R-help -
This code iterates over a function with 2 free parameters to find a list of
values (which are the number of incorrect predictions for a computational
model). I want to find the values of i,e when there is the minimum number
of incorrect predictions. In other words, the value of "i" and "e" when
variable "thesum" is at the lowest possible number. The bestDeltas variable
at the bottom worked with 1 free parameter.
structure(list(grid = c(3162L, 3162L, 3162L, 3162L, 3162L, 3162L,
3162L, 3162L, 3162L, 3162L, 3162L, 3162L, 3162L, 3162L, 3162L,
3162L, 3162L, 3162L, 3162L, 3162L), RepNum = c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), stimbinary = c(1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 1), choice = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 1), accuracy = c(0L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 0L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 0L), actualcorrect = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1), correctstim = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), choseright = c(0,
1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1), reward = c(0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L,
1L, 1L, 0L), resp = structure(c(5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
5L, 3L, 5L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L), .Label = c("f11.bmp",
"f13.bmp", "f14.bmp", "f4.bmp", "f7.bmp", "f9.bmp"), class =
"factor")), .Names = c("grid",
"RepNum", "stimbinary", "choice", "accuracy", "actualcorrect",
"correctstim", "choseright", "reward", "resp"), row.names = c(NA,
20L), class = "data.frame")
install.packages("Hmisc", dependencies = T)
library(Hmisc)
bestDeltas= 0; betterThanChance = 0; minErr = 0
fitProp<- 0
thresholded_test <- 0
calc.probs <- function(delta,gamma){
# initial starting probabilities
thList = 0
block = 0
# finMatrix <- 0
for (i in 1:length(dat2$choice))
{
if (dat2$RepNum[i] != block)
{
pL = 0.5
pR = 0.5
block = dat2$RepNum[i]
certState = 0.5
}
# Markov Transitions
pL <- pL*(1-delta) + pR*delta
pR <- 1-pL
# Apply feedback
#denom <- p(F|L,C) * p(L) + p(F|R,C) * p(R)
pflc <- ifelse(dat2$choice[i] == dat2$reward[i], .8, .2)
pfrc <- 1 - pflc
denom <- pflc * pL + pfrc * pR
# What's the new belief given observation
posteriorL <- pflc * pL/denom
posteriorR <- 1-posteriorL
pL <- posteriorL
pR <- posteriorR
certState = gamma * certState + (1 - gamma) * pL
thList[i] = ifelse(certState > 0.55, 1, ifelse(certState < 0.45, 0,
ifelse(pL > 0.5, 1, 0)))
}
return(thList)
}
thesum=0
j = 1
minx = 0.01; maxx = 0.5; incx = 0.05;
x = seq(minx, maxx, incx)
miny = 0.0; maxy = 1; incy = 0.05;
y = seq(miny, maxy, incy)
for (i in x)
{
for (e in y)
{
thresholded = calc.probs(i, e)
diff <- abs(dat2$choice - Lag(thresholded))
diff = ifelse(is.na(diff), 0, diff)
thesum[j] = sum(diff, na.rm = T)
j = j + 1
}
}
minSum = min(thesum)
minErr[s] = min(thesum)
bestDeltas[s] = min(which(thesum==minSum)) * incx + minx
# bestDeltasGammas[s] <- matrix(min(which(thsum==minSum))*inc + min)
betterThanChance[s] = sum(pbinom(0:minSum, length(dat2$choice), 0.5))
--
Edward H. Patzelt
Research Assistant TRiCAM Lab
University of Minnesota Psychology/Psychiatry
VA Medical Center
Office: S355 Elliot Hall - Twin Cities Campus
Phone: 612-626-0072 Email: patze...@umn.edu
Please consider the environment before printing this email
www.psych.umn.edu/research/tricam
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