log0001 is behaved very badly. From what we saw here it seems that Log1 and LogHalf are good choices for count data, they have low Type I error and Bias along the whole sample size gradient.

The issue is that an effect of exactly 0 never exist in real life where most of the effect are small (but non-zero) thus the Null Hypothesis will never be true. Let’s look know how the different models behaved when we vary b1 alone in a first time and crossed with sample size variation.

#range over b1
outpu<-compute.stats(NRep=500,b1.range = seq(-2,2,length=17))
ggplot(outpu,aes(x=b1,y=Reject,color=Model))+geom_path(size=2)+base_theme+labs(y="Proportion of Rejected H0")
ggplot(outpu,aes(x=b1,y=Bias,color=Model))+geom_path(size=2)+base_theme+labs(y="Bias in the slope coefficient")


Here the sample size was set to 100, what we see in the top graph is that for a slope of exactly 0 all model have a similar average proportion of rejection of the null hypothesis. As b1 become smaller or bigger the average proportion of rejection show very similar increase for all model expect for Log0001 which has a slower increase. This curves basically represent the power of the model to detect an effect and is very similar to the Fig2 in the Ives 2015 paper. Now the bottom panel show that all the LM models have bad behaviour concerning their bias, they have only small bias for very small (close to 0) coefficient, has the coefficient gets bigger the absolute bias increase. This means that even if the LM models are able to detect an effect with similar power the estimated coefficient is wrong. This is due to the value added to the untransformed count data in order to avoid -Inf for 0s. I have no idea on how one may take into account arithmetically these added values and then remove its effects …

Next let’s cross variation in the coefficient with sample size variation:

#range over n and b1
ggplot(output3,aes(x=n,y=Reject,color=Model))+geom_path(size=2)+scale_x_log10()+facet_wrap(~b1)+base_theme+labs(x="Sample size (log-scale)",y="Proportion of rejected H0")
ggplot(output3,aes(x=n,y=Bias,color=Model))+geom_path(size=2)+scale_x_log10()+facet_wrap(~b1)+base_theme+labs(x="Sample size (log-scale)",y="Bias in the slope coefficient")

Figure 3

The tope panel show one big issue of focussing only on the significance level: rejection of H0 depend not only on the size of the effect but also on the sample size. For example for b1=0.75 (a rather large value since we work on the exponential scale) less than 50% of all models rejected the null hypothesis for a sample size of 10. Of course as the effect sizes gets larger the impact of the sample size on the rejection of the null hypothesis is reduced. However most effect around the world are small so that we need big sample size to be able to “detect” them using null hypothesis testing. The top graph also shows that NB is slightly better than the other models and that Log0001 is again having the worst performance. The bottom graphs show something interesting, the bias is quasi-constant over the sample size gradient (maybe if we would look closer we would see some variation). Irrespective of how many data point you will collect the LMs will always have bigger bias than the GLMs (expect for the artificial case of b1=0)

To finish with in Ives 2015 the big surprise was the explosion of type I error with the increase in the variation in individual-level random error (adding a random normally distributed value to the linear predictor of each data point and varying the standard deviation of these random values) as can be seen in the Fig3 of the paper.

#range over b1 and sd.eps
ggplot(output4,aes(x=sd.eps,y=Reject,color=Model))+geom_path(size=2)+facet_wrap(~b1)+base_theme+labs(x="",y="Proportion of rejected H0")
ggplot(output4,aes(x=sd.eps,y=Bias,color=Model))+geom_path(size=2)+facet_wrap(~b1)+base_theme+labs(x="Standard Deviation of the random error",y="Bias of the slope coefficient")

Figure 4

Before looking at the figure in detail please note that a standard deviation of 2 in this context is very high, remember that these values will be added to the linear predictor which will be exponentiated so that we will end up with very large deviation. In the top panel there are two surprising results, the sign of the coefficient affect the pattern of null hypothesis rejection and I do not see the explosion of rejection rates for NB or QuasiP that are presented in Ives 2015. In his paper Ives reported the LRT test for the NB models when I am reporting the p-values from the model summary directly (Wald test). If some people around have computing power it would be interesting to see if changing the seed and/or increasing the number of replication lead to different patterns … The bottom panel show again that the LMs bias are big, the NB and QuasiP models have an increase in the bias with the standard deviation but only if the coefficient is negative (I suspect some issue with the exponentiating of large random positive error), as expected the GLMM perform the best in this context.

