library(lme4)## Warning: package 'lme4' was built under R version 4.1.2
## Loading required package: Matrix
## Warning: package 'Matrix' was built under R version 4.1.2
politeness<-
read.csv("http://www.bodowinter.com/tutorial/politeness_data.csv")
which(is.na(politeness)==T)## [1] 375
politeness <- politeness[complete.cases(politeness),]boxplot(frequency ~ attitude*gender,
col=c("white","lightgray"),politeness)
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.1.2
## Warning in register(): Can't find generic `scale_type` in package ggplot2 to
## register S3 method.
library(dplyr)##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
politeness %>% ggplot(aes(x= attitude, y=frequency)) +
geom_boxplot() 
politeness %>% ggplot(aes(x= attitude, y=frequency)) +
geom_boxplot() +
facet_grid(.~subject)
politeness %>% ggplot(aes(x= attitude, y=frequency)) +
geom_boxplot() +
facet_grid(.~scenario)
politeness %>% ggplot(aes(x= attitude, y=frequency)) +
geom_jitter() +
facet_grid(scenario~subject)
Syntax model = lmer(target ~ fixed1 + fixed2+ (1|random1) + (1|random2), data=data)
politeness.model = lmer(frequency ~ attitude +
(1|subject) +
(1|scenario) ,
data=politeness)
politeness.model## Linear mixed model fit by REML ['lmerMod']
## Formula: frequency ~ attitude + (1 | subject) + (1 | scenario)
## Data: politeness
## REML criterion at convergence: 793.4536
## Random effects:
## Groups Name Std.Dev.
## scenario (Intercept) 14.80
## subject (Intercept) 63.36
## Residual 25.42
## Number of obs: 83, groups: scenario, 7; subject, 6
## Fixed Effects:
## (Intercept) attitudepol
## 202.59 -19.69
politeness.model = lmer(frequency ~ attitude + gender +
(1 | subject) +
(1 | scenario),
data = politeness)
politeness.model## Linear mixed model fit by REML ['lmerMod']
## Formula: frequency ~ attitude + gender + (1 | subject) + (1 | scenario)
## Data: politeness
## REML criterion at convergence: 775.4547
## Random effects:
## Groups Name Std.Dev.
## scenario (Intercept) 14.81
## subject (Intercept) 24.81
## Residual 25.41
## Number of obs: 83, groups: scenario, 7; subject, 6
## Fixed Effects:
## (Intercept) attitudepol genderM
## 256.85 -19.72 -108.52
politeness.null = lmer(frequency ~ gender +
(1 | subject) + (1 | scenario),
data = politeness,
REML = FALSE)
politeness.null## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: frequency ~ gender + (1 | subject) + (1 | scenario)
## Data: politeness
## AIC BIC logLik deviance df.resid
## 816.7193 828.8135 -403.3597 806.7193 78
## Random effects:
## Groups Name Std.Dev.
## scenario (Intercept) 13.83
## subject (Intercept) 20.24
## Residual 27.42
## Number of obs: 83, groups: scenario, 7; subject, 6
## Fixed Effects:
## (Intercept) genderM
## 247.0 -108.2
politeness.model = lmer(frequency ~ attitude + gender +
(1 | subject) + (1 | scenario),
data = politeness,
REML = FALSE)anova(politeness.null,politeness.model)## Data: politeness
## Models:
## politeness.null: frequency ~ gender + (1 | subject) + (1 | scenario)
## politeness.model: frequency ~ attitude + gender + (1 | subject) + (1 | scenario)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## politeness.null 5 816.72 828.81 -403.36 806.72
## politeness.model 6 807.10 821.61 -397.55 795.10 11.618 1 0.0006532 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
coef(politeness.model)## $scenario
## (Intercept) attitudepol genderM
## 1 243.4860 -19.72207 -108.5173
## 2 263.3592 -19.72207 -108.5173
## 3 268.1322 -19.72207 -108.5173
## 4 277.2546 -19.72207 -108.5173
## 5 254.9319 -19.72207 -108.5173
## 6 244.8015 -19.72207 -108.5173
## 7 245.9618 -19.72207 -108.5173
##
## $subject
## (Intercept) attitudepol genderM
## F1 243.3684 -19.72207 -108.5173
## F2 266.9442 -19.72207 -108.5173
## F3 260.2276 -19.72207 -108.5173
## M3 284.3535 -19.72207 -108.5173
## M4 262.0575 -19.72207 -108.5173
## M7 224.1293 -19.72207 -108.5173
##
## attr(,"class")
## [1] "coef.mer"
politeness.model = lmer( frequency ~ attitude + gender +
(1 + attitude | subject) +
(1 + attitude | scenario),
data = politeness,
REML = FALSE)## boundary (singular) fit: see ?isSingular
coef(politeness.model)## $scenario
## (Intercept) attitudepol genderM
## 1 245.2630 -20.43990 -110.8062
## 2 263.3044 -15.94550 -110.8062
## 3 269.1439 -20.63124 -110.8062
## 4 276.8329 -16.30057 -110.8062
## 5 256.0602 -19.40628 -110.8062
## 6 246.8625 -21.94848 -110.8062
## 7 248.4714 -23.55653 -110.8062
##
## $subject
## (Intercept) attitudepol genderM
## F1 243.8065 -20.68446 -110.8062
## F2 266.7314 -19.16924 -110.8062
## F3 260.1482 -19.60436 -110.8062
## M3 285.6972 -17.91570 -110.8062
## M4 264.2019 -19.33643 -110.8062
## M7 227.3618 -21.77138 -110.8062
##
## attr(,"class")
## [1] "coef.mer"
politeness.model = lmer( frequency ~ attitude + gender +
(1 + attitude | subject) +
(1 + attitude | scenario),
data = politeness,
REML = FALSE)## boundary (singular) fit: see ?isSingular
politeness.null = lmer( frequency ~ gender +
(1 + attitude | subject) +
(1 + attitude | scenario),
data = politeness,
REML = FALSE)## boundary (singular) fit: see ?isSingular
anova(politeness.null,politeness.model)## Data: politeness
## Models:
## politeness.null: frequency ~ gender + (1 + attitude | subject) + (1 + attitude | scenario)
## politeness.model: frequency ~ attitude + gender + (1 + attitude | subject) + (1 + attitude | scenario)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## politeness.null 9 819.61 841.37 -400.80 801.61
## politeness.model 10 814.90 839.09 -397.45 794.90 6.7082 1 0.009597 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1