search for: fac2

...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, mlmfit0, X...

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

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 sensible...

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.factor(r...

...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<-fac? levels(fac2)[c(1,2,3)]<-"A" levels(fac2)[c(2,3,4)]<-"B" # not c(4,5,6) levels(fac2)[c(3,4,5,6)]<-"C" # not c(7,8,9,10) levels(fac2) print(fac2) #third method fac3<-fac levels(fac3)<-list("A"=c("a","b","c"...

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 grouping by "fac1" and the "mean" and "sd" fo...

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 = TRUE) d[, "fac3"] <- reco...

...-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 vector of column name(s) to split by, how can I supply split with these? I was thinking something like:...

...ple (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(sites)) anova(model1,model2,test="F") Analysis of Devianc...

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 these produce the same error: E...

...tr.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 0...

...ass 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))), n=length(na.omit(x))) # what I...

...les 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 only NA values for fac1=='b' > bwplot(fac2 ~ x | fac1,data=d) # not ok, if I try to use different scales in the X axis...

Hallo all Please help me. I am lost and do not know what is the problem. I have a factor called kvartaly. > attributes(kvartaly) $levels [1] "1Q.04" "2Q.04" "3Q.04" "4Q.04" "1Q.05" "2Q.05" "3Q.05" "4Q.05" $class [1] "factor" > mode(kvartaly) [1] "numeric" > str(kvartaly) Factor w/ 8

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 in as.character(unique(groups[subs...

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", "x", "y"), row.names = c("1", "2", "3", "4"),...

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 7.409010 -1.088588 But mean(y) gives 6.199327 and is different of In...

Hello mailing list! I would like to know, how I can introduce a covariate in a glm, I've two factors and a covariate. Thank you very much! _______________________________________________________________________________________________ Michelangelo La Spina Equipo de Protección de cultivos - Control Biológico Departamento de Biotecnología y Protección de Cultivos Instituto Murciano de

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 to los...

...ing lme (linear mixed effects) from the nlme library, it appears that there is rounding of the p-values to zero, if the p-value is less than about 1.0e-16. Is there a way we can obtain the exact p-values from lme without rounding? used commands: library(nlme) g<-lme(value~factor(fac1)+factor(fac2)+factor(fac1):factor(fac2),data=mydataframe,random=~1|factor(fac3)) ag<-anova(g) kind regards, R. Alberts