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Introduction to mixed-effects modelling

Posted on May 29, 2017 in stats

Introduction to mixed-effects modelling

Christophe Pallier
2017-05-29

Plan

  • Some generalities about statistical inference
  • Classic regression and its implementation in R
  • Random effects and mixed-effects models
  • Examples of mixed-effects analyses with R

Meaning of "X has an effect on Y" mean

What does it mean, from a statistical point of view, to say that the variable \( X \) has an effect on the variable \( Y \)?

\( Y \) is influenced by \( X \) if the *distribution of \( Y \) change when \( X \) takes different values.

Example

plot of chunk unnamed-chunk-1

As the two distributions differ, we can state that “\( X \) has an effect on \( Y \)”“ (not implying causality).

Remark: One is often only interested in whether the means of the distributions differ, e.g.: \[ E(Y|X=1) \stackrel{?}{=} E(Y|X=2) \]

Sampling

In real life, we rarely have access to the data of the whole population but only to random samples, that is, to a finite set of observations \( (y_i) \).

The distributions of the samples can (and often will) differ from one another because of sampling variability, that is, the fact that different individuals compose each sample.

For example, the mean heights of two groups of 20 boys will not be exactly the same.

Inference

To make statements about a population property from observed samples, we need statistical inference.

  1. hypothesis testing (e.g. does the mean of \( Y \) (e.g. height) varies with \( X \) (e.g. nationality)?)
  2. estimation (e.g. what is a confidence interval for the average height of French people?)
  3. prediction (e.g. what is the most probable height of a 18 year old German boy?)

Classical Simple Regression

  • One predictor variable \( X \)
  • One dependent variable \( Y \) (continuous).

The question of interest is whether \( E(Y|X) \) is constant or not:

\[ \cal{H}_0: \quad \rm E(Y|X=x) = \beta, \quad x \ \mbox{varying} \]

One alternative hypothesis is that the average of \( Y \) depends linearily on \( X \) :

\[ \cal{H}_{lin}: \quad \rm E(Y|X=x) = \alpha x + \beta \]

Graphical Representation

linear reg

Example

Effect of age on tot, a biomarker of kidney functioning.

plot of chunk unnamed-chunk-2

Ordinary Least Squares solution to Regression

The OLS method searches among all linear models one that minimizes the Euclidian distance between the model's predictions and the data (\( \sum (y_i - \hat{y}_i)^2 \)).

In R, this is accomplished by the fit function:

x = age
y = tot
fit = lm(y ~ x)
coef(fit)
(Intercept)           x 
 2.86002680 -0.07858842 

These coefficients estimate the population parameters \( \beta \) and \( \alpha \).

Prediction

The equation \( \alpha.x+\beta \) can be used to compute the expected value of \( Y \) for a given value \( x \) of \( X \)

plot(y ~ x, bty='l', pch=16)
abline(fit, col=2, lwd=2)

plot of chunk unnamed-chunk-4

Hyothesis testing in simple regression

To draw any inference from these coefficients to the population parameters, for example to know their precision, you need an additional hypothesis.

The simplest one is the the errors terms are statistically independent and each is distributed following a normal distribution with a standard deviation \( \sigma \) :

\[ y_i = \alpha . x_i + \beta + \epsilon_i , \quad \epsilon \sim \cal{N}(0,\sigma^2) \]

Testing the hypothesis of a non-null slope

The p-value for the hypothesis that \( X \) has no (linear) effect on \( Y \) can be obtained calling the function anova with the linear model object as a parameter.

anova(fit)
Analysis of Variance Table

Response: y
           Df Sum Sq Mean Sq F value    Pr(>F)    
x           1 244.29 244.288  75.312 5.182e-15 ***
Residuals 155 502.77   3.244                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Estimates and standard errors of coefficients

They can be displayed with the function summary, allowing to construct confidence intervals for them:

summary(fit)

Call:
lm(formula = y ~ x)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2018 -1.3451  0.0765  1.0719  4.5252 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.860027   0.359565   7.954 3.53e-13 ***
x           -0.078588   0.009056  -8.678 5.18e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.801 on 155 degrees of freedom
Multiple R-squared:  0.327, Adjusted R-squared:  0.3227 
F-statistic: 75.31 on 1 and 155 DF,  p-value: 5.182e-15

