Set working directory
setwd("C:/Users/Katie/Desktop/Research/Dissertation/STUDIES/Studies 1 and 2 Behavioral/RESULTS/Analyses/L1 French")Load data file
data<-read.csv("all native french decomp raw.csv",header=TRUE,na.strings = "NULL")Remove breaks and practice items
data<-data[data$Running !="PracList",]
data<-data[data$Procedure != "BreakProc",]Remove unneeded columns
data<-data[,c(1,2,17,19,21,31,32,35,42,44)]Give some cols more transparent names
library(dplyr)
data<-rename(data,List=ExperimentName)
data<-rename(data,TrialOrder=Block)
data<-rename(data,Accuracy=Slide1.ACC)
data<-rename(data,RT=Slide1.RT)Create mini table to tie the conditions to their relatedness
PrimeCondition<-c("Morph","Orth","Sem","ID","Unr")
Related<-c("related","related","related","related","unrelated")
RelatedTable<-cbind(PrimeCondition,Related)Add related column to data file
library(qdap)
data$Related<-lookup(data$PrimeCondition,RelatedTable)Load necessary packages
library(stats)
library(lme4)
library(lmerTest)Summarize accuracy data
aggregate(Accuracy~Condition,data=data,FUN=mean)## Condition Accuracy
## 1 ID 0.9119048
## 2 Morph 0.9325397
## 3 Nonce 0.9093254
## 4 Orth 0.8698413
## 5 Sem 0.9325397Logit model to analyze accuracy data
acc1<-glm(Accuracy~ Condition*Related+TrialOrder,data=data,family=binomial)
summary(acc1)##
## Call:
## glm(formula = Accuracy ~ Condition * Related + TrialOrder, family = binomial,
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4275 0.3857 0.4248 0.4469 0.5622
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.2648702 0.1559378 14.524 < 2e-16 ***
## ConditionMorph 0.3041848 0.2175117 1.398 0.16197
## ConditionNonce -0.1094407 0.1663687 -0.658 0.51065
## ConditionOrth -0.5091692 0.1857611 -2.741 0.00613 **
## ConditionSem 0.3816037 0.2221165 1.718 0.08579 .
## Relatedunrelated -0.0985317 0.1990132 -0.495 0.62053
## TrialOrder 0.0008255 0.0004167 1.981 0.04760 *
## ConditionMorph:Relatedunrelated -0.0373848 0.3004546 -0.124 0.90098
## ConditionNonce:Relatedunrelated 0.1332093 0.2243493 0.594 0.55267
## ConditionOrth:Relatedunrelated 0.1269867 0.2601242 0.488 0.62542
## ConditionSem:Relatedunrelated -0.1791552 0.3014735 -0.594 0.55233
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6075.1 on 10079 degrees of freedom
## Residual deviance: 6029.8 on 10069 degrees of freedom
## AIC: 6051.8
##
## Number of Fisher Scoring iterations: 5Remove nonce items
data<-data[data$Condition!="Nonce",]
head(data)## List Subject TrialOrder Condition Item Prime PrimeCondition
## 11 List1 101 11 Morph D014 gagnons Morph
## 15 List1 101 15 Sem D085 narrons Sem
## 16 List1 101 16 Sem D106 discutons Unr
## 20 List1 101 20 Orth D068 amassons Unr
## 23 List1 101 23 Sem D082 montons Sem
## 25 List1 101 25 Morph D013 fondons Morph
## Accuracy RT Target Related
## 11 1 757 GAGNE related
## 15 1 798 RACONTE related
## 16 1 708 AIDE unrelated
## 20 1 729 CHARGE unrelated
## 23 1 800 GRIMPE related
## 25 1 710 FONDE relatedLoad lexical property file
props<-read.csv("major paper stim properties.csv",header=TRUE)
