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- require(gsheet)
- data <- read.csv(text =
- gsheet2text('https://docs.google.com/spreadsheets/d/1QgtDcGJebyfW7TJsB8n6rAmsyAnlz1xkT3RuPFICTdk/edit?usp=sharing',
- format ='csv'))
- > head(data)
- Subject Auditorium Education Time Emotion Caffeine Recall
- 1 Jim A HS 0 Negative 95 125.80
- 2 Jim A HS 0 Neutral 86 123.60
- 3 Jim A HS 0 Positive 180 204.00
- 4 Jim A HS 1 Negative 200 95.72
- 5 Jim A HS 1 Neutral 40 75.80
- 6 Jim A HS 1 Positive 30 84.56
- library(ggplot2)
- p <- ggplot(data, aes(x = Caffeine, y = Recall, colour = Subject)) +
- geom_point(size=3) +
- geom_line(aes(y = predict(fit1)),size=1)
- print(p)
- p <- ggplot(data, aes(x = Caffeine, y = Recall, colour = Subject)) +
- geom_point(size=3) +
- geom_line(aes(y = predict(fit2)),size=1)
- print(p)
- > data$predict = predict(fit2)
- > head(data)
- Subject Auditorium Education Time Emotion Caffeine Recall predict
- 1 Jim A HS 0 Negative 95 125.80 132.45609
- 2 Jim A HS 0 Neutral 86 123.60 130.55145
- 3 Jim A HS 0 Positive 180 204.00 150.44439
- 4 Jim A HS 1 Negative 200 95.72 112.37045
- 5 Jim A HS 1 Neutral 40 75.80 78.51012
- 6 Jim A HS 1 Positive 30 84.56 76.39385
- $`Time:Subject`
- (Intercept) Caffeine
- 0:Jason 75.03040 0.2116271
- 0:Jim 94.96442 0.2116271
- 0:Ron 58.72037 0.2116271
- 0:Tina 70.81225 0.2116271
- 0:Victor 86.31101 0.2116271
- 1:Jason 59.85016 0.2116271
- 1:Jim 52.65793 0.2116271
- 1:Ron 57.48987 0.2116271
- 1:Tina 68.43393 0.2116271
- 1:Victor 79.18386 0.2116271
- 2:Jason 43.71483 0.2116271
- 2:Jim 42.08250 0.2116271
- 2:Ron 58.44521 0.2116271
- 2:Tina 44.73748 0.2116271
- 2:Victor 36.33979 0.2116271
- $Subject
- (Intercept) Caffeine
- Jason 30.40435 0.2116271
- Jim 79.30537 0.2116271
- Ron 13.06175 0.2116271
- Tina 54.12216 0.2116271
- Victor 132.69770 0.2116271
- > coef(fit2)[[1]][2,1]
- [1] 94.96442
- > coef(fit2)[[2]][2,1]
- [1] 79.30537
- > coef(fit2)[[1]][2,2]
- [1] 0.2116271
- > data$Caffeine[1]
- [1] 95
- > coef(fit2)[[1]][2,1] + coef(fit2)[[2]][2,1] + coef(fit2)[[1]][2,2] * data$Caffeine[1]
- [1] 194.3744
- > ranef(fit2)
- $`Time:Subject`
- (Intercept)
- 0:Jason 13.112130
- 0:Jim 33.046151
- 0:Ron -3.197895
- 0:Tina 8.893985
- 0:Victor 24.392738
- 1:Jason -2.068105
- 1:Jim -9.260334
- 1:Ron -4.428399
- 1:Tina 6.515667
- 1:Victor 17.265589
- 2:Jason -18.203436
- 2:Jim -19.835771
- 2:Ron -3.473053
- 2:Tina -17.180791
- 2:Victor -25.578477
- $Subject
- (Intercept)
- Jason -31.513915
- Jim 17.387103
- Ron -48.856516
- Tina -7.796104
- Victor 70.779432
- > summary(fit2)$coef[1]
- [1] 61.91827 # Overall intercept for Fixed Effects
- > ranef(fit2)[[1]][2,]
- [1] 33.04615 # Time:Subject random intercept for Jim
- > ranef(fit2)[[2]][2,]
- [1] 17.3871 # Subject random intercept for Jim
- > summary(fit2)$coef[2]
- [1] 0.2116271 # Fixed effect slope
- > data$Caffeine[1]
- [1] 95 # Value of caffeine
- summary(fit2)$coef[1] + ranef(fit2)[[1]][2,] + ranef(fit2)[[2]][2,] +
- summary(fit2)$coef[2] * data$Caffeine[1]
- [1] 132.4561
- > summary(fit2)
- Linear mixed model fit by REML ['lmerMod']
- Formula: Recall ~ (1 | Subject/Time) + Caffeine
- Data: data
- REML criterion at convergence: 444.5
- Scaled residuals:
- Min 1Q Median 3Q Max
- -1.88657 -0.46382 -0.06054 0.31430 2.16244
- Random effects:
- Groups Name Variance Std.Dev.
- Time:Subject (Intercept) 558.4 23.63
- Subject (Intercept) 2458.0 49.58
- Residual 675.0 25.98
- Number of obs: 45, groups: Time:Subject, 15; Subject, 5
- Fixed effects:
- Estimate Std. Error t value
- (Intercept) 61.91827 25.04930 2.472
- Caffeine 0.21163 0.07439 2.845
- Correlation of Fixed Effects:
- (Intr)
- Caffeine -0.365
- > ranef(fit2)
- $`Time:Subject`
- (Intercept)
- 0:Jason 13.112130
- 0:Jim 33.046151
- 0:Ron -3.197895
- 0:Tina 8.893985
- 0:Victor 24.392738
- 1:Jason -2.068105
- 1:Jim -9.260334
- 1:Ron -4.428399
- 1:Tina 6.515667
- 1:Victor 17.265589
- 2:Jason -18.203436
- 2:Jim -19.835771
- 2:Ron -3.473053
- 2:Tina -17.180791
- 2:Victor -25.578477
- $Subject
- (Intercept)
- Jason -31.513915
- Jim 17.387103
- Ron -48.856516
- Tina -7.796104
- Victor 70.779432
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