search for: cscore

...(custID=custIDs,rscore=rep(0,ncust), fscore=rep(0,ncust),mscore=rep(0,ncust)) rfmout$rscore<-cut(by(x$rscore,x[,1],min),breaks=rbreaks,labels=FALSE) rfmout$fscore<-cut(table(x[,1]),breaks=fbreaks,labels=FALSE) rfmout$mscore<-cut(by(x[,2],x[,1],sum),breaks=mbreaks,labels=FALSE) rfmout$cscore<-(weights[1]*rfmout$rscore+ weights[2]*rfmout$fscore+ weights[3]*rfmout$mscore)/sum(weights) return(rfmout[order(rfmout$cscore),]) } set.seed(12345) x2<-data.frame(ID=sample(1:50,250,TRUE), purchase=round(runif(250,5,100),2), date=paste(rep(2016,250),sample(1:12,250,TRUE), sample(1:...

In their paper, "Lexical Scope and Statistical Computing", the authors ( Gentleman and Ihaka ) go to great length explaining why R's use of lexical scoping creates advantages when doing statistical computations. If anyone has or is familiar with this paper, could they provide the main program code for how the "newton" function would be called in their example on page 500

I'm getting all the rows as NA in Cscore and almost most of the observation in R and F and M are also NA. what can be the reason for this. also suggest me the appropriate solution. On 9 October 2017 at 15:51, Jim Lemon <drjimlemon at gmail.com> wrote: > Hi Hemant, > Here is an example that might answer your questions. Please...

...out changing the breaks or the date format arguments. As you haven't provided any example that shows what you are doing, I can't guess what the problem is. Jim On Mon, Oct 9, 2017 at 9:40 PM, Hemant Sain <hemantsain55 at gmail.com> wrote: > I'm getting all the rows as NA in Cscore and almost most of the observation > in R and F and M are also NA. > what can be the reason for this. also suggest me the appropriate solution. > > On 9 October 2017 at 15:51, Jim Lemon <drjimlemon at gmail.com> wrote: >> >> Hi Hemant, >> Here is an example that...

...quot;, "5", "6", "7", "8")), .Names = c("origin", "destination")), class = "table") Diag <- as.factor(diag(1:8)) Rscore <- scale(as.numeric(row(occupationalStatus)), scale = FALSE) Cscore <- scale(as.numeric(col(occupationalStatus)), scale = FALSE) Uniform <- glm(Freq ~ origin + destination + Diag + Rscore:Cscore, family = poisson, data = occupationalStatus) residuals(Uniform)[as.logical(diag(8))] #zero/near-zero rstandard(Uniform)[as.logical(diag(8))] #mostly...

...arguments. As you > haven't provided any example that shows what you are doing, I can't > guess what the problem is. > > Jim > > > On Mon, Oct 9, 2017 at 9:40 PM, Hemant Sain <hemantsain55 at gmail.com> > wrote: > > I'm getting all the rows as NA in Cscore and almost most of the > observation > > in R and F and M are also NA. > > what can be the reason for this. also suggest me the appropriate > solution. > > > > On 9 October 2017 at 15:51, Jim Lemon <drjimlemon at gmail.com> wrote: > >> > >> Hi H...

Dear collegues, Using maxstat I am getting the following: > blood <- maxstat.test(Surv(SUPER, FV)~ZAP,data=zap70, smethod="LogRank") Error in maxstat(y = structure(c(24.4301369863014, 26.4164383561644, 18.7835616438356, : couldn't find function "cscores" I do not know the meaning of this problem. Could you please help me on dat? Thank you in advance for your time. Sincerely, Francesc Bosch, M.D. Senior specialist Department of Hematology, Hospital Clinic C/ Villarroel, 170 08036 - Barcelona Phone & Fax: +34-93-227 5428 e-mail: fbosc...

I'm trying to perform an RFM analysis on the attached dataset, I'm able to get the results using the auto_rfm function but i want to define my own breaks for RFM. as follow r <-c(30,60,90) f <-c(2,5,8) m <-c(10,20,30) but when i tried to define my own breaks i got the identical result for RFM i.e 111 for every ID. please help me with this with working R script so that i can get

Dear all, can somebody give me some pointer how I can fit a "linear-by-linear association model" (i.e. loglinear model for the ordinal variables) in R? A brief description can be found here 'https://onlinecourses.science.psu.edu/stat504/node/141'. Thanks for your help

...d for each customer rfmout$fscore<-cut(table(x[,1]),breaks=fbreaks,labels=FALSE) # categorize the amount purchased # by each customer rfmout$mscore<-cut(by(x[,2],x[,1],sum),breaks=mbreaks,labels=FALSE) # calculate the RFM score from the # optionally weighted average of the above rfmout$cscore<-round((weights[1]*rfmout$rscore+ weights[2]*rfmout$fscore+ weights[3]*rfmout$mscore)/sum(weights),2) return(rfmout[order(rfmout$cscore),]) } Now you can load the function into your workspace like this: source("qdrfm.R") Load your data: df<-read.csv("df.csv") Run...

...the number of individuals seen in each case) ... I tried to implment it as follow (for the 2 first components for instance, but I would be interesting at looking to the other components ...I did not manage to get the eigen values !) : A <- corresp(Table, nf = 2) biplot(A) hc <- hclust(dist(A$cscore), "ward") plot(hc) Is that ok ??? ... here it is exemplae using Ward method for clustering ... I would also be interested in using method of mutual neighbors to identify the clusters ... is it possible using hclust ? Any help would be really appreciated, Best regards ... Christophe...

...uot;, "7", > "8")), .Names =3D c("origin", "destination"))= , > class =3D "table") > Diag <- as.factor(diag(1:8)) > Rscore <- scale(as.numeric(row(occupationalStatus)), scale =3D FALSE) > Cscore <- scale(as.numeric(col(occupationalStatus)), scale =3D FALSE) > Uniform <- glm(Freq ~ origin + destination + Diag + > Rscore:Cscore, family =3D poisson, data =3D occupationalSta= tus) > residuals(Uniform)[as.logical(diag(8))] #zero/near-zero > rstandard(Uniform)[as....

Hello, I need help in performing a Van_der_Waerden normal scores test in R. I have two arrays of scores(final on therapy scores from drug and placebo) and want to use the normal scores procdeure to test for significance. (observations are unequal in number - due to dropouts). Could you please help me out with the coding or let me know if there is a package that can be used (for example,