head(props)## Item Condition Word Letters Frequency Syllables tar.rel.unrel
## 1 D113 ID laçons 6 0 2 unrel
## 2 D114 ID parons 6 0 2 unrel
## 3 D115 ID gazons 6 0 2 unrel
## 4 D116 ID misons 6 0 2 unrel
## 5 D120 ID privons 7 0 2 unrel
## 6 D121 ID prônons 7 0 2 unrel
## rel.overlap word.nonce repeat.
## 1 NA word 0
## 2 NA word 0
## 3 NA word 0
## 4 NA word 0
## 5 NA word 0
## 6 NA word 0Organizing property files in prep of integrating to data file
targets<-props[props$tar.rel.unrel=="target" & props$word.nonce=="word",]
primes<-props[props$tar.rel.unrel!="target"& props$word.nonce=="word",]
target_length<-targets[,c(3,4)]
target_freq<-targets[,c(3,5)]
target_syllables<-targets[,c(3,6)]
primes_length<-primes[,c(3,4)]
primes_length<-primes_length[unique(primes_length$Word),]
primes_freq<-primes[,c(3,5)]
primes_freq<-primes_freq[unique(primes_freq$Word),]
primes_syllables<-primes[,c(3,6)]
primes_syllables<-primes_syllables[unique(primes_syllables$Word),]Integrate lexical properties to data file first make all targets lower case in data file
data$targetfreq<-lookup(data$Target,target_freq)
data$targetlength<-lookup(data$Target,target_length)
data$targetsyllables<-lookup(data$Target,target_syllables)
data$primefreq<-lookup(data$Prime,primes_freq)
data$primelength<-lookup(data$Prime,primes_length)
data$primesyllables<-lookup(data$Prime,primes_syllables)Clean data in following order
* Remove 3000+ RTs
* Calculate zscores
* Remove 2.5+ zscores
* Keep only accurate items
* Re-level the Related column so that Unrelated is treated as the baselinelibrary(stats)
data<-data[data$RT<=3000,]
data$zRT<-ave(data$RT,data$Subject,FUN=scale,na.rm=T)
data<-data[data$zRT<=2.5,]
dim(data)## [1] 4871 18data<-data[data$Accuracy==1,]
data$Related<-as.factor(data$Related)
data$Related<-relevel(data$Related,ref="unrelated")Log transform RTs. Target and prime frequencies are not log-transformed because many values are zero
data$logRT<-log(data$RT)
data$logtargetfreq<-log(data$targetfreq)Summarize data frame to be used in analyses
str(data)## 'data.frame': 4469 obs. of 20 variables:
## $ List : Factor w/ 2 levels "List1","List2": 1 1 1 1 1 1 1 1 1 1 ...
## $ Subject : int 101 101 101 101 101 101 101 101 101 101 ...
## $ TrialOrder : int 11 15 16 20 23 25 26 29 32 34 ...
## $ Condition : Factor w/ 5 levels "ID","Morph","Nonce",..: 2 5 5 4 5 2 4 1 2 4 ...
## $ Item : Factor w/ 288 levels "D001","D002",..: 14 85 106 68 82 13 64 115 18 67 ...
## $ Prime : Factor w/ 486 levels "abjurons","aboyons",..: 219 296 145 19 289 207 97 326 409 368 ...
## $ PrimeCondition : Factor w/ 5 levels "ID","Morph","Orth",..: 2 4 5 5 4 2 5 1 2 5 ...
## $ Accuracy : int 1 1 1 1 1 1 1 1 1 1 ...
## $ RT : int 757 798 708 729 800 710 1023 691 652 828 ...
## $ Target : Factor w/ 297 levels "ABUSE","ACCEPTE",..: 119 233 10 55 127 110 19 207 254 53 ...
## $ Related : Factor w/ 2 levels "unrelated","related": 2 2 1 1 2 2 1 2 2 1 ...
## $ targetfreq : num 19.66 54.12 18.38 7.97 8.18 ...
## $ targetlength : num 5 7 4 6 6 5 6 4 5 6 ...
## $ targetsyllables: num 1 2 1 1 1 1 2 1 1 1 ...
## $ primefreq : num NA NA NA NA NA NA NA NA 0.41 1.01 ...
## $ primelength : num NA NA NA NA NA NA NA NA 7 8 ...
## $ primesyllables : num NA NA NA NA NA NA NA NA 2 3 ...
## $ zRT : num -0.107 0.136 -0.398 -0.273 0.148 ...
## $ logRT : num 6.63 6.68 6.56 6.59 6.68 ...
## $ logtargetfreq : num 2.98 3.99 2.91 2.08 2.1 ...head(data)## List Subject TrialOrder Condition Item Prime PrimeCondition
## 11 List1 101 11 Morph D014 gagnons Morph
## 15 List1 101 15 Sem D085 narrons Sem
## 16 List1 101 16 Sem D106 discutons Unr
## 20 List1 101 20 Orth D068 amassons Unr
## 23 List1 101 23 Sem D082 montons Sem
## 25 List1 101 25 Morph D013 fondons Morph
## Accuracy RT Target Related targetfreq targetlength targetsyllables
## 11 1 757 GAGNE related 19.66 5 1
## 15 1 798 RACONTE related 54.12 7 2
## 16 1 708 AIDE unrelated 18.38 4 1
## 20 1 729 CHARGE unrelated 7.97 6 1
## 23 1 800 GRIMPE related 