similar to: Split-split plot ANOVA

Dear all, I would like to use the biological symbols for male and female as plotting symbols in a scatterplot (ideally filled and non-filled). R does not seem to have these symbols using pch= in plot() nor are they implemented via expression() or at least I did not find them. I found that the symbols are e.g. available in the wasysym and the marvosym package in LaTeX. I have coded two very rough

Dear all, I would like to use the biological symbols for male and female as plotting symbols in a scatterplot (ideally filled and non-filled). R does not seem to have these symbols using pch= in plot() nor are they implemented via expression() or at least I did not find them. I found that the symbols are e.g. available in the wasysym and the marvosym package in LaTeX. I have coded two very rough

I am using lme to handle repeated measurements. So far i can follow the examples from the book from pinheiro and bates. Now i get the problem , that i have "nested" repeated measuremnts, and i cant find out how to do the grouping part of the lme formula. 1.The simple problem ist that i have different Samples , from which i make repeated measurements (each sample is measured 6 times) and

Hi R-people, I performed controlled experiments to evaluated the seeds germination of two palms under four levels of water treatments. I conducted a generalized linear model (GLM) with a Poisson distribution to verify whether there were significant differences in the number of seed germination (NS-count variable) between treatments and species (explanatory variables). Thus, my model and output

Dear Madam or Sir, I need to do multiple comparisons following nlme analysis (Compare the effects of different treatments on a response measured repeatedly over time; fixed = response ~ treat*time). On the web I found the notion that one might use the L argument from ANOVA. Do you have an example to show how this works together with nlme? Are there other ways to do a post-hoc analysis in

Hi, I am rather new to R, but both myself and another much more experience user cannot figure this out. I have a collegue who has a 800+ nonlinear regressions to run for seed germination (different species, treatments, etc.) over time. I created a looping structure to extract the parameters from each regression; I will then use the parameters themselves for further analysis. I would like to

Dear all, is this a bug or do a miss something (this concerns version 0.63.1 on Linux)? > boxplot (c (1, 2, 3, 4, 5), ylim= c (-5, 5)) Warning: parameter "ylim" couldn't be set in high-level plot() function > boxplot (c (1, 2, 3, 4, 5), pars= list (ylim= c (-5, 5))) Warning: parameter "ylim" couldn't be set in high-level plot() function > and actually in

Hi, I am a first user of R. I was hoping I could get some help on some data I need to analyze. The experimental design is a complete randomized design with 2 factors (Source material and Depth). The experimental design was suppose to consist of 4 treatments replicated 3 time, Source 1 and applied at 10 cm and source 2 applied at 20 cm. During the construction of the treatmetns the depths vary

I don't think, this has been answered: > I'm trying to run a 3-way within-subject anova in lme with 3 > fixed factors (Trust, Sex, and Freq), but get stuck with handling > the random effects. As I want to include all the possible random > effects in the model, it would be something more or less > equivalent to using aov > > > fit.aov <- aov(Beta ~ >

Dear all, Many thanks to all who always so quickly respond with helpful hints! Currently I am working with R 0.63.1 on SuSE 5.2 which I installed this morning within about half an hour: my deepest compliments to the developpers! (I had still 0.62.2 before.) This time I am trying the following: > xy <- list (a= c (1, 2, NA), b= c (2, 3)) > list.name <- "xy" > get

I am trying to perform a model 3 ANOVA for a 2 factor (say factor A and factor B) anova in which factor A is fixed and factor B is random. Therefore, the error term for the test of factor A should be the A:B interaction term and the error terms for B and A:B should be the model residual (within) term. I have tried to work out how to specify such error strata using aov, however, I have had

Dear all, I do not seem to grasp how contrasts are set for ordered factors. Perhaps someone can elighten me? When I work with ordered factors, I would often like to be able to reduce the used polynomial to a simpler one (where possible). Thus, I would like to explicetly code the polynomial but ideally, the intial model (thus, the full polynomial) would be identical to one with an ordered factor.

I do not think anyone has answered this. > I'm trying to run a simple one-way ANCOVA with the lmer > function in R package lme4, but have encountered some > conceptual problem. The data file MyData.txt is like this: > > Group Subj Cov Resp > A 1 3.90 4.05 > A 2 4.05 4.25 > A 3 4.25 3.60 > A 4 3.60 4.20 > A 5 4.20 4.05 > A 6 4.05 3.85

Dear all! I tried to fit Gompertz growth models to describe cummulative germination rates using nls. I used the following code: germ.model<-nls(percent.germ~a*exp(-b*exp(-k*day)),data=tab,start=list(a=100,b=10,k=0.5)) My problem is that I want that the fitted model goes through the origin, since germination cannot start before the experiment was started, and y-max should be 100. Does anyone

Hi everybody, I've found a very interesting problem in R: the if-else statement doesn't work in a main program. Sounds crazy, but true. I tried this very easy example, and I got syntax error at the "else" line. Example: ------- a <- 1 if ( a == 1 ) print("yes") else print("no") -------- I tried on Windows and on Linux, but none of them works.

Hi Keep your messages in the list, you increase your chance to get some answer. I changed your data to groupedData object (see below), but I did not find any problem in it. plot(wlg) gives reasonable picture and I am not such expert to see any problem with data. Seems to me, that something has to be wrong with nlsList function. > wheat.list <- nlsList(Prop ~ SSlogis(end,Asym, xmid,

Hi there Please excuse this elementary question, but I have been fumbling with this for hours and can't seem to get it right. I have a nested anova, with random factor "lakefac" nested within factor "fishfac" (fixed), with an additional fixed factor "Habfac". If I consider everything as fixed effects, it's addmittedly not the correct model, but I can at

Hi I try to implement a simple 2-factorial repeated-measure anova in the lme framework and would be grateful for a short feedback -my dependent var is a reaction-time (rt), -as dependent var I have -the age-group (0/1) the subject belongs to (so this is a between-subject factor), and -two WITHIN experimental conditions, one (angle) having 5, the other 3 (hands) factor-levels;

Dear R-users, I'm studing a DB, structured like this (just a little part of my dataset): _____________________________________________________________________________________________________________ Site Latitude Longitude Year Tot-Prod Total_Density dmp Dendoudi-1 15.441964 -13.540179 2005 3271.16 1007 16993.25 Dendoudi-2 15.397321 -13.611607

Hello all! Consider a dataset with a grouping structure, Group (factor) Several treatments, Treat (factor) Some sort of yield, Yield (numeric) Something, possibly important, measured for each group; GroupCov (numeric) To look for fixed effects from Treat on Yield, a first attempt could be: m1 <- lm(Yield ~ Treat) which gives, in a symmetric situation, the same estimated fixed effects as: