Mixed Models: Regression

Regression Models with Mixed Effects

When we conduct experiments over several years and/or at several locations, we have to decide if differences among years and/or differences among locations are of interest. In other words: are they fixed effects like the treatments we apply or we want to classify them random effects? If locations are picked at random it seems obvious to define locations as random effects. It means that the location contributes to the variation of the response that cannot be controlled. If the locations are picked according to their weed flora, soil type, and crop pattern, it would be wise to define locations as a fixed effect. Usually, one should use one's common sense and the knowledge of the experimental design and the objectivewhen determining whether locations are fixed or random. There is ongoing discussions among statisticians about what should be random and what should be fixed; but that discussion is somewhat outside this course.

Example 1: Sugarbeet yield

Within the many years that sugarbeet has been an economic crop, we incidentally did field experiments in 2006 and 2007, near Powell, Wyoming to determine the effects of duration of Salvia reflexa (lanceleaf sage) interference with sugarbeet (Odero et al. 2010). The experiment is also used in the ANCOVA chapter.

##   Replicate Duration Year Yield_Mg.ha Yield.loss.pct
## 1         1       36 2006       67.26          98.05
## 2         1       50 2006       60.34          87.96
## 3         1       64 2006       53.69          78.27

first of all we look at the distribution of data within years in Figure 1.

xyplot(Yield_Mg.ha ~ Duration | factor(Year), data = lanceleaf.sage,
    xlab="Duration of Competition",
       panel = function(x,y)

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Figure 1. The effect of the duration of weed competition on sugarbeet yield in two different years. it looks as if the relationships could be a linear one.

The ANCOVA chapter he analysis showed a linear regression was accepted by checking the regression against the most general ANOVA model. The ANCOVA also showed that the regression slopes were the same independent of years, but the intercepts differed between years. In this chapter we now assume that the variation between years are random, because we cannot control the climate.

To analyze this we need to use the lmer()function in the package lme4. The syntax for lmer()is almost the same as for lm(), but there are some limitations and some important additional arguments. In lm() we can convert a continuous x-variable to a factor just by writing factor(x). You can also do that with fixed effects in the lmer()function, but when it comes to random effects you cannot. In this particular experiment, there are two random effects: experimental years (1|Year) and Replicate because it is a block experiment. But the Replicate 1 in year 2006 is not the same as Replicate 1 in 2007. Consequently, we have to make the names for Replicates unambiguously defined with a unique name, so we get a total of 8 unique levels for Year and Replicate combination and the random effect takes the form (1|New.Rep) by defining New.Rep using the with() function.

lanceleaf.sage$New.Rep <- with(lanceleaf.sage, factor(Replicate):factor(Year))
## [1] "1:2006" "1:2007" "2:2006" "2:2007" "3:2006" "3:2007" "4:2006" "4:2007"

After this initial exercise, we are now ready to test whether we can assume linearity of Yield on duration of competition. We compare the regression model with the most general model, the ANOVA, as it is becoming standard practice in the course.

Anova.m1 <- lmer(Yield_Mg.ha ~ factor(Duration) + (1|Year) + (1|New.Rep),
                 data=lanceleaf.sage, REML=FALSE)
Regression.m2 <- lmer(Yield_Mg.ha ~ Duration + (1|Year) + (1|New.Rep),
                      data=lanceleaf.sage, REML=FALSE)
anova(Anova.m1, Regression.m2) #No indication of nonlinearity
## Data: lanceleaf.sage
## Models:
## Regression.m2: Yield_Mg.ha ~ Duration + (1 | Year) + (1 | New.Rep)
## Anova.m1: Yield_Mg.ha ~ factor(Duration) + (1 | Year) + (1 | New.Rep)
##               Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## Regression.m2  5 455 466   -222      445                        
## Anova.m1      11 461 485   -220      439  5.79      6       0.45

The test for lack of fit is non-significant as was also seen in the ANCOVA chapter.

Notice, we have an argument that is not used in the general lm() and it is REML (Rstricted Maximum Likelihood). There are two options: Maximum Likelihood estimation (ML) REML. REML=TRUE is the default. ML produces downwards biased estimated variance components (over-optimistic precision). REML reduces the bias (similar to dividing by n - p rather than n for the residual standard error in linear regression). In other words REML agrees with lm() in case of no random effects. To make a long story short, we should use ML (REML=FALSE) when comparing models using the anova() function.

However, when we look at the summary of the mixed effect regression:

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: Yield_Mg.ha ~ Duration + (1 | Year) + (1 | New.Rep)
##    Data: lanceleaf.sage
##      AIC      BIC   logLik deviance df.resid 
##    454.8    465.6   -222.4    444.8       59 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7195 -0.6532 -0.0976  0.5328  2.3680 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  New.Rep  (Intercept)  8.99    3.00    
##  Year     (Intercept) 14.09    3.75    
##  Residual             52.22    7.23    
## Number of obs: 64, groups:  New.Rep, 8; Year, 2
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)   67.856      3.402   19.94
## Duration      -0.214      0.023   -9.31
## Correlation of Fixed Effects:
##          (Intr)
## Duration -0.473

it looks rather different from an ordinary regression summary with the lm() function. The first thing we see is that there are no significance levels like in lm() output. This is because there is still no consensus about how to calculate degrees of freedom for mixed effects models. There is fairly extensive discussion of this topic in the R community, that can be left out here, but we will point you to a breif explanation provided by Douglas Bates, the author of the lmer function.

Variation between years (Year) and variation between reps within year (New.Rep) are both provided in the random effects portion of the output. What it means is that the variance between years and the variation among Replicates cannot be controlled by us; therefore we define them as random effect and they are now an integral part of the total variation we cannot explain.

Comparing the result above with the fixed year effect:

regression.fixed<-lm(Yield_Mg.ha ~ Duration + factor(Year), data=lanceleaf.sage)
## Call:
## lm(formula = Yield_Mg.ha ~ Duration + factor(Year), data = lanceleaf.sage)
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -16.348  -5.243  -0.725   3.572  17.956 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       72.0958     2.2052   32.69  < 2e-16 ***
## Duration          -0.2145     0.0247   -8.68  3.0e-12 ***
## factor(Year)2007  -8.4788     1.9389   -4.37  4.9e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Residual standard error: 7.76 on 61 degrees of freedom
## Multiple R-squared:  0.608,  Adjusted R-squared:  0.595 
## F-statistic: 47.2 on 2 and 61 DF,  p-value: 4.08e-13

We see that the slope for the mixed model is the same as for the fixed year model -0.21 with standard error of 0.02. The intercept of the mixed model is 67.9 (3.4). For the fixed effect model the intercepts were 72.1 and 72.1 -8.5 = 63.6with a standard error of 2.2 and 1.9, respectively. The changes of going from fixed year effect to random year effect is not dramatic when it comes to regression slopes, either. But if we decide year is random, then the mixed model is the one to use. We get a bonus, however by separating the variation of years from the residual. Reporting this information gives others who want to include our results in a meta-analysis knowledge of the actual variation in the experiments.

The illustration of the results is shown below. In order to make the plot neat we take the averages of the measurements by using the package dplyr, before plotting the data in Figure 2 .

averages<-lanceleaf.sage %.%
  group_by(Duration,Year) %.%
  summarise(YIELD = mean(Yield_Mg.ha))
plot(YIELD ~ Duration, data = averages,
     ylab = quote(Yield~(kg~ha^-1)), #Note how to use dimension accepted in some journals
     xlab = "Duration of competition",
     pch  = as.numeric(factor(Year)), ylim=c(10,80))

plot of chunk unnamed-chunk-7

Figure 2. The regression with year as random effect. Note that in this instance we have one slope and one intercept, and the regression line now is almost in the middle. Note that the fixef(Regression.m2) contains the fixed regressions parameters and the ordinary abline() can be used.

Example 2: Fungicide toxicity

Another example is with a total of six concentration-response experiments evaluating
the effect of a fungicide, vinclozolin on luminescence of ovary cells. Vinclozolin is a putative endogenous disruptor and therefore banned in Denmark. The concentration-response curves were run on separate days and it shows that the calibration of the instrument makes the response-curves somewhat different.

vinclozolin <- read.csv("http://rstats4ag.org/data/vinclozolin.csv")
##    conc effect experiment
## 1 0.025    908      10509
## 2 0.050    997      10509
## 3 0.100    744      10509
## 4 0.200    567      10509
## 5 0.390    314      10509
## 6 0.780    325      10509

The plot in Figure 3 show the individual response curves illustrates the variation in the slope and intercept of the straight lines. However the variation is kind of erratic because of daily calibration of the machine. Therefore, we consider days a random effect, the variation is intangible. As was the case with the duration of competition we will change the numeric name of each experiment to a factor

vinclozolin$Experiment <- factor(vinclozolin$experiment)

And then run the analysis, but first we illustrate thevariation in data.

xyplot(effect ~ log(conc) | Experiment, data = vinclozolin,
       xlab = "Log( Concentration of Vinclozolin)", ylab = "Luminescence",
       panel = function(x,y)

plot of chunk unnamed-chunk-10

Figure 3. Concentration-response curves for ovary cell luminescence on venclozolin.

The procedure to run the mixed regression model is the same as for Salvia reflexa.
First we make a test for lack of fit against the most general model the ANOVA.

vinclo.mixed.ANOVA <- lmer(effect ~ factor(conc) + (1|Experiment),
                           data = vinclozolin, REML = FALSE)
vinclo.mixed.Regression <- lmer(effect ~ log(conc) + (1|Experiment),
                                data = vinclozolin, REML = FALSE)
anova(vinclo.mixed.ANOVA, vinclo.mixed.Regression)
## Data: vinclozolin
## Models:
## vinclo.mixed.Regression: effect ~ log(conc) + (1 | Experiment)
## vinclo.mixed.ANOVA: effect ~ factor(conc) + (1 | Experiment)
##                         Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## vinclo.mixed.Regression  4 627 634   -310      619                        
## vinclo.mixed.ANOVA      10 628 647   -304      608  10.6      6        0.1

Apparently the test for lack of fit is non-significant so we can assume the relationship is linear whatever the day. Please note that we only have one replication per regression per day. But the concentrations are the same, so a test for lack of fit is still appropriate.

