similar to: R survival package error message - bug?!

Displaying 20 results from an estimated 700 matches similar to: "R survival package error message - bug?!"

2015 Aug 25
0
sprintf error: "only 100 arguments allowed"
From the sources: #define MAXNARGS 100 /* ^^^ not entirely arbitrary, but strongly linked to allowing %$1 to %$99 !*/ On 22/08/2015 04:21, Martin Bel wrote: > I'm trying to apply a function defined in the VW R docs, that attemps to > convert a data.table object to Vowpal Wabbit format. In the process i'm > getting the error in printf mentioned in the subject.
2015 Aug 26
1
sprintf error: "only 100 arguments allowed"
Wouldn't it make sense to have this in the man page? The 8192-byte limitation for 'fmt' is mentioned but not this one. Thanks, H. On 08/25/2015 02:08 AM, Prof Brian Ripley wrote: > From the sources: > > #define MAXNARGS 100 > /* ^^^ not entirely arbitrary, but strongly linked to > allowing %$1 to %$99 !*/ > > > > On 22/08/2015 04:21, Martin
2015 Aug 22
3
sprintf error: "only 100 arguments allowed"
I'm trying to apply a function defined in the VW R docs, that attemps to convert a data.table object to Vowpal Wabbit format. In the process i'm getting the error in printf mentioned in the subject. The original function is here: https://github.com/JohnLangford/vowpal_wabbit/blob/master/R/dt2vw.R Below there is a small example that reproduces the error. The function works great with
2008 Sep 02
0
Error in .local(object, ...) : test vector does not match model !
I am getting a really strange error when I am using predict on an ksvm model. The error is "Error in .local(object, ...) : test vector does not match model !". I do understand that this happens when the test vectors do not match the Model. But in this case it is not so. I am attaching a portion of both the test data used for prediction and the data used to build the model. I could
2006 Feb 01
2
sort columns
Hi. I have a simple (I think) question My dataset have these variables: names(data) [1] "v1" "v2" "v3" "v4" "v5" "v6" "v7" "v8" "v9" "v10" "v11" "v12" "v13" "v14" "v15" "v16" "v17"
2005 Jan 25
3
multi-class classification using rpart
Hi, I am trying to make a multi-class classification tree by using rpart. I used MASS package'd data: fgl to test and it works well. However, when I used my small-sampled data as below, the program seems to take forever. I am not sure if it is due to slowness or there is something wrong with my codes or data manipulation. Please be advised ! The data is described as the output from str()
2008 Jul 14
2
long data frame selection error
Hello, I am trying to select the following headers from a data frame but when I try and run the command it executes halfway through and give me an error at V188 and V359. Temp <- data.frame(V4, V5, V6, V7, V8, V9, V10, V11, V12, V13, V14, V15, V16, V17, V18, V19, V20, V21, V22, V23, V24, V25, V26, V27, V28, V29, V30, V31, V32, V33, V34, V35, V36, V37, V38, V39, V40, V41, V42, V43, V44, V45,
2010 Apr 06
1
Caret package and lasso
Dear all, I have used following code but everytime I encounter a problem of not having coefficients for all the variables in the predictor set. # code rm(list=ls()) library(caret) # generating response and design matrix X<-matrix(rnorm(50*100),nrow=50) y<-rnorm(50*1) # Applying caret package con<-trainControl(method="cv",number=10) data<-NULL data<- train(X,y,
2013 Mar 19
1
write random sampling as table output
Hello, I have a data matrix consists of species as columns and sites as rows. N Chlamydiae Deferribacteres GN12 Hyd24-12 KSB1 PAUC34f SC4 SPAM Thermi WPS-2 ZB3 AD3 Elusimicrobia GAL15 ABY1_OD1 OP9 Fusobacteria HDBW-WB69 OP11 WS1 SR1 ZB2 AC1 OP3 OP8 NC10 NKB19 TM7 WS3 Gemmatimonadetes Lentisphaerae GN02 Armatimonadetes Tenericutes Spirochaetes Epsilonproteobacteria TM6 Acidobacteria Chlorobi
2011 Jul 12
3
"as.numeric"
Dear R user, After I imported data (csv format) in R, I called it out. But it is in non-numeric format. Then using "as.numeric" function. However, the output is really awful !!!!! > PE[1,90:99] V90 V91 V92 V93 V94 V95 V96 V97 V98 V99 1 16.8467742 17.5853166 19.7400328 21.7277241
2013 Jan 05
5
Need help on dataframe
Dear R users, I came up to a problem by taking means (or other summary statistics) of a big dataframe. Suppose we do have a dataframe: ID V1 V2 V3 V4 ........................ V71 1 6 5 3 2 ........................ 3 2 3 2 2 1 ........................ 1 3 6 5 3 2 ........................ 3 4 12 15 3 2 ........................ 100
2005 Apr 22
4
I have a problem similar to FAQ 2 scenario, but reply packets don''t seem to be recognized.
