table <- read.delim("data/restable.txt", stringsAsFactors=T)
table
## participant condition age palscore retscore
## 1 1 B Y 100 100
## 2 1 B O 100 100
## 3 2 A Y 60 80
## 4 2 A O 80 80
## 5 3 A Y 100 80
## 6 3 A O 100 40
## 7 4 B Y 100 100
## 8 4 B O 100 100
## 9 5 A Y 100 100
## 10 5 A O 20 20
## 11 6 B Y 100 100
## 12 6 B O 100 100
## 13 7 A Y 100 100
## 14 7 A O 100 60
## 15 8 B Y 60 100
## 16 8 B O 100 100
## 17 9 A Y 100 100
## 18 9 A O 100 80
## 19 10 A Y 100 80
## 20 10 A O 80 100
## 21 11 A Y 80 80
## 22 11 A O 80 80
## 23 12 A Y 100 100
## 24 12 A O 80 100
## 25 13 A Y 100 80
## 26 13 A O 80 100
## 27 14 B Y 80 100
## 28 14 B O 0 0
## 29 15 A Y 100 100
## 30 15 A O 100 60
## 31 16 B Y 100 100
## 32 16 B O 100 100
## 33 17 B Y 80 40
## 34 17 B O 60 20
## 35 18 A Y 100 100
## 36 18 A O 100 100
## 37 19 B Y 60 100
## 38 19 B O 100 100
## 39 20 A Y 100 100
## 40 20 A O 100 100
## 41 21 B Y 100 100
## 42 21 B O 100 100
## 43 22 A Y 80 100
## 44 22 A O 100 100
## 45 23 B Y 100 100
## 46 23 B O 0 0
## 47 24 B Y 100 100
## 48 24 B O 0 0
## 49 25 A Y 100 80
## 50 25 A O 80 80
## 51 26 B Y 100 100
## 52 26 B O 100 100
## 53 27 A Y 100 80
## 54 27 A O 100 100
## 55 28 B Y 80 100
## 56 28 B O 0 0
## 57 29 A Y 100 100
## 58 29 A O 100 80
## 59 30 A Y 100 100
## 60 30 A O 100 100
## 61 31 B Y 80 80
## 62 31 B O 100 100
## 63 32 B Y 0 0
## 64 32 B O 100 80
## 65 33 B Y 100 80
## 66 33 B O 100 100
## 67 34 B Y 100 100
## 68 34 B O 100 100
## 69 35 A Y 100 100
## 70 35 A O 100 100
## 71 36 B Y 80 100
## 72 36 B O 100 100
## 73 37 A Y 100 100
## 74 37 A O 100 100
## 75 38 B Y 100 100
## 76 38 B O 100 100
## 77 39 B Y 100 100
## 78 39 B O 80 100
## 79 40 B Y 100 100
## 80 40 B O 100 100
## 81 41 B Y 100 100
## 82 41 B O 100 100
## 83 42 B Y 100 100
## 84 42 B O 100 100
## 85 43 B Y 60 100
## 86 43 B O 100 100
## 87 44 A Y 100 100
## 88 44 A O 100 100
## 89 45 B Y 100 100
## 90 45 B O 100 100
## 91 46 B Y 100 100
## 92 46 B O 100 100
## 93 47 A Y 100 100
## 94 47 A O 100 100
## 95 48 B Y 60 80
## 96 48 B O 100 100
## 97 49 A Y 100 100
## 98 49 A O 100 100
## 99 50 A Y 100 100
## 100 50 A O 100 80
## 101 51 A Y 100 100
## 102 51 A O 100 100
## 103 52 B Y 60 100
## 104 52 B O 100 100
## 105 53 A Y 100 100
## 106 53 A O 100 100
## 107 54 B Y 80 40
## 108 54 B O 100 80
## 109 55 B Y 60 80
## 110 55 B O 100 100
## 111 56 A Y 80 100
## 112 56 A O 100 100
## 113 57 B Y 20 100
## 114 57 B O 40 100
## 115 58 A Y 100 100
## 116 58 A O 100 80
## 117 59 A Y 100 100
## 118 59 A O 100 100
## 119 60 A Y 80 80
## 120 60 A O 100 60
## 121 61 B Y 40 80
## 122 61 B O 100 100
## 123 62 A Y 100 100
## 124 62 A O 100 100
## 125 63 B Y 100 100
## 126 63 B O 100 100
## 127 64 A Y 100 100
## 128 64 A O 100 80
## 129 65 A Y 100 100
## 130 65 A O 100 100
## 131 66 B Y 100 100
## 132 66 B O 100 80
## 133 67 A Y 100 100
## 134 67 A O 100 100
## 135 68 B Y 100 100
## 136 68 B O 100 100
## 137 69 B Y 100 100
## 138 69 B O 100 100
## 139 70 B Y 100 100
## 140 70 B O 100 100
## 141 71 A Y 80 60
## 142 71 A O 0 20
## 143 72 A Y 100 100
## 144 72 A O 80 100
## 145 73 B Y 100 100
## 146 73 B O 60 100
## 147 74 A Y 100 100
## 148 74 A O 100 100
## 149 75 A Y 100 100
## 150 75 A O 100 100
## 151 76 A Y 80 80
## 152 76 A O 60 100
## 153 77 B Y 100 80
## 154 