similar to: mangia a colori (PR#3914)

Dear all, I have the following problem. I have a List of time series dataframe.I'm trying to produce specific graph for each element of my list. The code is: This is my list: Lista_import<-lapply(Lista_import, function(x){ x2<-subset(x, select=c("ANNO","DICHIARANTE","PARTNER", "quota")) x2<-cast(x2, ANNO+DICHIARANTE~PARTNER) x2<-

Good day, Sometimes, sparse.model.matrix outputs a dgCMatrix which has column names consisting of factor levels that were not in the original dataset. The first factor appears to be correctly transformed, but the following factors don't. For example: diamonds <- as.data.frame(ggplot2::diamonds) > colnames(sparse.model.matrix(~ . -1, diamonds)) [1] "carat"

color and clarity are ordered factors, so sparse.model.matrix is generating orthogonal-polynomial contrasts (see ?contr.poly). This is by design ... what are you trying to do? Are you interested in fac2sparse? On 18-02-07 11:00 PM, Dario Strbenac wrote: > Good day, > > Sometimes, sparse.model.matrix outputs a dgCMatrix which has column names consisting of factor levels that were not

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Hi, All Thanks to Robert Baskin, Thomas Lumley, and Spencer Graves for the valuable helps. I have learned a lot from this discussion. I put all discussions together without editing, so we can see how things are evolved. Likely, I have a lot of articles to read. As in the discussion, mixed modeling approach is a poosible but may be over-kill in my posted data analyses. I will explore other

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On 30 Nov 2018, at 14:40, Kees Cook wrote: > On Fri, Nov 30, 2018 at 11:27 AM Jarkko Sakkinen > <jarkko.sakkinen at linux.intel.com> wrote: >> >> In order to comply with the CoC, replace **** with a hug. > > Heh. I support the replacement of the stronger language, but I find > "hug", "hugged", and "hugging" to be very weird

I hit a weird problem with the default PHP 5.1.6 on CentoS 5.2. It seems the default 5.1.6 PHP on CentOS has a bug in the functions escapeshellargs/escapeshellcmd: it "gulps" any special (non-ASCII) characters, so that they are missing from the output. I have been assured that this does not happen on other 5.1.6 or newer on other Linux distributions. I don't see this behavior

Dear Railers, I am toying with a new project and have a schema design question. I have a tree-like node structure. Each of the nodes will be associated with a single object. This associated object can be of a variety of types. I represent the tree through a Node class which acts_as_tree. Now imagine three other classes--Animals, Fruits and Vegetables. Each node will have an association

I've calculated as follows: matusita<-function(f1,f2,lw,up){ fmatu<-function(x) ( sqrt(f1(x)) - sqrt(f2(x)) )^2 sqrt (integrate( fmatu, lower=lw, upper=up, subdivisions=1000000)$value) } Josué Almansa Unitat de Recerca en Serveis Sanitaris Institut Municipal d'Investigació Mèdica, IMIM c/ Doctor Aiguader, 80 08003 Barcelona e-mail: jalmansa@imim.es

In consequence of this thread I went looking for a probe script that would send individualized email messages to each subscriber of a mailman list and found none. Does such a thing in fact exist? It seems to me that this would be an invaluable tool in tracking down which subscriber is the bot-bait. -- *** e-Mail is NOT a SECURE channel *** Do NOT transmit sensitive

On Sat, 2015-08-29 at 13:04 -0400, James B. Byrne wrote: > In consequence of this thread I went looking for a probe script that > would send individualized email messages to each subscriber of a > mailman list and found none. Does such a thing in fact exist? What might work, although the task is potentially daunting, is to examine the MX for each list member's domain name and from

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{BCC'ed to three Core groups} Following the recommendation of the mailman developers, I have activated occasional "VERP"ing for our mailing lists. (and will turn it off again, after about a day or so). Here is the mailman "comment-docu" on this : # These variables control the format and frequency of VERP-like delivery for # better bounce detection. VERP is Variable

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Dear R-users, I'd like to announce the release of version 1.0-0 of package JM (already available from CRAN) for the joint modeling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent

Dear R-users, I'd like to announce the release of version 1.0-0 of package JM (already available from CRAN) for the joint modeling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent