Displaying 20 results from an estimated 1000 matches similar to: "quantile regression"
2010 Oct 13
1
(no subject)
Dear all,
I have just sent an email with my problem, but I think no one can see the red part, beacuse it is black. So, i am writing again the codes:
rm(list=ls()) #remove almost everything in the memory
set.seed(180185)
nsim <- 10
mresultx <- matrix(-99, nrow=1000, ncol=nsim)
mresultb <- matrix(-99, nrow=1000, ncol=nsim)
N <- 200
I <- 5
taus <- c(0.480:0.520)
h <-
2010 Oct 13
4
loop
Dear all,
I am trying to run a loop in my codes, but the software returns an error: "subscript out of bounds"
I dont understand exactly why this is happenning. My codes are the following:
rm(list=ls()) #remove almost everything in the memory
set.seed(180185)
nsim <- 10
mresultx <- matrix(-99, nrow=1000, ncol=nsim)
mresultb <- matrix(-99, nrow=1000, ncol=nsim)
N
2010 Oct 15
0
nomianl response model
Is there a way to estimate a nominal response model?
To be more specific let's say I want to calibrate:
\pi_{v}(\theta_j)=\frac{e^{\xi_{v}+\lambda_{v}\theta_j}}{\sum_{h=1}^m
e^{\xi_{h}+\lambda_{h}\theta_j}}
Where $\theta_j$ is a the dependent variable and I need to estimate
$\xi_{h}$ and $\lambda_{h}$ for $h \in {1...,m}$.
Thank you,
Mauricio Romero
Quantil S.A.S.
Cel: 3112231150
2010 Oct 06
4
loop in R
Dear all,
I need to do a loop in R, but I am not sure the software is generating "n" times the variables I request differently. When I ask to print the last matrix created, I just can see the loop for n=1.
To be more precise, supose I need to simulate 10 times one variable and I want to fit the 10 variables simulated in a matrix. I dont really know what I am doing wrong, but I just
2006 Jul 20
1
Loss of numerical precision from conversion to list ?
I?m working on an R-implementation of the simulation-based finite-sample null-distribution of (R)LR-Test in Mixed Models (i.e. testing for Var(RandomEffect)=0) derived by C. M. Crainiceanu and D. Ruppert.
I'm in the beginning stages of this project and while comparing quick and dirty grid-search-methods and more exact optim()/optimize()-based methods to find the maximum of a part of the
2011 Nov 07
1
How do I return to the row values of a matrix after computing distances
## Package Needed
library(fields)
## Assumptions
set.seed(123)
nsim<-5
p<-2
## Generate Random Matrix G
G <- matrix(runif(p*nsim),nsim,p)
## Set Empty Matraces dmax and dmin
dmax<- matrix(data=NA,nrow=nsim,ncol=p)
dmin<- matrix(data=NA,nrow=nsim,ncol=p)
## Loop to Fill dmax and dmin
for(i in 1:nsim) {
dmax[i]<- max(rdist(G[i,,drop=FALSE],G))
dmin[i]<-
2011 Feb 17
1
How to speed up a for() loop
Dear all,
Does anyone have any idea on how to speed up the for() loop below.
Currently it takes approximately 2 minutes and 30 seconds.
Because of the size of Nsim and N, simulating a multivariate normal
(instead of simulating Nsim times a vector of N normal distributions)
would require too much memory space.
Many thanks for your kind help,
Simona
N=3000
PD=runif(N,0,1)
cutoff.=qnorm(PD)
2007 Oct 11
3
reason for error in small function?
Running the function below, tested using the cardiff dataset from
splancs generates the following error. What changes do I need to
make to get the function to work? Thanks. --Dale
> gen.rpoints(events, poly, 99)
> rpoints
Error: object "rpoints" not found
# test spatial data
library(splancs)
data(cardiff)
attach(cardiff)
str(cardiff)
events <- as.points(x,y)
###
2006 Mar 08
1
power and sample size for a GLM with Poisson response variable
Craig, Thanks for your follow-up note on using the asypow package. My
problem was not only constructing the "constraints" vector but, for my
particular situation (Poisson regression, two groups, sample sizes of
(1081,3180), I get very different results using asypow package compared
to my other (home grown) approaches.
library(asypow)
pois.mean<-c(0.0065,0.0003)
info.pois <-
2007 Dec 04
1
Metropolis-Hastings within Gibbs coding error
Dear list,
After running for a while, it crashes and gives the following error message: can anybody suggest how to deal with this?