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: effect ~ log(conc) + (1 | Experiment)
##    Data: vinclozolin
##      AIC      BIC   logLik deviance df.resid 
##    627.1    634.5   -309.5    619.1       43 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -1.936 -0.627 -0.235  0.611  3.003 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  Experiment (Intercept) 72092    268     
##  Residual               19987    141     
## Number of obs: 47, groups:  Experiment, 6
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)    478.8      112.7    4.25
## log(conc)     -199.0       13.3  -14.96
## Correlation of Fixed Effects:
##           (Intr)
## log(conc) 0.144

The variation among experimental days (268) is huge compared with the variation within days (141).

xyplot(effect~log(conc)|experiment, data=vinclozolin,
       xlab="Log( Concentration of Vinclozolin)", ylab="Luminescence",

plot of chunk unnamed-chunk-13

Figure 4. The regression lines for all 6 days shown in six panels to give an idea of the variation among days. Apparently, there are particularly large differences between the responses and the regression lines for the three days in the lower panel.

If we had run an ordinary regression by pooling the six concentration-response curves then we would have gotten the result below.

vinclo.fixed.Regression<-lm(effect ~ log(conc), data = vinclozolin)
## Call:
## lm(formula = effect ~ log(conc), data = vinclozolin)
## Residuals:
##    Min     1Q Median     3Q    Max 
## -584.6 -212.7  -58.8  179.0  920.4 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    478.9       58.0    8.25  1.5e-10 ***
## log(conc)     -198.5       29.3   -6.77  2.3e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Residual standard error: 313 on 45 degrees of freedom
## Multiple R-squared:  0.504,  Adjusted R-squared:  0.493 
## F-statistic: 45.8 on 1 and 45 DF,  p-value: 2.25e-08

The slope and the intercept are almost identical for the two lmer()and lm(). The standard errors for the intercept, though, roughly doubled, 58 for lm() to 113 for lmer(), whereas the slope was more that halved, 13, for the lmer()compared to 29 with lm(). Undoubtedly, the experimental dates improved the precision of the slope. We can compare the fixed and random effects models on these data using Akaike Information Criterion (AIC). For a given set of data, the model with the lowest AIC value is more likely to be the appropriate model.

AIC(vinclo.mixed.Regression, vinclo.fixed.Regression)
##                         df   AIC
## vinclo.mixed.Regression  4 627.1
## vinclo.fixed.Regression  3 677.4

As a gereral rule, differences in AIC values less than 10 indicate two models perform similarly in describing the data. For the vinclozolin data set, the mixed model has and AIC of 627 compared to 677 for the fixed model where the effect of day was not included. This indicates the mixed model is the best fit for these data.


  1. Hi Andrew,

    First of all, thanks for putting together this website. It's really useful! I'm a 3rd year PhD student and I've encountered mixed models through my lab, independent reading, and one class. While they seem very attractive for blocked designs and to help deal with missing data, I have never gotten a straight answer about how to communicate their results in the absence of p-values and R squared values. I would love to hear your thoughts on this.

    For instance, right now I am analyzing data on cover crop weed suppression. We have measures of cover crop ground cover and I would like to see which of them is the best predictor of weed biomass. So the first question is, without p and R2 values, how do I compare them? This seems easy enough, since I can look at the residual variance and the standard error of the fixed effects. So I can choose the best predictor. But how do I go from there to communicate to my colleagues how good of a predictor it is? All I have is the slope and some measure of how the best model compares to other models. Do I communicate the fit with graphs, and let the reader/audience judge how good the fit is?

    More concretely, how would I present the results of a mixed linear regression model at a scientific conferences (say, the trisocieties, next week)? I can plot the data and plot an abline of fixef. I can report the slope and standard error. But I'm not sure how else to communicate the goodness of fit, in a world where everyone is used to looking at p and R2.

    I would greatly appreciate hearing your take on how to translate the results of a mixed model to an audience. If you can help me here, you will stand out from many who have not given me an answer!


  2. Dear Mitch, I do not know if you have gotten a reply but mine would be that R^2 is on of the most over rated stat parameters of virtually no value.

    If you really want to tell the gain of of a mixed model you could include the information of how random effect model separates the unexplained variation ( see the website

    Random effects:
    ## Groups Name Variance Std.Dev.
    ## New.Rep (Intercept) 8.993 2.999
    ## Year (Intercept) 14.092 3.754
    ## Residual 52.217 7.226
    ## Number of obs: 64, groups: New.Rep, 8; Year, 2
    you can see the Residual is 7.23 and the other two shows the unexplained variation between years and within replications.
    In fact this information would be very valuable if anybody would compare your results with others

    Kind regards

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