similar to: comparing ancova models

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'm trying to perform ANCOVAs in R 1.14, on a Mac OS X, but I can't figure out what I am doing wrong. Essentially, I'm testing whether a number of quantitative dental measurements (the response variables in each ANCOVA) show sexual dimorphism (the sexes are the groups) independently of the animal's size (the concomitant variable). I have attached a 13-column matrix as a data

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, I am VERY new to R, just picking it up infact. I have got my head around the basics of ANOVA with post hoc tests but I am struggling with regression, especially with ANCOVAs. I have two sets of data, one of type A, one of type B. Both have been placed in a wind tunnel and sampled every week. The co variate is of course the days since the start. An example is day A B 0 10.0 10.0 7 9.0

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

Dear RUsers, I guess this is an easy question for someone a little familiar with programming...(which I am not)... I've got 2 colummns, one shows just dates(SST_date, Class 'Date' num), the other one shows the SeaSurfaceTemperature (SST, num) at that certain date. SST_date SST 2008-01-01 22.2 2008-01-02 21.8 2008-01-03 22.8 2008-01-04 22.9 2008-01-05 23.1 2008-01-06 23.2 ...

Hello! I have measurements (length and volume) of fish collected in two years. I want to know if the the relationship between length and volume is the same for both years. The number of fish measured is different for each year. I don't know whether lm or aov is more appropriate to use. Here are the two output options: Call: lm(formula = Volume ~ Length * Year) Residuals: Min 1Q

Hi, I have a puzzeling problem. I want to export graphics from R to TeX via postscript(). This works fine for some graphs, but for others, the eps remain empty when viewed with GSView. When such an empty eps is imported to TeX, the figure appears upside down and very small, irrespective of TeX width and height commands. If I transform the eps to pdf, the graphic shows up, but turned aqround 90°.

================================================= This is an automated summary of the status of the R-bugs repository. Note that this may be neither complete nor perfectly correct at any given instance: Not all bugs are reported, and some reported bugs may have been fixed, but the repository not yet updated. Some bug fixes are difficult to verify because they pertain to specific hardware or

Matthew Wiener <mcw@ln.nimh.nih.gov> writes: > t1 <- data.frame(matrix(rnorm(16), nc=4)) > t1$X1 <- 1 > t1$X2 <- 2 > print(t1) > Error: dim<- length of dims do not match the length of object Well, it is prototype-compatible. Splus 5.3 does likewise. A way out is t1<-data.frame(unclass(t1)) However, we do seem to have a bug in the area: > t1 <-

Matthew Wiener <mcw@ln.nimh.nih.gov> writes: > t1 <- data.frame(matrix(rnorm(16), nc=4)) > t1$X1 <- 1 > t1$X2 <- 2 > print(t1) > Error: dim<- length of dims do not match the length of object Well, it is prototype-compatible. Splus 5.3 does likewise. A way out is t1<-data.frame(unclass(t1)) However, we do seem to have a bug in the area: > t1 <-

Matthew Wiener <mcw@ln.nimh.nih.gov> writes: > > parameters (xxdefun in gram.y calls lang4). The arg count got changed > > from 0.64.2 to 0.65.0 (from 3 to 4) to accommodate the stored source > > attribute. I suppose that a function stored in a saved workspace might > > cause some kind of mess when restored. > > > > If I understand this correctly, it

The following commands illustrate a problem with graphing relatively large data sets using the "line" option to plot: temp <- runif(200000) # or whatever other numbers you like; # same thing happens with a sine wave plot(1:200000, temp) # everything fine plot(1:200000, temp, type="l") # data gets cut off The number of points isn't crucial. The cutoff point

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 <-

I have some data with two categorises plus/minus (p53) and a particular time (Time) and the outcome is a continuous vairable (Result). I set up a maximum model. ancova <- lm(Result~Time*p53) > summary(ancova) .. Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.05919 0.55646 0.106 0.916 Time -0.02134 0.01785 -1.195 0.241 p53plus

I'm a programmer in a biology lab who is starting to use R to automate some of our statistical analysis of growth rate determination. But I'm running into some problems as I re-code. 1) Hypotheses concerning Slope similarity/difference: I'm using R's anova(lm()) methods to analyse a model which looks like this: growth.metric ~ time * test.tube I understand that

I have a dataframe that looks like this: > str(chr) 'data.frame': 84 obs. of 7 variables: $ county: Factor w/ 3 levels "Broome","Nassau",..: 3 3 3 3 3 3 3 3 3 3 ... $ item : Factor w/ 28 levels "Access to healthy foods",..: 21 19 20 18 16 3 2 6 17 8 ... $ value : num 8644 15 3.5 3.9 7.7 ... $ low : num 7897 9 2.5 2.6 7 ... $ high : num 9390

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

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, all. I'm not sure whether this is a problem with ESS or R (or even a feature I don't understand!). Say I've got a function named f.count.these.patterns (as I do -- I'm using actual names in case they're somehow important). I previously had a version called f.count.these.patterns.3, but discarded it because it was worse. So the function is no longer there. If I create a