jake-westfall

library("scatterplot3d")
library("MASS")

path <- "/Users/jakewestfall/Desktop/"

# simulate data from gaussian copula
covmat <- matrix(.9, nrow=3, ncol=3)
diag(covmat) <- 1
dat <- pnorm(mvrnorm(n=3000, mu=c(0,0,0), Sigma=covmat))
# pairs(dat)

jake-westfall

library("RWiener")
library("bbmle")

# alpha = boundary separation parameter.
# tau = non-decision time parameter.
# beta = bias parameter.
# delta = drift rate parameter
dat <- rwiener(200, alpha=2, tau=.3, beta=.5, delta=1)
str(dat)
wiener_plot(dat)

jake-westfall

# function to fold data into k folds. this returns a list of matrices where
# the 1st column in each is the response and all other columns are predictors
fold <- function(y, X, k){
  n <- length(y)
  lapply(0:(k-1)*n/k + 1, function(i){
    cbind(y, X)[seq(from=i, length.out=n/k),]
  })
}

# function to compute MSE for datasets with different numbers of folds

jake-westfall

# replace this with a path on your own machine
path <- "/Users/Jake/Desktop/nested_power.png"

# define function to compute statistical power
pow <- function(d, E, LC, l, pl){
  t <- d/2/sqrt(E/(2^pl)/(2^l) + LC/(2^l))
  DF <- (2^l) - 1
  return(pt(qt(.975, DF), DF, ncp=t, lower.tail=F) + 
           pt(qt(.025, DF), DF, ncp=t))
}

jake-westfall

dat <- read.table("http://pcl.missouri.edu/exp/effectSizePuzzler.txt", header=TRUE)

str(dat)
# 'data.frame':	2500 obs. of  3 variables:
#  $ id  : int  1 1 1 1 1 1 1 1 1 1 ...
#  $ cond: int  1 1 1 1 1 1 1 1 1 1 ...
#  $ rt  : num  0.56 0.93 0.795 0.615 1.028 ...

(means <- with(dat, tapply(rt, cond, mean)))
#         1         2 

jake-westfall

library(MASS) # for mvrnorm()
library(lme4) # for lmer()

# function to simulate data, fit models, do model comparison
sim <- function(n, # number of subjects
                m, # number of items per test
                mu, # mean for both tests
                SD, # subject SD in test means
                r, # correlation between subject test means
                diff, # SD of item difficulties (intercepts)