Hi, everyone
I have a model like.
cpue=catch*1000/Hook
glmmodel=glm(log(cpue)~yy+qq+cc+pp, family=gaussian)
and I want to estimate yy, qq, cc, pp effect and CI
A senior scientist suggested to use
model <- cbind(yhat=predict.glm(glmmodel, se.fit=T), DATA)
yy_effect = with(model, tapply(fit, yy, mean))
yy_effect.se = with(model, tapply(se.fit, yy, mean))
STD_CPUE_yy = exp(yy_effect+yy_effect.se/2);
It's confusing me, because I don't understand 1). why calculate mean first
before exp, 2). why +se/2 and 3). How can I calculate CI for STD_CPUE_yy?
My previous code was.
yhat = predict.glm(glmmodel, se.fit=T,interval = "predict")
pcpue =exp(yhat$fit)
pcatch = pcpue*yftcpue$Hook/1000
model=cbind(yftcpue,pcatch,pcpue)
#calculate mean predication for each yy
yypcatch= with(model, tapply(pcatch, yy, sum))
yyhook= with(model, tapply(Hook, yy, sum))
yypcatch/yyhook*1000
# calculate CI for each yy
upp= model$fit+1.96*model$se.fit
low= model$fit-1.96*model$se.fit
thanks for help
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