similar to: survival function with a Weibull dist

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

Hi, I'm a new R user, with a lot of questions. At the moment I'm stoped on an error traying to fit a model: > x <- sandeel ## numeric data (2500-60000) > y <- Noss ## numeric data (0-1.2) > A <- 0.8 > B <- 0.6 > C <- 1/40000 > nls( y ~ A-B*exp(-C*x)) Error in match.call(definition, call, expand.dots) : .Primitive... is not a function I'm

Hi I am trying to install "lme4" package, but have problems intalling matrix package. I get the following error: RGui: Rgui.exe -Unable To Locate DLL the dynamic link library Rblas could not found in the specified path C:\Program

hello everyone, I would like to as for advice for the use of ?lmer? (package ?lme4?) and writing the proper syntax to best describe my data using a mixed-effects model. I have just started to use these models, and although I have read some good examples (Extending the Linear Model with R, Faraway 2005; and the R book, Crawley 2007), I am still not sure of the syntax to test my hypothesis.

Hi All, I have a vector of data, a vector of bin breakpoints and I want to put my data in the bins and then extract fanciful informations like the mean value of each bin. I know I can write my own function, but I would have thought that R should have somewhere a function that took as arguments something like (data, breaks, what to do with the data in the bins). I surey could not find it

Hi all, I try to represent a multiple curve graphic where the x-axis is the temperature and the different y-axes are the different X (X22,X43,X44...) some X corresponds to the same molecule (22 and 44 are for CO2 for instance) so I use the same colour for them. I wanna mix the linetype with the colour to be able to visually see the difference between X43 and X45 The best I have done up to now

Dear list, I made survival analysis using Weibull regression. I got significance in the analysis: >anova(m1) Df Deviance Resid. Df -2*LL P(>|Chi|) NULL NA NA 2158 4933.109 NA tratt -8 577.0669 2150 4356.042 1.988317e-119 and > summary(m1) Call: survreg(formula = Surv(tempo, sensore) ~ tratt) Value Std. Error z p

A number of as.data.frame methods do names(x) <- NULL Replacing that with if(!is.null(names(x))) names(x) <- NULL appears to save making one copy of the data (based on tracemem and Rprofmem in a copy of R compiled with --enable-memory-profiling) and gives a modest but consistent boost in speed, e.g.: # old new # user system

How can I from the summary function, decide which glm (fit1, fit2 or fit3) fits to data best? I don't know what to look after, so I would please explain the important output. > fit1 <- glm(Y~X, family=gaussian(link="identity")) > fit2 <- glm(Y~X, family=gaussian(link="log")) > fit3 <- glm(Y~X, family=Gamma(link="log")) > summary(fit1) Call:

On Mon, Apr 09, 2018 at 04:09:20AM +0000, haibinzhang(???) wrote: > > > On Fri, Apr 06, 2018 at 08:22:37AM +0000, haibinzhang(???) wrote: > > > handle_tx will delay rx for tens or even hundreds of milliseconds when tx busy > > > polling udp packets with small length(e.g. 1byte udp payload), because setting > > > VHOST_NET_WEIGHT takes into account only

----------------------------------------------------------------------------------------------------- Howdy, Gurus I am appying R package for regression analysis as followings. A dependent variable is jhnet that means ratio of dividing internal trip with all trips in a traffic zone. There are many indepentent variables including factor or dummy varibles such as parkfee, ohouse, Devt2, corridor1.

Hallo, I have this dataset with repeated measures. There are two within-subject factors, "formant" (2 levels: 1 and 2) and "f2 Ref" (25 levels: 670, 729, 788, 846, 905, 1080, 1100, 1120, 1140, 1170, 1480, 1470, 1450, 1440, 1430, 1890, 1840, 1790, 1740, 1690, 2290, 2210, 2120, 2040, 1950), and one between-subject factor, lang (2 levels:1 and 2). The response variable

Hi all, My question is not really urgent. I can write a loop and solve the problem. But I know that I'll be in a similar situation many more times so it would be useful to find out the answer Is there a fast way to perform linear fit to all the columns of a matrix? (or in the one dimension of a multi-dimensional array.) I'm talking about many single linear fits, not about a multiple fit.

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =