search for: myfactor

Hello! I have larger and a smaller data frame with 1 factor in each - it's the same factor: large.frame<-data.frame(myfactor=LETTERS[1:10]) small.frame<-data.frame(myfactor=LETTERS[c(9,7,5,3,1)]) levels(large.frame$myfactor) levels(small.frame$myfactor) table(large.frame$myfactor) table(small.frame$myfactor) myfactor has 10 levels in large.frame and 5 levels in small.frame. All 5 levels in small.frame are present in...

..."IN0019800021","IN0020020064") > mydata<-c("IN0020020155","IN0019800021","IN0020020064","IN0020020155","IN0019800021","IN0019800021","IN0020020064","IN0020020064","IN0019800021") > myfactor<-factor(mydata,levels=mylevels) > myfactor [1] IN0020020155 IN0019800021 IN0020020064 IN0020020155 IN0019800021 IN0019800021 IN0020020064 IN0020020064 IN0019800021 Levels: IN0020020155 IN0019800021 IN0020020064 > summary(myfactor) IN0020020155 IN0019800021 IN0020020064 2...

I have a data frame with some factor columns. I want to drop the rows with rare factor values (and remove the factor values from the factors). E.g., frame$MyFactor takes values A 1,000 times, B 2,000 times, C 30 times and D 4 times. I want to remove all rows which assume rare values (<1%), i.e., C and D. i.e., frame <- frame[[! (frame$MyFactor %in% c("A","B"))]] except that I probably got the syntax wrong and I want c("A",&...

...a mocked up data (one that I found online) here. require(nlme) require(multcomp) dv <- c(1,3,2,2,2,5,3,4,3,5) subject <- factor(c("s1","s1","s2","s2","s3","s3","s4","s4","s5","s5")) myfactor <- factor(c("f1","f2","f1","f2","f1","f2","f1","f2","f1","f2")) mydata <- data.frame(dv, subject, myfactor) rm(subject,myfactor,dv) attach(mydata) # paired t test (H0: f2-f1 = 0) t.test(...

...ng data issue as follows: tmtA <- c(20,26, 16,29,22,NA) tmtB <- c(12,24,17,21,NA,17) require(lme4) dv <- c(20,12,26,24,16,17,29,21,22,17) subject <- rep(c("s1","s2","s3","s4","s5","s6"),each=2) subject <- subject[-c(10,11)] myfactor <- rep(c("f1","f2"), 6) myfactor <- myfactor[-c(10,11)] mydata <- data.frame(dv, subject, myfactor) am2 <- lmer(dv ~ myfactor + (1|subject)), data = mydata) summary(am2) anova(am2) subject <- subject[-c(10,11)] Any help would be greatly appreciated. Thank you,...

...e data dv <- c(1,3,4,2,2,3,2,5,6,3,4,4,3,5,6) subject <- factor(c("s1","s1","s1","s2","s2","s2","s3","s3","s3", "s4","s4","s4","s5","s5","s5")) myfactor <- factor(c("f1","f2","f3","f1","f2","f3","f1","f2","f3", "f1","f2","f3","f1","f2","f3")) mydata <- data.frame(dv, subject, myfactor) ### Do t...

Hi, I searched for this in the mailing list, but found no results. I have a large dataframe ( dim(mydata)= 1297059 16, object.size(mydata= 145280576) ) , and I want to perform some calculations which can be done by a factor's levels, say, mydata$myfactor. So what I want is to split this dataframe into nlevels(mydata$myfactor) = 80 levels. But I must do this efficiently, that is, I can't actually split the dataframe and then save the resulting 80 smaller ones, as I would run out of memory. I don't know how to do it though. So, can anyone gi...

...group.1 2009-01-12 1.362 ... group.2 2008-12-29 2.250 group.2 2009-01-05 1.4057 group.2 2009-01-12 3.4411 ... Thanks a lot for your suggestions! The code is below: Dimitri ### Creating example data set: mydates<-rep(seq(as.Date("2008-12-29"), length = 43, by = "day"),2) myfactor<-c(rep("group.1",43),rep("group.2",43)) set.seed(123) myvalues<-runif(86,0,1) myframe<-data.frame(dates=mydates,group=myfactor,value=myvalues) (myframe) dim(myframe) ## Removing same rows (dates) unsystematically: set.seed(123) removed.group1<-sample(1:43,size=11,re...

