Hello,
I'm trying to obtain a maximum likelyhood estimate of a gaussian model
by the MLE command, as I did with a Poisson model:
x <- rep(1:2,each=500)
y <- rnorm(length(x), mean=10+3*x, sd=1)
glm1 <- glm(y ~ x , family=gaussian())
library(stats4)
func1 <- function(alfa=10, beta=3, sigma=1)
-sum(dnorm(y, mean=alfa+beta*x, sd=sigma), log=FALSE)
mle(func1, method = "BFGS")
func2 <- function(alfa=10, beta=3, sigma=1)
-sum((1/sqrt(2*pi*sigma^2))*exp(-0.5*(((y-alfa-beta*x)^2)/sigma^2)))
mle(func2, method = "BFGS")
I don't understand why it doesn't work.
Have you some suggestions?
Thank you so much for your help
Antonio Gasparrini
Public and Environmental Health Research Unit (PEHRU)
London School of Hygiene & Tropical Medicine
Keppel Street, London WC1E 7HT, UK
Office: 0044 (0)20 79272406
Mobile: 0044 (0)79 64925523
www.lshtm.ac.uk/pehru/
[EMAIL PROTECTED]
[[alternative HTML version deleted]]
______________________________________________
[email protected] 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.