Pre-conclusion, in real life of course we would rarely have a model with only one predictor, most of the time we will build larger models with complex interaction structure between the predictors. This will of course affect both H0 rejection and Bias, but this is material for a next post :)

Let’s wrap it up; we’ve seen that even if LM transformation seem to be a good choice for having a lower type I error rate than GLMs this advantage will be rather minimal when using empirical data (no effect are equal to 0) and potentially dangerous (large bias). Ecologists have sometime the bad habits to turn their analysis into a star hunt (R standard model output gives stars to significant effects) and focusing only on using models that have the best behavior towards significance (but large bias) does not seem to be a good strategy to me. More and more people call for increasing the predictive power of ecological model, we need then modelling techniques that are able to precisely (low bias) estimate the effects. In this context transforming the data to make them somehow fit normal assumption is sub-optimal, it is much more natural to think about what type of processes generated the data (normal, poisson, negative binomial, with or without hierarchical structure) and then model it accordingly. There are extensive discussion nowadays about the use and abuse of p-values in science and I think that in our analysis we should slowly but surely shifts our focus from “significance/p-values<0.05/star hunting” only to a more balanced mix of effect-sizes (or standardized slopes), p-values and R-square.

Confidence Intervals for prediction in GLMMs

With LM and GLM the predict function can return the standard error for the predicted values on either the observed data or on new data. This is then used to draw confidence or prediction intervals around the fitted regression lines. The confidence intervals (CI) focus on the regression lines and can be interpreted as (assuming that we draw 95% CI): “If we would repeat our sampling X times the regression line would fall between this interval 95% of the time”. On the other hand the prediction interval focus on single data point and could be interpreted as (again assuming that we draw 95% CI): “If we would sample X times at these particular value for the explanatory variables, the response value would fall between this interval 95% of the time”. The wikipedia page has some nice explanation about the meaning of confidence intervals.
For GLMM the predict function does not allow one to derive standard error, the reason being (from the help page of predict.merMod): “There is no option for computing standard errors of predictions because it is difficult to define an efficient method that incorporates uncertainty in the variance parameters”. This means there is for now no way to include in the computation of the standard error for predicted values the fact that the fitted random effect standard deviation are just estimates and may be more or less well estimated. We can however still derive confidence or prediction intervals keeping in mind that we might underestimate the uncertainty around the estimates.


#first case simple lmer, simulate 100 data points from 10 groups with one continuous fixed effect variable
f<-gl(n = 10,k = 10)
#the fixed effect coefficient
#the random effect
#the simulated response values


#first CI and PI using predict-like method, using code posted here:
#predict(m,newdat,re.form=NA) would give the same results
pvar1 <- diag(mm %*% tcrossprod(vcov(m),mm))
tvar1 <- pvar1+VarCorr(m)$f[1] # must be adapted for more complex models
newdat <- data.frame(
  , plo = newdat$y-1.96*sqrt(pvar1)
  , phi = newdat$y+1.96*sqrt(pvar1)
  , tlo = newdat$y-1.96*sqrt(tvar1)
  , thi = newdat$y+1.96*sqrt(tvar1)

#second version with bootMer
#we have to define a function that will be applied to the nsim simulations
#here we basically get a merMod object and return the fitted values
predFun<-function(.) mm%*%fixef(.) 
bb<-bootMer(m,FUN=predFun,nsim=200) #do this 200 times
#as we did this 200 times the 95% CI will be bordered by the 5th and 195th value
bb_se<-apply(bb$t,2,function(x) x[order(x)][c(5,195)])

legend("topleft",legend=c("Fitted line","Confidence interval","Prediction interval","Bootstrapped CI"),col=c("red","blue","orange","darkgreen"),lty=2,lwd=2,bty="n")


This looks pretty familiar, the prediction interval being always bigger than the confidence interval.
Now in the help page for the predict.merMod function the authors of the lme4 package wrote that bootMer should be the prefered method to derive confidence intervals from GLMM. The idea there is to simulate N times new data from the model and get some statistic of interest. In our case we are interested in deriving the bootstrapped fitted values to get confidence interval for the regression line. bb$t is a matrix with the observation in the column and the different bootstrapped samples in the rows. To get the 95% CI for the fitted line we then need to get the [0.025*N,0.975*N] values of the sorted bootstrapped values.

