similar to: getting CI's for certain y of nls fitted curve

hello, can anyone help with this: ########################################################### ###data: measurments (response = trans) run several times at the same predictor value level (press) por<-data.frame(list(structure(list(run = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L), .Label = c("1", "3", "4"), class = "factor"), press

Dear All, I am trying to use the bca.ci function on a boot.out object which consists a few NaN's and I want to ignore those NaN's, and get a ci only for the "normal" values. boot.out$t has R number of values for 3000 different statistics, so when I use boot.ci(boot.out, index=i) and i happens to be a column in boot.out$t with some NaN's in there I get an error message. I

I would like to put confidence intervals on a mantel corellogram they are already calculated in the pmgram object but I am unsure how I get the x value in order to plot them? package(ecodist) X<-1:100 Y<-rnorm(1:100) Z<-rnorm(1:100) XY<-dist(data.frame(X,Y)) YX<-dist(data.frame(Y,X)) my.mgram<-mgram(XY,XZ) plot(my.mgram) print(my.mgram) > print(my.mgram) $mgram

I?m entirely stumped on this particular issue and really hoping someone has some advice for me. I am running a covariant model in lm I would like to give the standard errors or the confidence intervals for the fitted lines. I?ve been using the dataset OrangeSprays where I want lines for each level of treatment over the covariant ?colpos?. I?ve been able to calculate intercepts and slopes for

hello, i need to calculate ci's for each of 4 groups within a dataset, to be able to infere about differences in the variable "similarity". the problem is that data within groups is dependent, as assigned by the blocking-factor "site". my guess was to use a block bootstrap but samples within in these blocks / sites are not of same length. i was not able to find a method to

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){ +

hey, how can i extract the values from the CI's when i use following code for a tukey test? the output shows three CI's and i know it should work with 'names but i don't really know how... thanks library(multcomp) data1$soort<-as.factor(data1$soort) amod<-aov(waarde~soort,data=data1) g<-glht(amod, linfct=mcp(soort = "Tukey")) confint(g) -- View this message in

Full_Name: Soren Feodor Nielsen Version: 2.5.0 OS: linux-gnu Submission from: (NULL) (130.225.103.21) The print-out of xtable in the following example is wrong; instead of yielding the correct ci's for the second model it repeats the ci's from the first model. require(xtable) require(MASS) data(cats) b1<-lm(Hwt~Sex,cats) b2<-lm(Hwt~Sex+Bwt,cats)

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

Dear List, I am relatively new to R and am trying to create more attractive plots than excel can manage! I have looked through the various programmes ggplot, lattice, hmisc etc but my case seems to be not metnioned, maybe it is but i have not noticed - if this is the case i apologise.

Hello everybody I'm trying to write a simple version of matlab's "quiver". The idea is that I have fluid with velocity defined on a grid. I have a matrix of x-components of velocity and a matrix of y-components and I want to see the overall flow pattern. (I work with 2D fluid mechanics problems). My first-stab function is below: quiver <- function(u,v,scale=1) # first

Hi all, I want to get results for cox proportional hazards on SNP data. I'm trying to get HRs, CI's, & p-values for each individual SNP - this is represented by cov[,i]. When I run my code, I get the following error: subscript out of bounds. I don't know why I am getting this error. I have looked through the R logs, but nothing that has been previously suggested has helped so

Hi, I'm trying to get confidence intervals to slopes from a linear model and I can't figure out how to get at them. As a cut 'n' paste example: ################# # dummy dataset - regression data for 3 treatments, each treatment with different (normal) variance x <- rep(1:10, length=30) y <- 10 - (rep(c(0.2,0.5,0.8), each=10)*x)+c(rnorm(10, sd=0.1), rnorm(10,

R-Users, I am working with gplots (in gregmisc bundle) plotting some posterior probabilities (using barplot2) of harvest bag limits for discrete data (x-axis from 0 to 12, data is counts) and I ran into a couple of questions whose solutions have evaded me. 1) When I create and include the confidence intervals, the lower bound of the confidence intervals for several of the posterior probabilities

Greetings, I have generated several ROC curves and would like to compare the AUCs. The data are cross sectional and the outcomes are binary. I am testing which of several models provide the best discrimination. Would it be most appropriate to report AUC with 95% CI's? I have been looking in to the "net reclassification improvement" (see below for reference) but thus far I can only

# I have some population estimates and confidence intervals for various size classes # of animals captured with two gear types. I'd like to plot the estimates along with # the 90 and 95% CI's by size class for each gear type. # The data can be read in as: estimates <- c(67.42,30.49,32.95,23.53,10.26,6.03,23.53,0.93,50.72,24.2,25.84,18.54,

dear all, I apologize for my delay in replying you. Here my contribution, maybe just for completeness: Similar to "earth", "segmented" also fits piecewise linear relationships with the number of breakpoints being selected by the AIC or BIC (recommended). #code (example and code from Martin Maechler previous email) library(segmented) o<-selgmented(y, ~x, Kmax=20,

I have a model with 5 parameters that I am optimising where the (best) value of the objective function is negative. I would like to use the Hessian matrix (from genoud and/or optim functions) to construct the covariance and correlation matrices. This is the code that I am using: est <- out$par # Parameter estimates H <- out$hessian # Hessian V <-

This is partly an R and partly a general statistics question. I'm trying to get confidence intervals of proportions (sometimes for subgroups) estimated from complex survey data. Because a function like prop.test() does not exist for the "survey" package I tried the following: 1) Define a survey object (PSU of clustered sample, population weights); 2) Use svyglm() of the package

Hi, I have the two vectors mean and sd of individual columns, but I am unsure how to generate bar charts with the standard deviation, even after looking the help of barplot and barplot.2. [[alternative HTML version deleted]]