Remark 1: X can be binary

The regression regression of \( Y \) on \( X \) is strictly equivalent to a Student T test.

x = rep(1:2, 20)
y = x + rnorm(40)

m1 <- lm(y ~ x)
plot(y ~ x, pch=16, xlim=c(.5, 2.5))
abline(m1)

plot of chunk unnamed-chunk-7

Case of binary X

summary(m1)

Call:
lm(formula = y ~ x)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.55111 -0.69204 -0.01078  0.32311  2.45041 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -0.4690     0.4822  -0.973 0.336914    
x             1.1057     0.3050   3.626 0.000842 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9644 on 38 degrees of freedom
Multiple R-squared:  0.257, Adjusted R-squared:  0.2375 
F-statistic: 13.15 on 1 and 38 DF,  p-value: 0.0008421
t.test(y ~ x, var.equal=T)

    Two Sample t-test

data:  y by x
t = -3.6258, df = 38, p-value = 0.0008421
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.7230634 -0.4883563
sample estimates:
mean in group 1 mean in group 2 
      0.6367533       1.7424631 

Remark 2: testing for non linear effect of X

x = age
y = tot
m1 <- lm(y ~ x)
summary(m2 <- lm(y ~ poly(x, 2))) # in '*linear model*', '*linear*' stands for a the relationship between the parameters and Y, not the predictors and Y

Call:
lm(formula = y ~ poly(x, 2))

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2024 -1.3492  0.0449  1.0755  4.4781 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.911e-04  1.442e-01  -0.001    0.999    
poly(x, 2)1 -1.563e+01  1.806e+00  -8.652 6.26e-15 ***
poly(x, 2)2 -4.758e-01  1.806e+00  -0.263    0.793    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.806 on 154 degrees of freedom
Multiple R-squared:  0.3273,    Adjusted R-squared:  0.3186 
F-statistic: 37.46 on 2 and 154 DF,  p-value: 5.522e-14

Remark 2: testing for non linear effect of X

plot(x, y)
lines(x, fitted(m1), col=2) # linear
lines(x, fitted(m2), col=3) # polynomial deg 2

plot of chunk unnamed-chunk-11

Remark 3: Non-parametric analyses as an alternative

plot(y ~ x, bty='l', pch=16)
lines(lowess(x, y), col='blue', lwd=2)

plot of chunk unnamed-chunk-12

Remark 4: nicer plot with ggplot2

plot of chunk unnamed-chunk-13

Multiple regression

Mutliple regression is a simple extension of simple regression: instead of considering only one predictor variable '\( X \)', several are taken into account '\( X_1, X_2, ... X_p \)'. The linear model then becomes:

\[ y_i = \beta_1.x_{1i}+ \beta_2.x_{2i} + ... + \beta_p.x_{pi} + \epsilon_i \]

Interpretation of coefficients

\( \beta_i \) represents the amount by which \( Y \) is modified when \( X_i \) increases by 1 while the other \( X_{j \neq i} \) remain constant (all other things equal).

Remark: The interpretation of \( \beta_i \) can actually become tricky when the \( \X_i \) are highly correlated (see variance inflation factor, VIF).

For categorical variables (factors), the interpretation of the betas depends on the coding contrast used: reference coding ( contr.treament) or deviation coding (contr.sum)

Recommended readings for linear modelling

  • John Fox (1997) Applied Regression Analysis, Linear Models, and Related Methods, Sage publications.
  • Julian Faraway (2005) Linear Models with R, Chapman & Hall.
  • Scott E. Maxwell and Harold D. Delaney (2000) Designing experiments and analyzing data: a model comparison perspective, Erlbaum.
  • Frank Harrell (2001) Regression Modeling Strategies

An issue with repeated measurements

Consider an experiment in which reaction times are recorded in two experimental conditions in human participants. Moreover let us suppose that each participant produced many data points in each condition (“repeated measures”).

 subj    cond   RT
 s01      c1    520
 s01      c1    497    
 s01      c1    489
 ...
 s01      c2    530
 s01      c2    513
 ...
 s02      c1    632
 s02      c1    567

An issue with repeated measurements

Newcomers to Psychology are often surprised when they are told that:

RTs within subject and condition must be averaged before running the Anova (or t-test).

  • Why it is not possible to perform the statistical analyses on the raw data set?
  • What is the purpose of having several trials per subject if we average in the end?

Justifications for averaging over repeated measurements

  • the within-subject within-condition RTs are not independent (for example RTs may be autocorrelated), violating the assumption of statistical independence.

  • Even if within subject data are independent, the mere fact that the the data is clustered by participants yields a lack of independence (see Intraclass Correlation Coefficient)

  • the unit of observation over which we want to perform inference is generally the population of humans, not the population of trials.

One wants to compare the observed effect size to the variability between participants, not the trial-to-trial variability (A similar issue exists in fMRI data analyses: see first level/fixed effect vs. second level/random effects analyses. Unless you are interested in aprecise individual participant; think about single case studies in neuropsychology or psychophysics).

One issue with averaging

This experiment actually has three sources of variations:

  • the factor condition
  • the factor subject
  • the factor trial

Averaging over trials entails confuses the trial-to-trial variability and the between subjects variability.

(Remark: The reason to increase the number of within subject measurements is to decrease the weight of the trial-to-trial variabililty).

What are fixed and random factors?

Fixed and Random factors

  • Fixed factor: we are interested in differences between the levels of the factor.

  • Random factor: the levels would vary in replications of the experiment. The effect for a given level is not of interest, but we may be interest in the variance.

Relations between factors: two factors can be crossed, nested or neither.

Knowing the relations between factors and their types allows one to compute the Expected Mean Squares in an ANOVA, and select the appropriate error term to assess an effect.

(See, e.g. Abdi, Edelman, Valentin, Dowling (2009) Experimental Design and Analysis for Psychology, OUP.)

The score model

One reasonable model for the score of the kth trial of the jth subject in condition i is the following: \[ Y_{ijk} = \mu + \beta_i + s_j + \epsilon_{ijk} \] Where:

  • \( \beta_i \) corresponds to the effect of the ith condition (measured as a distance from the global mean \( \mu \))
  • \( s_j \) corresponds to the effects of the jth subject
    • \( s_j \sim {\cal N}(0, \sigma_s^2) \)
  • \( \epsilon_{ijk} \) is the effect of trial 'ijk'
    • \( \epsilon_{ijk} \sim {\cal N}(0, \sigma_\epsilon^2) \)

Mixed-effects

The problem is that most data analysis softwares used to allow only a single random factor. And statistics textbooks for psychology only explained the procedures to analyse design with a single random factor even though most experiments have two or more.

“Mixed-effects” procedures allows one to take into account the presence of several random factors, and estimate the various sources of variation. They have many advantages over classic ANOVA.

Advantages of mixed-effect modelling over ANOVA and rANOVA

  • better handling of missing data, between group unbalance, heteroscestadicity.
  • avoid kludges like Greenhouse-Geisser correction for non-sphericity.
  • They provide less biased estimate of individual effects (by shrinkage; see B. Efron & C. Morris (1977) Stein's Paradox in Statistics. Scientific American, pp. 119–127).
  • allow more detailed modeling of effects as it is possible to incorporate trial level predictors in the model.
  • encourage to better look at the data.

one can get more from one's data than with traditional ANOVA techniques.

Drawback of mixed effect modelling

  • relative complexity (need to really master multiple regression first)

    • unnecessary in simple cases where the assumptions of ANOVA hold
  • synthesizing the results of analysis can be more challenging (one can hope that, one day, the results section of scientific articles will be replaced by the full outputs of data analysis scripts).

First example

Let us generate a dataset with 2 conditions and repeated measures within participants.

First example

data1 <- data.frame(sub, cond, y)
str(data1)
'data.frame':   800 obs. of  3 variables:
 $ sub : Factor w/ 20 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ cond: Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
 $ y   : num  -34.9 186.7 110.3 -58.5 140.4 ...
head(data1)
  sub cond         y
1   1    1 -34.86155
2   1    1 186.66035
3   1    1 110.26327
4   1    1 -58.45421
5   1    1 140.37307
6   1    1  72.39533

First example

plot of chunk data1plotplot of chunk data1plot

Classical Anova

bysub <- aggregate(y ~ cond + sub, data=data1, mean)
with(bysub, { print(tapply(y, cond, mean)); interaction.plot(cond, sub, y)})
        1         2 
 88.45269 106.35811 

plot of chunk anova1

Classical anova

summary(anov <- aov(y ~ cond + Error(sub/cond), data=bysub))

Error: sub
          Df Sum Sq Mean Sq F value Pr(>F)
Residuals 19  53106    2795               

Error: sub:cond
          Df Sum Sq Mean Sq F value  Pr(>F)   
cond       1   3206    3206   8.683 0.00828 **
Residuals 19   7016     369                   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Mixed-effects model

require(lme4)
require(lmerTest)
lme1 <- lmer(y ~ cond + (1 | sub), data=data1)
summary(lme1)
Linear mixed model fit by REML t-tests use Satterthwaite approximations
  to degrees of freedom [lmerMod]
Formula: y ~ cond + (1 | sub)
   Data: data1

REML criterion at convergence: 9761.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.7541 -0.6748 -0.0146  0.7129  3.3294 

Random effects:
 Groups   Name        Variance Std.Dev.
 sub      (Intercept)  1112     33.35  
 Residual             11406    106.80  
Number of obs: 800, groups:  sub, 20

Fixed effects:
            Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)   88.453      9.172  27.500   9.643 2.54e-10 ***
cond2         17.905      7.552 779.800   2.371    0.018 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
      (Intr)
cond2 -0.412

Estimated subject's' effects

$sub

plot of chunk unnamed-chunk-16

We assumed that the effect of condition was the same across all subjects, but we can fit a more flexible model allowing for a random effect of cond within subject

Fitting a more flexible model

lme2 <- lmer(y ~ cond + (1 + cond | sub), data=data1)
summary(lme2)
Linear mixed model fit by REML t-tests use Satterthwaite approximations
  to degrees of freedom [lmerMod]
Formula: y ~ cond + (1 + cond | sub)
   Data: data1

REML criterion at convergence: 9761.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.7542 -0.6746 -0.0121  0.7139  3.3247 

Random effects:
 Groups   Name        Variance  Std.Dev. Corr 
 sub      (Intercept) 1.143e+03  33.8018      
          cond2       8.068e-01   0.8982 -1.00
 Residual             1.141e+04 106.7993      
Number of obs: 800, groups:  sub, 20

Fixed effects:
            Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)   88.453      9.254  20.000   9.558 6.85e-09 ***
cond2         17.905      7.555 732.200   2.370    0.018 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
      (Intr)
cond2 -0.430

Let us compare both models:

anova(lme2, lme1)
Data: data1
Models:
..1: y ~ cond + (1 | sub)
object: y ~ cond + (1 + cond | sub)
       Df    AIC    BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)
..1     4 9781.6 9800.3 -4886.8   9773.6                         
object  6 9785.6 9813.7 -4886.8   9773.6 0.0115      2     0.9943

This allows us to test whether there is significant variance in the effect of condition across subjects.

Second example: simple regressions within subjects

To test if a continuous predictor \( X \) has an effect on a continuous variable \( Y \), the classic linear regression framework assumes that measurements are independent.

x <- rnorm(100)
y <- 5 + x + rnorm(100)
plot(y ~ x, pch=16)
summary(lm1 <- lm(y ~ x))

Call:
lm(formula = y ~ x)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.04035 -0.52200 -0.02043  0.72914  1.91953 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.88012    0.09464   51.57   <2e-16 ***
x            1.03755    0.09589   10.82   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.944 on 98 degrees of freedom
Multiple R-squared:  0.5443,    Adjusted R-squared:  0.5397 
F-statistic: 117.1 on 1 and 98 DF,  p-value: < 2.2e-16
abline(lm1)

plot of chunk unnamed-chunk-18

Clustered measurements

Let us suppose that the measures are “clustered”, that is, come from different participants. The independence assumption is violated.

