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...0 7 [16,] 2 1 8 Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, May 24, 2018 at 7:46 AM, Ioanna Ioannou <ii54250 at msn.com<mailto:ii54250 at msn.com>> wrote: Hello everyone, I want to transform a data.frame into an array (lets call it mydata), where: mydata[[1]] is the first imputed dataset...and for each mydata[[d]], the first p columns are covariates X, and the last one is the outcome Y. Lets...

...0 7 [16,] 2 1 8 Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, May 24, 2018 at 7:46 AM, Ioanna Ioannou <ii54250 at msn.com> wrote: > Hello everyone, > > > I want to transform a data.frame into an array (lets call it mydata), > where: mydata[[1]] is the first imputed dataset...and for each mydata[[d]], > the first p columns are covariates X, and the last one is the outcome Y. > >...

Hello all, I would appreciate your thoughts on a seemingly simple problem. I have a database, where each row represent a single record. I want to aggregate this database so I use the aggregate command : D<-read.csv("C:\\Users\\test.csv") attach(D) by1<-factor(Class) by2<-factor(X) W<-aggregate(x=Count,by=list(by1,by2),FUN="sum") The results I

Hello everyone, I want to transform a data.frame into an array (lets call it mydata), where: mydata[[1]] is the first imputed dataset...and for each mydata[[d]], the first p columns are covariates X, and the last one is the outcome Y. Lets assume a simple data.frame: Imputed = data.frame( X1 = c(1,2,1,2,1,2,1,2, 1,2,1,2,1,2,1,2), X2 =

Hello all, I have data in the form of a table: X Y1 Y2 0.1 3 2 0.2 2 1 And I would like to transform in the form: X Y 0.1 Y1 0.1 Y1 0.1 Y1 0.1 Y2 0.1 Y2 0.2 Y1 0.2 Y1 0.2 Y2 Any ideas how? Thanks in advance, IOanna [[alternative HTML version deleted]]

Hello, I am trying to perform a logistic regression using counts. For example: cedegren <- read.table("http://www.cloudstat.org/index.php?do=/attachment/download/id_95 /", header=T) attach(cedegren) ced.del <- cbind(sDel, sNoDel) ced.logr <- glm(ced.del ~ cat + follows + factor(class), family=binomial("logit")) This works. However, if I change the family to

Hello, A very simple question but I am stuck. I have an excel file each row is an address. However, I cannot make geocode read each line and come up with the latitude longitude. Could you please correct my code? library(ggmap) X<-c (2 Afxentiou Ampelokipi Thessaloniki Greece, 2 Afxentiou Ampelokipi Thessaloniki Greece, 4 Afxentiou Ampelokipi Thessaloniki Greece, 55 Agathonos

Hello all, I would like to use the Nadaraya-Watson estimator assuming a Gaussian kernel: So far I sued the library(sm) library(sm) x<-runif(5000) y<-rnorm(5000) plot(x,y,col='black') h1<-h.select(x,y,method='aicc') lines(ksmooth(x,y,bandwidth=h1)) which works fine. What if my data were clustered requiring a bandwidth that varies with x? How can I do that? Thanks in

Hello, I run a linear regression I get the summary, e.g.: > summary(lm.r) Call: lm(formula = signal ~ conc) Residuals: 1 2 3 4 5 0.4 -1.0 1.6 -1.8 0.8 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.60000 1.23288 2.92 0.0615 . conc 1.94000 0.05033