similar to: Problem building dll in R-2-15-1

I would like to plot two data sets (frequency (y-axis) of mean values for 0-1(x=axis)) on a single histogram for comparison. The hist() only allow the overlay of two histograms, and although barplot() allows beside=TRUE, it does not show frequency values (like hist) but rather all of the values. Is there any way that I can use the hist() to plot two data sets similar to the barplot(). Any help or

Dear all with image I can plot only one set of values in one plot. Do somebody have any insight how to put those 2 matrices into one picture so that in one cell in image picture are both values from mat[1,1] and mat2[1,1]. mat<-matrix(1:4, 2,2) mat2<-matrix(4:1,2,2) x <-1:2 y <-1:2 image(x, y, mat) image(x, y, mat2) The only way I found is to mix x or y for both matrices let

Sorry for the repeat. The PRESS statistic is defined as sum(y-yhat(i))^2, where yhat(i) denotes the ith predicted value using all the data except the ith case (as used typically in linear models). Thanks again Jacob Jacob L van Wyk Department of Mathematics and Statistics Rand Afrikaans University P O Box 524 Auckland Park 2006 South Africa Tel: +27-11-489-3080 Fax: +27-11-489-2832

Hello everyone, Peter (see my earlier post) recommended the following script for finding the means and standard deviations and putting them in table form. However, I would like the standard deviations under the means in brackets. Can anyone check this code to see how this can be adjusted? library(xtable) dataset1 = matrix( c(1,2,3,4, 5, 6 ), 2 , 3) dataset2 = matrix( c(4,3,5,10, 1, 0), 2, 3)

Hallo Could anybody please help. I have a simple linear regression model with 5 predictors. I want to use "bootstrap residuals" to make inferences regarding beta(2)hat. After fitting the model y=b0+b1+b2+b3+b4+b5 I tried the following: mod <- lm(y ~ x1+x2+x3+x4+x5) res <- resid(mod) pred <- predict(mod) Now, I have tried boot(res, lm(res+pred ~ x1+x2+x3+x4+x5)$coef[3],

Hi everyone I want help in graduating the attached rates and checking for goodness of fit and smoothness using R please help. Many thnk TOo every one around the world This message and attachments are subject to a disclaimer. Please refer to www.it.up.ac.za/documentation/governance/disclaimer/ for full details. / Hierdie boodskap en aanhangsels is aan 'n vrywaringsklousule onderhewig.

Dear R users, Overdispersion is often a problem in binomial data. I attempt to model a binary response (sex-ratio) with three categorical explanatory variables, using GLM, which could assume the form: y<-cbind(sexf, sample-sexf) model<-glm(y ~ age+month+year, binomial) summary(model) Output: (Dispersion parameter for binomial family taken to be 1) Null deviance: 8956.7 on 582

I gave you a lengthy description of the Fedora lists. It's rather rude to see your question here again. Met vriendelijke groet, Hans Witvliet, J, Ing., DMO/OPS/I&S/APH, Kennis Team Opensource Coldenhovelaan 1 Maasland 3531RC Coldehovelaan 1, kamer B213 -----Original Message----- From: CentOS [mailto:centos-bounces at centos.org] On Behalf Of Turritopsis Dohrnii Teo En Ming Sent: vrijdag

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Dear R Users; I have been using the space-time K-function analysis to detect the existence of clustering in both space and time. I wish to ask and know what the minimum number of cases should be for such analyses to be valid. I have data with 23 cases and don't know how careful i should be in the interpreting the results. Secondly, i wish to find out if there is any bond between the