Error in if (ratio0[i] < log(runif(1))) { :
missing value where TRUE/FALSE needed
################### original program ########
p2 <- function (Nsim=1000){
x<- c(0.301,0,-0.301,-0.602,-0.903,-1.208, -1.309,-1.807,-2.108,-2.71) # logdose
2009 Sep 02
1
problem in loop
Hi R-users,
I have a problem for updating the estimates of correlation coefficient in simulation loop.
I want to get the matrix of correlation coefficients (matrix, name: est) from geese by using loop(500 times) .
I used following code to update,
nsim<-500
est<-matrix(ncol=2, nrow=nsim)
for(i in 1:nsim){
fit <- geese(x ~ trt, id=subject, data=data_gee, family=binomial,
2007 Oct 03
2
Speeding up simulation of mean nearest neighbor distances
I've written the function below to simulate the mean 1st through nth
nearest neighbor distances for a random spatial pattern using the
functions nndist() and runifpoint() from spatsat. It works, but runs
relatively slowly - would appreciate suggestions on how to speed up
this function. Thanks. --Dale
library(spatstat)
sim.nth.mdist <- function(nth,nsim) {
D <- matrix(ncol=nth,
2012 Nov 23
1
Spatstat: Mark correlation function
I normally use the following code to create a figure displaying the mark
correlation function for the point pattern process "A":
M<-markcorr(A)
plot(M)
I have now started to use the following code to perform 1000 Monte Carlo
simulations of Complete Spatial Randomness (CSR). It is a Monte Carlo test
based on envelopes of the Mark correlation function obtained from simulated
point
2010 Nov 08
1
try (nls stops unexpectedly because of chol2inv error
Hi,
I am running simulations that does multiple comparisons to control.
For each simulation, I need to model 7 nls functions. I loop over 7 to do
the nls using try
if try fails, I break out of that loop, and go to next simulation.
I get warnings on nls failures, but the simulation continues to run, except
when the internal call (internal to nls) of the chol2inv fails.
2009 Dec 09
1
Why cannot get the expected values in my function
Hi,
In the following function, i hope to save my simulated data into the
"result" dataset, but why the final "result" dataset seems not to be
generated.
#Function
simdata<-function (nsim) {
result<-matrix(NA,nrow=nsim,ncol=2)
colnames(result)<-c("x","y")
for (i in 1:nsim) {
set.seed(i)
result[i,]<- cbind(runif(1),runif(1))
}
2006 Apr 10
1
Generic code for simulating from a distribution.
Hello all,
I have the code below to simulate samples of certain size from a
particular distribution (here,beta distribution) and compute some
statistics for the samples.
betasim2<-function(nsim,n,alpha,beta)
{
sim<-matrix(rbeta(nsim*n,alpha,beta),ncol=n)
xmean<-apply(sim,1,mean)
xvar<-apply(sim,1,var)
xmedian<-apply(sim,1,median)
2011 Apr 07
2
Two functions as parametrs of a function.
Hi R users:
I'm trying to make a function where two of the parameters are
functions, but I don't know how to put each set of parameters for
each function.
What am I missing?
I try this code:
f2<-function(n=2,nsim=100,fun1=rnorm,par1=list(),fun2=rnorm,par2=list()){
force(fun1)
force(fun2)
force(n)
p1<-unlist(par1)
p2<-unlist(par2)
force(p1)
force(p2)
2011 Apr 06
1
Use of the dot.dot.dot option in functions.
Hi R users:
I try this code, where "fun" is a parameter of a random generating
function name, and I pretend to use "..." parameter to pass the parameters
of different random generating functions.
What am I doing wrong?
f1<-function(nsim=20,n=10,fun=rnorm,...){
vp<-replicate(nsim,t.test(fun(n,...),fun(n,...))$p.value)
return(vp)
}
This works!
f1()
2006 Apr 27
1
? bug in 'sample' (PR#8813)
I have found that specifying different "sizes" in the sample command has
a funny effect on the random sampling. The code below is a condensed
version of a function I wrote to simulate a bootstrap method. For
simplicity, I eliminated the internal bootstrap loop, but kept a
statement to draw one bootstrap sample, because this is where the
problem occurs. The output (mean(y)^2) should be
2011 Nov 26
1
Constrained linear regression
Dear all,
I need to run a simple linear regression such that:
y = b0 + b1*x1 + (1-b1)*x2 + e
which I know I can use:
lm(y ~ I(x1 - x2) + offset(x2)).
However, I also need to restrict the coefficient b1 to be between 0 and 1.
Is there any way to include such restriction in the linear regression estimation?
I saw suggestion related with the function Solve.QP, but I really did not understand such