77 B O 100 80
## 155 78 A Y 100 100
## 156 78 A O 100 100
## 157 79 B Y 100 100
## 158 79 B O 100 100
## 159 80 A Y 100 100
## 160 80 A O 100 80
## 161 81 A Y 100 100
## 162 81 A O 100 80
## 163 82 A Y 100 100
## 164 82 A O 80 80
## 165 83 A Y 100 100
## 166 83 A O 100 100
## 167 84 B Y 100 100
## 168 84 B O 100 100
## 169 85 B Y 100 100
## 170 85 B O 100 100
## 171 86 B Y 100 80
## 172 86 B O 100 100
## 173 87 B Y 100 100
## 174 87 B O 100 100
## 175 88 A Y 100 80
## 176 88 A O 100 80
## 177 89 A Y 100 100
## 178 89 A O 100 100
## 179 90 A Y 80 100
## 180 90 A O 100 100
## 181 91 A Y 100 100
## 182 91 A O 100 100
## 183 92 A Y 100 100
## 184 92 A O 100 100
## 185 93 B Y 80 60
## 186 93 B O 100 80
## 187 94 B Y 100 100
## 188 94 B O 100 100
## 189 95 B Y 100 100
## 190 95 B O 100 100
## 191 96 A Y 100 100
## 192 96 A O 100 80
## 193 97 B Y 100 100
## 194 97 B O 100 100
## 195 98 B Y 100 100
## 196 98 B O 100 100
## 197 99 A Y 20 60
## 198 99 A O 60 100
## 199 100 A Y 100 100
## 200 100 A O 80 100
## 201 101 A Y 100 100
## 202 101 A O 100 100
## 203 102 B Y 100 100
## 204 102 B O 100 100
## 205 103 B Y 100 100
## 206 103 B O 100 100
## 207 104 B Y 100 100
## 208 104 B O 100 100
## 209 105 A Y 100 100
## 210 105 A O 100 100
## 211 106 B Y 100 80
## 212 106 B O 100 100
## 213 107 B Y 100 100
## 214 107 B O 100 100
## 215 108 B Y 100 100
## 216 108 B O 100 100
## 217 109 B Y 100 100
## 218 109 B O 100 100
## 219 110 B Y 40 60
## 220 110 B O 60 60
## 221 111 A Y 100 100
## 222 111 A O 100 100
## 223 112 B Y 60 80
## 224 112 B O 100 100
## 225 113 B Y 40 40
## 226 113 B O 20 20
## 227 114 B Y 100 100
## 228 114 B O 100 100
## 229 115 A Y 100 100
## 230 115 A O 100 100
## 231 116 A Y 100 100
## 232 116 A O 80 100
## 233 117 A Y 80 100
## 234 117 A O 100 100
## 235 118 B Y 100 100
## 236 118 B O 100 100
## 237 119 A Y 100 100
## 238 119 A O 100 80
## 239 120 A Y 100 100
## 240 120 A O 100 100
## 241 121 B Y 60 60
## 242 121 B O 100 100
## 243 122 B Y 40 0
## 244 122 B O 60 40
## 245 123 B Y 100 100
## 246 123 B O 100 100
## 247 124 B Y 100 100
## 248 124 B O 100 100
## 249 125 B Y 80 60
## 250 125 B O 100 100
## 251 126 A Y 100 100
## 252 126 A O 80 100
## 253 127 A Y 100 100
## 254 127 A O 100 100
## 255 128 A Y 80 0
## 256 128 A O 60 40
## 257 129 B Y 80 60
## 258 129 B O 100 100
## 259 130 A Y 100 100
## 260 130 A O 100 100
## 261 131 B Y 100 100
## 262 131 B O 100 60
## 263 132 A Y 100 100
## 264 132 A O 100 100
## 265 133 A Y 100 100
## 266 133 A O 100 100
## 267 134 B Y 100 100
## 268 134 B O 100 100
## 269 135 A Y 100 100
## 270 135 A O 100 100
## 271 136 B Y 100 100
## 272 136 B O 100 100
## 273 137 A Y 100 100
## 274 137 A O 100 100
## 275 138 B Y 60 60
## 276 138 B O 80 100
## 277 139 B Y 100 80
## 278 139 B O 100 100
## 279 140 A Y 100 80
## 280 140 A O 80 100
levels (table$condition) # condition to which participants were exposed
## [1] "A" "B"
contrast <- cbind (c(-1/2, +1/2))
colnames (contrast) <- c("-A+B")
contrasts (table$condition) <- contrast
contrast
## -A+B
## [1,] -0.5
## [2,] 0.5
levels (table$age) # age of the speakers
## [1] "O" "Y"
contrast <- cbind (c(-1/2, +1/2))
colnames (contrast) <- c("-O+Y")
contrasts (table$age) <- contrast
contrast
## -O+Y
## [1,] -0.5
## [2,] 0.5
library (lmerTest)
