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

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

#generate data (columns of drift matrix 'cause' rows)
genmodel <- ctModel(n.manifest=3,n.latent=3,LAMBDA=diag(3),Tpoints=10,
  DRIFT=matrix(c(
    -.3, .1,0,
    0,-.3,.1,
    0,0,-.3), nrow=3, ncol=3),
  DIFFUSION=diag(1,3))

cdriveraus

#get ctsem
install.packages("devtools")
library(devtools)
install_github("cdriveraus/ctsem")

#load lorenz data
ldat=read.csv(file='ldat.csv')
ldat=ldat[seq(1,nrow(ldat),5),] #subsample

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.