8.18 6 1
## 25 1 710 FONDE related 2.03 5 1
## primefreq primelength primesyllables zRT logRT logtargetfreq
## 11 NA NA NA -0.1068966 6.629363 2.9785861
## 15 NA NA NA 0.1364956 6.682109 3.9912038
## 16 NA NA NA -0.3977799 6.562444 2.9112631
## 20 NA NA NA -0.2731157 6.591674 2.0756845
## 23 NA NA NA 0.1483683 6.684612 2.1016922
## 25 NA NA NA -0.3859071 6.565265 0.7080358summary(data)## List Subject TrialOrder Condition Item
## List1:1909 Min. :101.0 Min. : 11.0 ID :1123 D002 : 35
## List2:2560 1st Qu.:109.0 1st Qu.: 82.0 Morph:1151 D006 : 35
## Median :209.0 Median :156.0 Nonce: 0 D027 : 35
## Mean :258.5 Mean :155.6 Orth :1059 D033 : 35
## 3rd Qu.:403.0 3rd Qu.:229.0 Sem :1136 D048 : 35
## Max. :411.0 Max. :301.0 D060 : 35
## (Other):4259
## Prime PrimeCondition Accuracy RT
## ajoutons : 20 ID : 567 Min. :1 Min. : 314.0
## arrimons : 20 Morph: 578 1st Qu.:1 1st Qu.: 602.0
## assistons: 20 Orth : 532 Median :1 Median : 683.0
## assumons : 20 Sem : 578 Mean :1 Mean : 727.6
## boucle : 20 Unr :2214 3rd Qu.:1 3rd Qu.: 803.0
## caressons: 20 Max. :1 Max. :2446.0
## (Other) :4349
## Target Related targetfreq targetlength
## AIDE : 35 unrelated:2214 Min. : 0.07 Min. :3.000
## BOUCLE : 35 related :2255 1st Qu.: 5.58 1st Qu.:5.000
## CACHE : 35 Median : 15.20 Median :5.000
## CHERCHE: 35 Mean : 28.42 Mean :5.284
## COUPE : 35 3rd Qu.: 29.80 3rd Qu.:6.000
## DANSE : 35 Max. :257.57 Max. :8.000
## (Other):4259
## targetsyllables primefreq primelength primesyllables
## Min. :1.000 Min. : 0.000 Min. :4.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.: 0.000 1st Qu.:6.000 1st Qu.:2.000
## Median :1.000 Median : 0.200 Median :7.000 Median :2.000
## Mean :1.269 Mean : 2.117 Mean :7.139 Mean :2.156
## 3rd Qu.:2.000 3rd Qu.: 0.470 3rd Qu.:8.000 3rd Qu.:2.000
## Max. :2.000 Max. :50.540 Max. :9.000 Max. :3.000
## NA's :2819 NA's :2819 NA's :2819
## zRT logRT logtargetfreq
## Min. :-2.2922 Min. :5.749 Min. :-2.659
## 1st Qu.:-0.6376 1st Qu.:6.400 1st Qu.: 1.719
## Median :-0.2909 Median :6.526 Median : 2.721
## Mean :-0.1392 Mean :6.560 Mean : 2.478
## 3rd Qu.: 0.1942 3rd Qu.:6.688 3rd Qu.: 3.395
## Max. : 2.4867 Max. :7.802 Max. : 5.551
## TrialOrder is kept in all models to control for the improvement of reaction time due to practice throughout the experiment
NOTE: In the below analyses the following levels are treated as baselines.
* Condition: ID
* Related: Unrelated
These baselines mean that an effect of condition means as a whole, that condition differs from ID only.
An effect of relatedness means for the ID condition ONLY there is a difference between related and unrelated primes.
Interactions of Condition * Related means that the effect of relatedness for a given condition is different from the effect of relatedness in the ID condition
test1<-lmer(logRT~Condition*Related+TrialOrder+(1|Item)+(1|Subject),data)
test2<-lmer(logRT~Condition*Related+logtargetfreq+TrialOrder+(1|Item)+(1|Subject),data)
test3<-lmer(logRT~Condition*Related+logtargetfreq+targetlength+TrialOrder+(1|Item)+(1|Subject),data)
anova(test1,test2,test3)## Data: data
## Models:
## object: logRT ~ Condition * Related + TrialOrder + (1 | Item) + (1 |
## object: Subject)
## ..1: logRT ~ Condition * Related + logtargetfreq + TrialOrder + (1 |
## ..1: Item) + (1 | Subject)
## ..2: logRT ~ Condition * Related + logtargetfreq + targetlength +
## ..2: TrialOrder + (1 | Item) + (1 | Subject)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## object 12 -2037.8 -1960.9 1030.9 -2061.8
## ..1 13 -2089.9 -2006.6 1057.9 -2115.9 54.0806 1 1.924e-13 ***
## ..2 14 -2089.7 -2000.1 1058.9 -2117.7 1.8602 1 0.1726
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(test2)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: logRT ~ Condition * Related + logtargetfreq + TrialOrder + (1 |
## Item) + (1 | Subject)
## Data: data
##
## REML criterion at convergence: -2032.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2216 -0.6544 -0.1243 0.5339 4.5559
