cdriveraus

b=.8
n=100
nruns=10000
out<-rep(NA,nruns)

for(i in 1:nruns){
x=rnorm(n,3,2)
y=b*x + rnorm(n, 0, 3)

fit=lm(y~x)

cdriveraus

library("qgraph")
library("igraph")
library("IsingSampler")
library("IsingFit")
library('rstan')

set.seed(1337)
Kappa <- as.matrix(get.adjacency(watts.strogatz.game(1,10,1,0)))
Kappa[1,5] <- Kappa[5,1] <- Kappa[3,9] <- Kappa[9,3] <-Kappa[7,1] <-Kappa[1,7] <- Kappa[5,9] <-Kappa[9,5] <-Kappa[7,3] <-Kappa[3,7] <-.5

cdriveraus

n=10000
beta=.4
#no effect

p=c()
for(i in 1:n){
s=rnorm(30,0,1)
sm=s+rnorm(30,0,1) #with measurement error
x=rnorm(30,0,1)
p=c(p,summary(lm(sm~x))$coefficients[2,4])

cdriveraus

N=10000 #iterations
obs=20 #observations per iteration
beta=exp(rnorm(N,-2,1))

Bs=c() #bias in estimated Beta (standardised) for no error condition
Bsm=c() #bias in estimated Beta (standardised) for with error condition
betas2=c() #unstandardised true beta for no error iterations that came out significant
betasm2=c() #unstandardised true beta for with error iterations that came out significant

for(i in 1:N){

cdriveraus

n=100
a<-rnorm(n)
b<-a*.5 + rnorm(n)
bs <- scale(b)
as<-scale(a)

summary(lm(b~a))
summary(lm(a~b))
summary(lm(bs~as))
summary(lm(as~bs))

cdriveraus

##install ctsem (If after May 2019, CRAN is recommended)
install.packages("devtools")
library(devtools)
install_github("cdriveraus/ctsem")
library(ctsem)


#specify generative model (linear sde only at present for data generation -- on the to do list)
gm <- ctModel(
  type='omx', #omx is older model type still needed for data generation

cdriveraus

install.packages('ctsem')
library(ctsem)

#### example 1 
scode <- "
parameters {
  real y[2]; 
} 
model {
  y[1] ~ normal(0, .5);

cdriveraus

#' ctStanFit
#'
#' Fits a ctsem model specified via \code{\link{ctModel}} with type either 'stanct' or 'standt', using Bayseian inference software
#' Stan. 
#' 
#' @param datalong long format data containing columns for subject id (numeric values, 1 to max subjects), manifest variables, 
#' any time dependent (i.e. varying within subject) predictors, 
#' and any time independent (not varying within subject) predictors.
#' @param ctstanmodel model object as generated by \code{\link{ctModel}} with type='stanct' or 'standt', for continuous or discrete time
#' models respectively.

cdriveraus

n <- 100000
pe <- .2 #patriarchy benefit to male ability
ba <- rnorm(n) #baseline ability
m <- rbinom(n,1,.5) #male
h <- ba + pe * m + rnorm(n)#h index
e1 <- ba + pe *m + rnorm(n)#earnings assuming caused by ability
e2 <- h + rnorm(n)#earnings assuming caused by h-index

lm(e1~h+m)
lm(e2~h+m)

cdriveraus

stn_md> stancode <- 'data {real y_mean;} parameters {real y;} model {y ~ normal(y_mean,1);}'

stn_md> mod <- stan_model(model_code = stancode, verbose = TRUE)

TRANSLATING MODEL '73fc79f8b1915e8208c736914c86d1a1' FROM Stan CODE TO C++ CODE NOW.
successful in parsing the Stan model '73fc79f8b1915e8208c736914c86d1a1'.
The NEXT version of Stan will not be able to pre-process your Stan program.
Please open an issue at
 https://github.com/stan-dev/stanc3/issues 
if you can share or at least describe your Stan program. This will help ensure that Stan