Dear friends,
I used R to analyze my data with the models of generalized linear models,
and found three models were relatively good, but i can't decide which is the
best,how should i do ?
*Model1:*
glm(formula = snail ~ grass + gheight + humidity + altitude + soiltem +
airtem + grass:altitude, *family = Gamma(link = inverse*),
data = model, na.action = na.exclude, control = list(epsilon = 1e-04,
maxit = 50, trace = T))
(Dispersion parameter for Gamma family taken to be 0.2644025)
Null deviance: 63.635 on 161 degrees of freedom
Residual deviance: 42.324 on 151 degrees of freedom
AIC: 1528.1
*Model2:*
glm(formula = snail ~ grass + gheight + humidity + altitude + soiltem +
airtem + grass:altitude, *family = quasi(link = inverse, variance = "mu^2")*,
data = model, na.action = na.exclude, control = list(epsilon = 1e-04, maxit
= 50, trace = F))
(Dispersion parameter for quasi family taken to be 0.2644025)
Deviance Residuals:
Null deviance: 63.635 on 161 degrees of freedom
Residual deviance: 42.324 on 151 degrees of freedom
AIC: NA
* *
*Model3:*
glm(formula = snail ~ grass + gheight + humidity + altitude + soiltem +
airtem + grass:altitude, *family = quasi(link = log, variance =
"mu^3"),*data = model,
na.action = na.exclude,
control = list(epsilon = 1e-04, maxit = 50, trace = F))
(Dispersion parameter for quasi family taken to be 0.005042872)
Deviance Residuals:
Null deviance: 1.4113 on 161 degrees of freedom
Residual deviance: 1.0080 on 151 degrees of freedom
AIC: NA
How should i evaluate my models in R? Thanks very much!
--
Kind Regards,
Zhi Jie,Zhang ,PHD
Department of Epidemiology
School of Public Health
Fudan University
Tel:86-21-54237149
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