The bootstrapped CI falls pretty close to the “normal” CI, even if for each bootstrapped sample new random effect values were calculated (because use.u=FALSE per default in bootMer)

Now let’s turn to a more complex example, a Poisson GLMM with two crossed random effects:

#second case more complex design with two crossed RE and a poisson response
f1<-gl(n = 10,k = 10)




#for GLMMs we have to back-transform the prediction after adding/removing the SE
pvar1 <- diag(mm %*% tcrossprod(vcov(m),mm))
tvar1 <- pvar1+VarCorr(m)$f1[1]+VarCorr(m)$f2[1]  ## must be adapted for more complex models
newdat <- data.frame(
   plo = exp(y-1.96*sqrt(pvar1))
  , phi = exp(y+1.96*sqrt(pvar1))
  , tlo = exp(y-1.96*sqrt(tvar1))
  , thi = exp(y+1.96*sqrt(tvar1))

#second version with bootMer
predFun<-function(.) exp(mm%*%fixef(.)) 
bb_se<-apply(bb$t,2,function(x) x[order(x)][c(5,195)])

legend("topleft",legend=c("Fitted line","Confidence interval","Prediction interval","Bootstrapped CI"),col=c("red","blue","orange","darkgreen"),lty=2,lwd=2,bty="n")


Again in this case the bootstrapped CI falled pretty close to the “normal” CI. We have here seen three different way to derive intervals representing the uncertainty around the regression lines (CI) and the response points (PI). Choosing among them would depend on what you want to see (what is the level of uncertainty around my fitted line vs if I sample new observations which value will they take), but also for complex model on computing power, as bootMer can take some time to run for GLMM with many observations and complex model structure.

Exploration of Functional Diversity indices using Shiny


Biological diversity (or biodiversity) is a complex concept with many different aspects in it, like species richness, evenness or functional redundancy. My field of research focus on understanding the effect of changing plant diversity on higher trophic levels communities but also ecosystem function. Even if the founding papers of this area of research already hypothesized that all components of biodiversity could influence ecosystem function (See Fig1 in Chapin et al 2000), the first experimental results were focusing on taxonomic diversity (ie species richness, shannon diversity, shannon evenness …). More recently the importance of functional diversity as the main driver of changes in ecosystem function has been emphasized by several authors (ie Reiss et al 2009). The idea behind functional diversity is basically to measure a characteristic (the traits) of the organisms under study, for example the height of a plant or the body mass of an insect, and then derive an index of how diverse these traits values are in a particular sites. A nice introduction into the topic is the Chapter from Evan Weiher in Biological Diversity.

Now as taxonomic diversity has many different indices so do functional diversity, recent developments of a new multidimensional framework and of an R package allow researchers to easily derive functional diversity index from their dataset. But finding the right index for his system can be rather daunting and as several studies showed there is not a single best index (See Weiher Ch.13 in Biological Diversity) but rather a set of different index each showing a different facet of the functional diversity like functional richness, functional evenness or functional divergence.

Here I show a little Shiny App to graphically explore in a 2D trait dimension the meaning of a set of functional diversity indices. The App is still in its infancy and many things could be added (ie variation in trait distribution …) but here it is:

#load libraries

#launch App
runGitHub("JenaExp",username = "Lionel68",subdir = "FD/Shiny_20150426")

All codes are available here:


Chapin III, F. Stuart, et al. “Consequences of changing biodiversity.” Nature 405.6783 (2000): 234-242.

Reiss, Julia, et al. “Emerging horizons in biodiversity and ecosystem functioning research.” Trends in ecology & evolution 24.9 (2009): 505-514.

Weiher, E. “A primer of trait and functional diversity.” Biological diversity, frontiers in measurement and assessment (2011): 175-193.

Villéger, Sébastien, Norman WH Mason, and David Mouillot. “New multidimensional functional diversity indices for a multifaceted framework in functional ecology.” Ecology 89.8 (2008): 2290-2301.