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...measures anova. This time around teaching it, we are using R for all of our computations. We are starting by covering the univariate approach to repeated measures anova. Doing a basic repeated measures anova (univariate approach) using aov() seems straightforward (e.g.: +> myModel<-aov(myDV~myFactor+Error(Subjects/myFactor),data=myData) +> summary(myModel) Where I am currently stuck is how best to deal with the issue of the assumption of homogeneity of treatment differences (in other words, the sphericity assumption) - both how to test it in R and how to compute corrected df for the F-test...

...0.03 0.00 0.04 > system.time(replicate(1e4,{q1<-c1[100:200];1})) user system elapsed 0.04 0.00 0.04 > system.time(replicate(1e4,{q1<-f1[100:200];1})) user system elapsed 0.67 0.00 0.68 Putting the levels vector in an environment speeds up subsetting: myfactor <- function(...) { f <- factor(...) g <- unclass(f) class(g) <- "myfactor" attr(g,"mylevels") <- as.environment(list(levels=attr(f,"mylevels"))) g } `[.myfactor` <- function (x, ...) { y <- NextMethod("[")...

Dear All! For descriptive purposes I would like to add attributes to objects. These attributes should be kept, even if by indexing only part of the object is used. I noted that some attributes like levels and class of a factor exist also after indexing, while others, like comment or label vanish. Is there a way to make an arbitrary attribute to be kept after indexing? This would be especially

Help! model1 <- siarmcmcdirichletv4(data, source, tef, concdep=0, 500000, 50000) Error in matrix(1, ncol = (numsources + numiso) * numgroups, nrow = (siardata$iterations - : invalid 'ncol' value (too large or NA) In addition: Warning message: In Ops.ordered((numsources + numiso), numgroups) : '*' is not meaningful for ordered factors Previous message, but more

Hi R-experts! Every once in a while I need to convert a factor to a vector of numeric values. as.numeric(myfactor) of course returns a nice numeric vector of the indexes of the levels which is usually not what I had in mind: > v <- c(25, 3.78, 16.5, 37, 109) > f <- factor(v) > f [1] 25 3.78 16.5 37 109 Levels: 3.78 16.5 25 37 109 > as.numeric(f) [1] 3 1 2 4 5 > What I really want is...

...ed from R's NLS to a correlation and compare it to SAS's correlation. They are identical; e.g., cov2cor(summary.nls.L$cov.unscaled) I have even written a function to convert an NLS object into a "cov.scaled" element that is equivalent to SAS: scaledCOV = function (nlsObject) { myFactor = nlsObject$cov.unscaled[1,1]/(nlsObject$coefficients[1,2]^2); myScaled = nlsObject$cov.unscaled/myFactor; myScaled; } so with this function I can determine the factor difference between R and SAS (which is different with each data run of NLS and NLIN). This factor is based on the same standard...

Dear All, I would like to run a generalized linear mixed model with the software R (one categorical predictor, one random factor, the distribution of the dependent variable is binomial, and the link is logit). Thereafter, I would like to perform multiple comparisons (post hoc test) among the groups of the categorical predictor. Is it possible with the software R? Are traditional methods of

I'm trying to create a graph using plot() with an axis that I essentially want to plot the categories in the reverse alphanumeric order - opposite of the typical R fashion. Is there a function to do this? In other words, I have categories "a", "c", "g", "z" which is the order they'll be plotted, but I want to plot them as "z",

Dear all, I've taken a subset of data from a data frame using crb<-subset(all.raw, creek %in% c("CR") & year %in% c(2000,2001) & substrate %in% ("b")) this works fine, except that all of the original factor levels are maintained. This results in NA's for these empty levels when I try to do summaries based on factors using by(). Is there a simple way to drop

Dear R Help, I would be very grateful if somebody could explain why this is happening. I am trying to plot a lattice barchart of a vector of numbers with age bandings that I have brought together into a data frame. When I plot the variables in their raw vector form, it works. When I try to plot them in the df, R labels the numeric axis with default factor values and treats the numeric vector

I would like to create a family of barplots with the same xlimits. Is there a way to "read" the xlimits from the first graph so I can apply it to the subsequent ones? I have tried just taking the min and max of the x data and the plot doesn't show. cheers [[alternative HTML version deleted]]