##
## Random effects:
## Groups Name Variance Std.Dev.
## Item (Intercept) 0.002485 0.04985
## Subject (Intercept) 0.018795 0.13709
## Residual 0.034046 0.18452
## Number of obs: 4469, groups: Item, 144; Subject, 35
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.666e+00 2.748e-02 6.400e+01 242.570
## ConditionMorph -6.736e-03 1.619e-02 2.190e+02 -0.416
## ConditionOrth -1.942e-02 1.647e-02 2.250e+02 -1.179
## ConditionSem -5.661e-04 1.631e-02 2.200e+02 -0.035
## Relatedrelated -5.324e-02 1.112e-02 4.345e+03 -4.788
## logtargetfreq -2.577e-02 3.287e-03 1.440e+02 -7.839
## TrialOrder -1.096e-04 3.300e-05 4.335e+03 -3.320
## ConditionMorph:Relatedrelated 1.058e-02 1.563e-02 4.340e+03 0.677
## ConditionOrth:Relatedrelated 3.692e-02 1.596e-02 4.343e+03 2.314
## ConditionSem:Relatedrelated 6.040e-02 1.568e-02 4.342e+03 3.853
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## ConditionMorph 0.677708
## ConditionOrth 0.239608
## ConditionSem 0.972341
## Relatedrelated 1.74e-06 ***
## logtargetfreq 9.20e-13 ***
## TrialOrder 0.000907 ***
## ConditionMorph:Relatedrelated 0.498730
## ConditionOrth:Relatedrelated 0.020740 *
## ConditionSem:Relatedrelated 0.000118 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CndtnM CndtnO CndtnS Rltdrl lgtrgt TrlOrd CndM:R CndO:R
## ConditnMrph -0.290
## ConditnOrth -0.317 0.496
## ConditionSm -0.265 0.503 0.484
## Relatedrltd -0.208 0.350 0.345 0.347
## logtargtfrq -0.285 -0.010 0.094 -0.097 0.008
## TrialOrder -0.181 -0.029 -0.016 -0.015 -0.001 0.004
## CndtnMrph:R 0.143 -0.488 -0.246 -0.247 -0.712 -0.004 0.027
## CndtnOrth:R 0.146 -0.244 -0.491 -0.241 -0.697 -0.011 0.003 0.496
## CndtnSm:Rlt 0.148 -0.248 -0.245 -0.489 -0.709 -0.008 0.002 0.505 0.494The “big” model (test2) shows:
* an effect of relatedness: for ID, Related primes elicit faster RTs than Unrelated primes
* an effect of target frequency: for ID, RT decreases as target frequency increases
* an effect of trial order: as the experiment progresses, RTs get faster
* an interaction of ConditionOrth x Related: the pattern of relatedness differs between ID condition and Orth condition
* an interaction of ConditionSem x Related: the pattern of relatedness differs between ID conditition and Sem conditionInteraction of ConditionORth x Related Create dataframe Orth_only
Orth_only<-data[data$Condition=="Orth",]Run model on just Orth data to check effect of relatedness
orth1<-lmer(logRT~Related+targetfreq+TrialOrder+(1|Item)+(1|Subject),Orth_only)
summary(orth1)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: logRT ~ Related + targetfreq + TrialOrder + (1 | Item) + (1 |
## Subject)
## Data: Orth_only
##
## REML criterion at convergence: -378
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8460 -0.6489 -0.1095 0.5196 4.3843
##
## Random effects:
## Groups Name Variance Std.Dev.
## Item (Intercept) 0.003416 0.05844
## Subject (Intercept) 0.015619 0.12498
## Residual 0.034509 0.18577
## Number of obs: 1059, groups: Item, 36; Subject, 35
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.612e+00 2.778e-02 7.530e+01 238.016 <2e-16 ***
## Relatedrelated -1.616e-02 1.156e-02 9.947e+02 -1.398 0.1623
## targetfreq -4.525e-04 2.217e-04 2.610e+01 -2.041 0.0515 .
## TrialOrder -1.183e-04 7.028e-05 9.954e+02 -1.684 0.0926 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Rltdrl trgtfr
## Relatedrltd -0.213
## targetfreq -0.221 -0.004
## TrialOrder -0.395 0.012 -0.022The above model shows that there is no effect of prime relatedness when the target is in the Orth condition, explaining the interaction in the “big” model
Interaction of ConditionSem x Related Create dataframe Sem_only
Sem_only<-data[data$Condition=="Sem",]sem1<-lmer(logRT~Related+targetfreq+TrialOrder+(1|Item)+(1|Subject),Sem_only)
summary(sem1)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: logRT ~ Related + targetfreq + TrialOrder + (1 | Item) + (1 |
## Subject)
## Data: Sem_only
##
## REML criterion at convergence: -547.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7648 -0.6387 -0.1179 0.5069 4.6413
##
## Random effects:
## Groups Name Variance Std.Dev.
## Item (Intercept) 0.00235 0.04848
## Subject (Intercept) 0.01775 0.13324
## Residual 0.03061 0.17496
## Number of obs: 1136, groups: Item, 36; Subject, 35
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.607e+00 2.786e-02 6.610e+01 237.189 <2e-16 ***
## Relatedrelated 7.706e-03 1.050e-02 1.078e+03 0.734 0.4630
## targetfreq -4.725e-04 2.178e-04 3.290e+01 -2.169 0.0374 *
## TrialOrder -1.083e-04 6.259e-05 1.079e+03 -1.730 0.0840 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Rltdrl trgtfr
## Relatedrltd -0.189
## targetfreq -0.257 -0.016
## TrialOrder -0.348 0.003 -0.016The above model shows that there is no effect of prime relatedness when the target is in the Sem condition, explaining the interaction in the “big” model
including ID follow up and morph follow up models just to demonstrate the effect of relatedness in those conditions only
id_only<-data[data$Condition=="ID",]
morph_only<-data[data$Condition=="Morph",]
id1<-lmer(logRT~Related+targetfreq+(1|Item)+(1|Subject),id_only)
morph1<-lmer(logRT~Related+targetfreq+(1|Item)+(1|Subject),morph_only)
summary(id1)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: logRT ~ Related + targetfreq + (1 | Item) + (1 | Subject)
## Data: id_only
##
## REML criterion at convergence: -405.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3589 -0.6729 -0.1137 0.5331 4.5240
