search for: wieghts

I am using the function 'spectrum'. It returns two arrays that are interesting to me. One would be the wieght or density of a given frequency with the irequency given in another array. I would like to take the top 'n' weights which would be the top 'n' frequencies contributing to the signal. One way to do this would be to use 'sort' which would sort the weights but

I would like to turn on auditing for a particular share and have all auditing go to the username.machinename.log files. If I turn on audit then no matter which way I configure it, it either goes to just syslog, or both. My goal is to just log to the samba files and take the wieght off of syslog. I have searched and searched but can't find but a solution that works. Any help would be

I'm using an R program (which I did not write) to simulate multilevel data (subjects in locations) used in power calculations. It uses lmer to fit a mixed logistic model to the simulated data based on inputs of means, variances, slopes and proportions: ? (fitmodel <- lmer(modelformula,data,family=binomial(link=logit),nAGQ=1)) where modelformula is set up in another part of the program.?

...quot;trend"][outliers] } } My question is, "is there a better way?". One improvement would be to use the square of the remainder as a stopping criteria rather than a hard-coded loop. Not being familiar with the arguments to stl (inner, outer, etc.) and their bearing on the wieghts I don't know if there is a better way by simply specifying these arguments. So far increasing these arguments above the default values does not seem to reduce the remainder or weights array. I realize that I could look at the source but before I do I would like to request some comments from tho...

Hello, I know this must be a very simple problem, but I can't work it out from the documentation that is available. I've got a list of data I would like to plot (the weights of a single neuron that was trained using the neural package). The problem I'm encountering is that this set of weights, are in the form of a list. > network$weigth[1] [[1]] [,1] [1,]

Hello, Why would I get an error message with the following code for gamm? I want to fit the a gam with different variances per stratum. library(mgcv) library(nlme) Y<-rnorm(100) X<-rnorm(100,sd=2) Z<-rep(c(T,F),each=50) test<-gamm(Y~s(X),weights=varIdent(form=~1|Z)) summary(test$lme) #ok summary(test$gam) Gives an error message: Error in inherits(x, "data.frame")

Dear list members, Could any expert on factor analysis be so kind to explain how to calculate AIC on the output of factanal. Do I calculate AIC wrong or is factanal$criteria["objective"] not a negative log-likelihood? Best regards Jens Oehlschl?gel The AIC calculated using summary.factanal below don't appear correct to me: n items factors total.df rest.df model.df

Dear list members, Could any expert on factor analysis be so kind to explain how to calculate AIC on the output of factanal. Do I calculate AIC wrong or is factanal$criteria["objective"] not a negative log-likelihood? Best regards Jens Oehlschl?gel The AIC calculated using summary.factanal below don't appear correct to me: n items factors total.df rest.df model.df

...like 'conn', 'nconn, 'nsunits', n and 'nunits' mean, and how weights are calculated. The package odf has little or no explanations, and the C +R surce code availabe at CRAN is too difficult to comprehend. Can anyone please help? Also, i wish to know how the *number* of wieghts is calculated. when the nnet() command is run, it ouputs, on the console, the number of weights, and values of 'value'. But how do you calculate the bnumber of weights in nnet, say, if you are feeding it an MxN inputs dataframe (i.e. M observations, each having N inputs, like the iris datas...