similar to: plotting effect confidence intervals

Hi, I'm regressing various body dimensions upon body mass using the 'lmodel2' function, as I'm keen to obtain both OLS and RMA slope values. I also wish to create a plot of the regressions, with the 95% confidence interval of both the slope and intercept. I know how to plot 95% ci bands of the OLS slope using lm with the 'predict' function and 'matlines'. Does

Hi, I have a question about the computation of confidence intervals in the zyp package, in particular using the functions zyp.sen and confint.zyp, or zyp.yuepilon. (1) I'm a bit confused about the confidence intervals given by zyp.sen and confint.zyp. When I request a certain confidence interval in the function, the R output seems to deliver another confidence interval, e.g. when I set

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-helpers, I am using the package "drc" to fit a 4 parameter logistic model. When I use the predict function to get prediction on a new dataset, I am not getting the requested confidence or prediction intervals. Any idea what is going on? Here is code to reproduce the problem: --- library(drc) # Fit model to existing dataset in package spinach.model <- drm(SLOPE~DOSE, data =

Hello all, This is something that I am sure has a really suave solution in R, but I can't quite figure out the best (or even a basic) way to do it. I have a simple linear regression that is fit with lm for which I would like to estimate the x intercept with some measure of error around it (confidence interval). In biology, there is the concept of a developmental zero - a temperature under

This is a follow-up mail of "the effect of blocking on the size of confidence intervals - analysis using aov". In both mails I pursue the idea of using blocking factors in order to reduce the width of confidence intervals. My dataset comprises, a quantitative response variable, namely: "response", and three categorical eplanatory variables, namely: "method",

I want to compute confidence intervals for the random effect estimates for each subject. From checking on postings, this is what I cobbled together using Orthodont data.frame as an example. There was some discussion of how to properly access lmer slots and bVar, but I'm not sure I understood. Is the approach shown below correct? Rick B. # Orthodont is from nlme (can't have both nlme and

Hi, I am a method developer in drug discovery. I have developed a similarity searching method for small (drug-like) molecules. I want to compare the performance of my method to the performance of other methods. Similarity searching methods are commonly assessed by their ability to (re)discover a set of molecules that is avtive versus a given target, given one randomly selected query molecule.

Hi list, is there any direct way to obtain confidence intervals for the regression slope from lm, predict.lm or the like? (If not, is there any reason? This is also missing in some other statistics softwares, and I thought this would be quite a standard application.) I know that it's easy to implement but it's for explanation to people who faint if they have to do their own programming...

Dear all, Does someone know an R function implementing the method of Sison and Glaz (1995) (see full ref below) for computing Simultaneous confidence intervals for multinomial proportions? As alternative method, I think to boostrap the mean of each proportion and get in that way confidence interval of the mean. I observed 21 times a response that could be one out of 8 categories

Hello R users I'm using MuMIn but for some reason I'm not getting the adjusted confidence interval and uncoditional SE whe I use model.avg(). I took into consideration the steps provided by Grueber et al (2011) Multimodel inference in ecology and evolution: challenges and solutions in JEB. I created a global model to see if malaria prevalence (binomial distribution) is related to any

Dear R-helpers, i get some strange results using a linear mixed-effects model (lme), of the type: lme1 <- lme(y ~ x, random=~x|group, ...) For some datasets, i obtain very small standard deviations of the random effects. I compared these to standard deviations of the slope and intercept using a lmList approach. Of course, the SD from the lme is always smaller (shrinkage estimator), but in

Hello, Setup: I have data with ~10K observations. Observations come from 16 different laboratories (labs). I am interested in how a continuous factor, X, affects my dependent variable, Y, but there are big differences in the variance and mean across labs. I run this model, which controls for mean but not variance differences between the labs: lm(Y ~ X + as.factor(labs)). The effect of X is

Hello, We are comparing some features of our product against a competitor. Since the product is produced in lots and we have proof that ambient temperature is relevant for its functioning we used a mixed effect model (MEM) structured this way (NB: R lme4 notation): `out ~ Brand * Temperature + (Temperature | Lot)` with out being the various outcomes, a random intercept on Lot and a random slope

Dear all, I am working on a data set in which I have sequentially measured egg temperatures ("eggtemp") in birds incubating in different ambient temperatures ("treat", sample data set below), "id" is not replicated within treatment. id treat eggtemp 1 79 3 30.90166 2 42 3 34.94044 3 10 3 32.69945 4 206 3 36.64127 5 23 3 31.80055 6

Dear R-helpers and graphics gurus, I have two problems with plotting confidence bands: 1. First is relatively simple. I am using the Passing-Bablok procedure to obtain "unbiased" regression coefficients. This procedure yields the "a" & "b" coefficient values along with their confidence intervals. I then plot the raw data with the regression line, but I would

Hi, I'm doing some modelling (lm) for my 3rd year dissertation and I want to do some resampling, especially as I'm working with microbes, getting them to evolve resistance to antimicrobial compounds, and after each exposure I'm measuring the minimum concentration required to kill them (which I'm expecting to rise over time, or exposures), I have 5 lineages per cleaner, and

I have already obtained my confidence intervals from a bootstrapping procedure and now I want to plot the estimates and the confidence intervals similar to the plots obtained when the geom_smooth function is used in ggplot2. Thanks -- View this message in context: http://r.789695.n4.nabble.com/Plotting-Confidence-Intervals-with-a-proiri-calculated-Intervals-tp4322581p4322581.html Sent from the R

Hi, I would like to plot a set of paired means (as X Y data) with unique confidence intervals for each (creating a set of ellipses, each with it's own centre point and shape). Would appreciate any advice out there! Cheers, Ged [[alternative HTML version deleted]]

Dear all, I am using the Instrumental Variable approach to estimate the causal effects of TWO endogenous variables in a Mendelian Randomization study. As long as point estimation is concerned, I have no problem: both "ivreg" in library "AER" and "tsls" in library "sem" do the job perfectly. The problems begin when I try to obtain confidence intervals for