similar to: Unexplained behavior of level names when using ordered factors in lm?

Dear list: I have tried MASS's mca function and SAS's PROC corresp on the farms data (included in MASS, also used as mca's example), the results are different: R: mca(farms)$rs: 1 2 1 0.059296637 0.0455871427 2 0.043077902 -0.0354728795 3 0.059834286 0.0730485572 4 0.059834286 0.0730485572 5 0.012900181 -0.0503121890 6

I made an ANOVA with repeated mesures (aov(Mesure~Distance*Genre*Correct+Error(Sujet/(Distance*Genre*Correct)), data)) and I would like to extract the pvalues. The output is: ----------------------------------------------------------- Error: Sujet Df Sum Sq Mean Sq F value Pr(>F) Residuals 21 97.082 4.623 Error: Sujet:Distance Df Sum Sq Mean Sq F value Pr(>F) Distance

A rather notorious aspect of TableGen is the time required to run the -emit-dag-isel backend on some targets, including AMDGPU and X86. I added a timing feature to TableGen and timed the AMDGPU run. ===-------------------------------------------------------------------------=== TableGen Phase Timing

I wouldn't want to be too hasty about simply removing the relaxation algorithm. The size and speed of the compiler affects all users, but the time to compile the compiler "only" affects us compiler developers. And I speak as a developer who is heavily affected by the time to compile the AMDGPU backend. One off-the-cuff idea (I haven't even looked at the code yet): could we pass

I'd like to have a data.frame structured something like the following: d <- data.frame ( x=list( c(1,2), c(5,2), c(9,1) ), y=c( 1, -1, -1) ) The reason is this: 'd' is the training data for a machine learning algorithm. d$x is the independent data, and d$y is the dependent data. In general my machine learning code will work where each element of d$x is a vector of one or

Can anyone please tell me if there is a function to calculate confidence intervals for the results of the quantile function. Some of my data is normally distributed but some is also a squewed distribution or a capped normal distribution. Some of the data sets contain about 700 values whereas others are smaller with about 100-150 values, so I would like to see how the confidence intervals change

AF_E PS_E 90.838 2.206 83.139 1.751 134.272 3.710 84.043 2.076 105.184 2.788 157.249 3.783 50.280 1.027 96.973 2.355 123.582 3.398 60.417 1.236 123.501 3.315 90.128 1.566 193.783 5.167 116.036 2.994 100.289 2.216 56.943 1.106 102.272 2.692 145.579 3.810 53.105 1.202 127.212 3.061 102.838 2.383 126.352 2.723 13.661 0.190 164.352 4.870 159.945 4.160 54.382 0.884 128.253 3.598 181.208 4.767 145.118

Hello, I am a new R -user and request your help for the following problem. I need to merge two dataset of longitudinal study which has two column (id and respose) common. when I used merge option to join the datas side be side, because of the repeated subject id, I got larger data set which is not accurate. I would like to connect twi data sets by id and response in such a way that data are

Hi, everyone, My question is: It's not every time that you can get a converged result from the nls function. Is there any solution for me to get a reasonable result? For example: x <- c(-0.06,-0.04,-0.025,-0.015,-0.005,0.005,0.015,0.025,0.04,0.06) y <- c(1866760,1457870,1314960,1250560,1184850,1144920,1158850,1199910,1263850,1452520) fitOup<- nls(y ~ constant + A*(x-MA)^4 +

Hello, I am trying to filter a data set like below so that the peaks in the Phase value are more obvious and can be identified by a peak finding function following the useful advise of Carl Witthoft. I have written the following for(i in length(data$Phase)){ newphase=if(abs(data$Phase[i+1]-data$Phase[i])>6){ data$Phase[i+1] }else{data$Phase[i] } } I get the following error which I have not

Hi all, I have been trying to calculate Type III SS in R for an unbalanced two-way anova. However, the Type III SS are lower for the first factor compared to type I but higher for the second factor (see below). I have the impression that Type III are always lower than Type I - is that right? And a clarification about how to fit Type III SS. Fitting model<-aov(y~a*b) in the base package and

I have some data with two categorises plus/minus (p53) and a particular time (Time) and the outcome is a continuous vairable (Result). I set up a maximum model. ancova <- lm(Result~Time*p53) > summary(ancova) .. Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.05919 0.55646 0.106 0.916 Time -0.02134 0.01785 -1.195 0.241 p53plus

Does anyone actually use sshd on a system that doesn't have socketpair? It's used elsewhere so the don't-have path seems like it'd never be exercised these days. Index: monitor.c =================================================================== RCS file: /usr/local/src/security/openssh/cvs/openssh/monitor.c,v retrieving revision 1.147 diff -u -p -r1.147 monitor.c --- monitor.c

Hello I would like to transfer some Splus functions in R. But I have a problem first about this assignation in Splus : xnom <- deparse(substitute(x)) I am a bad programmer : I don't understand the R help How to modify these functions ? Thank you very much for your help Here are the four functions and a data test

Dear R users, I'm trying to do betareg on my dataset. Dependent variable is not normally distributed and is proportion (of condom use (0,1)). But I'm having problems: gyl<-betareg(cond ~ alcoh + drug, data=results) Error in optim(par = start, fn = loglikfun, gr = gradfun, method = method, : initial value in 'vmmin' is not finite Why is R returning me error in optim()? What

All, Given the following commands: > ag.m35<-read.table("m35.txt",header=TRUE,sep=",") > ag.m35.lp<-subset(ag.m35, race=="lp") > aov.m35.lp=aov(year~time,data=ag.m35.lp) > anova.m35.lp=anova(aov.m35.lp) > anova.m35.lp Analysis of Variance Table Response: year Df Sum Sq Mean Sq F value Pr(>F) time 1 11.56 11.5649 1.7282

Dear Sir or Madam? I am a doctor of urology,and I am engaged in developing a nomogram of bladder cancer. May I ask for your help on below issue? I set up a dataset which include 317 cases. I got the Binary Logistic Regression model by SPSS.And then I try to reconstruct the model ?lrm(RECU~Complication+T.Num+T.Grade+Year+TS)? by R-Project,and try to internal validate the model through

I'm using R for Mac OSX version 1.14 (2129) and the geoR package version 1.6-5 (the current version in the R repository). I'm running R in OS 10.4.6 on a Mac G4 iBook (933MHz, 640 MB DDR SDRAM). I searched the R archive and did not find a posting on this issue. I want to use the variog and variog4 functions of geoR to characterize the pattern of spatial autocorrelation of tree

Hi All. The recent changes to scp caused build failures on Solaris and AIX 4.2 which showed up on the tinderbox[1]. I mentioned the first to djm in email yesterday but I'm posting after finding the second, in case anyone else has seen similar problems. Solaris' nanosleep is in librt (or libposix4 in older versions) which is not linked. Adding them to configure works fine, however ldd

i've applied albert chin's patch on configure.in file: http://lists.samba.org/pipermail/rsync/2002-January/005726.html, but i still getting the error: cc: Error: lib/getaddrinfo.c, line 180: In this statement, "EAI_MAX" is not declared. (undeclared) thanks for any advice Martin Sigilfredo _________________________________________________________________ Join the world’s