similar to: Comparing Type 2 regressions

For starters, let me say again how impressive R is, and how all of the effort that has gone into R shines through clearly. It is an amazing program, and I am frequently pleasantly surprised to find out how powerful and capable R is. Finding the interface to xgobi is the latest pleasant surprise. Are there functions in R for Major Axis and Reduced Major Axis regression? I have OBS-RESID graphs of

First, I am really liking what I have seen so far in R. The demos are quite fascinating, and I look forward to doing some really neat stuff over the next little while with R. Being but an intiate to R, I am having quite a bit of trouble figuring out how to use the leaps library. Rather than ask several questions to the list at large, I would rather explain what I'm trying to do to someone in

Hi R-Users, I am working with sm.ancova (in the package sm) and I have two problems with the graph, which is automatically generated when sm.ancova() is run. 1-Besides of the fitted lines, the observed data appeared automatically in the graph. I prefer that only fitted lines appear. I check the sm.options, but I could not find the way that the observed data do not appear in the graph. 2-I

How to Ancova in R? I know this has got to be an FAQ, because I see it asked in the lists, but I haven't seen an answer to it. I see the R-sm has the ancova thing happening, but I kind of doubt that what I'm trying to do is "smoothing"... -- Pete Hurd phurd at uts.cc.utexas.edu http://www.zo.utexas.edu/research/phurd Section of Integrative Biology, University of Texas, Austin

Hi there, This problem has been dogging me for a bit, and I'm trying to figure out why. When running the the subsets function in the leaps library, R is giving me the following error message > lvodsub <- subsets(pred, resp$LVOD) Warning message: XHAUST returned error code -999 in: leaps.exhaustive(a, really.big = really.big) but this still happens if I add the really.big option:

Hi there, I want to do a nonparametric covariance analysis and I have tried to use the package "sm" function "sm.ancova" but it didn't work for me because I have more then one covariates (I have 18 covariates and 3 factors). I want to analyse for one factor (who has 13 levels) where the differences for my response are, using the explanatory (covariates). (The other two

Thanks to John Fox, Brian Ripley, and Peter Dalgaard for responding. The short answer (as in Peter Dalgaard's reply, already posted to the list) is that the models I'm concerned with can in fact be compared using ancova. The key fact is that while the parameters may not be nested, the subspaces I'm examining are. An additional note from Prof. Ripley on AIC and BIC (which I quote in

I have to admit that I'm at a bit of a loss here; any pointers would be greatly appreciated. I've been making correlation matricies from some of my datasets, and have been instructed to get the probability values for each of these correlations. I've checked the online help for info on both the cor and cov functions, but I was unable to find any relevant info on finding how to obtain

Hi there, I realize that this is a bit of a strange question, but here goes. In SAS, one can use the REG procedure to carry out a least-squares regression analysis. By specifying the R-SQUARE option in the SELECTION command, the program carries out regressions for every combination of every independant variable against the dependant variable. This is useful in smaller datasets, though difficult

Dear R Users, Recently I began to use R. I`m interested about comparing several regression curves. REcently I found the package sm and the function sm.ancova which I understand allows me to do this. I applied this function to my data (seven regression curves) and I found that there are significant differences. Nevertheless, when I want to find out where are the differences, the package tells that

Dear R-help list, I am attempting to understand the proper formulation of ANCOVA's in R. I would like to test both parallelism and intercept equality for some data sets, so I have generated an artificial data set to ease my understanding. This is what I have done #Limits of random error added to vectors min <- -0.1 max <- 0.1 x <- c(c(1:10), c(1:10))+runif(20, min, max) x1 <-

Please help me interpret the following results. The full model (Schwa~Dialect*Prediction*Reduction) was reduced via both update() and step(). The minimal adequate model is: ancova<-lm(Schwa~Dialect+Prediction+Reduction+Dialect:Prediction) Schwa is response variable Dialect is factor, two levels ("QF","SF") Prediction is factor, two levels ("High","Low")

Hi, I have some blocked transactions. Does anybody have some advise on how I could mend this because I am unsure where to start? I believe this broke after I issued some set auth.allow commands: # gluster volume set oem-shared auth.allow 10.54.54.57,10.54.54.160,10.54.54.161,10.54.54.213,10.54.54.214,10.22.9.73,10.22.9.74 Kind regards, Sophie [2017-06-20 13:28:24.052623] E

Hello R experts I have two linear regressions for sexes (Male, Female, Unknown). All have a good correlation between body length (response variable) and head length (explanatory variable). I know it is not recommended, but for a good practical reason (the purpose of study is to find a single conversion factor from head length to body length), the regressions need to go through the origin (0

Could you please help me on the following ANCOVA issue? This is a part of my dataset: sampling dist h 1 wi 200 0.8687212 2 wi 200 0.8812909 3 wi 200 0.8267464 4 wi 0 0.8554508 5 wi 0 0.9506721 6 wi 0 0.8112781 7 wi 400 0.8687212 8 wi 400 0.8414646 9 wi 400 0.7601675 10 wi 900 0.6577048 11 wi 900

Hello, all. I've got what is probably a simple question about comparison of models using anova, specifically about the situations in which it's valid. I understand, I think, what's going on when the models are strictly nested (as most are in the demo(lm) examples). My question involves what happens when the models aren't strictly nested. In my particular case, I'm doing

Hello R experts, I have having a difficult time figuring out how to perform and interpret an ANCOVA of my nested experimental data and would love any suggestions that you might have. Here is the deal: 1) I have twelve tanks of fish (1-12), each with a bunch of fish in them 2) I have three treatments (1-3); 4 tanks per treatment. (each tank only has one treatment applied to it) 3) I sampled

Dear R users, i'm using a custom function to fit ancova models to a dataset. The data are divided into 12 groups, with one dependent variable and one covariate. When plotting the data, i'd like to add separate regression lines for each group (so, 12 lines, each with their respective individual slopes). My 'model1' uses the group*covariate interaction term, and so the coefficients

Hi, I am looking for some tips on how to incorporate known measurement error into the comparison of slopes in an analysis of covariance. Specifically, if I know that each measurement comes with a 5% error, is it possible to 'expand' the confidence intervals around the estimates for the slope of the line passing through the data defined by the grouping variable? With standard linear

Hello, I was wondering if anyone could please help me with some simple questions regarding ANCOVA and the assumption of homogeneity of slopes. The standard design of ANCOVA assumes the homogeneity of regression coefficients of the different groups. This assumption can be tested using the factor ?? covariate interaction, which should subsequently be removed. However if this assumption is not