similar to: xyplot() with segments() superposed?

Hi dear listers Do you know how to made a mean by one or more categorical variables? I can do that quite easy on SAS by in R I just can't do it E.g. Some fishes Sex Lenght male 15 fema 20 fema 17 fema 19 male 18 So the idea is mean(Lenght) by sex, in order to have sex: Male mean=XX Sex: Fema mean=YY In this case is quite easy to do it by a loop, but I have a huge DB so is not an

Hi Bill, I put stringsAsFactors = FALSE still did not work. tdat <- read.table(textConnection("A B C Y A12 B03 C04 0.70 A23 B05 C06 0.05 A14 B06 C07 1.20 A25 A23 A12 3.51 A16 A25 A14 2,16"),header = TRUE ,stringsAsFactors = FALSE) tdat$D <- 0 tdat$E <- 0 tdat$D <- (ifelse(tdat$B %in% tdat$A, tdat$A[tdat$B], 0)) tdat$E <- (ifelse(tdat$B %in% tdat$A, tdat$A[tdat$C], 0))

Hello, Here is one way. tdat$D <- ifelse(tdat$B %in% tdat$A, tdat$A[tdat$B], 0) tdat$E <- ifelse(tdat$B %in% tdat$A, tdat$A[tdat$C], 0) Hope this helps, Rui Barradas On 12/13/2017 9:36 PM, Val wrote: > Hi all, > > I have a data frame > tdat <- read.table(textConnection("A B C Y > A12 B03 C04 0.70 > A23 B05 C06 0.05 > A14 B06 C07 1.20 > A25 A23 A12 3.51

Thank you Rui, I did not get the desired result. Here is the output from your script A B C Y D E 1 A12 B03 C04 0.70 0 0 2 A23 B05 C06 0.05 0 0 3 A14 B06 C07 1.20 0 0 4 A25 A23 A12 3.51 1 1 5 A16 A25 A14 2,16 4 4 On Wed, Dec 13, 2017 at 4:36 PM, Rui Barradas <ruipbarradas at sapo.pt> wrote: > Hello, > > Here is one way. > > tdat$D <- ifelse(tdat$B %in% tdat$A,

Use the stringsAsFactors=FALSE argument to read.table when making your data.frame - factors are getting in your way here. Bill Dunlap TIBCO Software wdunlap tibco.com On Wed, Dec 13, 2017 at 3:02 PM, Val <valkremk at gmail.com> wrote: > Thank you Rui, > I did not get the desired result. Here is the output from your script > > A B C Y D E > 1 A12 B03 C04 0.70 0 0

Hi all, I have a data frame tdat <- read.table(textConnection("A B C Y A12 B03 C04 0.70 A23 B05 C06 0.05 A14 B06 C07 1.20 A25 A23 A12 3.51 A16 A25 A14 2,16"),header = TRUE) I want match tdat$B with tdat$A and populate the column values of tdat$A ( col A and Col B) in the newly created columns (col D and col E). please find my attempt and the desired output below Desired output

Hey Folks, Could somebody help me rewrite the following code? I am looping through all records across 5 fields to calculate the cumulative percentage of each record (relative to each individual field). Is there a way to rewrite it so I don't have to loop through each individual record? ##### tdat is my data frame ##### j is my field index ##### k is my record index ##### tsum is the sum of

Is there a function that calculates the power of a repeated-measure ANOVA design, e.g., 2 groups, 4 within-subject factors, an average 0.40 correlation between the 4 within factors, etc. I don't think I can use power.anova.test() because it does not consider corr=0.40. I am hoping that someone has already implemented the method by Muller & Barton (1989, JASA 84: 549-555) on how to

... and similar to Jim's suggestion but perhaps slightly simpler (or not!): > cross <- xtabs( Y ~ stat + year, data = tdat) > keep <- apply(cross, 1, all) > keep <- names(keep)[keep] > cross[keep,] year stat 2003 2004 2006 2007 2009 2010 AL 38 21 20 12 16 15 NY 50 51 57 98 183 230 > ## for counts just do: > xtabs( ~ stat + year, data

