search for: nresidual

...fit <- nlminb(start, fmin, gmin, X=x, y=y, w=wt, method = "BFGS", ...) names(fit$par) <- dn cat("\nCoefficients:\n"); print(fit$par) # R: use fit$value and fit$convergence cat("\nResidual Deviance:", format(fit$objective), "\n") cat("\nConvergence message:", fit$message, "\n") invisible(fit) } I have been using "lower=.25" in the call for logitreg: options(contrasts = c("contr.treatment&qu...

Hello, I am looking for a routine to do a logistic regression. In the book "Modern Applied Statistics with S-PLUS" a function is described which uses the 'ms' command. Is there an analogue for R, or an alternative approach that can accomplish the same thing? Thanks, John. -- ========================================== John Janmaat Department of Economics Acadia

....05), upper = c(Inf, Inf), ...) #fit <- optim(start, fmin, gmin, X = x, y = y, w = wt, control = list(trace = 6), method = "L-BFGS-B", lower = c(-Inf, 0.05), upper = c(Inf, Inf), ...) names(fit$par) <- dn cat("\nCoefficients:\n"); print(fit$par) cat("\nResidual Deviance:", format(fit$value), "\n") if(fit$convergence > 0) cat("\nConvergence code:", fit$convergence, "\n") return(list(fitpar = fit$par)) invisible(fit) } And here is my data, # ---------------------- # Dose 0 1 emp.prop # ----------------...

...= c(-Inf, 0.05), upper = c(Inf, Inf), ...) fit <- optim(start, fmin, gmin, X = x, y = y, w = wt, control = list(trace = 6), method = "L-BFGS-B", lower = c(-Inf, 0.05), upper = c(Inf, Inf), ...) names(fit$par) <- dn cat("\nCoefficients:\n"); print(fit$par) cat("\nResidual Deviance:", format(fit$value), "\n") if(fit$convergence > 0) cat("\nConvergence code:", fit$convergence, "\n") return(list(fitpar = fit$par)) invisible(fit) } And here is my data, # ---------------------- # Dose 0 1 emp.prop # ----------------------...

Dear list, I want to retrieve the p-value of a two-polynomial regression. For a one-polynomial lm I can easily do this with: summary(lm(b~a, data=c)[[4]][[8]]. But how do I find the final p-value in the two-polynomial regression? Under $coefficients I don't find it Any suggestions? Patrick alt <-(2260,2183,2189,1930,2435, 2000,2100,2050,2020,2470, 1700,2310,2090,1560,2060,

I apologize for my earlier posting that, unbeknownst to me before, apparently was not in the correct format for this list. Hopefully this attempt will go through, and no-one will hold the newbie mistake against me. I could really use some help in writing a new glm link function in order to run an analysis of daily nest survival rates. I've struggled with this for weeks now, and can at least

...ficients: (", nsingular, " not defined because of singularities)\n", # sep = "") # else cat("\nCoefficients:\n") print(roundfun(x$coefficients, digits = digits), quote = FALSE, ...) cat("\nResidual standard error:", format(signif(x$sigma, digits)), "on", rdf, "degrees of freedom\n") if (!is.null(x$fstatistic)) { cat("Multiple R-Squared:", format(signif(x$r.squared, digits))) ca...

...yahoo.es> > Hola, Manuel: > > Si descomprimes el tar.gz indicado por Marcuzzi, y accedes a la subcarpeta > R, en el archivo legends.R buscas la cadena p-value, y en el archivo > strucplot.R es donde creo que modificas el "Pearson residuals" (busca por > "Pearson\nresiduals"). > > Saludos. > Eva > > > ------------------------------ > *De:* Manuel Spínola <mspinola10@gmail.com> > *Para:* "Marcuzzi, Javier Rubén" <javier.ruben.marcuzzi@gmail.com> > *CC:* R <r-help-es@r-project.org> > *Enviado:* Viernes 5...

Muchas gracias Javier, Lo que yo quiero es cambiar el título de la leyenda que dice "Pearson residuals" a español "Residuales de Pearson", pero no me doy cuenta como. También en lugar de "p-value", que diga "Valor de P". Es posible? Manuel 2013/7/4 Marcuzzi, Javier Rubén <javier.ruben.marcuzzi@gmail.com> > Estimado Manuel Spíndola > >

...= c(-Inf, 0.05), upper = c(Inf, Inf), ...) fit <- optim(start, fmin, gmin, X = x, y = y, w = wt, control = list(trace = 6), method = "L-BFGS-B", lower = c(-Inf, 0.05), upper = c(Inf, Inf), ...) names(fit$par) <- dn cat("\nCoefficients:\n"); print(fit$par) cat("\nResidual Deviance:", format(fit$value), "\n") if(fit$convergence > 0) cat("\nConvergence code:", fit$convergence, "\n") return(list(fitpar = fit$par)) invisible(fit) } And here is my data, # ---------------------- # Dose 0 1 emp.prop # ----------------------...