plot of chunk unnamed-chunk-19

Classical approaches: group as a fixed factor

summary(lm(y ~ x + g))

Call:
lm(formula = y ~ x + g)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.62106 -0.63273  0.07911  0.58336  1.80567 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  5.22779    0.29648  17.633   <2e-16 ***
x            1.02948    0.10095  10.198   <2e-16 ***
g2           0.01246    0.42167   0.030   0.9765    
g3          -0.28507    0.41968  -0.679   0.4987    
g4          -0.42151    0.42364  -0.995   0.3225    
g5          -0.79667    0.41880  -1.902   0.0604 .  
g6          -0.36249    0.41832  -0.867   0.3885    
g7          -0.21226    0.41848  -0.507   0.6133    
g8          -0.69352    0.41885  -1.656   0.1013    
g9          -0.77835    0.42721  -1.822   0.0718 .  
g10          0.05507    0.42028   0.131   0.8960    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9354 on 89 degrees of freedom
Multiple R-squared:  0.5937,    Adjusted R-squared:  0.5481 
F-statistic: 13.01 on 10 and 89 DF,  p-value: 1.05e-13

Classical approach II: random regressions

Another approach would be to compute the regression lines within each subgroups and see if the slopes are significantly different from 0.

require(lme4)
summary(ll <- lmList(y ~ x | g, data=data.frame(g, x, y), pool=FALSE))
Call:
  Model: y ~ x | NULL 
   Data: data.frame(g, x, y) 

Coefficients:
   (Intercept) 
   Estimate Std. Error  t value     Pr(>|t|)
1  5.279415  0.3127493 16.88066 4.317584e-28
2  5.148072  0.3222263 15.97657 1.304357e-26
3  4.926271  0.2932141 16.80093 5.807291e-28
4  4.561703  0.3194453 14.28007 1.034148e-23
5  4.431121  0.2911254 15.22066 2.440710e-25
6  4.757161  0.2985573 15.93383 1.536352e-26
7  4.966494  0.3131747 15.85854 2.050923e-26
8  4.595111  0.3130507 14.67849 2.086530e-24
9  4.784621  0.3326146 14.38488 6.774336e-24
10 5.275362  0.2963988 17.79819 1.508026e-29
   x 
    Estimate Std. Error  t value     Pr(>|t|)
1  0.7709056  0.5723844 1.346832 1.818374e-01
2  0.7469003  0.4233653 1.764198 8.151595e-02
3  0.9081946  0.2575459 3.526340 7.007535e-04
4  0.5024107  0.2833711 1.772978 8.003867e-02
5  0.9475340  0.3446890 2.748953 7.387759e-03
6  1.6784043  0.3972504 4.225054 6.284580e-05
7  1.1840832  0.3638882 3.253976 1.668477e-03
8  0.8814299  0.2800796 3.147069 2.317080e-03
9  1.5374619  0.2438057 6.306095 1.474008e-08
10 0.9924809  0.2746013 3.614262 5.247836e-04

Residual standard error: 0.9206192 on 80 degrees of freedom
t.test(coef(ll)[,2])

    One Sample t-test

data:  coef(ll)[, 2]
t = 8.9013, df = 9, p-value = 9.343e-06
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 0.7570356 1.2729255
sample estimates:
mean of x 
 1.014981 

More principled approach: fit a multilevel regression

summary(lmer(y ~ x + (1 + x | g)))
Linear mixed model fit by REML t-tests use Satterthwaite approximations
  to degrees of freedom [lmerMod]
Formula: y ~ x + (1 + x | g)

REML criterion at convergence: 273.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.8193 -0.5823 -0.0392  0.7140  1.9780 

Random effects:
 Groups   Name        Variance Std.Dev. Corr 
 g        (Intercept) 0.006856 0.0828        
          x           0.053852 0.2321   -0.68
 Residual             0.829530 0.9108        
Number of obs: 100, groups:  g, 10

Fixed effects:
            Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)  4.88407    0.09661 9.02700   50.56 2.18e-12 ***
x            1.01993    0.12170 9.67900    8.38 9.64e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
  (Intr)
x -0.085

Price of houses as a function of latitude (Galwey; 2006)

Data set from  Galwey (2006) _Introduction to Mixed Modelling_

Models with several random factors are not a novelty !

