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You've specified simulate.p.value = TRUE, so chisq.test will use a test statistic and P-value based on a Monte Carlo approach. In this case, there is no assumed chi-square distribution for the test st …

Do you want to take the average of the predicted probabilities, or the average of the coefficients? They will give different results, because a logistic regression involves a nonlinear transform of th …

Have you tried logLik(lrfit1)?

In R, you can fit them using a variety of functions, with lme in package nlme probably being a good place to start. …

You can use constrOptim to minimise the RSS, subject to the constraints. There's an example in Modern Applied Statistics with S of using optim to do logistic regression via direct minimisation of the …

To put every observation into its own leaf, use minbucket=2, minsplit=1, cp=-1. The negative value for cp is to ensure that rpart doesn't end splitting prematurely. You probably don't want to put eve …

When R is involved, IML generates R code to treat x as a symbol rather plain text. … Alternatively, you could use the ExportMatrixToR module to send the data to R: start func(a); run ExportMatrixToR(a, "a"); submit / R; print(a); endsubmit; finish; Caveat: untested code …

Note that the covariance matrix of $(x, y)$ doesn't have unit diagonals, so specifying corr as an argument to pmvnorm provides incomplete information. You need to specify sigma, which is the covarianc …

I've got a model that I've developed in R, but also need to express in SAS. … I can fit this in R using gls() and the varComb and varIdent functions. …

x <- seq_along(r) f <- function(par) { m <- par[1] sd <- par[2] k <- par[3] rhat <- k * exp(-0.5 * ((x - m)/sd)^2) sum((r - rhat)^2) } optim(c(15, 2, 1), f, method="BFGS", control …

You're confused; $\alpha$ and $\lambda$ are totally different. $\alpha$ sets the degree of mixing between ridge regression and lasso: when $\alpha = 0$, the elastic net does the former, and when $\al …

I was wondering if anyone had experience with SAS/IML and R, and can give some pointers as to the relative advantages/disadvantages of the two. … I've used R extensively for programming and statistical analyses, but haven't had much experience with IML. …

There are a number of general-purpose optimization routines in base R that I'm aware of: optim, nlminb, nlm and constrOptim (which handles linear inequality constraints, and calls optim under the hood) …

You can set the constraints for the unconstrained parameters to $\pm \infty$ (and the ceiling for the non-negative parameters to $+\infty$). optim(par=theta, fn=min.RSS, lower=c(0, -Inf, -Inf, 0), u …

I'm using Rob Hyndman's groovy new tidyverts family of packages (the replacement for forecast). I was just wondering how you'd specify that the data is seasonal, especially in the presence of groups. …