similar to: How to solve differential and integral equation using R?

Hi I'd really like to get a bar plot showing the means of my anova data. I have looked everywhere and can only seem to find instructions for 2 way anova's. I basically want to look at the mean condition of my subjects spilt by age, sex and year (as a factor rather than a continuous variable, hence Anova and not Ancova). and want to show it firstly as a bar graph with standard error. I

Dear R user, I am new with split-plot designs and I have problems with multiple comparisons. This data correspond to an split-plot experiment with two replications (bloque).(Hoshmand, 2006 pp 138). Briefly, the whole-plot factor is Nitrogen concentration ("nitrogeno") and the subplot factor is the variety of corn ("hibrido"). The aim is to determine if major differences

I have difficulties in interpreting high order interaction terms in high-way ANOVA. According to Introductory Statistics with R by Peter Dalgaard (Section 12.5), "The exact definition of the interaction terms and the interpretation of their associated regression coefficients can be elusive. Some peculiar things happen if an interaction term is present but one or more of the main effects are

I'm interested in plotting a y with an x factor as the combination of 2 factors and colour with respect to a third, which the code below does with interaction.plot(). However, this is because I redefine the x to be 1 factor. Is there a way of getting it to plot without redefining it, and ideally to not join up the lines BETWEEN levels a and b, but just join those between after and before for

R-devel, I'm interested in looking at some examples of existing R packages that rely heavily on S4 classes to get a feel for varying styles and package organization techniques. Could you recommend any packages that might serve as a good starting point? Thanks in advance, Jeff

R-devel, I'm interested in looking at some examples of existing R packages that rely heavily on S4 classes to get a feel for varying styles and package organization techniques. Could you recommend any packages that might serve as a good starting point? Thanks in advance, Jeff

I am trying to plot an interaction in a multilevel model. Here is some sample data. In the following example, it is longitudinal (i.e., repeated measures), so the outcome, score (at each of the three time points), is nested within the individual. I am interested in the interaction between gender and happiness predicting score. id <- c(1,1,1,2,2,2,3,3,3) age <-

Hi R-devels, recently, we have seen a new warning by "R CMD check" appearing for the devel-version of our package distrEx available on r-forge, e.g. as *|install.packages("/distrEx/",repos="http://r-forge.r-project.org")|* Here is the warning: %--------------------- [...snip ...] * checking for unstated dependencies in R code ... WARNING .get_S4_methods_list():

HI, I am trying to perform ANOVA with 2 factors: material (3), temperature(3). The interaction is significant. I tried something like, ( summary(avt, split=list("temp:mat"=list("15"=1, "70"=2, "125"=3))) is not correct). Thanks, av <- aov(time ~ mat*temp, data=dados) avt <- aov(time ~ temp/mat) summary(avt,

Hi, I'm a bit new to R and I would like to know how can I compare simple main effects when using the aov function. I'm doing a mixed model ANOVA with two between subjects variables and one within. When I get an interaction of two of the variables I don't know how to check for simple main effect of that interaction (A at B1 and A at B2 for example). The aov function is very simple but

Dear list, i have a quick and (hopefully) straightforward question regarding the plot-function after running aov. if i plot an equation like this: plot(dataSubjects~factorA, data=mydata) R gives me the boxplots for this particular factor A. my model, however contains several factors. is there a straightforward way to plot barplots for a specific factor with the constraint that those values

The characteristic function is the inverse Fourier transform of the distribution function. The characteristic function of a normaly distributed random variable is exp(-t^2/2). x=seq(-2,2,length=100) fft(pnorm(x),inverse=T)/length(x) exp(-x^2/2) Why aren't the inverse fft and the mentioned function the same? Thanks for help, Thomas

Dear all, I have a dataset with qualitative variables (factors) and I want to test the null hypothesis of independance between two variables for each pair by using appropriate tests on contingency tables. I first applied chisq.test and obtained dependance in almost all cases with extremely small p-values and warning messages. > chisq.test(table(data$ins.f, data$ins.st))$p.val [1]

Dear All Can R compute the expected value of a random variable? Thanks in advance, Paul

Doese anyone know a R function to find the median of a gamma distribution? [[alternative HTML version deleted]]

Is it possible to suppress compiling for the x64 arch when doing 'Rcmd build <mypkg>? Such that the pre-compiled package for binary distribution only contains a 'lib/i386/mypkg.dll'. For CHECK (and also INSTALL) there is a '--no-multiarch' argument, but I didn't find something similar for build. Otherwise, I suppose, I could - handle the problem in Makevars or

Hello, Given a probability function: p(x) = 12 / (25(x+1)) , x=0, 1, 2, 3 we generate the following values: C1 C2 0 0.48 1 0.24 2 0.16 3 0.12 Now, I'm supposed to create 50 random values using this table. In MiniTab, I simply selected Calc -> Random Data -> Discrete, and selected the columns, and it created 50 random values in a new column.[1] How do I do the

Hello to everyone! I have built CRAN task view for genetics. For now I have not submit it to CRAN yet and it can be accessible from: http://www.bfro.uni-lj.si/MR/ggorjan/software/R/Genetics.html http://www.bfro.uni-lj.si/MR/ggorjan/software/R/Genetics.ctv I have not submitted it to CRAN, since I would like first some opinion about it. Genetics is really so broad field that I belive one person

Hi All Please see the Rprofile file which i have modified as follows and after that when I start R then I see that R says to me "TRUE" for all the packages implying that all loaded at once. But when i try to use commands as simple as help("lm"), it doesnt work nor any of the menu "Packages" is not working. Although the regression using lm ( Y ~ X ) is working

Hi all, We know that Hermite polynomial is for Gaussian, Laguerre polynomial for Exponential distribution, Legendre polynomial for uniform distribution, Jacobi polynomial for Beta distribution. Does anyone know which kind of polynomial deals with the log-normal, Student抯 t, Inverse gamma and Fisher抯 F distribution? Thank you in advance! David [[alternative HTML version deleted]]