similar to: Intercept in model formulae

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

To anyone who can help: I have two brief questions concerning sapply. Following below is the code for my example. The two problems are described at the end of the code: site <- rep(2:6, each = 12) tillage <- rep(c(1,-1), each = 6, times = 5) carbon <- c(18.23, 16.06, 17.81, 16.07, 17.26, 17.08, 14.92, 15.88, 12.11, 14.23, 16.99, 13.57, 20.34, 20.3,

Dear List, i am trying to produce a 3d plot using wireframe using the code: wireframe(Residuals_FD ~ Elevation * Temperature, data = data2, scales = list(arrows = FALSE), drape = TRUE, colorkey = TRUE) As you can see when the code (using the data below) is run the plot area is set-up correctly but the actual surface is missing? Any help would be greatly appreciated. Chris #data Elevation

Hello R users, For the last few days I am struggling with the following task: my data.frame: A1 A2 A3 B1 B2 B3 58.81 53.292 54.501 13.013 17.39 19.407 56.02 56.251 54.033 20.099 13.15 10.411 55.376 53.099 57.625 13.396 21.031 13.22 58.584 53.194 54.218 13.038 16.854 19.289 55.7 55.921 53.847 19.942 13.153 9.828 55.093 52.934

Dear R members, I have been trying to find out whether one can use multivariate regression trees (for example mvpart) to analyze repeated measures data. As a non-parametric technique, CART is insensitive to most of the assumptions of parametric regression, but repeated measures data raises the issue of the independence of several data points measured on the same subject, or from the same plot

Hi folks, I'm trying to estimate bias-corrected percentile (BCP) confidence intervals on a vector from a simple for loop used for resampling. I am attempting to follow steps in Manly, B. 1998. Randomization, bootstrap and monte carlo methods in biology. 2nd edition., p. 48. PDF of the approach/steps should be available here: https://wyocoopunit.box.com/s/9vm4vgmbx5h7um809bvg6u7wr392v6i9 If

Hello, I'm EDWIN, I create (make) GUI, with call many function but I don't know why when I call function I can't. if without function, Yes I can.. can you help me ? can you make this, become true with full code? Can you help me to create data.entry with interface LM - BETA1.HAT - BETA2.HAT SD.BETA1.HAT HAT SD.BETA2.HAT RLM - BETA1.HAT - BETA2.HAT SD.BETA1.HAT HAT SD.BETA2.HAT 2 10

Is there a way to get confidence intervals around an ordered regression like polr() in the MASS package? ------------------------------------------- Joe King, M.A. Ph.D. Student University of Washington - Seattle 206-913-2912 <mailto:jp@joepking.com> jp@joepking.com ------------------------------------------- Ad astra per aspera - "Through hardships to the stars"

Is there any way to put an argument into an object name. For example, say I have 5 objects, model1, model2, model3, model4 and model5. I would like to make a vector of the r.squares from each model by code such as this: rsq <- summary(model1)$r.squared for(i in 2:5){ rsq <- c(rsq, summary(model%i%)$r.squared) } So I assign the first value to rsq then cycle through models 2 through

>I am trying to use the Hmisc function transace to transform predictors > > test<-cbind(flowstress,pressres,alloy) > xtrans<-transace(x,binary=pressres',monotonic='flowstress', categorical='alloy') > > >and I am getting the following message?? >Error in ace(x[, -i], x[, i], monotone = im, categorical = ic) : > unused argument(s) (monotone ...)

I am trying to find out if someone has implemented a (McFadden-type) multivariate binary logistic regresssion package for R? From what I can tell, this is not available for R. Thank you, Lynne Baker [[alternative HTML version deleted]]

Hello. I am trying to do a non-linear fit using the 'nls' command. The data that I'm using is as follows pH k 1 3.79 34.21 2 4.14 25.85 3 4.38 20.45 4 4.57 15.61 5 4.74 12.42 6 4.92 9.64 7 5.11 7.30 8 5.35 5.15 9 5.67 3.24 with a transformation of pH to H <- 10^-pH When using the nls command for a set of parameters - a, b and c, I receive two sets of errors: >

Hi every one, I am using the package 'drc' to model root elongation using dose response data. I don't know which model I should use. Though I don't know which model I should use, I tried the following codes given below. But it produced the error messages.Can any one tell me the code in 'drc' package to find out the EC (Effective Concentration) values and Confidence

I?ve been using Frank Harrell?s rms package to do bootstrap model validation. Is it the case that the optimum penalization may still give a model which is substantially overfitted? I calculated corrected R^2, optimism in R^2, and corrected slope for various penalties for a simple example: x1 <- rnorm(45) x2 <- rnorm(45) x3 <- rnorm(45) y <- x1 + 2*x2 + rnorm(45,0,3) ols0 <- ols(y

Christoph -- There was just a thread on this earlier this week. You can search in the archives for the title: "refitting lm() with same x, different y". (Actually, it doesn't turn up in the R site search yet, at least for me. But if you just go to the archive of recent messages, available through CRAN, you can search on refitting and find it. The original post was from William

R-Listers: I am doing a quasi-maximum likelihood estimation and I get a "subscript out of bound" error message, Typically I would think this means that a subscript used in the function is literally out of bounds however I don't think this is the case. All I change in the code is a constant, that is hard-wired in (not data dependent and not parameter dependent), furthermore,

I think I am doing something wrong when I try to bootstrap R square obtained from lm. My code is included below. No matter how many times I run the simulation, I always get exactly the same result, the bias and std.error are always zero. I would think that these values should be non-zero. I would appreciate any suggestions as to what I am doing wrong, or perhaps what I fail to understand. R 2.1.0

I've been using R to do logistic regresssion, and that's working well, but there are two things I haven't figured out how to do. (1) Is there some pre-existing function that will let you compute the odds ratios and confidence intervals for them for a specific fit. I know how to do this manually or even write a function that I can call with the coefficients and se, but

Hello, I am trying to filter a data set like below so that the peaks in the Phase value are more obvious and can be identified by a peak finding function following the useful advise of Carl Witthoft. I have written the following for(i in length(data$Phase)){ newphase=if(abs(data$Phase[i+1]-data$Phase[i])>6){ data$Phase[i+1] }else{data$Phase[i] } } I get the following error which I have not

On 5 Feb 2017, at 19.49, Timo Sirainen <tss at iki.fi> wrote: > > On 4 Feb 2017, at 20.03, Ruga <ruga at protonmail.com> wrote: >> >> https://github.com/libressl-portable/portable/issues/278 > > I've no idea why that would happen. The only idea I had got rejected by someone. Oh, that's with OSX. I think that's the reason. Nothing to do with