search for: resmat

...e trees grown by rpart fits in an array, > in order to be able to use them later to predict new data. I have > tried to use parse and eval to do this, without success. I'd be > very grateful if someone could explain how to do it. > > The kind of thing I hope to do is: > > resmat=array(NA, 100) > for (run in 1:100) resmat[run]=rpart(y~., data=train[run]) > > So that, later, I can do: > for (run in 1:100) pred[run]=predict(resmat[run], newdat) > > But, resmat[run] does not work, even though it saves the cptables. > > Thanks, in advance, > >...

...n in the archives, but nothing apart from that. Is it there or am I missing something? If it is not there, is there a specific reason for that? I am aware that the required code is not special or long, but the one I could come up with is rather ugly and a pain to use: htest <- function(object, resmat, target = 0, level = 0.95, n = 0) UseMethod("htest") # Testing liner restrictions of the form: resmat %*% cfs = target # resmat: matrix/vector of restriction multipliers (i x k) ARRAY!! # target: restriction value vector/scalar (k) htest.lm <- function(object, resmat, target = 0...

...ter/more efficient ways than my slogging through one case at a time ... apologies for the long message, but I am temporarily cut off from any way to post these files to the web. cheers Ben Bolker code that tests various combinations of numbers of parameters and algorithms: ----------- resmat = array(dim=c(3,2,3), dimnames=list(npar=1:3,c("fit","confint"),c("default","plinear","port"))) resmat.fix <- resmat ## sim. values npts=1000 set.seed(1001) x = runif(npts) b = 0.7 y = x^b+rnorm(npts,sd=0.05) a =0.5 y2 = a*x^b+rnorm(npts,sd=0.05)...

...quot;,"UL","UU","UU","UU","UU","UU","UU", "CU","CU","CU","CU","CU","CU") splabel=c("A","B","C","D","E","F") resmat=data.frame(Effect, Per, Spp, Treat) ## Graphing functions library(lattice) key.aug <- list(corner=c(0,0.93), text = list(levels(resmat$Treat)), points = list(pch = c(21,2,21,2), cex=c(1.2,1,1.2,1), col = c("red3","red","black","lightgrey")), lw...

Dear R user, I have made many logistic regression (glm function) with a second order polynomial formula on a data set containing 440 observation of 96 variables. I?ve made the plot of AIC versus the frequency (presence/observations) of each variable and I obtain a nearly perfect arch effect with a symmetric axe for a frequency of 0.5 . I obtain the same effect with deterministic data. Maybe

Dear all, I am trying to make a series of waffle plot-like figures for my data to visualize the ratios of amino acid residues at each position. For each one of 37 positions, there may be one to four different amino acid residues. So the data consist of the positions, what residues are there, and the ratios of residues. The ratios of residues at a position add up to 100, or close to 100 (more on

...catch: Warning in glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, ?: ?algorithm did not converge Warning in glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, ?: ?fitted probabilities numerically 0 or 1 occurred script ngood<-28 nbad<-21 resmat<-"DFr" j<-0 nsim<-100 res<-matrix(NA, nrow=nsim, ncol=6) ow <- options("warn") options(warn= 1) outcome<-rep(c("G","B"), times=c(ngood, nbad)) outcome<-as.factor(outcome) agood<-rep(c(0,1), times=c(27,1)) abad<-rep(c(0,1), times=c(1...

I wrote an ugly algorithm to set certain elements of a matrix to 1 without looping and below works and you can see what The output is below the code. K<-6 lagnum<-2 restrictmat<-matrix(0,nrow=K,ncol=K*3) restrictmat[((col(restrictmat) - row(restrictmat) >= 0 ) & (col(restrictmat)-row(restrictmat)) %% K == 0)]<-1 restrictmat[,(lagnum*K+1):ncol(restrictmat)]<-0 restrictmat