similar to: estimated variance for parametric fit

Dear all, I fitted a non-parametric model using GAM function in R. i.e., gam(y~s(x1)+s(x2)) #where s() is the smooth function Then I obtained the coefficients(a and b) for the non-parametric terms. i.e., y=a*s(x1)+b*s(x2) Now if I want to use this estimated model to do optimization or sensitivity analysis, I am not sure how to incorporate the smooth function since s() may not

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

Hello R, I am looking for non-parametric simulation in IRT. Is there any IRT package that does non-parametric simulation? helen L [[alternative HTML version deleted]]

Hi, I am running a simple linear model with (say) 5 independent variables. Is there a simple way of getting the variance-covariance matrix of the coeffcient estimates? None of the values of the lm() seem to provide this. Thanks in advance, Ritwik Sinha rsinha@darwin.cwru.edu Grad Student Case Western Reserve University [[alternative HTML version deleted]]

Hi, I was trying to fit a parametric survival model with Weibull distribution on counting process type of data (NOT interval censor data), but the survreg(Surv(T1,T2,event)~x,data,dist="weibull") did not seem to work. Anyone can help me with that? Thanks, Rachel Memorial Sloan-Kettering Cancer Center -- View this message in context:

Hi all, I have probably a basic question, but I can't seem to find the answer in the literature or in the R-archives. I would like to do a robust ANCOVA (using either rlm or lmRob of the MASS and robust packages) - my response variable deviates slightly from normal and I have some "outliers". The data consist of 2 factor variables and 3-5 covariates (fdepending on the model).

Hello, Is there a way to obtain the variance-covariance matrix of the estimated parameters from GLM? my.glm<-glm(mat ~X,family = binomial, data =myDATA) out1<-predict(my.glm,se.fit = TRUE) std<-out1$se.fit se.fit is for getting the standard errors of the estimated parameters (\betas). Is there a way to get the variance-covariance matrix of the estimated parameters? Many thanks,

Greetings -- I would like to estimate a proportional hazard model with a weibull or lognormal baseline. I have looked at both the coxph() and survreg() functions and neither appear (to me ) to do it. Am I missing something in the docs or is there another terrific package out there that will do this. Many Thanks. Carl Mason

I know of no existing functions for estimating the parameters of this model using MCMC or MML. Many years ago, I wrote code to estimate this model using marginal maximum likelihood. I wrote this based on the using nlminb and gauss-hermite quadrature points from statmod. I could not find that code to share with you, but I do have code for estimating the 3PL in this way and you could modify the

There has been recent discussion about alternatives to the model-based standard error estimators for lm. While some people like the sandwich estimator and others don't, it is clear that neither estimator dominates the other for any sane loss function. It is also worth noting that the sandwich estimator is the default for t.test(). I think it would be useful for models using other

Good day Sir/Ma'am! This is Alyssa Fatmah S. Mastura taking up Master of Science in Statistics at Mindanao State University-Iligan Institute Technology (MSU-IIT), Philippines. I am currently working on my master's thesis titled "Comparing the Three Estimation Methods for the Four Parametric Logistic (4PL) Item Response Model". While I am looking for a package about Markov chain

Dear R experts, I have a problem that is a related to the question raised in this earlier post https://stat.ethz.ch/pipermail/r-help/2007-January/124064.html My situation is different in that I have only 2 predictors (coordinates x,y) for local regression but a number of global ("parametric") offsets that I need to consider. Essentially, I have a spatial distortion overlaid over a

I have spatial data in 2 dimensions - say (x,y). The correlation between x and y is fairly substantial. My goal is to use a non-parametric approach to estimate the multivariate density describing the spatial locations. Ultimately, I would like to use this estimated density to determine the area associated with a 95% probability contour for the data. Given the strong correlation between x and

Greetings to you all, I am performing a semi parametric bootstrap in R on a Gamma Distributed data and a Binomial distributed data. The main challenge am facing is the fact that the residual variance depends on the mean (if I am correct). I strongly feel that the script below may be wrong due to mean-variance relationship #####R code####### fit1s

I am new to R. I'm using the function GAM and wanted to get standard errors and p-values for the parametric terms (I fitted a semi-parametric models). Using the function anova() on the object from GAM, I only get p-values for the nonparametric terms. Does anyone know if and how to get standard errors for the parametric terms? Thanks. Jean G. Orelien

Dear List, Is there a way to extract variance components from lmeObjects or summary.lme objects without using intervals()? For my purposes I don't need the confidence intervals which I'm obtaining using parametric bootstrap. Thanks, Mike [[alternative HTML version deleted]]

Dear everyone, I am doing some work about species richness estimation. Nonparametric estimation (such as Chao1, Jacknife1) can be done just using function "specpool()" and "estimateR()" in package "vegan". The problem is that I can not found any functions for parametric estimation (such as MMMeans, MMruns, Michaelis-Menten). Do you know any function for doing this?

Dear R-users I am relatively new to R, i hope my many novice questions are welcome. I have problems accessing some objects (specifically the random effects, correlation structure and variance function) from an object of class gls and lme. I used the following models: yah <- gls (outcome~ -1 + as.factor(Trial):as.factor(endpoint)+

Dear friends, After fitting a generalized linear models ,i hope to get the variance of alpha,variance of beta and their covariance, that is , the variance-covariance matrix/information of alpha and beta , suppose *B* is the object of GLMs, i use attributes(B) to look for the options ,but can't find it, anybody knows how to get it? > attributes(B) $names [1] "coefficients"

Dear all, I am trying to find a suitable R-function for parametric proportional hazard regressions. The package survival contains the coxph() function which performs a Cox regression which leaves the base hazard unspecified, i.e. it is a semi-parametric method. The package Design contains the function pphsm() which is good for parametric proportional hazard regressions when the underlying base