similar to: confidence intervals

Hi, I'm a database admin for a database which manage chromatographic results of products during stability studies. I use R for the reporting of the results in MS Word through R2wd. But now I think I need your help: suppose we have the following data frame: ID rrt Mnd Result 1 0.45 0 0.10 1 0.48 0 0.30 1 1.24 0 0.50 2 0.45 3 0.20 2 0.48 3 0.60 2 1.22 3 0.40 3

for unweighted fits using `nls' I compute confidence intervals for the fitted model function by using: #------------------- se.fit <- sqrt(apply(rr$m$gradient(), 1, function(x) sum(vcov(rr)*outer(x,x)))) luconf <- yfit + outer(se.fit, qnorm(c(probex, 1 - probex))) #------------------- where `rr' contains an `nls' object, `x' is the independent variable vector, `yfit'

Hi, I have a model, which has a logaritmic response, but there is an offset for this response. I use optimize to find a value to minimise the residuals of the model: f2 <- function(x,df) sum(summary(lm(log((y-x)/Mnd) ~ I(1e+05/(8.617*(Temp + 273.16))), df))$residuals^2) start <- optimize(f2,c(0,min(y)),df=df)$minimum mod <- lm(log((y-start)/Mnd) ~ I(1e+05/(8.617*(Temp + 273.16))), df)

I have data that looks like O.lengthO.age 176 1 179 1 182 1 ... 493 5 494 5 514 5 606 5 462 6 491 6 537 6 553 6 432 7 522 7 625 8 661 8 687 10 704 10 615 12 (truncated) with a simple VonB growth model from within nls(): plot(O.length~O.age, data=OS) Oto = nls(O.length~Linf*(1-exp(-k*(O.age-t0))), data=OS, start=list(Linf=1000, k=0.1, t0=0.1), trace=TRUE) mod <- seq(0, 12)

Dear All I am quite new to R and would appreciate some help fitting 95% confidence intervals to a nls function. I have the data DOY CET 90 5.9 91 8 92 8.4 93 7.7 95 6.6 96 6.8 97 7.1 98 9.7 99 12.3 100 12.8 102 11 103 9.3 104 9.8 105 9.9 107 7.7 110 6.2 111 5.9 112 5.9 113 3.4 114 3.5 116 3.3 117 5.4 118 6.3 119 9.7 120 11.2 121 7.3 124 7.8 etc I am trying to use some code that has been

dear list members, I apologize in advance for posting a second time, but probably after one week chances are, the first try went down the sink.. my question concerns computation of confidence intervals in nonlinear fits with `nls' when weigthing the fit. the seemingly correct procedure does not work as expected, as is detailed in my original post below. any remarks appreciated. greetings

dear list members, my question concerns computation of confidence intervals in nonlinear fits with `nls' when weigthing the fit. the seemingly correct procedure does not work as (I) expected. I'm posting this here since: (A) the problem might suggest a modification to the `m' component in the return argument of `nls' (making this post formally OK for this list) and (B) I got no

Hi folks! I run the following code to get a CI for a Poisson with lambda=12.73 library(MASS) set.seed(125) x <- rpois(100,12.73) lambda_hat<-fitdistr(x, dpois, list(lambda=12))$estimate #Confidence Intervals - Normal Approx. alpha<-c(.05,.025,.01) for(n in 1:length(alpha)) { LowerCI<-mean(x)-(qnorm(1-alpha[n]/2, mean = 0, sd = 1)*sqrt(var(x)/length(x)))

hello, i managed to get CI's for my curve - but now I need the intervall for a certain y point (y_tenth) of the curve.. can anyone help me with this? #####data: 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 = c(15, 21, 24, 29.5, 15, 21,

I am trying to get confidence bands for a non-linear power function (y=mx^b). I thought I should be able to figure it out, but can't. Are there any R consultant? I would be willing to pay some amount of money, but not sure such consultants exist. I fit power functions to lots of data, and this would be very useful. I would ideally like to have confidence bands for the mean function and a

Hi, this should be an easy one, but I can't figure it out. I have a vector of tests, with their units between brackets (if they have units). eg tests <- c("pH", "Assay (%)", "Impurity A(%)", "content (mg/ml)") Now I would like to hava a function where I use a test as input, and which returns the units like: f <- function (x) sub("\\)",

Hi. The short story... Rush job, never done clustered file systems before, vlan didn't support multicast. Thus I ended up with drbd working ok between the two servers but cman / gfs2 not working, resulting in what was meant to be a drbd primary/primary cluster being a primary/secondary cluster until the vlan could be fixed with gfs only mounted on the one server. I got the single server

Dear R-Help members, I am using Simon Wood`s mgcv package version1.7-22and R version 3.0.0 (2013-04-03) for fitting a GAM-Model to the LIDAR Data contained in the "SemiPar" package. Here is the code for fitting the model and for plotting the result: data("lidar") attach(lidar) ### # mgcv fitting ### gam_fit <- gam(logratio ~ s(range, k = 40, bs = "cr"), gamma

Hi I really hope someone can help me. I have just started to work with S-plus, and have not yet understood how it really works. I am now trying to fit a mixed effects model with lme. My goal is to compare four different groups, at several different time points, and I therefore would like to create confidence intervals for linear combinations of my estimated parameters (as I usually do with

To Whom It May Concern, I estimated a model with 6 states (3 time-varying Regression parameters and 3 quarterly seasonality trends). The model is saved in the object titled "mod." Following the example in the documentation and using the commands below, I am attempting to use the function "dlmSvd2var" to implement SVD and calculate the 90% confidence errors for each

To Whom It May Concern, I estimated a model with 6 states (3 time-varying Regression parameters and 3 quarterly seasonality trends). The model is saved in the object titled "mod." Following the example in the documentation and using the commands below, I am attempting to use the function "dlmSvd2var" to implement SVD and calculate the 90% confidence errors for each

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

Hello, I have a couple of Dell 2950 III, both of them with CentOS 5.3, Xen, drbd 8.2 and cluster suite. Hardware: 32DB RAM, RAID 5 with 6 SAS disks (one hot spare) on a PERC/6 controller. I configured DRBD to use the main network interfaces (bnx2 driver), with bonding and crossover cables to have a direct link. The normal network traffic uses two different network cards. There are two DRBD

https://bugzilla.mindrot.org/show_bug.cgi?id=1635 Summary: It is useful to transfer locale-related environment variables acros the conection Product: Portable OpenSSH Version: 5.2p1 Platform: Other OS/Version: Linux Status: NEW Severity: normal Priority: P2 Component: Miscellaneous

I wonder if anyone know why survest (a function in Design package) and standard survfit.coxph (survival) returned different confidence intervals on survival probability estimation (say 5 year). I am trying to estimate the 5-year survival probability on a continuous predictor (e.g. Age in this case). Here is what I did based on an example in "help cph". The 95% confidence intervals