##
## Random effects:
## Groups Name Variance Std.Dev.
## Item (Intercept) 0.004437 0.06661
## Subject (Intercept) 0.018061 0.13439
## Residual 0.034764 0.18645
## Number of obs: 1123, groups: Item, 36; Subject, 35
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.604e+00 2.780e-02 5.710e+01 237.564 < 2e-16 ***
## Relatedrelated -5.488e-02 1.127e-02 1.062e+03 -4.868 1.3e-06 ***
## targetfreq -5.437e-04 3.371e-04 3.040e+01 -1.613 0.117
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Rltdrl
## Relatedrltd -0.208
## targetfreq -0.296 0.002summary(morph1)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: logRT ~ Related + targetfreq + (1 | Item) + (1 | Subject)
## Data: morph_only
##
## REML criterion at convergence: -352.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7696 -0.6371 -0.1232 0.5082 4.1130
##
## Random effects:
## Groups Name Variance Std.Dev.
## Item (Intercept) 0.004143 0.06437
## Subject (Intercept) 0.022836 0.15111
## Residual 0.036757 0.19172
## Number of obs: 1151, groups: Item, 36; Subject, 35
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.600e+00 3.021e-02 5.360e+01 218.458 < 2e-16 ***
## Relatedrelated -4.111e-02 1.143e-02 1.087e+03 -3.597 0.000336 ***
## targetfreq -8.820e-04 3.903e-04 3.120e+01 -2.260 0.030958 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Rltdrl
## Relatedrltd -0.193
## targetfreq -0.295 0.013Checking if all subjects show id priming
morph<-data[data$Condition=="Morph",]
orth<-data[data$Condition=="Orth",]
sem<-data[data$Condition=="Sem",]
id<-data[data$Condition=="ID",]
morph_means<-aggregate(RT~Related,data=morph,FUN=mean)
orth_means<-aggregate(RT~Related,data=orth,FUN=mean)
sem_means<-aggregate(RT~Related,data=sem,FUN=mean)
id_means<-aggregate(RT~Related,data=id,FUN=mean)
morph_priming<-morph_means[2,2]-morph_means[1,2]
orth_priming<-orth_means[2,2]-orth_means[1,2]
sem_priming<-sem_means[2,2]-sem_means[1,2]
id_priming<-id_means[2,2]-id_means[1,2]
priming<-rbind(id_priming,morph_priming,orth_priming,sem_priming)
priming## [,1]
## id_priming -28.396549
## morph_priming -26.733473
## orth_priming -4.844977
## sem_priming 4.891555id_rel<-id[id$Related=="related",]
id_unrel<-id[id$Related=="unrelated",]
identity_rel_priming<-aggregate(RT~Subject,data=id_rel,FUN=mean)
identity_unrel_priming<-aggregate(RT~Subject,data=id_unrel,FUN=mean)
id_priming<-cbind(identity_rel_priming,identity_unrel_priming)
id_priming<-id_priming[,c(1,2,4)]
id_priming<-rename(id_priming,Related=RT,Unrelated=RT.1)
id_priming## Subject Related Unrelated
## 1 101 693.2778 800.8000
## 2 102 551.6111 616.2667
## 3 103 524.2941 661.5000
## 4 104 653.4118 738.6667
## 5 105 661.0000 749.5385
## 6 106 607.8824 691.4375
## 7 107 578.7647 710.8125
## 8 108 632.8125 696.7647
## 9 109 576.9412 581.0714
## 10 201 632.9412 692.6875
## 11 202 876.1176 865.1765
## 12 203 579.2941 609.9375
## 13 204 612.5000 638.3125
## 14 205 692.1875 676.5556
## 15 206 716.9333 702.2941
## 16 207 685.8750 661.1875
## 17 208 748.1875 754.1111
## 18 209 784.1875 764.3529
## 19 301 761.9286 839.6000
## 20 302 885.0000 822.9286
## 21 303 842.0000 905.8750
## 22 304 666.0000 741.2941
## 23 305 677.5556 755.0000
## 24 306 777.1250 893.2667
## 25 401 801.8333 859.7333
## 26 402 1309.7333 1112.0000
## 27 403 708.7222 715.1667
## 28 404 661.8333 666.6667
## 29 405 811.0000 798.5625
## 30 406 713.0000 681.6471
## 31 407 723.5000 754.7222
## 32 408 596.3333 674.9412
## 33 409 690.0000 619.8000
## 34 410 731.5000 766.8235
## 35 411 791.6667 687.7059id_priming$priming<-id_priming$Related-id_priming$Unrelated
id_priming$shows_id_priming<-id_priming$priming<0
