similar to: Joint a Samba DC AD to a Windows 2019 DC AD

But initially I try a full path and nothing: This is just a new created for testing: samba-tool user create 'John' '1234' --userou='OU=Trabajadores,OU=Usuarios' --surname='Doe' --given-name='John' --initials='JD' --job-title='Network Administrator' --department='IT' --company='EPAPR' --description='IT

This is just a new created for testing: samba-tool user create 'John' '1234' --userou='OU=Trabajadores,OU=Usuarios' --surname='Doe' --given-name='John' --initials='JD' --job-title='Network Administrator' --department='IT' --company='EPAPR' --description='IT Technical Support Account'

SUBMISSION DEADLINE 16th International Joint Conference on e-Business and Telecommunications Submission Deadline: February 28, 2019 http://www.icete.org/ July 26 - 28, 2019 Prague, Czech Republic. In Cooperation with: Photonics21 and EOS. Proceedings will be submitted for indexation by: DBLP, Thomson Reuters, EI, SCOPUS, Semantic Scholar and Google Scholar.

Dear All, I would like to be able to estimate confidence intervals for a linear combination of coefficients for a GLS model. I am familiar with John Foxton's helpful paper on Time Series Regression and Generalised Least Squares (GLS) and have learnt a bit about the gls function. I have downloaded the gmodels package so I can use the estimable function. The estimable function is very

Dear R users, The package 'mfp' that fits fractional polynomial terms to predictors. Example: data(GBSG) f <- mfp(Surv(rfst, cens) ~ fp(age, df = 4, select = 0.05) + fp(prm, df = 4, select = 0.05), family = cox, data = GBSG) print(f) To describe the association between the original predictor, eg. age and risk for different values of age I can plot it the polynomials

Dear R-users, I'd like to announce the release of the new package JM (JM_0.1-0 available from CRAN) for the joint modelling of longitudinal and time-to-event data. The package has a single model-fitting function called jointModel(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either

Dear R-users, I'd like to announce the release of the new package JM (JM_0.1-0 available from CRAN) for the joint modelling of longitudinal and time-to-event data. The package has a single model-fitting function called jointModel(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either

Hi Michael, with res.uc$conf you'll get the single configurations for each rater. You can use these to produce the plot you want to have. Best, Patrick r-help-request at r-project.org wrote: > Send R-help mailing list submissions to > r-help at r-project.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://stat.ethz.ch/mailman/listinfo/r-help >

Hello, I am using sm.density() to find the bivariate joint PDFof events: For eg, x<-cbind(rnorm(30),rnorm(30)) den<-sm.density(x) Then I get the joint pdf from den$estimate in order to constructthe joint cumulative PDF. However, summing up all the values from den$estimateisnot equal to 1(have multipliedby the grid size). Anyone could help? Thanks. mc [[alternative HTML version

Hi, I've just read this on Vorbis Xtreme site: "11. YOU SAY THAT OGG VORBIS IS PATENT-FREE, BUT I SAW A PATENT NAMED 'JOINT-STEREO' ON FRAUNHOFER'S PATENT LIST? SO OGG VORBIS ACTUALLY ISN'T PATENT-FREE SINCE IT ALSO USES JOINT-STEREO? No. You can't judge on a patent just by looking at its name - what's 'inside' is what matters. So if the name of the

Dear all, Please, look at the following code: attach(geyser) f1 <- kde2d(duration, waiting, n = 5) a <- 0 for (i in 1:5){ for (j in 1:5){ a <- a + f1$z[i,j] } } As far as I understood from Help kde2d returns matrix elements of which are values of joint probability mass function Pr(X=x,Y=y) therefore, sum of its elements should sum to 1. Which is not the case from my check. Where is

Dear all, Given rr<-data.frame(r1<-rnorm(1000,10,5),r2<-rnorm(1000,220,5)) How can I add a column (rr$p) for the joint probability of each r1 & r2 pair? I know how to add the column.. I just dont know how to compute the p value for joint probabilities given the two samples. //M

Dear R family, I want to calculate the joint probability (distribution) of two random continuous variables X and Y. Could to please tell me how to do it?Thanks in advance.. elisa [[alternative HTML version deleted]]

Dear R users, For a uni-variable distribution represented in a numerical vector, we can obtain a distribution function using 'ecdf', and then calculate corresponding p-values. But if I have a 2-column dataframe representing a bi-variable joint distribution, given a pair of values, how can I get the p-value? And how can I plot out the density of the joint distribution? Best

Hi all, I'm fairly new to R, so I'm still trying to feel out what is available to me. I would like to be able to plot joint density in a two dimensional plot where density is indicated by color or darkness gradients, like a 2d color coded topographic map. Ideally, the output would be something I could then plot other points or lines on. Currently, I'm calculating joint density with

Hello, I am interested in fitting a segmented model with unknown joint points in nls and perhaps eventually in nlme. I can fit this model in sas (see below, joint points to be estimated are a41 and a41), but am unsure how to specify this in the nlm function. I would really appreciate any suggestions or example code. Thanks a lot. -andy proc nlin data=Stems.Trees; params b41=-3 b42=1.5

For a homework question. I was wondering if rcmdr has a function to plot a graph of a bivariate function of X and Y. I have a function with joint pdf fX,Y(x,y) = x+y for 0<x<1 , 0<y<1 I've tried > x <- seq(0,1,.001) > y <- seq(0,1,.001) > r = x+y > plot(r) but it seems to just add them together say .2+.2 .3+.3 not other possibilities like .9 + .1 Thanks

Suppose I make two discrete variables -- > D <- data.frame(f1=sample(1:5,100,replace=T), f2=sample(1:5,100,replace=T)) I know I can do: > table(D$f1, D$f2) 0 1 2 3 4 0 5 5 5 5 4 1 4 2 6 7 3 2 5 3 5 3 6 3 3 1 3 1 2 4 6 4 3 3 6 > table(D$f1) 0 1 2 3 4 24 22 22 10 22 > table(D$f2) 0 1 2 3 4 23 15 22 19 21 which is all great. But how do I produce the

Dear colleagues, I have a dataset that looks as below. I would like to make a new dataset that excludes the cases which are joint conjunctions of particular state names and years, so Connecticut and 2010, Maryland and 2010 and Vermont and 2010. I'm trying the following subset code: newdata<- subset(bpa, (!State=="Connecticut" & year<"2010")) It appears that

Hi, I conduct a panel data estimation and obtain estimators for two of the coefficients beta1 and beta2. R tells me the mean and covariance of the distribution of (beta1, beta2). Now I would like to find the distribution of the quotient beta1/beta2, and one way to do it is to simulate via the joint distribution (beta1, beta2), where both beta1 and beta2 follow t distribution. How could we