similar to: Confidence interval for response variable in mixed effects models

Dear R help gurus, I have the following problem and I would be delighted if you could help me. >From a large (1500) cohort of patients we have been taking some measurements (ECG measurements, but its not important). The measurements are ordinal in 4 grades (Grade I-IV, grade IV being the most severe form). Every patients has been measured several times (usually once per year). The

I'm fairly new to R and am wondering if anybody knows of R code to calculate confidence intervals for parameters (fixed effects and variance components) from mixed effects models based on Sattherthwaite's method? I'm also interested in Satterthwaite-based confidence intervals for linear combinations (mostly sums) of various variance components. [[alternative HTML version

Hello all, I've come across an online posting http://www.biostat.wustl.edu/archives/html/s-news/2001-10/msg00119.html that described how to get confidence intervals for predicted values from predict.glm. These instructions were meant for S-Plus. Yet, it generally seems to work with R too, but I am encountering some problems. I am explaining my procedure in the following and would be most

Hello, I have the following r-codes for solving a quasilikelihood estimating equation: >library(geepack) >fit<-geese(y~x1+x2+x3,jack=TRUE,id=id,scale.fix=TRUE,data=dat,mean.link = "logit", corstr="independence") Now my question is how can I calculate the confidence interval of the parameters of the above model "fit"? [[alternative HTML version deleted]]

Say I fit a logistic model and want to calculate an odds ratio between 2 sets of predictors. It is easy to obtain the difference in the predicted logodds using the predict() function, and thus get a point-estimate OR. But I can't see how to obtain the confidence interval for such an OR. For example: model <- glm(chd ~age.cat + male + lowed, family=binomial(logit)) pred1 <-

> > Hi All: > > I am using R to calculate exact 95% confidence interval using Clopper > Pearson method. I am using the following code but it seems to get into a > loop and not get out of it, it goes on forever although I am looping it > only 10 times across 63 sites with 10 observations per site. I was hoping > to get some help. > > Thanks > Anamika >

I am analyzing a dataset on the effects of six pesticides on population growth rate of a predatory mite. The response variable is the population growth rate of the mite (ranges from negative to positive) and the exploratory variable is a categorical variable (treatment). The experiment was blocked in time (3 blocks / replicates per block) and it is unbalanced - at least 1 replicate per block. I am

Hello, I have the following r-codes for solving a quasilikelihood estimating equation: >library(geepack) >fit<-geese(y~x1+x2+x3,jack=TRUE,scale.fix=TRUE,data=dat,mean.link = "logit", corstr="independence") Now my question is how can I calculate the confidence interval of the parameters of the above model "fit"? [[alternative HTML version deleted]]

Howdy everyone I?m trying to get Odds ratio and OR confidence intervals using a probit model, but I'm not getting. Do you think you can help me? I?m new with R L naive = summary(glm(pcr.data[,7]~boldBeta_individual+pcr.data$age,family=binomial(link=probit))) naive_answer = c(naive$coefficients[,1:3]) #naive estimates for

Hi R-users, >From the last week I've been working fitting a linear mixed model with random intercept and fixed shape (model4) for a data set with 37 individuals measured over time, using lme package. Results are at the end of this message. The outcome is score and the covariate is age. My question is: is possible (and how) to estimate both 95% confidence and prediction intervals for the

I want to construct a logit model, plot the probability curve with the confidence intervals, and then I want to print out a data frame with the predictor, response value, predicted value, the low ci predicted value, and the high ci predicted value. So it should look something like: value low_ci prob hi_ci 5 0.10 0.12 0.13 6 0.11 0.13 0.16 7 0.13 0.15

Hello! I would like to draw a graph like the following: http://www.optics.rochester.edu/workgroups/cml/opt307/spr04/pavel/plot_small.jpg Aim is to plot confidence intervals of treatments for X(=response1) and Y(=response2) axis simultaneously to visualize aggreement of confidence interval for two responses. Can anyone please provide me some direction to start with? Thanks! -- Kim.

Hello, I am running the program COMPAR.GEE within the package APE. My dependent variable is binomial, while my independent variable is a multi-state categorical variable. The output reports an estimate for each state of the independent variable except the first one. For example, for the variable X with 3 states, the output is: intercept (estimate) X2 (estimate) X3 (estimate)

Using the survey package I find it is convenient and easy to get estimated proportions using svymean, and their corresponding estimated standard errors. But is there any elegant/simple way to calculate corresponding confidence intervals for those proportions? Of course +/- 1.96 s.e. is a reasonable approximation for a 95% CI, but (incorrectly) assumes symmetrical distribution for a proportion.

Dear Friends, I have been following the discussions of mixed-effects models with some confusion, and I realize that much of this is work in progress, and that much of the discussion is beyond my knowledge of statistics. My question is simple, though: Is there a set of commands that will produce an output equivalent to the exceedingly useful predict(bl.lm, newdata = bl.new, type =

Hello everyone, I want to fit a baseline category logit model (with 3-4 categories) with nested random effects. (For example, I have clusters(i) and households within clusters (j) resulting in the nested random effects structure : b_i +d_j(i)). Is there a R function/package that I can use ? Any help will be much appreciated. Thanks and regards, Dhiman Bhadra [[alternative HTML version

Sub: using glmer to fit a mixed-effects model with gamma-distributed response variable Hello, I'm currently trying to fit a mixed effects model , i.e.: > burnedmodel1.2<-glmer(gpost.f.crwn.length~lg.shigo.av+dbh+leaf.area+ bark.thick.bh+ht.any+ht.alive+(1|site/transect/plot), family=gaussian, na.action=na.omit, data=rws30.BL) If I run this code, I get the error below: Error:

Hi everyone, I'm building a matrix in R with a cycle for like this: pp_ris2=matrix(NA,6,6) for(i in 0:6){ for(j in 0:6){ if(i>j){ pp_ris2[i,j]=myfunction} else if(i==j){ print(c(i,j)) pp_ris2[i,j]=myfunction} }} but the result is: [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.062 NaN NaN NaN NaN NaN [2,] 0.083 0.022 NaN NaN NaN NaN [3,]

I am working with Probit regression (I cannot switch to logit) can anybody help me in finding out how to obtain with R Finney's fiducial confidence intervals for the levels of the predictor (Dose) needed to produce a proportion of 50% of responses(LD50, ED50 etc.)? If the Pearson chi-square goodness-of-fit test is significant (by default), a heterogeneity factor should be used to calculate

Hi, I would be very grateful for advice on getting confidence intervals for the ordinary (non smoothed) parameter estimates from a gam. Motivation I am studying hospital outcomes in a large data set. The outcomes of interest to me are all binary variables. The one in the example here, Dead30d, is death within 30 days of admission. Sexf is gender (M or F), Age is age in years at the start