summary(id_priming$shows_priming)## Length Class Mode
## 0 NULL NULLshows_id_priming<-id_priming[,c(1,5)]
shows_id_priming## Subject shows_id_priming
## 1 101 TRUE
## 2 102 TRUE
## 3 103 TRUE
## 4 104 TRUE
## 5 105 TRUE
## 6 106 TRUE
## 7 107 TRUE
## 8 108 TRUE
## 9 109 TRUE
## 10 201 TRUE
## 11 202 FALSE
## 12 203 TRUE
## 13 204 TRUE
## 14 205 FALSE
## 15 206 FALSE
## 16 207 FALSE
## 17 208 TRUE
## 18 209 FALSE
## 19 301 TRUE
## 20 302 FALSE
## 21 303 TRUE
## 22 304 TRUE
## 23 305 TRUE
## 24 306 TRUE
## 25 401 TRUE
## 26 402 FALSE
## 27 403 TRUE
## 28 404 TRUE
## 29 405 FALSE
## 30 406 FALSE
## 31 407 TRUE
## 32 408 TRUE
## 33 409 FALSE
## 34 410 TRUE
## 35 411 FALSEdata$shows_id_priming<-lookup(data$Subject,shows_id_priming)
head(data)## List Subject TrialOrder Condition Item Prime PrimeCondition
## 11 List1 101 11 Morph D014 gagnons Morph
## 15 List1 101 15 Sem D085 narrons Sem
## 16 List1 101 16 Sem D106 discutons Unr
## 20 List1 101 20 Orth D068 amassons Unr
## 23 List1 101 23 Sem D082 montons Sem
## 25 List1 101 25 Morph D013 fondons Morph
## Accuracy RT Target Related targetfreq targetlength targetsyllables
## 11 1 757 GAGNE related 19.66 5 1
## 15 1 798 RACONTE related 54.12 7 2
## 16 1 708 AIDE unrelated 18.38 4 1
## 20 1 729 CHARGE unrelated 7.97 6 1
## 23 1 800 GRIMPE related 8.18 6 1
## 25 1 710 FONDE related 2.03 5 1
## primefreq primelength primesyllables zRT logRT logtargetfreq
## 11 NA NA NA -0.1068966 6.629363 2.9785861
## 15 NA NA NA 0.1364956 6.682109 3.9912038
## 16 NA NA NA -0.3977799 6.562444 2.9112631
## 20 NA NA NA -0.2731157 6.591674 2.0756845
## 23 NA NA NA 0.1483683 6.684612 2.1016922
## 25 NA NA NA -0.3859071 6.565265 0.7080358
## shows_id_priming
## 11 TRUE
## 15 TRUE
## 16 TRUE
## 20 TRUE
## 23 TRUE
## 25 TRUELooking if all show morph priming
morph_rel<-morph[morph$Related=="related",]
morph_unrel<-morph[morph$Related=="unrelated",]
morph_rel_priming<-aggregate(RT~Subject,data=morph_rel,FUN=mean)
morph_unrel_priming<-aggregate(RT~Subject,data=morph_unrel,FUN=mean)
morph_priming<-cbind(morph_rel_priming,morph_unrel_priming)
morph_priming<-morph_priming[,c(1,2,4)]
morph_priming<-rename(morph_priming,Related=RT,Unrelated=RT.1)
morph_priming## Subject Related Unrelated
## 1 101 693.4375 768.7222
## 2 102 554.8889 595.2778
## 3 103 506.6000 577.2941
## 4 104 666.0000 735.4375
## 5 105 723.1250 718.9444
## 6 106 657.9444 665.7222
## 7 107 557.6250 617.8824
## 8 108 721.7647 706.7778
## 9 109 555.3529 579.1250
## 10 201 660.7059 754.3750
## 11 202 864.5000 858.0000
## 12 203 578.8824 590.6875
## 13 204 658.2500 649.5455
## 14 205 635.0000 743.1250
## 15 206 694.4706 682.5333
## 16 207 643.5333 656.2500
## 17 208 766.2353 780.0000
## 18 209 728.9444 770.1765
## 19 301 798.1667 862.6429
## 20 302 857.2500 824.5556
## 21 303 981.3333 942.5556
## 22 304 699.4706 753.3333
## 23 305 687.2000 754.1250
## 24 306 711.0667 880.3333
## 25 401 805.4375 993.0000
## 26 402 1396.6429 1114.5882
## 27 403 668.6471 655.6875
## 28 404 667.0000 654.3889
## 29 405 859.7647 738.2500
## 30 406 659.5556 751.9412
## 31 407 777.7222 742.2353
## 32 408 617.8824 645.6667
## 33 409 679.3889 611.6111
## 34 410 708.0588 767.2222
## 35 411 648.2222 683.0769morph_priming$priming<-morph_priming$Related-morph_priming$Unrelated
morph_priming$shows_morph_priming<-morph_priming$priming<0