Must read:

  • Clark, H. H. (1973) “The Language-as-Fixed-Effect Fallacy: A Critique of Language Statistics in Psychological Research.” Journal of Verbal Learning and Verbal Behavior 12: 335–59.

Clark remarked that most psycholinguistics experiments have (at least) two important random factors: subjects and items (words, sentences, …).

Expected Mean Squares

Expected Mean squares for SxW<T> design

Table of EMS 2

Notice that there is no simple ratio between sources of variance to test the hypothesis that \( \sigma_t > 0 \).

For example, EMS\( _T \) exceeeds EMS\( _{T{\rm x}S} \) by \( \sigma_w^2 + qr\sigma_t^2 \).

MinF'

Clark proposed a minimal bound, MinF' for a quasi-F ratio, that can be computed from \( F_1 \) (by subject) and \( F_2 \) (by item) analyses.

But using a mixed effect procedure, it is possible to avoid this cumbersome approach and to be more sensitive to detect an effect of Treatment (see Baayen (2006) Analysing Linguistics Data)

Example of a lexical decision experiment

Dataset lexdec described in Baayen (2006) Analyzing Linguistic Data and provided in the languageR package.

require(languageR)
data(lexdec)
help(lexdec)
str(lexdec)
'data.frame':   1659 obs. of  28 variables:
 $ Subject       : Factor w/ 21 levels "A1","A2","A3",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ RT            : num  6.34 6.31 6.35 6.19 6.03 ...
 $ Trial         : int  23 27 29 30 32 33 34 38 41 42 ...
 $ Sex           : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
 $ NativeLanguage: Factor w/ 2 levels "English","Other": 1 1 1 1 1 1 1 1 1 1 ...
 $ Correct       : Factor w/ 2 levels "correct","incorrect": 1 1 1 1 1 1 1 1 1 1 ...
 $ PrevType      : Factor w/ 2 levels "nonword","word": 2 1 1 2 1 2 2 1 1 2 ...
 $ PrevCorrect   : Factor w/ 2 levels "correct","incorrect": 1 1 1 1 1 1 1 1 1 1 ...
 $ Word          : Factor w/ 79 levels "almond","ant",..: 55 47 20 58 25 12 71 69 62 1 ...
 $ Frequency     : num  4.86 4.61 5 4.73 7.67 ...
 $ FamilySize    : num  1.386 1.099 0.693 0 3.135 ...
 $ SynsetCount   : num  0.693 1.946 1.609 1.099 2.079 ...
 $ Length        : int  3 4 6 4 3 10 10 8 6 6 ...
 $ Class         : Factor w/ 2 levels "animal","plant": 1 1 2 2 1 2 2 1 2 2 ...
 $ FreqSingular  : int  54 69 83 44 1233 26 50 63 11 24 ...
 $ FreqPlural    : int  74 30 49 68 828 31 65 47 9 42 ...
 $ DerivEntropy  : num  0.791 0.697 0.475 0 1.213 ...
 $ Complex       : Factor w/ 2 levels "complex","simplex": 2 2 2 2 2 1 1 2 2 2 ...
 $ rInfl         : num  -0.31 0.815 0.519 -0.427 0.398 ...
 $ meanRT        : num  6.36 6.42 6.34 6.34 6.3 ...
 $ SubjFreq      : num  3.12 2.4 3.88 4.52 6.04 3.28 5.04 2.8 3.12 3.72 ...
 $ meanSize      : num  3.48 3 1.63 1.99 4.64 ...
 $ meanWeight    : num  3.18 2.61 1.21 1.61 4.52 ...
 $ BNCw          : num  12.06 5.74 5.72 2.05 74.84 ...
 $ BNCc          : num  0 4.06 3.25 1.46 50.86 ...
 $ BNCd          : num  6.18 2.85 12.59 7.36 241.56 ...
 $ BNCcRatio     : num  0 0.708 0.568 0.713 0.68 ...
 $ BNCdRatio     : num  0.512 0.497 2.202 3.591 3.228 ...

