similar to: flexible processing

Hi everyone, I have a problem using rpart (R 2.0.1 under Unix) Indeed, I have a large matrix (9271x7), my response variable is numeric and all my predictor variables are categorical (from 3 to 8 levels). Here is an example : > mydata[1:5,] distance group3 group4 group5 group6 group7 group8 pos_1 0.141836040224967 a c e a g g pos_501

Hi I needed some help with ANOVA I have a problem with My ANOVA analysis. I have a dataset with a known ANOVA p-value, however I can not seem to re-create it in R. I have created a list (zzzanova) which contains 1)Intensity Values 2)Group Number (6 Different Groups) 3)Sample Number (54 different samples) this is created by the script in Appendix 1 I then conduct ANOVA with the command >

Sorry i had a misprint in the appendix code in the last email Hi I needed some help with ANOVA I have a problem with My ANOVA analysis. I have a dataset with a known ANOVA p-value, however I can not seem to re-create it in R. I have created a list (zzzanova) which contains 1)Intensity Values 2)Group Number (6 Different Groups) 3)Sample Number (54 different samples) this is created by the

Hi, I wish to compute multivariate test statistics for a within-subjects repeated measures design with anova.mlm. This works great if I only have two factors, but I don't know how to compute interactions with more than two factors. I suspect, I have to create a new "grouping" factor and then test with this factor to get these interactions (as it is hinted in R News 2007/2), but

Dear R users, I have a data frame in the form below, on which I would like to make normality tests on the values in the ExpressionLevel column. > head(df) ID Plant Tissue Gene ExpressionLevel 1 1 p1 t1 g1 366.53 2 2 p1 t1 g2 0.57 3 3 p1 t1 g3 11.81 4 4 p1 t2 g1 498.43 5 5 p1 t2 g2 2.14 6 6 p1 t2 g3 7.85 I

Let's say I have a within-subject experiment with 2 observables, obs1 and ob2 and 2 independent factors, fac1 and fac2. I can do summary( aov( obs1~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) summary( aov( obs2~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) to test the 2 observables separately. > summary( fit<-manova( cbind(obs1,obs2)~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) gives

Hi, I wonder how to pass several functions and their arguments as arguments to a function. For example, the main function is f = function(X ) { process1(X) ... process2(X) } I have a few functions that operate on X, e.g. g1(X, par1), g2(X, par2), g3(X, par3). par1, par2 and par3 are parameters and of different types. I would like to pass g1, g2, g3 and their arguments to f and g1,

Dear list, I've recently came across a problem that I think I've solved and that I wanted to share with you for two reasons: - Maybe others come across the same problem. - Maybe someone has a much simpler solution that wants to share with me ;-) The problem is as follows: expand.grid() allows you to generate a data.frame with all combinations of a set of values, e.g.: >

Hi all, I had some trouble in?regrouping factor levels for a variable. After some experiments, I have figured out how I can recode to modify the factor levels. I would now like some help to understand why some methods work and others don't. Here's my code : rm(list=ls()) ###some trials in recoding factor levels char<-letters[1:10] fac<-factor(char) levels(fac) print(fac) ##first

Hello Again... I?m making a faceted plot of a response on two categorical variables using ggplot2 and having troubles with the coloring. Here is a sample that produces the desired plot: compareCats <- function(data, res, fac1, fac2, colors) { require(ggplot2) p <- ggplot(data, aes(fac1, res)) + facet_grid(. ~ fac2) jit <- position_jitter(width = 0.1) p <- p +

hello list, i'd very much appreciate help with setting up the contrast for a 2-factorial crossed design. here is a toy example: library(multcomp) dat<-data.frame(fac1=gl(4,8,labels=LETTERS[1:4]), fac2=rep(c("I","II"),16),y=rnorm(32,1,1)) mod<-lm(y~fac1*fac2,data=dat) ## the contrasts i'm interressted in: c1<-rbind("fac2-effect in

Hi, I have a data frame DATA, which (simplified of course) looks like this: know1 = c("Y","N","N","Y","N","N","Y","Y","N") par1=c(1,4,5,3,3,2,3,3,5) know2 = c("Y","Y","N","Y","N","N","N","Y","Y")

Dear List, I am using function recode, from package car, within sapply, as follows: L3 <- LETTERS[1:3] (d <- data.frame(cbind(x = 1, y = 1:10), fac1 = sample(L3, 10, replace=TRUE), fac2 = sample(L3, 10, replace=TRUE), fac3 = sample(L3, 10, replace=TRUE))) str(d) d[, c("fac1", "fac2")] <- sapply(d[, c("fac1", "fac2")], recode, "c('A',

Dear R Users, I am trying to write a wrapper around summarize and xYplot from Hmisc and am having trouble understanding how to pass arguments from the function I am writing to the nested functions. There must be a way, but I have not been able to figure it out. An example is below. Any advice would be greatly appreciated. Thanks, Dan # some example data df=expand.grid(rep=1:4,

Dear R-users I seem to be stumped on something simple. I want to split a data frame by factor levels given in one or more columns e.g. given dat <- data.frame(x = runif(100), fac1 = rep(c("a", "b", "c", "d"), each = 25), fac2 = rep(c("A", "B"), 50)) I know I can split it by fac1, fac2 by:

Hey, hopefully there is an easy way to solve my problem. All that i think off is lengthy and clumsy. Given a data.frame d with columns VALUE, FAC1, FAC2, FAC3. Let FAC1 be something like experiment number, so that there are exactly the same number of rows for each level of FAC1 in the data.frame. Now i would like to scale all values according to the center of its experiment. So i can apply s

Dear all, I am fitting a glm to count data using poison errors with the log link. My goal is to test for the significance of model terms by calling the anova function on two nested models following the recommendation in Michael Crawley's guide to Statistical Computing. Without going into too much detail, essentially, I have a small overdispersion problem (errors do not fit the poisson

I've come across the following error when using free scales with bwplot (I use a small example data set just to illustrate the problem): > d <- data.frame( x=c(34.4, 12.4, NA, 65.3, NA, 12.0, 45.0, 645.0, 644.0,323.0), fac1=c('a','a','b','a','b','a','a','c','c','c'),

Hello Again R Folk: I have found items about this in the archives, but I?m still not getting it right. I want to use ggplot2 with facet_grid inside a function with user specified variables, for instance: p <- ggplot(data, aes_string(x = fac1, y = res)) + facet_grid(. ~ fac2) Where data, fac1, fac2 and res are arguments to the function. I have tried p <- ggplot(data,

Hi R users: I'm trying to make a function where two of the parameters are functions, but I don't know how to put each set of parameters for each function. What am I missing? I try this code: f2<-function(n=2,nsim=100,fun1=rnorm,par1=list(),fun2=rnorm,par2=list()){ force(fun1) force(fun2) force(n) p1<-unlist(par1) p2<-unlist(par2) force(p1) force(p2)