shows_morph_priming<-morph_priming[,c(1,5)]
shows_morph_priming## Subject shows_morph_priming
## 1 101 TRUE
## 2 102 TRUE
## 3 103 TRUE
## 4 104 TRUE
## 5 105 FALSE
## 6 106 TRUE
## 7 107 TRUE
## 8 108 FALSE
## 9 109 TRUE
## 10 201 TRUE
## 11 202 FALSE
## 12 203 TRUE
## 13 204 FALSE
## 14 205 TRUE
## 15 206 FALSE
## 16 207 TRUE
## 17 208 TRUE
## 18 209 TRUE
## 19 301 TRUE
## 20 302 FALSE
## 21 303 FALSE
## 22 304 TRUE
## 23 305 TRUE
## 24 306 TRUE
## 25 401 TRUE
## 26 402 FALSE
## 27 403 FALSE
## 28 404 FALSE
## 29 405 FALSE
## 30 406 TRUE
## 31 407 FALSE
## 32 408 TRUE
## 33 409 FALSE
## 34 410 TRUE
## 35 411 TRUELooking if any show orth priming
orth_rel<-orth[orth$Related=="related",]
orth_unrel<-orth[orth$Related=="unrelated",]
orth_rel_priming<-aggregate(RT~Subject,data=orth_rel,FUN=mean)
orth_unrel_priming<-aggregate(RT~Subject,data=orth_unrel,FUN=mean)
orth_priming<-cbind(orth_rel_priming,orth_unrel_priming)
orth_priming<-orth_priming[,c(1,2,4)]
orth_priming<-rename(orth_priming,Related=RT,Unrelated=RT.1)
orth_priming## Subject Related Unrelated
## 1 101 712.8571 793.8571
## 2 102 585.7647 676.6875
## 3 103 564.1250 588.0000
## 4 104 756.0667 702.7692
## 5 105 700.0588 739.5333
## 6 106 631.1765 707.8462
## 7 107 581.7500 610.7143
## 8 108 734.2353 712.5625
## 9 109 543.7692 598.0588
## 10 201 688.4615 827.6667
## 11 202 791.4667 835.7500
## 12 203 627.1333 573.7059
## 13 204 633.8462 612.9000
## 14 205 756.8571 708.4118
## 15 206 733.0667 667.4375
## 16 207 679.9333 705.4375
## 17 208 759.7857 776.3750
## 18 209 835.1176 778.2222
## 19 301 826.7500 808.6364
## 20 302 758.0667 834.7857
## 21 303 887.4706 979.0714
## 22 304 680.0556 747.2222
## 23 305 622.8750 689.0667
## 24 306 859.0000 740.0000
## 25 401 881.0667 954.5000
## 26 402 1137.7857 960.1667
## 27 403 651.8667 673.4667
## 28 404 694.8000 643.5000
## 29 405 764.3571 786.1250
## 30 406 715.0714 760.1667
## 31 407 773.7500 762.4706
## 32 408 687.6923 661.3333
## 33 409 634.6667 644.7500
## 34 410 720.9333 702.2000
## 35 411 750.8824 646.4000orth_priming$priming<-orth_priming$Related-orth_priming$Unrelated
orth_priming$shows_orth_priming<-orth_priming$priming<0
shows_orth_priming<-orth_priming[,c(1,5)]
shows_orth_priming## Subject shows_orth_priming
## 1 101 TRUE
## 2 102 TRUE
## 3 103 TRUE
## 4 104 FALSE
## 5 105 TRUE
## 6 106 TRUE
## 7 107 TRUE
## 8 108 FALSE
## 9 109 TRUE
## 10 201 TRUE
## 11 202 TRUE
## 12 203 FALSE
## 13 204 FALSE
## 14 205 FALSE
## 15 206 FALSE
## 16 207 TRUE
## 17 208 TRUE
## 18 209 FALSE
## 19 301 FALSE
## 20 302 TRUE
## 21 303 TRUE
## 22 304 TRUE
## 23 305 TRUE
## 24 306 FALSE
## 25 401 TRUE
## 26 402 FALSE
## 27 403 TRUE
## 28 404 FALSE
## 29 405 TRUE
## 30 406 TRUE
## 31 407 FALSE
## 32 408 FALSE
## 33 409 TRUE
## 34 410 FALSE
## 35 411 FALSELooking if any show sem priming
sem_rel<-sem[sem$Related=="related",]
sem_unrel<-sem[sem$Related=="unrelated",]
sem_rel_priming<-aggregate(RT~Subject,data=sem_rel,FUN=mean)
sem_unrel_priming<-aggregate(RT~Subject,data=sem_unrel,FUN=mean)
sem_priming<-cbind(sem_rel_priming,sem_unrel_priming)
sem_priming<-sem_priming[,c(1,2,4)]
sem_priming<-rename(sem_priming,Related=RT,Unrelated=RT.1)
sem_priming## Subject Related Unrelated