Hello, I am running Shorewall 2.0.2f, on SuSE 9.2 distro, kernel 2.6.8-24.11-default My ip addr show output is as follows: 1: lo: <LOOPBACK,UP> mtu 16436 qdisc noqueue link/loopback 00:00:00:00:00:00 brd 00:00:00:00:00:00 inet 127.0.0.1/8 brd 127.255.255.255 scope host lo inet6 ::1/128 scope host valid_lft forever preferred_lft forever 2: eth0:
2018 Jan 15
0
barplot that displays sums of values of 2 y colums grouped by different variables
It is not generally advisable to get too fancy with stat functions in ggplot... things can easily get more complicated than ggplot is ready to handle when it comes to calculations. It is better to create data that corresponds directly to the graphical representations you are mapping them to. Read [1] for more on this philosophy. [1] H. Wickham, Tidy Data, Journal of Statistical Software,
2015 Apr 19
2
[LLVMdev] remove redundant load by GVN() does not work
Hi, Assume I have the following code. The first four instructions in each BB does the same thing. So I think GVN() can remove the redundant code. However, after I apply GVN to my module by "Passes.add(createGVNPass())" and "Passes.run(*myModule)". It seems GVN does not remove the redundant instructions. Can anyone give me a hint what's going on here? Any hint is
2010 Nov 16
1
simulate survival data using median survival time
Dear All, I like to know how to simulate survival data using median (or mean) survival time. Any help will be greatly appreciated. Best wishes, Kere Kerenaftali Klein PhD| Biostatistician | Queensland Clinical Trials & Biostatistics Centre The University of Queensland | School of Population Health | Building 33, Level 1| Princess Alexandra Hospital |Ipswich Road | Woolloongabba QLD 4102 |
2012 Feb 23
2
Survival analysis and comparing survival curves
Hei, I have a one simple question which does not seem to be that simple as I cannot find any solution/answer: Is it possible to compare multiple survival curves in R with survdiff-function when there is interaction term involved in predictor variables (and this interaction is significant)? Example: survdiff(Surv(death,status)~treatment*gapsize) R is making "problems" with it ie.e.
2011 Oct 01
4
Is the output of survfit.coxph survival or baseline survival?
Dear all, I am confused with the output of survfit.coxph. Someone said that the survival given by summary(survfit.coxph) is the baseline survival S_0, but some said that is the survival S=S_0^exp{beta*x}. Which one is correct? By the way, if I use "newdata=" in the survfit, does that mean the survival is estimated by the value of covariates in the new data frame? Thank you very much!
2001 Sep 27
1
kidney survival data
Dear all, in survival5, kidney data set, appears in help page: "survival5 does not reproduce the original analysis." What does it means? thanks in advance TCM -------------- next part -------------- An HTML attachment was scrubbed... URL: https://stat.ethz.ch/pipermail/r-help/attachments/20010927/e006b08d/attachment.html
2013 Apr 29
1
R help - bootstrap with survival analysis
Hi, I'm not sure if this is the proper way to ask questions, sorry if not. But here's my problem: I'm trying to do a bootstrap estimate of the mean for some survival data. Is there a way to specifically call upon the rmean value, in order to store it in an object? I've used print(...,print.rmean=T) to print the summary of survfit, but I'm not sure how to access only rmean
2013 Jan 24
0
Royston Parmar adjusted survival curves using flexsurv
Dear R I am trying to understand and use the flexible parametric survival model suggested by Royston and Parmar. However I am stuck trying to plot the adjusted survival curves for different covariates in the following code: library(flexsurv) library(graphics) spl <- flexsurvspline(Surv(futime, fustat) ~ rx+ecog.ps+resid.ds+age, data = ovarian, k=2, scale="odds") spl the code