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2001 Aug 30
1
MCMC coding problem
...t distribution
# mu = mean of second mode
# phi2 = multiplier of variance factor 2.38
# varwt = weight (0 < varweight <= 1) applied to variance of second part of
mixture.
# ntimes = number of iterations
#
# phi is the standard deviation of the proposal distribution.
# y is the data matrix, ystar is the proposal state
phi <- (2.38^2)/d * diag(phi2,d)
y <- matrix(0,ncol=d,nrow=ntimes)
ystar <- matrix(0,ncol=d,nrow=1)
len <- ntimes/1000 - 1
meanmat <- matrix(0,nrow=len,ncol=d)
quantmat <- array(0, dim = c(3,d,len))
count <- 0
# print("Comment out the browser before...
2006 Jan 19
2
Tobit estimation?
...s,
Based on
http://www.biostat.wustl.edu/archives/html/s-news/1999-06/msg00125.html
I thought I should experiment with using survreg() to estimate tobit
models.
I start by simulating a data frame with 100 observations from a tobit model
> x1 <- runif(100)
> x2 <- runif(100)*3
> ystar <- 2 + 3*x1 - 4*x2 + rnorm(100)*2
> y <- ystar
> censored <- ystar <= 0
> y[censored] <- 0
> D <- data.frame(y, x1, x2)
> head(D)
y x1 x2
1 0.0000000 0.86848630 2.6275703
2 0.0000000 0.88675832 1.7199261
3 2.7559349 0.38341782 0.6247869
4 0....
2012 Oct 25
2
How to extract auc, specificity and sensitivity
I am running my code in a loop and it does not work but when I run it
outside the loop I get the values I want.
n <- 1000; # Sample size
fitglm <- function(sigma,tau){
x <- rnorm(n,0,sigma)
intercept <- 0
beta <- 0
ystar <- intercept+beta*x
z <- rbinom(n,1,plogis(ystar))
xerr <- x + rnorm(n,0,tau)
model<-glm(z ~ xerr, family=binomial(logit))
int<-coef(model)[1]
slope<-coef(model)[2]
pred<-predict(model)
result<-ifelse(pred>.5,1,0)
accuracy<-length(whic...
2012 Oct 20
1
Logistic regression/Cut point? predict ??
...t various cut points; however, my
output was garbage (at x equal zero, I did not get .50)
I am basically testing the performance of classifiers.
Here is the code:
n <- 1000; # Sample size
fitglm <- function(sigma,tau){
x <- rnorm(n,0,sigma)
intercept <- 0
beta <- 5
* ystar <- intercept+beta*x*
* z <- rbinom(n,1,plogis(ystar))* *# I believe plogis accepts the a
+bx augments and return the e^x/(1+e^x) which is then used to generate 0
and 1 data*
xerr <- x + rnorm(n,0,tau) # error is added here
model<-glm(z ~ xerr, family=binomial(logit))...
2009 Apr 23
2
Two 3D cones in one graph
...<-t(G$vector%*%t(A))
E2<-t(diag(sqrt(G$values))%*%t(E1))
mu<-c(0.1,0.2)
E3<-sweep(E2,2,-mu)
a<-sqrt(max(rowSums(sweep(E3,2,mu)**2)))
b<-sqrt(min(rowSums(sweep(E3,2,mu)**2)))
astar<-as.numeric(a+abs(mu[1]))
bstar<-as.numeric(b+abs(mu[2]))
xstar<-seq(-astar,astar,len=50)
ystar<-seq(-bstar,bstar,len=50)
g<-expand.grid(x=xstar,y=ystar)
p1<-2*g$x*mu[1]/a**2+2*g$y*mu[2]/b**2
p2<-(g$x**2/a**2+g$y**2/b**2)
p3<-mu[1]**2/a**2+mu[2]**2/b**2-1
q<-(p1+sqrt(p1**2-4*p2*p3))/(2*p2)
z<-sqrt(1-(q*g$x)**2-(q*g$y)**2)
zstar<-(z/q)
ind0<-!(q<1)
g$z<-zsta...
2008 Sep 15
0
Simple censored quantile regression question
I start by doing a simple gaussian tobit by MLE:
x1 <- runif(1000) # E() = 0.5
x2 <- runif(1000)*2 # E() = 1
x3 <- runif(1000)*4 # E() = 2
ystar <- -7 + 4*x1 + 5*x2 + rnorm(1000) # is mean 0
y <- ystar
censored <- ystar <= 0
y[censored] <- 0
library(AER)
m <- tobit(y ~ x1 + x2, left=0, data=D)
summary(m)
Which gives:
Call:
tobit(formula = y ~ x1 + x2, left = 0, data = D)
Observations:
Total Left-cen...
2002 Feb 11
0
profile
..., eta2)*log(Popn/PopStd)
Ymax <- Ymax*PotYield3*Popn/1000
Ymax <- Ymax*ifelse(Dmax<=DIs*AWC, 1, 1 - beta*(Dmax
-DIs*AWC)/SumEp)
Nstar <- (Nsupply- MnmN*Ymax) / (OptN*Ymax - MnmN*Ymax)
Nstar<-pmax ( 0,Nstar)
Ystar<-ifelse(Nstar<1, (1 + gN*(1 - Nstar))* Nstar^(gN), 1)
Ystar<-pmax ( 0, Ystar)
Y.model <- Ystar*Ymax
Y.model
}
# simulate experimental data for predictors
nsim <- 300
Popn <- rnorm(nsim,PopStd,0.1*P...
2012 Sep 17
2
Problem with Stationary Bootstrap
Dear R experts,
I'm running the following stationary bootstrap programming to find the parameters estimate of a linear model:
X<-runif(10,0,10)
Y<-2+3*X
a<-data.frame(X,Y)
coef<-function(fit){
fit <- lm(Y~X,data=a)
return(coef(fit))
}
result<- tsboot(a,statistic=coef(fit),R = 10,n.sim = NROW(a),sim = "geom",orig.t = TRUE)
Unfortunately, I got this
2008 Dec 16
1
Prediction intervals for zero inflated Poisson regression
...ro inflated poisson regression
fm_zip <- zeroinfl(art ~ fem | 1, data = bioChemists)
fit <- predict(fm_zip)
Pearson <- resid(fm_zip, type = "pearson")
VarComp <- resid(fm_zip, type = "response") / Pearson
fem <- bioChemists$fem
bootstrap <- replicate(999, {
yStar <- pmax(round(fit + sample(Pearson) * VarComp, 0), 0)
predict(zeroinfl(yStar ~ fem | 1), newdata = newdata)
})
newdata0 <- newdata
newdata0$fit <- predict(fm_zip, newdata = newdata, type = "response")
newdata0[, 3:4] <- t(apply(bootstrap, 1, quantile, c(0.025, 0.975)))
new...
2012 Oct 26
0
Problems getting slope and intercept to change when do multiple reps.
library(ROCR)
n <- 1000
fitglm <- function(iteration,intercept,sigma,tau,beta){
x <- rnorm(n,0,sigma)
ystar <- intercept+beta*x
z <- rbinom(n,1,plogis(ystar))
xerr <- x + rnorm(n,0,tau)
model<-glm(z ~ xerr, family=binomial(logit))
*int*<-coef(model)[1]
*slope*<-coef(model)[2] # when add error you are suppose to get slightly
bias slope. However when I change the beta in the origina...