## 1 101 771.7222 757.6250
## 2 102 568.6111 624.3750
## 3 103 575.4706 589.2353
## 4 104 718.0588 679.4375
## 5 105 713.2941 696.1111
## 6 106 749.1176 628.3333
## 7 107 603.0625 631.2500
## 8 108 710.9412 680.3125
## 9 109 596.8333 558.0000
## 10 201 696.8125 676.1875
## 11 202 854.9375 845.2222
## 12 203 592.5000 602.5556
## 13 204 660.1875 612.6923
## 14 205 715.6875 728.8333
## 15 206 716.4375 710.7059
## 16 207 666.6471 649.6250
## 17 208 809.4118 812.6250
## 18 209 865.4706 847.2667
## 19 301 889.0000 830.9286
## 20 302 825.1875 794.3077
## 21 303 934.5000 874.5294
## 22 304 672.0588 657.1176
## 23 305 685.8889 728.6250
## 24 306 820.5294 842.9286
## 25 401 845.3750 963.9333
## 26 402 1047.0000 1197.7500
## 27 403 708.8000 689.1176
## 28 404 682.7222 671.4444
## 29 405 820.0000 859.0000
## 30 406 729.1111 703.9375
## 31 407 748.6000 786.2353
## 32 408 634.6000 679.2500
## 33 409 669.6471 680.2000
## 34 410 780.7059 804.8000
## 35 411 791.5625 679.4706sem_priming$priming<-sem_priming$Related-sem_priming$Unrelated
sem_priming$shows_sem_priming<-sem_priming$priming<0
shows_sem_priming<-sem_priming[,c(1,5)]
shows_sem_priming## Subject shows_sem_priming
## 1 101 FALSE
## 2 102 TRUE
## 3 103 TRUE
## 4 104 FALSE
## 5 105 FALSE
## 6 106 FALSE
## 7 107 TRUE
## 8 108 FALSE
## 9 109 FALSE
## 10 201 FALSE
## 11 202 FALSE
## 12 203 TRUE
## 13 204 FALSE
## 14 205 TRUE
## 15 206 FALSE
## 16 207 FALSE
## 17 208 TRUE
## 18 209 FALSE
## 19 301 FALSE
## 20 302 FALSE
## 21 303 FALSE
## 22 304 FALSE
## 23 305 TRUE
## 24 306 TRUE
## 25 401 TRUE
## 26 402 TRUE
## 27 403 FALSE
## 28 404 FALSE
## 29 405 TRUE
## 30 406 FALSE
## 31 407 TRUE
## 32 408 TRUE
## 33 409 TRUE
## 34 410 TRUE
## 35 411 FALSEConcatenate which participants show which kinds of priming
subject_number<-shows_id_priming[,1]
I<-shows_id_priming[,2]
M<-shows_morph_priming[2]
O<-shows_orth_priming[,2]
S<-shows_sem_priming[,2]
all_priming<-cbind(subject_number,I,M,O,S)
all_priming<-rename(all_priming,M=shows_morph_priming)
all_priming$subject_number<-as.character(all_priming$subject_number)
summary(all_priming)## subject_number I M O
## Length:35 Mode :logical Mode :logical Mode :logical
## Class :character FALSE:11 FALSE:13 FALSE:15
## Mode :character TRUE :24 TRUE :22 TRUE :20
## NA's :0 NA's :0 NA's :0
## S
## Mode :logical
## FALSE:20
## TRUE :15
## NA's :0all_priming## subject_number I M O S
## 1 101 TRUE TRUE TRUE FALSE
## 2 102 TRUE TRUE TRUE TRUE
## 3 103 TRUE TRUE TRUE TRUE
## 4 104 TRUE TRUE FALSE FALSE
## 5 105 TRUE FALSE TRUE FALSE
## 6 106 TRUE TRUE TRUE FALSE
## 7 107 TRUE TRUE TRUE TRUE
## 8 108 TRUE FALSE FALSE FALSE
## 9 109 TRUE TRUE TRUE FALSE
## 10 201 TRUE TRUE TRUE FALSE
## 11 202 FALSE FALSE TRUE FALSE
## 12 203 TRUE TRUE FALSE TRUE
## 13 204 TRUE FALSE FALSE FALSE
## 14 205 FALSE TRUE FALSE TRUE
## 15 206 FALSE FALSE FALSE FALSE
## 16 207 FALSE TRUE TRUE FALSE
## 17 208 TRUE TRUE TRUE TRUE
## 18 209 FALSE TRUE FALSE FALSE
## 19 301 TRUE TRUE FALSE FALSE
## 20 302 FALSE FALSE TRUE FALSE
## 21 303 TRUE FALSE TRUE FALSE
## 22 304 TRUE TRUE TRUE FALSE
## 23 305 TRUE TRUE TRUE TRUE
## 24 306 TRUE TRUE FALSE TRUE
## 25 401 TRUE TRUE TRUE TRUE
## 26 402 FALSE FALSE FALSE TRUE
## 27 403 TRUE FALSE TRUE FALSE
## 28 404 TRUE FALSE FALSE FALSE
## 29 405 FALSE FALSE TRUE TRUE
## 30 406 FALSE TRUE TRUE FALSE
## 31 407 TRUE FALSE FALSE TRUE
## 32 408 TRUE TRUE FALSE TRUE
## 33 409 FALSE FALSE TRUE TRUE
## 34 410 TRUE TRUE FALSE TRUE
## 35 411 FALSE TRUE FALSE FALSE