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

	[[alternative HTML version deleted]]

