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]
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