similar to: New Mailbox Format Plugin Help

Hi, folks. I was wondering if anyone has some sort of guide or reference on plugin development. We are trying to develop a non-filesystem backing store. So basically we do not store mailboxes for users in local filesystem. We do RPC call to fetch from fileservers. Thank you in advance. Chang

Dear Sacha, use glm() in this case. I'd rather code the covariable as TRUE / FALSE or as a factor. Best regards, ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance thierry.onkelinx at inbo.be

Dear R-experts, I am trying to fit a GAM with 2 binary predictors (variables coded 0,1). I guess I cannot just smooth binary variables. By the way I code them as 0=no,1=yes, then mgcv will think those variables are numeric.? I have tried to change 0 and 1 in no and yes. It does not work. How to solve my problem. Here below my toy example. Many thanks. Best, Sacha ? ########################

R-experts, I have fitted many different lines. The fast-tau estimator (yellow line) seems strange to me?because this yellow line is not at all in agreement with the other lines (reverse slope, I mean the yellow line has a positive slope and the other ones have negative slope). Is there something wrong in my R code ? Is it because the Y variable is 1 vector and should be a matrix ? Here is the

On Mon, 4 Apr 2016, varin sacha via R-help wrote: > Hi Deepak, > > In econometrics there is another test very often used : the white test. > The white test is based on the comparison of the estimated variances of > residuals when the model is estimated by OLS under the assumption of > homoscedasticity and when the model is estimated by OLS under the > assumption of

You need to pay attention to the documentation more closely. If you don't know what something means, that is usually a signal that you need to study more... in this case about the difference between an input variable and a design (model) matrix. This is a concept from the standard linear algebra formulation for regression equations. (Note that I have never used RobPer, nor do I regularly

Hello, Can you say me which is the key to add in the registry for Windows XP to Enable Plain Text Password? Best Regards

On 31/03/2018 11:57 AM, varin sacha via R-help wrote: > Dear R-experts, > > Here below my reproducible R code. I want to add many straight lines to a plot using "abline" > The last fit (fast Tau-estimator, color yellow) will not appear on the plot. What is going wrong ? > Many thanks for your reply. > It's not quite reproducible: you forgot the line to create

Hello, Sacha, le ven. 08 févr. 2019 18:00:22 +0100, a ecrit: > On  Debian GNU/Linux 9.7 (stretch) amd64, we have a bug on the last Xen > Hypervisor version: > > xen-hypervisor-4.8-amd64 4.8.5+shim4.10.2+xsa282 (Read: 4.8.5+shim4.10.2+xsa282-1+deb9u11) > The rollback on the previous package version corrected the problem: > > xen-hypervisor-4.8-amd64

Hi Deepak, In econometrics there is another test very often used : the white test. The white test is based on the comparison of the estimated variances of residuals when the model is estimated by OLS under the assumption of homoscedasticity and when the model is estimated by OLS under the assumption of heteroscedastic. The White test with R install.packages("bstats") library(bstats)

Hi Rui, Many thanks. The R code works BUT the results I get are quite weird I guess ! MIC = 0.2650 Normal 95% CI = (0.9614, 1.0398) The MIC is not inside the confidence intervals ! Is there something wrong in the R code ? Here is the reproducible example : ########## C=c(2,4,5,6,3,4,5,7,8,7,6,5,6,7,7,8,5,4,3,2) D=c(3,5,4,6,7,2,3,1,2,4,5,4,6,4,5,4,3,2,8,9) library(minerva) mine(C,D)$MIC

You need: myCor <- function(data, index){ mine(data[index, ])$MIC[1, 2] } results=boot(data = cbind(C,D), statistic = myCor, R = 2000) boot.ci(results,type="all") Look at the differences between: mine(C, D) and mine(cbind(C, D)) The first returns a value, the second returns a symmetric matrix. Just like cor() David L. Carlson Department of Anthropology Texas A&M

It took me a little while to figure this out, but: the problem is that if your resampling leaves out any countries (which is very likely), your model applied to the bootstrapped data will have fewer coefficients than your original model. I tried this: cc <- unique(e$Country) func <- function(data, idx) { coef(lm(Score~ Time + factor(Country, levels =cc),data=data[idx,])) } but lm()

> On Dec 10, 2015, at 10:40 AM, Leon Fauster <leonfauster at googlemail.com> wrote: > > Am 10.12.2015 um 16:16 schrieb Noam Bernstein <noam.bernstein at nrl.navy.mil>: >> I guess this is really a RedHat, not CentOS question, but I?m hoping that someone here will be familiar enough with the upstream policy to have some useful information. >> >> How does

Dear Duncan, Dear Ivan, I really thank you a lot for your response. So, if I correctly understand your answers the problem is coming from this line: coef(lm(Score~ Time + factor(Country)),data=data[idx,]) This line should be: coef(lm(Score~ Time + factor(Country),data=data[idx,])) If yes, now I get an error message (code here below)! So, it still does not work. Error in t.star[r, ] <-

Hi Sacha, On second thought, perhaps this is more the direction that you want ... X2 = cbind(X_train,y_train) colnames(X2)[3] = "y" regr2<-randomForest(y~x1+x2, data=X2,maxnodes=10, ntree=10) regr regr2 #Make prediction predictions= predict(regr, X_test) predictions2= predict(regr2, X_test) HTH, Eric On Tue, May 9, 2023 at 6:40?AM Eric Berger <ericjberger at gmail.com>

Hello! I am doing an analysis on a questionnaire of hunters taken in 4 different districts of some mysterious foreign country. The aim of the study was to gather info on the factors that determine the hunting success of a peculiarly beautiful bird in that area. All variables are factors, i.e. they are variables such as "Use of Guns - yes / no", "Use of Dogs - yes / no" and

I guess this is really a RedHat, not CentOS question, but I?m hoping that someone here will be familiar enough with the upstream policy to have some useful information. How does RedHat decide which versions to release patches for, e.g. https://access.redhat.com/security/cve/CVE-2015-7613 <https://access.redhat.com/security/cve/CVE-2015-7613> which has only a RH7 erratum, not 6? And are

I have been using ddply to do aggregation, and I frequently define a single aggregation function that I use to aggregate over different groups. For example, require(plyr) dat <- data.frame(x = sample(3, 100, replace=TRUE), y = sample(3, 100, replace = TRUE), z = rnorm(100)) f <- function(x) { data.frame(mean.z = mean(x$z), sd.z = sd(x$z)) } ddply(dat, "x", f) ddply(dat,

?s 20:12 de 23/10/2023, varin sacha via R-help escreveu: > Dear R-experts, > > I really thank you all a lot for your responses. So, here is the error (and warning) messages at the end of my R code. > > Many thanks for your help. > > > Error in UseMethod("predict") : > ? no applicable method for 'predict' applied to an object of class