Filtering out mistakes and long RTs

nrow(lexdec)
[1] 1659
plot(density(lexdec$RT))

plot of chunk unnamed-chunk-24

lexdec2 <- subset(lexdec, RT < 7)
nrow(lexdec2)
[1] 1618
lexdec3 <- subset(lexdec2, Correct=="correct")
nrow(lexdec3)
[1] 1557

Inspect subject data to see if there is an effect of Trial

plot of chunk unnamed-chunk-25

Is it worth including Trial as a random slope within Subject?

require(lme4)
require(lmerTest)
lexdec3$cTrial = lexdec3$Trial - mean(lexdec3$Trial)
summary(m1 <- lmer(RT ~ Frequency + Length + NativeLanguage + cTrial + (1 | Subject) + (1 | Word), data=lexdec3 ))
Linear mixed model fit by REML t-tests use Satterthwaite approximations
  to degrees of freedom [lmerMod]
Formula: 
RT ~ Frequency + Length + NativeLanguage + cTrial + (1 | Subject) +  
    (1 | Word)
   Data: lexdec3

REML criterion at convergence: -1282.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3184 -0.6624 -0.1181  0.5248  4.4652 

Random effects:
 Groups   Name        Variance Std.Dev.
 Word     (Intercept) 0.002072 0.04552 
 Subject  (Intercept) 0.014709 0.12128 
 Residual             0.022569 0.15023 
Number of obs: 1557, groups:  Word, 79; Subject, 21

Fixed effects:
                      Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)          6.414e+00  5.472e-02  7.130e+01 117.218  < 2e-16 ***
Frequency           -3.219e-02  5.569e-03  7.320e+01  -5.780 1.71e-07 ***
Length               9.447e-03  3.806e-03  7.200e+01   2.482   0.0154 *  
NativeLanguageOther  1.351e-01  5.404e-02  1.900e+01   2.499   0.0218 *  
cTrial              -1.841e-04  8.169e-05  1.491e+03  -2.253   0.0244 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
            (Intr) Frqncy Length NtvLnO
Frequency   -0.660                     
Length      -0.618  0.427              
NtvLnggOthr -0.422 -0.002  0.001       
cTrial      -0.002  0.005 -0.003  0.001

Comparing with a model with Trial as a random effect within Subject

m2 <- lmer(RT ~ Frequency + Length + NativeLanguage + cTrial + (1 + cTrial | Subject) + (1 | Word), data=lexdec3 )
anova(m1, m2)
Data: lexdec3
Models:
object: RT ~ Frequency + Length + NativeLanguage + cTrial + (1 | Subject) + 
object:     (1 | Word)
..1: RT ~ Frequency + Length + NativeLanguage + cTrial + (1 + cTrial | 
..1:     Subject) + (1 | Word)
       Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)    
object  8 -1311.2 -1268.4 663.59  -1327.2                             
..1    10 -1341.8 -1288.3 680.92  -1361.8 34.651      2  2.991e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

See Baayen (2006, section 7.1 for more detailed analysis)

Difficulties

Specifying the model, especially selecting the random effect structure can be difficult.

In classical ANOVA/hypothesis testing framework, one specifies the maximally complex model and consult the p-values. The model is often overfitting and lacks robustness. For prediction, a model leaving higher order interactions will typically perform better. But model selection is complicated and Psychologist usually avoid it.

  • Barr, D. J., Levy, R., Scheepers, C., and Tily, H. J. (2013). Random effects structure for confirma- tory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3):255–278.

  • Bates, Douglas, Reinhold Kliegl, Shravan Vasishth, and Harald Baayen. 2015. “Parsimonious Mixed Models.” arXiv Preprint arXiv:1506.04967. http://arxiv.org/abs/1506.04967.

Books on mixed-effects

  • R. H. Baayen (2008) Analyzing linguistic data. A practical introduction to Statistics using R, Cambridge University Press.
  • N. W. Galwey (2006) Introduction to Mixed Modelling: Beyond Regression and Analysis of Variance, John Wiley & Sons.
  • Andrew Gelman and Jennifer Hill (2007). Data Analysis Using Regression and Multilevel/Hierachical Models. Cambridge University Press.