Thank you Bert and Jim, Jim, FYI , I have an error message generated as Error in allstates : object 'allstates' not found Bert, it is working. However, If I want to chose to include only mos years example, 2003,2004,2007 and continue the analysis as before. Where should I define the years to get as follow. 2003 2004 2007 AL 2 1 1 NY 1 1 2 Thank you

I get the following message when using the reshape2 package line > tDat.m<- melt(Dataset) Using Item, Subject as id variables > tDatCast<- acast(tDat.m,Subject~Item) Aggregation function missing: defaulting to length Note Problem Statement- convert dataframe Subject Item Score 1 Subject 1 Item 1 1 2 Subject 1 Item 2 0 3 Subject 1 Item 3 1 4 Subject 2 Item 1 1 5

I want to replicate between two Solaris hosts. The source is Solaris 2.6 and the target is Solaris 2.8. Using rsh, the basic program seems fine (not running in daemon mode), but I have a problem. The directory tree is about 150,000 files, with about 5 gb of data. I tar'd the directory and moved it to the other side, but when I run an rsync against that directory, it still wants to move

Hello I am trying to add a ternary plot as a corner inset graph to a larger main ternary plot. I have successfully used add.scatter in the past for different kinds of plots but It doesn't seem to work for this particular function. It overlays the old plot rather than plotting as an inset. Here is a simple version of what I'm trying. Note that if I change the inset plot to be an ordinary

Hi All, I have a sample of data set show as below. tdat <- read.table(textConnection("stat year Y AL 2003 25 AL 2003 13 AL 2004 21 AL 2006 20 AL 2007 12 AL 2009 16 AL 2010 15 FL 2006 63 FL 2007 14 FL 2007 25 FL 2009 64 FL 2009 47 FL 2010 48 NY 2003 50 NY 2004 51 NY 2006 57 NY 2007 62 NY 2007 36 NY 2009 87 NY 2009 96 NY 2010

Hi, I am looking for some help concerning the npudens function in the np package. I am trying to find a kernel density function of a multivariate dataset and the density evaluated at each of the 176 points. I have 2 continuous and 3 ordered discrete variables. My sample size is 176. So edata is a 176x(2+3) data frame, while tdat is a 1x(2+3) vector. bw_cx[i,] is a 1x (2+3) vector

I would appreciate some thoughts on using the bootstrap functions in the library "bootstrap" to estimate confidence intervals of ICC values calculated in lme. In lme, the ICC is calculated as tau/(tau+sigma-squared). So, for instance the ICC in the following example is 0.116: > tmod<-lme(CINISMO~1,random=~1|IDGRUP,data=TDAT) > VarCorr(tmod) IDGRUP = pdLogChol(1)

R-helpers, I recently submitted a help request for the predict.drm function found in the drc package. I am still having issues with the function and I am submitting reproducible code hoping that somebody can help me figure out what is going on. -------- library(drc) # Fit a 4 parameter logistic model to ryegrass dataset fit <- drm(rootl ~ conc, data = ryegrass, fct = LL.4()) summary(fit) #

I am using the following script to make .jpg files. jpeg('plotx.jpg') ddat <-read.table("file",header=T) attach(ddat) tdat<-read.table("file1") plot(xx1,yy1,type='p',pch=1,col="blue",cex=0.2,xlim=c(0,3.5),ylim=c(-75,75)) points(tdat,col="green",pch=1,cex=0.2) dev.off() The problem is that I want the points to be very small;

Hi all, At some point recently my customers can no longer see GPFS snapshots under the Windows Previous Versions tab. It simply says "No previous versions available". If a fileset is exported with the flag "force user = root" then Previous Versions *are* displayed. [2016/07/06 10:07:35.602080, 3] ../source3/smbd/vfs.c:1322(check_reduced_name) check_reduced_name:

I tried multiple imputation with aregImpute() and fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1. The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored. "Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use