# [R] Predicition and CI for lognormal model

Chien-Pang Chin chienpang.c at gmail.com
Mon Oct 31 10:17:48 CET 2016

```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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