similar to: confidence intervals

Hi everybody, I'm having trouble getting an eval-parse combination to work with lme in a for loop. Consider the code below. The call to lme outside the loop works. The call to aov inside the loop works, but the call to to lme inside the loop does not. The latter fails with Error in model.frame.default(formula = ~meanCol + Group + c1 + c3 + Subject, : variable lengths differ (found for

Dear List: I am working to understand some differences between the results of the svymean() function in the survey package and from code I have written myself. The results from svymean() also agree with results I get from SAS proc surveymeans, so, this suggests I am misunderstanding something. I am never comfortable with "I did what the software" does mentality, so I am working to

Dear R folk: Perhaps I'm just dense today, but I am having trouble reproducing the principal components plotted and summarized by clusplot. Here is a brief example using the pluton dataset. clusplot reports that the first two principal components explain 99.7% of the variability. But this is not what princomp is reporting. I would greatly appreciate any advice. With best regards, -- Tom

For block effects with small variance, lmer will sometimes estimate the variance as being very close to zero and issue a warning. I don't have a problem with this -- I've explored things a bit with some simulations (see below) and conclude that this is probably inevitable when trying to incorporate random effects with not very much data (the means and medians of estimates are plausibly

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

Hello, I am using R.12.2.0. I am trying to generate bootstrap confidence intervals using bootcov from the rms package. I am able to impute the missing data using aregImpute and to perform a linear regression on the imputed datasets using fit.mult.impute, but I am unable to use bootcov to generate the confidence intervals for the R-squared. Here is a small example that should duplicate the

Hi, I am attempting to set the confidence limits on a ls means plot as follows: mult<-glht(lm(effectModel, data=statdata, na.action = na.omit), linfct=mcp(mainEffect="Means")) meanPlot <- sub(".html", "meanplot.jpg", htmlFile) jpeg(meanPlot) plot(mult, main=NA, xlab=unlist(strsplit(Args[4],"~"))[1]) This produces 95% CIs by default but I would

Hello R Experts, I started to work with the qcc package and it wprks quite nicely. Heres My Code: n <- c(55,5,94,25,10,15,15,40,44,34,90,114,204,37,30,28,12,68,64,29,24,14,31,16,62,45,55,20,24,14,9,19,76,57,55,42,6, 54,32,117,19,32,9,11,13,31,27,33,44,28) x <- c(6,0,30,5,2,1,4,5,2,9,12,24,64,3,9,21,9,48,15,4,3,0,1,0,6, 7,2,0,3,2,0,5,1,2,6,9,1,6,2,0,1,4,8,0,1,1,3,0,0,0)

violating.runs I read from the news cran.r-project.org/doc/Rnews/Rnews_2004-1.pdf that the criteria for the violating is 5 but 1)I cannot find "5" in the code of the function. Where is the "5" ? 2)What is the easiest way to change it ? 3)Is there any more criterias made somewhere ? Yours sincerelly, Tommi Viitanen

I have windows version of R v1.6.2 I have downloaded the qtoolbox.zip from http://www.cmis.csiro.au/S-PLUS/qtoolbox/ My R command line is > install.packages("C:/Program Files/R/qtoolbox.zip", .libPaths()[1], CRAN = NULL) and I get the following error message: updating HTML package descriptions Warning message: error -1 in extracting from zip file > Is there another way to

Hi all, I wonder if you could help me with (what is surely) a simple R question? I've written a simple R script (attached) to perform multiple ANOVAs on the columns in a table loaded in from a a file (also attached). I loop through the list of columns for which I want to perform the ANOVAs, storing the column name in a variable. When I try to perform the ANOVA using the variable name as a

Hi all, I've done some anovas, found some significance and proceeded to do the post hoc tests to determine the source of the significance. I used the multcmp package as suggested by Andy Liaw (thank you). I am, however, unsure how to interpret the results of the call to simint. Could someone help me with this? I've attached the code and the results it produced. There are 3 groups

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 know this is not exactly an R question but may be someone will nice enough to give me a pointer. I'm using nnet to fit a non linear univariate function. I wonder if there is a way to estimate a confidence interval for the predicted output beside using the residuals? The data set I use is relatively small and I use only a few (5:10) of these as test set or should I say validation set.

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

I'm using the plotmeans function to plot the means and 95% CI of my groups, however I also want to get the actual numbers of the CI, i.e. the upper and lower bounds. Is there anyway to get that information out of plotmeans? If not, is there an easy way to calculate the CIs? -- View this message in context: http://old.nabble.com/Confidence-intervals-tp26225647p26225647.html Sent from the R

fixed @ R-forge. New version should appear on CRAN soon. Thanks for the report! Torsten > > ---------- Forwarded message ---------- > Date: Sat, 3 Sep 2011 23:56:35 +0200 > From: Ulrich Halekoh <Ulrich.Halekoh at agrsci.dk> > To: "r-help at r-project.org" <r-help at r-project.org> > Subject: [R] glht (multcomp): NA's for confidence intervals using

Dear All, Is it possible to get confidence intervals for Harrell's concordance index or, equivalently, Somer's D using the rms package or in some other way ? I have survival data it would be the c-index in the Cox model setting Many thanks Dr Bernard North Statistical Consultant Statistical Advisory Service Advice and Courses on Research Design and Methodology Imperial College South

Hi, Sorry for repeated question. I performed logistic regression using lrm and penalized it with pentrace function. I wanted to get confidence intervals of odds ratio of each predictor and summary(MyModel) gave them. I also tried to get bootstrapping standard errors in the logistic regression. bootcov function in rms package provided them. Then, I found that the confidence intervals provided by

Hi all! I would like to estimate confidence intervals for a non lm model. For example, I use a mixed model of the form: md=lme(y~x1+I(x1^2)+x2 ...) Parameters x1+I(x1^2) are fixed effects and I would like to plot the predicted (partial) curve corresponding to these ones, along with 90% CI bands. Thus, I simulate (partial) predictions: Lx=c() Ux=c() Mx=c() z=c() for (j in