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Hello friends and fellow R users, I have successfully tabulated and entered my survival data into R and have generated survival curves. But I would like to be able to determine what the survival rates are now at one month, three months, six months and one year. I have a data set, via.wall, which I have entered into R, and which generates the following Surv object: Surv(Days,Status==1) [1]

Hello, My name is Rodrigo, I am using R program and I have a trouble. I am trying to do a dendrogram with genetics information. Let me explain... The Similarity Matrix was already did, and with this matrix I want to construct a dendrogram. So, the distance is done. I need to transform this matrix (that I have) in a dendrogram, I woud be very grateful if someone could help me. PS: I am sending

Hello, I am frastruated with this graph, just cannot get what I need. Thank you for any suggestions or help. I really appreciate it. I wrote the following code, but there are 3 problems 1, the red line is added on the graph but without any marker on the y-axis. I want to display the number '.1361' on the y-axis. So people can easily tell 'method 2' gets a constant estimate, which

Hi, Does anyone know of a function in R that can handle a fractional variable as the dependent variable? The catch is that the function has to be inclusive of 0 and 1, which betareg() does not. It seems like GLM might be able to handle the fractional logit model, but I can't figure it out. How do you format GLM to do so? Best, Rachael [[alternative HTML version deleted]]

Hi R users: What is the difference between in the computation of the diag of the "hat" matrix in: "lm.influence" and the matrix operations with "solve()" and "t()"? I mean, this is my X matrix x1 x2 x3 x4 x5 [1,] 0.297 0.310 0.290 0.220 0.1560 [2,] 0.360 0.390 0.369 0.297 0.2050 [3,] 0.075 0.058 0.047 0.034 0.0230 [4,] 0.114 0.100

Hallo all, I have the following glm model: f1 <- as.formula(paste("factor(y.fondi)~", "flgsess + segmeta2 + udm + zona.geo + ultimo.prod.", "+flg.a2 + flg.d.na2 + flg.v2 + flg.cc2", " +(flg.a1 + flg.d.na1 + flg.v1 + flg.cc1)^2", " + flg.a2:flg.d.na2 + flg.a2:flg.v2 +

Dear Rusers, I have met a difficult problem on explaining the differences of principal component analysis(PCA) between R,S-PLUS and SAS/STATA/SPSS, which wasn't met before. Althought they have got the same eigenvalues, their coeffiecients were different. First, I list my results from R,S-PLUS and SAS/STATA/SPSS, and then show the original dataset, hoping sb. to try and explain it.

Hi there, Does anyone know how to extract elements from the table returned by Manova()? Using the univariate equivalent, Anova(), it's easy: a.an<-Anova(lm(y~x1*x2)) a.an$F This will return a vector of the F-values in order of the terms of the model. However, a similar application using Manova(): m.an<-Manova(lm(Y~x1~x2)) m.an$F Returns NULL. So does any attempt at calling the

*Problem-1* CASE-I---------(works fine) > var1<-"tom" > var1 [1"tom" > var1<-as.character(var1) > var1 [1] "tom" > var2<-c("tom","harry","kate") > logc<-(var1 %in% var2) > logc [1] TRUE > typeof(var1) [1] "character" > typeof(var2) [1] "character"

Hello R community, I am computing weighted average statistic by using ddply function: My data set is: N1 T1 S1 I1 C1 FY-4 ROE11 EPS11 MKT11 N1 T1 S1 I1 C1 FY-3 ROE12 EPS12 MKT12 N1 T1 S1 I1 C1 FY-2 ROE13 EPS13 MKT13 N1 T1 S1 I1 C1 FY-1 ROE14 EPS14 MKT14 N1 T1 S1 I1 C1 FY0 ROE15 EPS15 MKT15 N1 T1 S1 I1 C1 FY1 ROE16 EPS16 MKT16 N1 T1 S1 I1 C1 FY2 ROE17

Full_Name: Hallgeir Grinde Version: 2.1.1 OS: Windows XP Submission from: (NULL) (144.127.1.1) While using lm(y~(x*z*c*...*v)^2) R crashes/closes if the numbers of variables are at least 8.

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to

Hello, I hope you can help me! I`m trying to tune svm parameters: cost and gamma for a landsat image classification, but I get an error and I can't understand it. I write this: > tune(svm, Class~., data = mdt01bis, ranges = list(gamma = 2^(-15:3), cost > = 2^(-5:15))) and R gives: Error en predict.svm(model, if (!is.null(validation.x)) validation.x else if (useFormula)

Greetings everyone, I'm trying to add a specific table or a specific number of rows (e.g.44) to a table with no success. This is my basic table > head(dataA) year plot spp prop.B DCA1 DCA2 DCA3 DCA4 1 2000 1 a1 0.031079 -0.0776 -0.0009 0.0259 -0.0457 2 2000 1 a2 0.968921 -0.0448 0.1479 -0.1343 0.1670 3 2000 2 a1 0.029218

Dear R-listers Is there anyone who knows why I get different eigenvectors when I run MatLab and R? I run both programs in Windows Me. Can I make R to produce the same vectors as MatLab? #R Matrix PA9900<-c(11/24 ,10/53 ,0/1 ,0/1 ,29/43 ,1/24 ,27/53 ,0/1 ,0/1 ,13/43 ,14/24 ,178/53 ,146/244 ,17/23 ,15/43 ,2/24 ,4/53 ,0/1 ,2/23 ,2/43 ,4/24 ,58/53 ,26/244 ,0/1 ,5/43) #R-syntax

Dear R-Users, I can't understand the behaviour of quasibinomial in lmer. It doesn't appear to be calculating a scaling parameter, and looks to be reducing the standard errors of fixed effects estimates when overdispersion is present (and when it is not present also)! A simple demo of what I'm seeing is given below. Comments appreciated? Thanks, Russell Millar Dept of Stat U.

Dear All, I am learning the ropes about logistic regression in R. I found some interesting examples http://bit.ly/Vq4GgX http://bit.ly/W9fUTg http://bit.ly/UfK73e but I am a bit lost. I have several questions. 1) For instance, what is the difference between glm.out = glm(response ~ poverty + gender, family=binomial(logit), data=mydata) and glm.out = glm(response ~ poverty * gender,

Hello, I have used R in the past to conduct multiple comparisons on standard linear models, but am a bit confused as to how to go about doing it with a mixed effects model. I am conducting a bioindication study using carabid beetles in which I have four treatment types (forest harvest types with varying levels of canopy structure retention), and am using canopy closure percent as a covariate in

Hi, I've got a little problem using auto.arima. I run the following command auto.arima(drivers,ic="aic",d=1,D=1,max.order=10,max.p=5,max.q=5,max.P=5,max.Q=5,stepwise=FALSE,allowdrift=FALSE) and I get the following output : Series: drivers ARIMA(0,1,1)(5,1,1)[12] Coefficients: ma1 sar1 sar2 sar3 sar4 sar5 sma1 -0.6421

Dear prof. Harrell, I'm not able to use the force option with fastbw, here an example of the error I've got (dataset stagec rpart package): > fitstc <- cph(Surv(stagec$pgtime,stagec$pgstat) ~ age + eet + g2 + grade + gleason + ploidy, data=stagec) > fbwstc <- fastbw(fitstc,rule="aic",type="individual") > fbwstc Deleted Chi-Sq d.f. P Residual d.f.