similar to: Bootstrap estimates and variance

I am fitting quantile regression models using data collected from a sample of 124 patients. When modeling cross-sectional associations, I have noticed that nonparametric bootstrap estimates of the variances of parameter estimates are much greater in magnitude than the empirical Huber estimates derived using summary.rq's "nid" option. The outcome variable is severely skewed, and I am

I would appreciate some thoughts on using the bootstrap functions in the library "bootstrap" to estimate confidence intervals of ICC values calculated in lme. In lme, the ICC is calculated as tau/(tau+sigma-squared). So, for instance the ICC in the following example is 0.116: > tmod<-lme(CINISMO~1,random=~1|IDGRUP,data=TDAT) > VarCorr(tmod) IDGRUP = pdLogChol(1)

I'm using the lme function in nmle to estimate the variance components of a fully nested two-level model: Y_ijk = mu + a_i + b_j(i) + e_k(j(i)) lme computes estimates of the variances for a, b, and e, call them v_a, v_b, and v_e, and I can use the intervals function to get confidence intervals. My understanding is that these intervals are probably not that robust plus I need intervals on the

Dear R users, I?m student of Master in Statistic and Data analysis, in New University of Lisbon. And now i?m writting my dissertation in variance estimation.So i?m using Survey Package to compute the principal estimators and theirs variances. My data is from Incoming and Expendire Survey. This is stratified Multi-stage Survey care out by National Statistic Institute of Mozambique. My domain of

I want to use boot.ci to generate confidence intervals over the bootstrapped mean(s) of a group of observations (i.e. I have 10 observations and I want to know how confident I can be on the value for the mean). I don't know (or want to know) the details of bootstrapping - I just have the simplistic idea of taking samples, measuring a statistic on the sample, and getting some confidence in the

I'm using R 2.15.1 on a 64-bit machine with Windows 7 Home Premium and package 'boot'. I've found that using a number of bootstrap resamples in boot() that is less than the number of data results in a fatal error. Once the number of resamples meets or exceeds the number of data, the error disappears. Sample problem (screwy subscripted syntax is a relic of edited down a more

Dear R community, Please, can you help me with a problem concerning bootstrap. The data table called «RMika», contained times (Tps) and corresponding concentration of a chemical in a soil (SolA). I would like to get, by bootstraping, 10 estimations of the parameters C0 and k from the function: SolA = C0*exp(-k*Tps). # First, I fit the data and all is OK >

Dear R community, I have currently some problems with non linear regression analysis in R. My data correspond to the degradation kinetic of a pollutant in two different soil A and B, x data are time in day and y data are pollutant concentration in soil. In a first time, I want to fit the data for the soil A by using the Ct = C0*exp(-k*Tpst) with Ct the concentration of pollutant at time t, C0

< In Stata, I can just use "bs" function ...> in STATA the bs command runs a bootstrap If you want to use the bootstrap function (or you could linearize b*c/a :) look under ?boot (you may need to load the package first). Usage: boot(data, statistic, R, sim="ordinary", stype="i", strata=rep(1,n), L=NULL, m=0, weights=NULL,

Dear List: I'm trying to use the boot function to estimate some standard errors. I actually programmed a bootstrap using some homebrew code and it worked fine. But, I am trying to use the more efficient boot function. I have placed some sample data for replication of my problem at the bottom of this email. For the sample problem, I have 10 subjects each with 5 observations Y_t = (t_1, ...,

Hi list members, I saw a couple lavaan posts here so I think I?m sending this to the correct list. I am trying to run a multigroup analysis with lavaan in order to compare behavioural correlations across two populations. I?m following the method suggested in the paper by Dingemanse et al. (2010) in Behavioural Ecology. In one of the groups, lavaan returns negative variance for one path and I?m

Dear list, In a mixed model I selected I find a non positive definite random effects variance-covariance matrix G, where some parameters are estimated close to zero, and related confidence intervals are incredibly large. Since simplification of the random portion is not an option, for both interest in the parameters and significant increase in the model fit, I would like to collect

I assume I'm missing something obvious here... The short form of my main question is: how do I extract variance components from an lme object? The longer form (plus optional supplementary question!): I'm looking at some quantitative genetics, and want to estimate two variance components so that I can then calculate a statistic called Qst from them. So I have this: reg1 <- lme(y ~

Dear listers, I would like to get the bootstrap estimates form my lme model. I have an HLM (multillevel) 2-level model with the dichotomous outcome. I used glmmPQL procedure. However I have a problem since I have a rather unbalanced proportion (90-99% of events, i.e. ones and only 1-10% of nonevents - zeros) although sample sizes are not that small between 500 and 1000 I get pretty weird and

When a variance components mixed model is run in Stata, if some of the variance components are zero, the model may not converge, for rational reasons. However, when the same model is run in SAS, the models with variance components that estimate to zero nonetheless converge. If I'm interested in looping through a set of such models, the SAS behavior is preferred. However, in Stata

Dear Faiz, Bootstrapping R^2 using Boot() is straightforward: Simply write a function that returns R^2, possibly in a vector with the regression coefficients, and use it as the f argument to Boot(). That will get you, e.g., bootstrapped confidence intervals for R^2. (Why you want that is another question.) See the example in ?Boot that shows how to bootstrap the estimated error variance (without

How might you fit a generalized linear model (glm) with variance = mu+theta*mu^2 (where mu = mean of the exponential family random variable and theta is a parameter to be estimated)? This appears in Table 2.7 of Fahrmeir and Tutz (2001) Multivariate Statisticial Modeling Based on Generalized Linear Models, 2nd ed. (Springer, p. 60), where they compare "log-linear model fits to

Hi all, I didn't get a response to my post of this issue a week ago, so I've tried to clarify: When I use lme to analyze a model of nested random effects, the variance estimates of levels higher in the hierarchy appear to have much more variance than they should. In the example below with 4 levels, I simulate variance in level 2 (sd=1.0) and level 4 (sd=0.1), but levels 1 and 3 do

Dear Friends and Colleagues, I'm puzzling over how to interpret or use some bootstrap intervals. I think that I know what I should do, but I want to check with knowledgeable people first! I'm using a studentized non-parametric bootstrap to estimate 95% confidence intervals for three parameters. I estimate the variance of the bootstrap replicates using another bootstrap. The script

Dear R-Users, I have a question concerning extraction of parameter estimates of variance function from lme fit. To fit my simulated data, we use varConstPower ( constant plus power variance function). fm<-lme(UPDRS~time,data=data.simula,random=~time,method="ML",weights=varConstPower(fixed=list(power=1))) I extract the results of this function by using the following codes: