similar to: standard errors for predict.nls?

Dear all, How can I replace text in objects that are of class "formula"? y="a * x + b" class(y)="formula" grep("x",y) y[1] Suppose I would like to replace the "x" by "w" in the formula object "y". How can this be done? Somehow, the methods that can be used in character objects do not work 1:1 in formula objects... Many

Dear R users, Let?s say I have 10 models, each named m1,m2,m3..., and I would like to summarize them automatically and simultaneously - e.g., to extract parameter estimates later on from all models; how can I do that? I have tried: x=1:10 #this creates some example data y=rnorm(10) m1=lm(x~y) m2=lm(x~1) sum.lms=function(x)summary(paste("m",x,sep="")) sum.lms(1:2) but

Dear all, Suppose I have a nested, repeated measure lme model. Which of the following formulae is correct? (assuming data are sampled from several plots in an agricultural experiment) (1) y~explanatory.variables,random=~time|block/plot/subplot/individual (2) y~explanatory.variables,random=~time|unique.ID.of.every.individual I have read that (2) is the only approach that works. But how could I

Dear R users, suppose I have a matrix of observations for which I calculate all pair-wise correlations: m=matrix(sample(1:100,replace=T),10,10) w=cor(m,use="pairwise.complete.obs") How do I extract only those correlations that are >0.6? w[w>0.6] #obviously doesn?t work, and I can?t find a way around it. I would very much appreciate any help! Best wishes Christoph (using R

Dear R users, I am analyzing several response variables (all scaled to [0;1]) using a multivariate linear model. After fitting the model, I set up a hypothesis matrix to test specific contrasts for these response variables; for example: "a always increases significantly more than b when regressed against x". What I am stuck with now is how to calculate the correct F-values (and

Dear all, How can I extract the total and residual d.f. from a gnls object? I have tried str(summary(gnls.model)) and str(gnls.model) as well as gnls(), but couldn?t find the entry in the resulting lists. Many thanks! Best wishes Christoph -- Dr. rer.nat. Christoph Scherber University of Goettingen DNPW, Agroecology Waldweg 26 D-37073 Goettingen Germany phone +49 (0)551 39 8807 fax +49

Dear R users, Assume I have three models with the following AIC values: model AIC df model1 -10 2 model2 -12 5 model3 -11 2 Obviously, model2 would be preferred, but it "wastes" 5 df compared to the other models. Would it be allowed to select model3 instead, simply because it uses up less df and the delta-AIC between model2 and model3 is just 1? Many thanks for any

Dear all, I am fitting a multivariate linear model with 7 response variables and 1 explanatory variable. The following matrix P: P <- cbind( c(1,-1,0,0,0,0,0), c(2,2,2,2,2,-5,-5), c(1,0,0,-1,0,0,0), c(-2,-2,0,-2,2,2,2), c(-2,1,0,1,0,0,0), c(0,-1,0,1,0,0,0)) should consist of orthogonal elements (as can be shown using %*% on the individual columns). However, when I use

Dear all, For an introductory course on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into

Dear R users, Suppose I have a set of 10 candidate models, and these all differ by a delta-AIC of 5. Would it be sensible, then, to choose the best-fit model as the one that has *both* minimal AIC *and* minimal number of d.f.? Are there any references for such an approach? Many thanks for your help! Best wishes, Christoph -- Dr. rer.nat. Christoph Scherber University of Goettingen DNPW,

Dear all, I am forced to work in an environment without administrator rights. When using R2.13.1 on Windows 7 (64-Bit), I found that I can?t install or update any packages due to missing writing permissions. I managed to get full access to a directory on my C:\ drive now - but how do I specify that all libraries shall be installed into this directory? In Rcmd_environ I have the following

Dear all, Is it possible to create a pdf file with layers using the pdf() device in R? Many thanks for your help! Christoph (using R 2.15.1 on Windows 7 64-Bit) -- PD Dr Christoph Scherber Georg-August University Goettingen Department of Crop Science Agroecology Grisebachstrasse 6 D-37077 Goettingen Germany phone 0049 (0)551 39 8807 fax 0049 (0)551 39 8806 http://www.gwdg.de/~cscherb1

Dear R experts! I'd to fit data by 'nls' with me-supplied function 'fcn'. 1) I'd like 'fcn' to accept arbitrary arguments, i.e. I defined it as f(...) {<body>}. (Ok, that's not actually impotant). 2) Second, I would NOT like to supply every parameter in the formula. To illustrate this, let's look at the last example of 'nls' help

Hello, We are attempting to use nlme to fit a linear mixed model to explain bird abundance as a function of habitat: lme(abundance~habitat-1,data=data,method="ML",random=~1|sampleunit) The data consist of repeated counts of birds in sample units across multiple years, and we have two questions: 1) Is it necessary (and, if so, how) to specify the repeated measure (years)? As written,

Dear all, I got this error message > library(dprep) > mardia(Savg) Error in cov(data) : 'x' is empty But with the same data, I got > library(mvnormtest) > mshapiro.test(Savg) Shapiro-Wilk normality test data: Z W = 0.9411, p-value = 0.6739 What does the error message "Error in cov(data) : 'x' is empty" mean? Thanks a lot! Jiao

I have data that looks like YC Age Num 82 11 2 83 10 0 84 9 8 85 8 21 86 7 49 87 6 18 88 5 79 89 4 28 90 3 273 91 2 175 with a program mod1=lm(log(Num+1)~YC, data=box44) plot(log(Num+1)~YC, data=box44, pch=19, xlab="Year Class", ylab="Loge Number at age", ylim=c(0,6), xlim=c(91,82)) abline(lm(log(Num+1)~YC), col="blue", lwd=2) summary(mod1) I need to

Dear R Community, I am attempting to perform a weighted non-linear least squares fit. It has already been noted that the weights option is not yet implemented for the nls function, but no one seems to offer any suggestions for getting around this problem. I am still curious if a) anyone has code they have written which includes a weight options for nls, or b) if there is another model which

Hi there, I'm trying to fit a growth curve to some data and need to use a weighted least squares estimator to account for heteroscedasticity in the data. A weights argument is available in nls that would appear to be appropriate for this purpose, but it is listed as 'not yet implemented'. Is there another package which could implement this procedure? Regards, Robert Brown

Hello I followed the example in page 59, chapter 11 of the 'Introduction to R' manual. I entered my own x,y data. I used the least squares. My function has 5 parameters: p[1], p[2], p[3], p[4], p[5]. I plotted the x-y data. Then I used lines(spline(xfit,yfit)) to overlay best curves on the data while changing the parameters. My question is how do I calculate the residual sum of squares.

Dear R users, How can I do a regression analysis in R where there is more than one observation per x value? I tried the example in Sokal&Rohlf (3rd edn., 1995), page 476 ff., but I somehow couldn??t find a way to partition the sums of squares into "linear regression", "deviations from regression", and within-groups SS. I tried