search for: fac1

...ompute 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 I don't really know how to use this approach. Here is my current code: Two Factors: fac1, fac2 mlmfit <- lm(mydata~1) mlmfit0 <- update(mlmfit, ~0) % test fac1, works, produces same output as SAS anova(mlmfit, mlmfit0, M = ~ fac1 + fac2, X = ~ fac2, idata = idata, test = "Wilks") % test fac1*fac2 interaction, also works, also the same output as SAS anova(mlmfit, mlmf...

Hi! Suppose I have a factor: > fac1 <- factor(rep(c("dog","cat","tree"),c(2,3,4))) > fac1 [1] dog dog cat cat cat tree tree tree tree Levels: cat dog tree > length(fac1) [1] 9 Now I want to get rid of the dogs: > fac1 <- fac1[3:9] > fac1 [1] cat cat cat tree tree tree tree Leve...

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 <- by(d[1], FAC1, scale). But i don't want...

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 results that seem...

Hello, unfortunately, I don't know a better subject. I would like to be very flexible in how to process my data. Assume the following dataset: par1 <- seq(0,1,length.out = 100) par2 <- seq(1,100) fac1 <- factor(rep(c("group1", "group2"), each = 50)) fac2 <- factor(rep(c("group3", "group4", "group5", "group6"), each = 25)) df <- data.frame(par1, par2, fac1, fac2) Now, I would like to calculate e.g. the "sum" for par1...

...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 method of recoding factors fac1<-fac levels(fac1)[c("a","b","c")]<-"A" levels(fac1)[c("d","e","f")]<-"B" levels(fac1)[c("g","h","i","j")]<-"C" levels(fac1) print(fac1) ##second method fac2&...

...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, fac1=c("a","b"), fac2=c("c","d"), fac3=c ("e","f")) df$resp1=rnorm(length(df$fac1)) df # define a funciton to compute mean, std error and n. meanstderr <- function(x) c(Mean=mean(x,na.rm=TRUE),se=sqrt(var (x,na.rm=TRUE)/length(na.omit(x))...

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 + layer(geom = "jitter", position = jit, color = colors) print(p) } test <- data.frame(res = rnorm(100), fac1 = as.fa...

Stefan Roepcke <stefan.roepcke at metagen.de> writes: > 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 <- by(d[1],...

...ts. Is there some "user's guide" document somewhere that describes this in language accessible to "generic scientists" (= non-statisticians)? 3) I just applied rpart to a dataset, and saw something that seems counter-intuitive at the least: a branch is made (n=84) between fac1=A (n=28) and fac1=B,C (n=56). The fac1=A branch is an end-node, the fac1=B,C branch is itself branched into fac1=C (n= 28) and (!) fac1=A,B (n=28)! According to the n-counts, there should be no more A in that latter branch (or in the fac1=B,C branch in general). Do I not understand something ess...

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', 'B') = 'XX'", as.factor.result = T...

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: split(dat, list(dat$fac1, dat$fac2)) which gives a list of length 8 - as desired. My question is, given a...

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'), fac2=c('v2','v2','v1','v2','v2','v2','v1','v2','v1','v2') ) # ok, although "x" has o...

...ally I don't know how to report the result). The following example (while errors are not overdispersed) otherwise generates a comparable analysis of deviance table to my analysis. Any help would be much appreciated. Jonathan counts <- c(rpois(100,5),rpois(100,20)) sites <- rep(100,200) fac1 <- factor(c(rep("A",100),rep("B",100))) fac2 <- factor(c(rep("C",50),rep("D",100),rep("C",50))) model1 <- glm(counts ~ fac1 * fac2,family=quasipoisson, offset=log(sites)) model2 <- glm(counts ~ fac1 + fac2,family=quasipoisson, offset=log...

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 A"=c(0,1,0,0,0,0,0,0), "fac2-effect in B"=c(0,1,0,0,0,1...

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, aes_string(x = fac1, y = res)) + facet_grid(. ~ as.name(fac2)) and p <- ggplot(data, aes_string(x = fac1, y = res)) + facet_grid(?. ~ fac2?) But all of...

Hello. I know that lattice is still in beta beta but. . . Nick Ellis wrote to S-news with an example of 'trellis' with several lines in each panel. df<-expand.grid(fac1=letters[1:2],x=seq(0,1,0.1),fac2=LETTERS[1:4]) df$y<-df$x*codes(df$fac1)+codes(df$fac2)*df$x^2+rnorm(nrow(df))/3 xyplot(y ~ x | fac2, groups=fac1, data=df, panel=function(x,y,subscripts,groups){ panel.superpose(x,y,subscripts,groups) for(g i...

Suppose you have a dataframe with two factors, but not all the factor levels appear together. For instance: stuff<- structure(list(fac1 = structure(c(2, 1, 2, 2), .Label = c("down", "up"), class = "factor"), fac2 = structure(c(1, 2, 1, 2), .Label = c("left", "right"), class = "factor"), x = c(1, 1, 2, 2), y = c(1, 2, 2, 1)), .Names = c("fac1", "fac2"...

Hi all, Suppose the following data and the simple model y<-1:12+rnorm(12) fac1<-c(rep("A",4),rep("B",4),rep("C",4)) fac2<-rep(c("D","C"),6) dat<-data.frame(y,fac1,fac2) tmp<-aov(y~fac1+fac2,dat) the command tmp$coeff gives the fllowing results : (Intercept) fac1B fac1C fac2D 3.307888 2.898187...

...1] "contr.sum" "contr.poly" > scores <- rep(seq(-2, 2), 3); scores [1] -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 > fac <- gl(3, 5); fac [1] 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 Levels: 1 2 3 > > # I get this: > model.matrix(~ scores:fac) (Intercept) scores:fac1 scores:fac2 scores:fac3 1 1 -2 0 0 2 1 -1 0 0 3 1 0 0 0 4 1 1 0 0 5 1 2 0 0 6 1...