similar to: Predicting ordinal outcomes using lrm{Design}

I've been playing with weakforced, so it fills in the 'fail2ban across a cluster' niche (not to mention RBLs). It seems to work well, once you've actually read the docs :) I was curious if anyone had played with it and was *very* curious if anyone was using it in high traffic production. Getting things to 'work' versus getting them to work *and* handle a couple hundred

Hi all, I used the following code and data to get auc values for two sets of predictions: library(caret) > table(predicted1, trainy) trainy hard soft 1 27 0 2 11 99 > aucRoc(roc(predicted1, trainy)) [1] 0.5 > table(predicted2, trainy) trainy hard soft 1 27 2 2 11 97 > aucRoc(roc(predicted2, trainy)) [1] 0.8451621 predicted1: 1 1 2

Hi, I'm trying to run an anderson-darling test for normality on a given variable 'Y': ad.test(Y) I think I need the 'nortest' package, but since it does not appear in any of the Ubuntu repositories for 2.10.1, I am wondering if it goes by the name of something else now? Thanks -- View this message in context:

On 19.07.2017 02:38, Mark Moseley wrote: > I've been playing with weakforced, so it fills in the 'fail2ban across a > cluster' niche (not to mention RBLs). It seems to work well, once you've > actually read the docs :) > > I was curious if anyone had played with it and was *very* curious if anyone > was using it in high traffic production. Getting things to

Hi all, I have just been informed that Netboot (http://netboot.sourceforge.net/) can be used to produce PXE boot ROMs. I wonder if anyone has tried running this combination with PXELINUX? This is obviously a really big deal... :) -hpa

Dear Colleagues, I hope to know how ordinal logistic regression with a mixed model is made in R. We (My colleague and I) are studying the behavior of a beetle. The attraction of beetles to a stimulus are recorded: the response is Slow, Mid, or Fast. They are based on the time after the presentation of the stimulus to the beetles. Because we do not observe the behavior continuously but do

Dear all, I have been using the lrm function in R to run an ordinal logistic regression and I am a bit confused about the methods for checking the model assumptions. I have produced residual plots in R of the score.binary type which I think look ok. However, the partial type plots show bell shaped patterns and have crossing lines, indicating violation of parallelism. However, I noticed

On Tue, Jul 18, 2017 at 10:40 PM, Aki Tuomi <aki.tuomi at dovecot.fi> wrote: > > > On 19.07.2017 02:38, Mark Moseley wrote: > > I've been playing with weakforced, so it fills in the 'fail2ban across a > > cluster' niche (not to mention RBLs). It seems to work well, once you've > > actually read the docs :) > > > > I was curious if

This is the named article: http://ije.oxfordjournals.org/content/37/5/1161.long maybe it can help you to help me... :-( -- View this message in context: http://r.789695.n4.nabble.com/INSTRUMENTAL-VARIABLES-WITH-BINARY-OUTCOMES-tp4642361p4642363.html Sent from the R help mailing list archive at Nabble.com.

Hi, I am simulating an event that has 15 possible outcomes and I have a vector 'pout' that gives me the probability of each outcome - different outcomes have different probabilities. Does anyone know a simple way of simulating the outcome of my event? If my event had only two possible outcomes (0/1) I would pick a uniform random number between 0 and 1 and use it to choose between the two

Hello, In a study exploring transgenerational transmission of anxiety disorder we investigate whether infants react to experimentally induced mood changes of their mothers. We measured the time that an infant needed to cross a cliff (=crossing time) depending on whether his mother had previously undergone a mood induction (treatment) or not (control). The treatment is thus a

I have been using generalized linear models in SPSS 18, in order to build models and to calculate the P values. When I was building models in Excel (using the intercept and Bs from SPSS), I noticed that the graphs differed from my expectations. When I ran the dataset again in R, I got totally different outcomes for both the P values as well as the Bs and the intercepts. The outcomes of R seem much

I will like to analyse a binary cross over design using the random effects model. The probability of success is assumed to be logistic. Suppose as an example, we have 4 subjects undergoing a crossover design, where the outcome is either success or failure. The first two subjects receive treatment "A" first followed by treatment "B". The remaining two subjects receive

Good Afternoon I have noticed results similar to the following several times as I have used R over the past several years. My .csv file has a header row and 3073 rows of data. > rskreg<-read.table('D:/data/riskregions.csv',header=T,sep=",") > dim(rskreg) [1] 2722 13 > rskreg<-read.csv('D:/data/riskregions.csv',header=T) > dim(rskreg) [1] 3073

Dear List: I have searched the archives and my R books and cannot find a method to transform a continuous variable into a binary variable. For example, I have test score data along a continuous scale. I want to create a new variable in my dataset that is 1=above a cutpoint (or passed the test) and 0=otherwise. My instinct tells me that this will require a combination of the transform

Dear All, I wonder if there is an efficient way to fit the generalized linear mixed model for multivariate outcomes. More specifically, Suppose that for a given subject i and at a given time j we observe a multivariate outcome Yij = (Y_ij1, Y_ij2, ..., Y_ijK). where Y_ijk is a binomial(n_ijk, p_ijk). One way to jointly model the data is to use the following specification: g(p_ijk) =

Hi all, I came across this article on LinuxMedNews (http://www.linuxmednews.com) this morning: http://www.linuxmednews.com/linuxmednews/1076524250/index_html This refers to an integrated data management and analysis system (OIO), which includes R and utilizes the RSessionDA package (Greg Warnes). More information is available here for those interested:

Dear all, I have 3 models (from simple to complex) and I want to compare them in order to see if they fit equally well or not. From the R prompt I am not able to see where I can get this information. Let´s do an example: fit1<- lm(response ~ stimulus + condition + stimulus:condition, data=scrd) #EQUIVALE A lm(response ~ stimulus*condition, data=scrd) fit2<- lm(response ~ stimulus +

Dear All, I have a problem in understanding how the interactions of 2 ways ANOVA work, because I get conflicting results from a t-test and an anova. For most of you my problem is very simple I am sure. I need an help with an example, looking at one table I am analyzing. The table is in attachment and can be imported in R by means of this command: scrd<-

Dear all, I have got two vectors coding for a stimulus presented in the current trial (mydat$Stimulus) and a prediction in the same trial (mydat$Prediciton), respectively. By applying an if-conditional I want to create a new vector that indicates if there is a match between both vectors in the same trial. That is, if the prediction equals the stimulus. When I pick out some trials randomly, I get