Kate Stark wrote:> Hi,
>
> I was wondering if someone could please help me. I am doing a logistic
> regression to compare size at maturity between 3 seasons. My model is:
>
> fit <- glm(Mature ~ Season * Size - 1, family = binomial, data=dat)
>
> where Mature is a binary response, 0 for immature, 1 for mature. There
> are 3 Seasons.
>
> The Season * Size interaction is significant. I would like to compare the
> size at 50% maturity between Seasons, which I have calculated as:
>
> Mat50_S1 <- -fit$coef[1]/fit$coef[4]
> Mat50_S2 <- -fit$coef[2]/(fit$coef[4] + fit$coef[5])
> Mat50_S3 <- -fit$coef[3]/(fit$coef[4] + fit$coef[6])
>
> But I am not sure how to calculate the standard error around each of
> these estimates. The p.dose function from the MASS package does this
> automatically, but it doesn?t seem to allow interaction terms.
>
> In Faraway(2006) he has an example using the delta method to calculate
> the StdErr, but again without any interactions. I can apply this for the
> first Season, as there is just one intercept and one slope coefficient,
> but for the other 2 Seasons, the slope is a combination of the Size
> coefficient and the Size*Season coefficient, and I am not sure how to use
> the covariance matrix in the delta calculation.
>
> I could divide the data and do 3 different logistic regressions, one for
> each season, but while the Mat50 (i.e. mean Size at 50% maturity) is the
> same as that calculated by the separate lines regression, Im not sure how
> this may change the StdErr?
>
> Regards,
>
> Kate
>
Hi,
Maybe you can re-parameterize the logistic model in such a way that the
size at 50% maturity for each season are explicit parameters in the
model, so the support function is maximized for those arguments and you
get the standard errors directly. For example, the following code
estimate the size at 50% maturity when the only predictor variable is
size. I guess you can use it to generalize to the case of an additional
factor such as season. I wrote this code as a translation from ADMB to R
so it is rather detailed (it uses nlm for maximization). count is real
data from a squid population and size (l) is in cm. Note in prop.ini,
prop.est, and prop.fit the reparameterization that introduces the size
at 50% maturity directly (along with the size at 95% maturity).
Rub?n Roa-Ureta
l<-sort(c(8:33,8:33))
mat<-rep.int(x=c(0,1),times=26)
imat<-rep.int(x=c(1,0),times=26)
count<-c(2,0,3,0,3,0,6,0,7,0,9,0,6,0,3,0,6,0,2,0,4,0,1,0,4,1,2,1,2,0,1,2,1,1,2,0,1,2,2,2,0,0,1,1,0,0,0,0,0,0,0,1)
l.plot<-8:33
contot<-vector(mode="numeric",length=26)
prop.obs<-vector(mode="numeric",length=26)
for (i in 1:26) {
countot[i]<-count[i*2-1]+count[i*2]
ifelse(countot[i]>0,prop.obs[i]<-count[i*2]/countot[i],NA)
}
l_50=25
l_95=35
prop.ini<-1/(1+exp((log(1/19))*((l.plot-l_50)/(l_95-l_50))))
plot(l.plot,prop.obs)
lines(l.plot,prop.ini)
fn<-function(p){
prop.est<-1/(1+exp(log(1/19)*(l-p[1])/(p[2]-p[1])));
iprop.est<-1-prop.est;
negloglik<--sum(count*(mat*log(prop.est)+imat*log(iprop.est)));
}
prop.lik<-nlm(fn,p=c(25.8,33.9),hessian=TRUE)
prop.lik
L_50<-prop.lik$estimate[1]
L_95<-prop.lik$estimate[2]
prop.covmat<-solve(prop.lik$hessian)
seL_50<-sqrt(prop.covmat[1,1])
seL_95<-sqrt(prop.covmat[2,2])
covL_50_95<-prop.covmat[1,2]
prop.fit<-1/(1+exp((log(1/19))*((l_plot-L_50)/(L_95-L_50))))
plot(l.plot,prop.obs,pch=19,xlab="Length (cm)",ylab="Proportion
Mature")
lines(l.plot,prop.fit)
text(x=12.5,y=0.9,expression(paste("p(l)=",frac(1,1+e^frac(ln(1/19)(l-l[50]),(l[95]-l[50]))))))
text(x=12.5,y=0.7,expression(paste(hat(l)[50],"=25.8")))
text(x=12.5,y=0.55,expression(paste(hat(l)[95],"=34.0")))