Tom, Allow me to give a few comments.
x <- c(x=(rep(33:55,1))) #Is a very uggly way to write x <- 33:55 #This (untested) code will probably generate a smooth plot too. Maybe it's not the plot that you intended to create. I'm just guessing as I can not reproduce your code (because I don't have het dataframe 'data') newdata <- data.frame(temp = seq(33, 55, length = 101)) predicted <- cbind(newdta, predict(glm.mort, type='response', newdata = newdata)) plot(fit ~ newdata, type = "l") HTH, Thierry ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest Cel biometrie, methodologie en kwaliteitszorg / Section biometrics, methodology and quality assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 [EMAIL PROTECTED] www.inbo.be Do not put your faith in what statistics say until you have carefully considered what they do not say. ~William W. Watt A statistical analysis, properly conducted, is a delicate dissection of uncertainties, a surgery of suppositions. ~M.J.Moroney -----Oorspronkelijk bericht----- Van: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Namens Tom Willems Verzonden: maandag 29 oktober 2007 16:51 Aan: r-help@r-project.org Onderwerp: Re: [R] plot for binomial glm Dear Jonh, there is probably an easier way, but i find this to give nice smooth plots. good luck with it. ### R-file alive <- data$num - data$numdead numdead <- data$numdead temp <- data$temp data.table <- cbind(numdead, alive) points.graph <- data$alive/data$num glm.mort<-glm(data.table ~ temp, family=binomial) fit <- predict(glm.mort, type='response' ) a <- glm.mort$coef[1] # writes model parameters to named variable, you can also use them directly in a function, as you like b <- glm.mort$coef[2] x2 <- c((logit(fit)-(a))/b) p2 <- c ((inv.logit(a+b*x2)) ) y2 <- c ( a+b*x2) plot(c(30,55), c(0,1),type="n", main= "survival",xlab = "Log x", ylab = "Probability") lines( sortedXyData( (logit(p2)-(a))/b,p2),type="l",lty=1 ,col="blue",ylim=c(0,1.2) ) points(temp,fit,pch=4,type= "p",col="black") ## This will plot a smooth cuve x <- c(x=(rep(33:55,1))) p <- c ((inv.logit(a+b*x)) ) y <- c ( a+b*x) plot(c(30,55), c(0,1),type="n", main= "survival",xlab = "Log x", ylab = "Probability") lines( sortedXyData( (logit(p)-(a))/b,p),type="l",lty=1 ,col="blue",ylim=c(0,1.2) ) points(temp,fit,pch=4,type= "p",col="black") ### END Willems Tom E-mail: [EMAIL PROTECTED] Disclaimer: click here [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
