Description Usage Format Source Examples
This data set contains responses of 11 Parkinsons' patients and 13 age-matched controls on the Weather Prediction Task. Both groups were tested twice. The PD patients were either on or off dopaminergic medication.
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A data.frame with 9600 observations on the following variables.
id
a factor with participant IDs
group
a factor with group IDs (Parksinson's patient or control)
med
a factor indicating, for the PD patients, whether they were on dopaminergic medicine or not
occ
a numeric vector with testing occassions
trial
a numeric vector with trial numbers
c1
a numeric (binary) vector indicating whether the first cue was present (1) or not (0)
c2
a numeric (binary) vector indicating whether the second cue was present (1) or not (0)
c3
a numeric (binary) vector indicating whether the third cue was present (1) or not (0)
c4
a numeric (binary) vector indicating whether the fourth cue was present (1) or not (0)
y
a factor with the actual outcome (Rainy or Fine)
r
a factor with participants' prediction of the outcome
Speekenbrink, M., Lagnado, D. A., Wilkinson, L., Jahanshahi, M., & Shanks, D. R. (2010). Models of probabilistic category learning in Parkinson's disease: Strategy use and the effects of L-dopa. Journal of Mathematical Psychology, 54, 123-136.
Corresponding author: m.speekenbrink@ucl.ac.uk
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | data(WPT)
# set up predictors for the different strategies
WPT$sngl <- 0 # singleton strategy
WPT$sngl[WPT$c1 == 1 & rowSums(WPT[,c("c1","c2","c3","c4")]) == 1] <- -1
WPT$sngl[WPT$c2 == 1 & rowSums(WPT[,c("c1","c2","c3","c4")]) == 1] <- -1
WPT$sngl[WPT$c3 == 1 & rowSums(WPT[,c("c1","c2","c3","c4")]) == 1] <- 1
WPT$sngl[WPT$c4 == 1 & rowSums(WPT[,c("c1","c2","c3","c4")]) == 1] <- 1
WPT$sc1 <- 1 - 2*WPT$c1
WPT$sc2 <- 1 - 2*WPT$c2
WPT$sc3 <- -1 + 2*WPT$c3
WPT$sc4 <- -1 + 2*WPT$c4
WPT$mc <- sign(-WPT$c1 - WPT$c2 + WPT$c3 + WPT$c4)
rModels <- list(
list(GLMresponse(formula=r~-1,data=WPT,family=binomial())),
list(GLMresponse(formula=r~sngl-1,data=WPT,family=binomial())),
list(GLMresponse(formula=r~sc1-1,data=WPT,family=binomial())),
list(GLMresponse(formula=r~sc2-1,data=WPT,family=binomial())),
list(GLMresponse(formula=r~sc3-1,data=WPT,family=binomial())),
list(GLMresponse(formula=r~sc4-1,data=WPT,family=binomial())),
list(GLMresponse(formula=r~mc-1,data=WPT,family=binomial()))
)
transition <- list()
for(i in 1:7) {
transition[[i]] <- transInit(~1,nstates=7,family=multinomial(link="identity"))
}
inMod <- transInit(~1,ns=7,data=data.frame(rep(1,48)),family=multinomial("identity"))
mod <- makeDepmix(response=rModels,transition=transition,
prior=inMod,ntimes=rep(200,48),stationary=TRUE)
fmod <- fit(mod)
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