A function to help graphical model checks of lm and ANOVA

As always a more colourful version of this post is available on rpubs.

Even if LM are very simple models at the basis of many more complex ones, LM still have some assumptions that if not met would render any interpretation from the models plainly wrong. In my field of research most people were taught about checking ANOVA assumptions using tests like Levene & co. This is however not the best way to check if my model meet its assumptions as p-values depend on the sample size, with small sample size we will almost never reject the null hypothesis while with big sample even small deviation will lead to significant p-values (discussion). As ANOVA and linear models are two different ways to look at the same model (explanation) we can check ANOVA assumptions using graphical check from a linear model. In R this is easily done using plot(model), but people often ask me what amount of deviation makes me reject a model. One easy way to see if the model checking graphs are off the charts is to simulate data from the model, fit the model to these newly simulated data and compare the graphical checks from the simulated data with the real data. If you cannot differentiate between the simulated and the real data then your model is fine, if you can then try again!

EDIT: You can also make the conclusion form your visual inspection more rigorous following the protocols outlined in this article. (Thanks for Tim comment)

Below is a little function that implement this idea:


  #the model frame
  #the model matrix
  #the standard deviation of the residuals
  #sample size
  #get the right-hand side of the formula  
  #rhs<-all.vars(update(f, 0~.))
  #simulate 8 response vectors from model
  ys<-lapply(1:8,function(x) rnorm(n,modmat%*%coef(m),sd.resid))
  #refit the models
  ms<-llply(ys,function(y) lm(y~modmat[,-1]))
  #put the residuals and fitted values in a list
  df<-llply(ms,function(x) data.frame(Fitted=fitted(x),Resid=resid(x)))
  #select a random number from 2 to 8
  #put the original data into the list





This function print the two basic plots: one looking at the spread of the residuals around the fitted values, the other one look at the normality of the residuals. The function return the position of the real model in the 3×3 window, counting from left to right and from top to bottom (ie position 1 is upper left graph).

Let’s try the function:

#a simulated data frame of independent variables
dat<-data.frame(Temp=runif(100,0,20),Treatment=gl(n = 5,k = 20))
#the model matrix
#the coefficient
#simulate response data
#the model
#model check




Can you find which one is the real one? I could not, here is the answer:


[1] 4

Happy and safe modelling!

Generating ANOVA-like table from GLMM using parametric bootstrap


[UPDATED: I modified a bit the code of the function, now you do not need to pass as character the random effect terms]

This article may also be found on RPubs:

In the list of worst to best way to test for effect in GLMM the list on state that parametric bootstrapping is among the best options. PBmodcomp in the pbkrtest package implement such parametric bootstrapping by comparing a full model to a null one. The function simulate data (the response vector) from the null model then fit these data to the null and full model and derive a likelihood ratio test for each of the simulated data. Then we can compare the observed likelihood ratio test to the null distribution generated from the many simulation and derive a p-value. The advantage of using such a method over the classical p-values derived from a chi-square test on the likelihood ratio test is that in the parametric bootstrap we do not assume any null distribution (like chi-square) but instead derive our own null distribution from the model and the data at hand. We do not make the assumption then that the likelihood ratio test statistic is chi-square distributed. I have made a little function that wraps around the PBmodcomp function to compute bootstrapped p-values for each term in a model by sequentially adding them. This lead to anova-like table that are typically obtained when one use the command anova on a glm object.

#the libraries used
#the function with the following arguments
#@model the merMod model fitted by lmer or glmer
#@w the weights used in a binomial fit
#@seed you can set a seed to find back the same results after bootstrapping
#@nsim the number of bootstrapped simulation, if set to 0 return the Chisq p-value from the LRT test
#@fixed should the variable be dropped based on the order given in the model (TRUE) or the the dropping goes from worst to best variable (FALSE)
  rand<-lapply(rand,function(x) as.character(x))
  rand<-lapply(rand,function(x) paste("(",x[2],x[1],x[3],")",sep=" "))
  rand<-paste(rand,collapse=" + ")
  #generate a list of reduced model formula
  fs[[1]]<-as.formula(paste(resp,"~ 1 +",rand))
    #the next two line will make that the terms in the formula will add first the most important term, and the least important one at the end 
    #going first through the interactions and then through the main effects
    mat<-data.frame(term=attr(terms(model),"term.labels"),SSQ=anova(model)[,3],stringsAsFactors = FALSE)
    for(i in 1:nb_terms){
    mat<-data.frame(term=attr(terms(model),"term.labels"),stringsAsFactors = FALSE)