## Loading required package: lme4
## Loading required package: Matrix
##
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
##
## lmer
## The following object is masked from 'package:stats':
##
## step
modelpal <- lmer (palscore ~ age * condition + (1 | participant), data = table, REML = TRUE)
summary (modelpal)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: palscore ~ age * condition + (1 | participant)
## Data: table
##
## REML criterion at convergence: 2461
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1665 0.1020 0.2995 0.4725 1.6690
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 114.1 10.68
## Residual 311.9 17.66
## Number of obs: 280, groups: participant, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 91.0090 1.3895 138.0000 65.497 <2e-16 ***
## age-O+Y 0.2369 2.1117 138.0000 0.112 0.9108
## condition-A+B -5.6291 2.7790 138.0000 -2.026 0.0447 *
## age-O+Y:condition-A+B -6.5850 4.2233 138.0000 -1.559 0.1212
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ag-O+Y cn-A+B
## age-O+Y 0.000
## conditn-A+B -0.029 0.000
## ag-O+Y:-A+B 0.000 -0.029 0.000
coefficients <- coef (summary(modelpal))
coefficients
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 91.0089869 1.389505 138 65.4973985 7.978760e-106
## age-O+Y 0.2369281 2.111673 138 0.1121992 9.108285e-01
## condition-A+B -5.6290850 2.779011 138 -2.0255715 4.473657e-02
## age-O+Y:condition-A+B -6.5849673 4.223346 138 -1.5591824 1.212436e-01
confint (modelpal)
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 6.650476 13.8763603
## .sigma 15.664482 19.8065643
## (Intercept) 88.286476 93.7314982
## age-O+Y -3.900555 4.3744108
## condition-A+B -11.074107 -0.1840624
## age-O+Y:condition-A+B -14.859933 1.6899980
library (lmerTest)
modelret <- lmer (retscore ~ age * condition + (1 | participant), data = table, REML = TRUE)
summary (modelret)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: retscore ~ age * condition + (1 | participant)
## Data: table
##
## REML criterion at convergence: 2475.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9763 0.1081 0.3123 0.3440 1.7703
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 154.3 12.42
## Residual 304.5 17.45
## Number of obs: 280, groups: participant, 140
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 90.404 1.480 138.000 61.073 <2e-16 ***
## age-O+Y 1.920 2.087 138.000 0.920 0.359
## condition-A+B -3.309 2.961 138.000 -1.118 0.266
## age-O+Y:condition-A+B -4.395 4.173 138.000 -1.053 0.294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ag-O+Y cn-A+B
## age-O+Y 0.000
## conditn-A+B -0.029 0.000
## ag-O+Y:-A+B 0.000 -0.029 0.000
coefficients <- coef (summary(modelret))
coefficients
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 90.404412 1.480267 138 61.0730264 9.010335e-102
## age-O+Y 1.919935 2.086511 138 0.9201651 3.590922e-01
## condition-A+B -3.308824 2.960535 138 -1.1176438 2.656611e-01
## age-O+Y:condition-A+B -4.395425 4.173022 138 -1.0532954 2.940464e-01
confint (modelret)
## Computing profile confidence intervals ...
## 2.5 % 97.5 %
## .sig01 8.866771 15.526908
## .sigma 15.477828 19.570547
## (Intercept) 87.504067 93.304757
## age-O+Y -2.168247 6.008116
## condition-A+B -9.109513 2.491866
## age-O+Y:condition-A+B -12.571787 3.780938