similar to: bootstrap: boot package

Is it possible for R to "read in" S objects. For example, suppose that when using S I did: > x <- c(1,2,3) > y <- x^2 > q() These objects are now in the .Data directory. If I then want to use x and y in an R session, how can I do so? Thanks, Rob -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hello, I have a question regarding shading regions under curves to display 95% confidence intervals. I generated bootstrap results for the slope and intercept of a simple linear regression model using the following code (borrowed from JJ Faraway 2005): > attach(allposs.nine.d) > x<-model.matrix(~log(d.dist,10))[,-1] > bcoef<-matrix(0,1000,2) > for(i in 1:1000){ +

Can anyone please tell me if there is a function to calculate confidence intervals for the results of the quantile function. Some of my data is normally distributed but some is also a squewed distribution or a capped normal distribution. Some of the data sets contain about 700 values whereas others are smaller with about 100-150 values, so I would like to see how the confidence intervals change

Hi! With the following script, I'm trying to make a demonstration of a Confidence Interval, but I'm observing some differences on tails. # Teste de média entre uma amostra e uma população normal # Autor: Raphael de Freitas Saldanha # Agosto de 2008 n <- 200 # Sample size xbar <- 100 # Sample mean s <- 2 # Sample SD nc <- 0.95 # Confidence level (95%

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

Hi, I am trying to figure out exactly what the bootcov() function in the Design package is doing within the context of clustered data. From reading the documentation/source code it appears that using bootcov() with the cluster argument constructs standard errors by resampling whole clusters of observations with replacement rather than resampling individual observations. Is that right, and is

Dear R users, I am new to R. I would like to calculate bootstrap confidence intervals using the BCa method for a parameter of interest. My situation is this: I already have a set of 1000 bootstrap replicates created from my original data set. I have already calculated the statistic of interest for each bootstrap replicate, and have also calculated the mean for this statistic across all the

Hi, I understand bootstrap can be used to estimate 95% confidence interval for some statistics, e.g. variance, median, etc. I have someone suggesting that by resampling certain proportion of the total samples (e.g. 80%) without replacement, we can also get the estimate of confidence intervals. Here we have an example of 1000 obsevations, we would like to estimate 95% confidence intervals for odds

Hi All: I have to compute bootstrap confidence intervals, the statistic (incremental cost effectiveness ratio) is computed from two samples (intervention and control) of different sizes. All the bootstrap functions that I have seen use one dataset as argument. I may go ahead and get the desired number of bootstrap replications separately. I would appreciate if you could point me to a source of a

There appear to be reports in the literature that transform continuous independent variablea by the use of splines, e.g., assume the dependent variable is hot dogs eaten per week (HD) and the independent variable is waistline (WL), a normal linear regression model would be: nonconfusing_regression <- lm(HD ~ WL) One might use a spline, confusion_inducing_regression_with_spline <- lm(HD

I tried to reproduce a result from a former colleague which he got with S-plus bootstrap method. I don't have S-plus at hand. In R, there are 2 packages related to bootstrap method, bootstrap and boot. The former has a function called 'bootstrap' but this does not seem to conform either to the function used in S-plus nor to that described in MASS, 3d ed., p.144. The latter seems to be

I like to bootstrap regression models, saving the entire set of bootstrapped regression coefficients for later use so that I can get confidence limits for a whole set of contrasts derived from the coefficients. I'm finding that ordinary bootstrap percentile confidence limits can provide poor coverage for odds ratios for binary logistic models with small N. So I'm exploring BCa confidence

Sometimes one might like to obtain pointwise bootstrap bias-corrected, accelerated (BCA) confidence intervals for a large number of statistics computed from a single dataset. For instance, one might like to get (so as to plot graphically) bootstrap confidence bands for the fitted values in a regression model. (Example: Chiu S et al., Early Acceleration of Head Circumference in Children with

Hello, I am doing a simple linear regression analysis that includes few variables. I am using a bootstrap analysis to obtain the variation of my variables to replacement. I am trying to obtain the coefficients 95% confidence interval from the bootstrap procedure. Here is my script for the bootstrap: N = length (data_Pb[,1]) B = 10000 stor.r2 = rep(0,B) stor.r2 = rep(0,B) stor.inter =

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

Hello, I have a question regarding bootstrap confidence intervals. Suppose we have a data set consisting of single measurements, and that the measurements are independent but the distribution is unknown. If we want a confidence interval for the population mean, when should a bootstrap confidence interval be preferred over the elementary t interval? I was hoping the answer would be

Hi there, I'm stuck, but since I just started learning R, this might be a trivial problem. I need to do a bootstrap on the variance among the eigenvalues of a matrix. I can get this variance doing this: >var.eigenvalues=function(x) >var(eigen(cov(x), symmetric = T, only.values = T)$values) but if I try to run: >matrix=read.table("matrix.txt", header=T)

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)

Hi, All: I only have 4 samples. I wish to get a confidence interval around the mean. Is it reasonable? If not, is there a way to compute a confidence interval for such small sample size's mean? Many thanks, U [[alternative HTML version deleted]]

I tried 2 methods to estimate C.I. of correlation coefficient of variables x and y: > x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1) > y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8) #METHOD 1: Pearson's ********************************************************** > cor.test(x, y, method = "pearson", conf.level = 0.95) Pearson's