  #fit the reduced model to the data

    m_fit<-lapply(fs,function(x) lmer(x,data,REML=FALSE))
  else if(fam=="binomial"){
    m_fit<-lapply(fs,function(x) glmer(x,data,family=fam,weights=weight))
    m_fit<-lapply(fs,function(x) glmer(x,data,family=fam))

    else if(fam=="binomial"){
  #compare nested model with one another and get LRT values (ie increase in the likelihood of the models as parameters are added)
  for(i in 1:(length(m_fit)-1)){
      #here are reported the bootstrapped p-values, ie not assuming any parametric distribution like chi-square to the LRT values generated under the null model
      #these p-values represent the number of time the simulated LRT value (under null model) are larger than the observe one
      print(paste("Variable ",term_added," tested",sep=""))
  print(paste("Seed set to:",seed))

You pass your GLMM model to the function together with the random part as character (see example below), if you fitted a binomial GLMM you also need to provide the weights as a vector, you can then set a seed and the last argument is the number of simulation to do, it is set by default to 50 for rapid checking purpose but if you want to report these results larger values (ie 1000, 10000) should be used.

Let’s look at a simple LMM example:

## Linear mixed model fit by REML ['lmerMod']
## Formula: TICKS ~ cHEIGHT + YEAR + (1 | BROOD)
##    Data: grouseticks
## REML criterion at convergence: 2755
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -3.406 -0.246 -0.036  0.146  5.807 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  BROOD    (Intercept) 87.3     9.34    
##  Residual             28.1     5.30    
## Number of obs: 403, groups:  BROOD, 118
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)   5.4947     1.6238    3.38
## cHEIGHT      -0.1045     0.0264   -3.95
## YEAR96        4.1910     2.2424    1.87
## YEAR97       -4.3304     2.2708   -1.91
## Correlation of Fixed Effects:
##         (Intr) cHEIGH YEAR96
## cHEIGHT -0.091              
## YEAR96  -0.726  0.088       
## YEAR97  -0.714  0.052  0.518
## [1] "Variable cHEIGHT tested"
## [1] "Variable YEAR tested"
## [1] "Seed set to: 23"
##     term      LRT    p_value
## 1 cHEIGHT 14.55054 0.00990099
## 2    YEAR 14.40440 0.00990099

The resulting table show for each term in the model the likelihood ratio test, which is basically the decrease in deviance when going from the null to the full model and the p value, you may look at the PBtest line in the details of ?PBmodcomp to see how it is computed.

Now let’s see how to use the function with binomial GLMM:

#simulate some binomial data
group<-gl(n = 20,k = 5)
y<-rbinom(n = 100,size = 10,prob = invlogit(p))
#fit a model
m<-glmer(prop~x1+x2+(1|group),family="binomial",weights = rep(10,100))
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: prop ~ x1 + x2 + (1 | group)
## Weights: rep(10, 100)
##      AIC      BIC   logLik deviance df.resid 
##    288.6    299.1   -140.3    280.6       96 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -2.334 -0.503  0.181  0.580  2.466 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  group  (Intercept) 1.38     1.18    
## Number of obs: 100, groups:  group, 20
## Fixed effects:
##             Estimate Std. Error z value Pr(&gt;|z|)    
## (Intercept)    0.748      0.287    2.61   0.0092 ** 
## x1             0.524      0.104    5.02  5.3e-07 ***
## x2            -2.083      0.143  -14.56  &lt; 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Correlation of Fixed Effects:
##    (Intr) x1    
## x1  0.090       
## x2 -0.205 -0.345</code></pre>
#takes some time
anova_merMod(m,w = rep(10,100),nsim=100)</pre>
## [1] "Variable x1 tested"
## [1] "Variable x2 tested"
## [1] "Seed set to: 98"
##   term      LRT p_value
## 1   x1   0.0429 0.80392
## 2   x2 502.0921 0.01961

For binomial model, the model must be fitted with proportion data and a vector of weights (ie the number of binomial trial) must be passed to the ‘w’ argument. Some warning message may pop up at the end of the function, this comes from convergence failure in PBmodcomp, this do not affect the results, you may read the article from the pbkrtest package: to understand better where this comes from.

Happy modeling and as Ben Bolker say: “When all else fails, don’t forget to keep p-values in perspective:

Evenness effect on ecosystem functioning


Most biodiversity ecosystem function (BEF) experiment focus on one aspect of diversity: species richness, and had to it some functional composition flavour. The treatment manipulate the number of species and the number of broad functional group occurring. The issue there is that diversity is not only species richness, the classical definition of diversity tells us that it is the combination between richness and evenness that makes diversity (Tuomitso 2012). Diversity is therefore the number of biological unit present at a given location at a given time taking into account how are the individuals distributed amongst the species.

Some definition:

A community with 10 species where 99% of individuals belong to one species is thought to be less diverse than a community with 10 species and equal repartition of the individuals between the species. The difference in the distribution of the total abundance between the different species in a community is called evenness. A community where all species have the same abundance will get the highest possible evenness value while a community where most of the individual belong to one species will have the lowest evenness value. There is a whole bunch of index that can be used to represent these three different components: species richness, evenness and diversity, people interested by knowing how to measure these should read Magurran and McGill 2011.

Here in this post I will talk about the ways by which a community of changing evenness will affect its ecosystem function.

The early literature on BEF already highlighted the potential impacts of changing evenness on the function of the ecosystem (Chapin et al 2000), as changes in the evenness will impact the distribution of the functional traits values (see figure 1 in Hillebrand et al 2008).

A community evenness will shift much earlier than its species richness, before a species is loss its abundance is likely to decline gradually leading to variation in evenness if this decline is not uniformly affecting all species in the system (ie if all species loose 5 individuals between time 1 and time 2, evenness will be unchanged). Tracking community evenness gives us some kind of early warning signals of what is going on in the community.

We can make three different scenarios on how changes in evenness will affect ecosystem function:

– if the function comes from synergies between the species in the community (ie complementarity, mutualism …) a reduction in evenness would lead to decline in the function rates as the beneficial supports between the species declines.

– if the function rates is dominated by the output of one species (ie one dominant species) and this species performance for the focal function is below the average, then a decrease in evenness due to the increased dominance of this species will lead to decreasing function rates

– if the function rates is dominated by the output of one species performing above average (a superproductive species), then the decrease in evenness caused by the increased dominance of this species will lead to an increase in the function.

Empirical results of the relation of evenness and ecosystem function are still rather rare, Wilsey and Potvin (2000) found in a grassland system that in plots with higher evenness there were higher variation in the light-capturing strategies used by the plant and so higher biomass production.

Evenness and dominance are two closely linked concept that often correlates between one another, Hillebrand et al (2008) reviewed some of the literature investigating links between evenness or dominance and ecosystem function, all kind of relation have already been found ie negative, positive or no relation.

So evenness effect on ecosystem function may take different form and will be sensible to spatial and time scale under study but knowledge on these relations will help us predict how the ecosystem functions will respond to the continuous shifts in the abundance of the species under anthropogenic pressures.


Chapin III, F. Stuart, et al. “Consequences of changing biodiversity.” Nature 405.6783 (2000): 234-242.

Hillebrand, Helmut, Danuta M. Bennett, and Marc W. Cadotte. “Consequences of dominance: a review of evenness effects on local and regional ecosystem processes.” Ecology 89.6 (2008): 1510-1520.

Magurran, Anne E., and Brian J. McGill, eds. Biological diversity: frontiers in measurement and assessment. Vol. 12. Oxford: Oxford University Press, 2011.

Tuomisto, Hanna. “An updated consumer’s guide to evenness and related indices.” Oikos 121.8 (2012): 1203-1218.

Wilsey, Brian J., and Catherine Potvin. “Biodiversity and ecosystem functioning: importance of species evenness in an old field.” Ecology 81.4 (2000): 887-892.


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