search for: ll0

...beta<-numeric(nx*ny) negll<- function(beta,y,xi) { beta[rest]<-0 beta[(((c.base-1)*nx)+1):(c.base*nx)]<-0 lli <- y * (xi%*%matrix(beta,nx,ny) - log ( apply(exp( xi%*%matrix(beta,nx,ny)) ,1,sum ) ) ) lli<-lli*w -sum(lli) } pi<- apply((y*w),2,mean)/mean(w) ll0 <- (t(pi)%*%log(pi))*sum(w) result<-c( optim(par = rep(0,nx*(ny)), fn = negll, y=y, xi=xi, hessian=T, method=method), list(varnames=xi.names, rest=rest, nx=nx, ny=ny, npar=nx*(ny-1)-length(rest), ll0=ll0, pi=pi, xi=xi, n.obs=n.obs,c.base=c.base,w=w)) result$par <- result$par[-(((...

......) { add.line <- trellis.par.get("add.line") zz <- surf[[packet.number()]] clines <- contourLines(zz,nlevels = nlevels) colreg <- heat.colors(max(unlist(lapply(clines,function(ll) ll$level)))) for (i in 2:length(clines)) { ll <- clines[[i]] ll0 <- clines[[i-1]] m <- ltransform3dto3d(rbind(ll$x-.5, ll$y-.5, zlim.scaled[1]), rot.mat, distance) m0 <- ltransform3dto3d(rbind(ll0$x-.5, ll0$y-.5, zlim.scaled[1]), rot.mat, distance) xvec <- c(m0[1,],m[1,ncol(m):1]) yvec <- c(m0[2,],m[2,ncol(m):1]) pane...

...ion # mult: sample squared multiple correlation in full model # n: sample size # Returns: # confidence interval z <- qnorm(1 - alpha/2) mult0 <- mult - part^2 zr <- log((1 + part)/(1 - part))/2 a <- (mult^2 - 2*mult + mult0 - mult0^2 + 1)/(1 - part^2)^2 se <- sqrt(a/(n - 3)) LL0 <- zr - z*se UL0 <- zr + z*se LL <- (exp(2*LL0) - 1)/(exp(2*LL0) + 1) UL <- (exp(2*UL0) - 1)/(exp(2*UL0) + 1) CI <- c(LL, UL) return(CI) } CIsemipartcorr(.05, .3638, .7803, 22) Thanks for your time,

...ax=15, xhalf=6) -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) ## fix one parameter to get 1D profile fit2 <- mle(ll, fixed=list(xhalf=6)) profile(fit2) ## same again with method="L-BFGS-B" fit3 <- mle(ll, fixed=list(xhalf=6),method="L-BFGS-B") profile(fit3) ll0 <- function(zzz) { ymax <- 15 xhalf <- 6 -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) } ## try mle() with all-fixed parameters with various methods ... methods = eval(formals(optim)$method) sapply(methods, function(m) { -logLik(mle(ll, start=list(ymax=15,...

...xhalf=6) -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) ## fix one parameter to get 1D profile fit2 <- mle(ll, fixed=list(xhalf=6)) profile(fit2) ## same again with method="L-BFGS-B" fit3 <- mle(ll, fixed=list(xhalf=6),method="L-BFGS-B") profile(fit3) ## BUG ll0 <- function(zzz) { ymax <- 15 xhalf <- 6 -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) } ## try mle() with all-fixed parameters with various methods ... methods = eval(formals(optim)$method) sapply(methods, function(m) { -logLik(mle(ll, start=list(ymax=15,xhalf=6),...