vasishth

model
{
# The model for each observational unit 
# (each row is a subject's data point)
for( j in 1:N )
{
mu[j] <- beta[1] + beta[2] * ( so[j] ) + u[subj[j]] + w[item[j]]
rt[j] ~ dnorm( mu[j], tau.e )
##change the above line to:
#rt[j] ~ dt(mu[j],tau.e, 2)

vasishth

newcomb <-
c(28,26,33,24,34,-44,27,16,40,-2,
29,22,24,21,25,30,23,29,31,19,
24,20,36,32,36,28,25,21,28,29,
37,25,28,26,30,32,36,26,30,22,
36,23,27,27,28,27,31,27,26,33,
26,32,32,24,39,28,24,25,32,25,
29,27,28,29,16,23)

# Data as a list

vasishth

> summary(lm<-lm(wear~material-1,BHHshoes))
> X<-model.matrix(lm)
> 2.49^2*solve(t(X)%*%X)
          materialA materialB
materialA   0.62001   0.00000
materialB   0.00000   0.62001

vasishth

> (lm.full<-lmer(wear~material-1+(1|Subject), data = BHHshoes))
Linear mixed model fit by REML 
Formula: wear ~ material - 1 + (1 | Subject) 
   Data: BHHshoes 
  AIC  BIC logLik deviance REMLdev
 62.9 66.9  -27.5     53.8    54.9
Random effects:
 Groups   Name        Variance Std.Dev.
 Subject  (Intercept) 6.1009   2.470   
 Residual             0.0749   0.274   

vasishth

> b1.vals<-subset(BHHshoes,material=="A")$wear
> b2.vals<-subset(BHHshoes,material=="B")$wear
> 
> vcovmatrix<-var(cbind(b1.vals,b2.vals))
> 
> covar<-vcovmatrix[1,2]
> sds<-sqrt(diag(vcovmatrix))
> covar/(sds[1]*sds[2])
b1.vals 
0.98823 

vasishth

## Our simulated scientist will declare 
## significance only if he/she gets 
## 2 replications with p<0.05:
stringent<-FALSE
## Set the above to FALSE if you want to 
## have the scientist publish a single
## expt. as soon as it's significant:
#stringent <- FALSE

## num of scientists to simulate:

vasishth

“He seems to have a solid stats background back in the day”

A lot of people claim a lot of things about themselves. I have met a lot of people who characterize themselves as “fluent in Japanese” (oddly, these are always Americans), where their actual on-the-ground fluency level is pretty laughable.

I couldn’t find any clear statements about what his educational background is. He says on his linkedin page: “Facultés universitaires ‘Notre-Dame de la Paix’ Ph.D., Statistics, Mathematics, Science, 1983 – 1993″, Then he lists two courses he did there, “Stochastic Geometry, Markov Processes.” I find it odd that a guy does a PhD somewhere, over 10 years, and lists two courses under that PhD.

Also, I searched for this mysterious uni I have never heard of: Facultés universitaires ‘Notre-Dame de la Paix.

I ended up at a weird Jesuits page in Belgium:

vasishth

<<>>=
# n: no of operations
# x: no of deaths
# N: no of hospitals
dat<-list(n=c(47,211,810,148,196,360,119,207,97,
              256,148,215),
          x=c(0,8,46,9,13,24,8,14,8,29,18,31),
          N=12)

cat("model

vasishth

### R code from vignette source 'recoveringcorrelationsV2.Rnw'

###################################################
### code chunk number 1: recoveringcorrelationsV2.Rnw:98-156
###################################################
new.df <- function(cond1.rt=487, effect.size=123, 
                   sdev=544,
                   sdev.int.subj=160, sdev.slp.subj=195, 
                   rho.u=0.6,
                   nsubj=37,

vasishth

## store proportion of false positives
## in one lifetime of 200 experiments:
prop_fps<-rep(NA,1000)
## run k=1000 scientists, each with
## a lifetime of 200 experiments:
for(k in 1:1000){
## number of experiments for each scientist:
nexp<-200
## prob of sampling from a population
## where the null is true: