similar to: Help in Bootstrapping

Respected Sir, I am working on Hidden Markov Models for count data. My data consist of number of new infectives in a ward per month. I have data for about 50 months. How can I fit a Hidden Markon Model to my data. I am using 'repeated' package('hidden' and 'chidden' arguments). I would like to know how can I estimate the initial values of the parameters(mu and

Hi All, I want to upload J K Lindsey's "repeated" in a LINUX OS.. I had tried the command.. [root@localhost Desktop] # R CMD INSTALL repeated.gz " WARNING: invalid package 'repeated' " *Installing to library '/usr/local/lib/R/library' ERROR: No packages

I am comfortable with windows based R. But recently I had shifted to LINUX(Red Hat Linux Enterprise Guide 4) 1) I want to load J K Lindsey's repeated library in R. How to install the packge? 2) How to create the shared library if I ve the fortran codes(I haven't done creation of shared library in windows also). I had run the command Rcmd in bin directory but an error message "bash:

Hi all, I am currently doing a consistency analysis using an lmer model and trying to use parametric bootstrapping for the confidence intervals. My model is like this: model<-lmer(y~A+B+(1|C/D)+(1|E),binomial) where E is the individual level for consistency analysis, A-D are other fixed and random effects that I have to control for. Following Nakagawa and Scheilzeth I can work out the

Hi, Sorry for repeated question. I performed logistic regression using lrm and penalized it with pentrace function. I wanted to get confidence intervals of odds ratio of each predictor and summary(MyModel) gave them. I also tried to get bootstrapping standard errors in the logistic regression. bootcov function in rms package provided them. Then, I found that the confidence intervals provided by

Hello, I have been successfully using nls to fit a non-linear, self-limiting function to several sets of data collected in 2010 (example found below). To generate confidence intervals for parameter estimates, I've been attempting to bootstrap my sample. Unfortunately, I have meet with singularity gradients and failures to converge. I have altered maxiter and minFactor statements, used

Hi, Let me start my saying that I am new to R hence my grasp of the appropriate used of R coding is undoubtedly way behind many on this forum. I am trying to use boostrapping to derive errors around my parameter estimate for the fixed effects in the following model. It is simply estimating the number of times an animal might cross a road based on the road's distance from a stream. I have

Dear all, I'm looking for someone that help me to write an R function to simulate survival data under complex situations, namely time-varying hazard ratio, marginal distribution of survival times and covariates. The algorithm is described in the reference below and it should be not very difficult to implement it. However I tried but without success....;-( Below there the code that I used; it

Hi, I'm having trouble understanding how to construct a random number generator for a parametric bootstrap. My aim is to bootstrap a Likelihood Ratio statistic (under the null) for a linear model. The function at this point is given by boot.test.n01 <- function(data, indeces, maxit=20) { y1 <- fit1+se(e2)*rnorm(314) mod1 <- glm(y1 ~ X1-1, maxit=maxit) y2 <-

Dear Lillian, I tried to estimate parameters for time series regression using time series bootstrapping as described on page 434 in Davison & Hinkley (1997) - bootstrap methods and their application. This approach is based on an AR process (ARIMA model) with a regression term (compare also with page 414 in Venable & Ripley (2002) - modern applied statistics with S) I rewrote the code

Dear R Help List, I have a vector of n and a vector of n-1 and I want to use boot() to bootstrap the ratio of their respective medians. I want to eventually use boot.ci() to generate confidence intervals. Because the vectors are not equal in length, I tried a few things, but have yet to be successful. Try 1: > x <- runif(20) > > y <- c(runif(19), NA) > > median(x) [1]

Hello all, I am low down on the learning curve of R but so far I have had little trouble using most of the packages. However, recently I have run into a wall when it comes to bootstrapping a Levene's test (from the car package) and thought you might be able to help. I have not been able to find R examples for the "boot" package where the test statistic specifically uses a

hi Thomas/All, I went through the thread( https://stat.ethz.ch/pipermail/r-sig-finance/2006q1/000682.html which concerns with swaps). Yeah it is correct that i would like to quote both David and Krishna that the curve interpolation may vary considerably (for e.g. any polynomial/parametric fit is very different from and curve fitting whether it is free hand or by NURBS ( complex version of

Hello , I want to compare two quadratic regression models with non-parametric bootstrap. However, I do not know which R package can serve the purpose, such as boot, rms, or bootstrap, DeltaR. Please kindly advise and thank you. Elaine The two quadratic regression models are y1=a1x^2+b1x+c1 y1= observed migration distance of butterflies() y2=a2x^2+b2x+c2 y2= predicted migration distance of

I would like to experiment with testing the fit of a loess model against the fit from an ordinary linear regression. The 1988 JASA paper by Cleveland and Devlin *appears* to indicate that this can be done, at least ``approximately''. They, as I read it, advocate the use of an ANOVA type test with degree of freedom chosen to make the ``F ratio'' have an approximate F distribution

Dear R-users, I want to perform a One-Sample parametric bootstrapped Kolmogorov-Smirnov GoF test (note package "Matching" provides "ks.boot" which is a 2-sample non-parametric bootstrapped K-S version). So I wrote this code: ---[R Code] --- ks.test.bootnp <- function( x, dist, ..., alternative=c("two.sided", "less", "greater"), B = 1000 ) {

Dear R-users, I would like to show you a simple example that gives an overview of one of my current issue. Although my working setting implies a different parametric model (which cannot be framed in the glm), I guess that what I'll get from the following example it would help for the next steps. Anyway here it is. Firstly I simulated from a series of exposures, a series of deaths (given a

Hello, I am have fitted GLS models to time series data. Now I wish to bootstrap this data to produce confidence intervals for the model. However, because this is time series data, normal bootstrapping is not applicable. Secondly, 'tsboot' appears to only be useful for ar models - and does not seem to be applicable to GLS models. I have written code in R to randomly sample blocks of

Dear R experts, I'm hoping someone can help me to interpret the results of building gam's with mgcv in R. Below are summaries of two gam's based on the same dataset. The first gam (named "gam.mod") has six predictor variables. The second gam (named "gam.mod2") is exactly the same except it is missing one of the predictor variables. What is confusing me is

Hi, this is a question about bootstrapping, it relates more to the concept than to the R package boot. But I wonder below if boot can help me. I have the below to calculate a certain point estimate: estimate= (0.9 * 0.03 * 700000 * (((77 * (76 / 76.0)) / 83107) - ((174 * (154 / 154.0)) / 376354))) = 8.77311 Now I want to calculate confidence intervals around that estimate so I thought that