Felipe Carrillo <mazatlanmexico <at> yahoo.com> writes:
## snip
In the absence of any other information, I would say your
best bet would just be to take the weekly average across the
previous years. There are lots of ways to do this (tapply,
aggregate, etc.), but cast() works:
fallavg <- cast(fallmelt,value="value",WEEK~.,fun.aggregate=mean,
na.rm=TRUE)
names(fallavg)[2] <- "value"
fallavg$variable <- "predicted"
ggplot(fallmelt,aes(WEEK,value/1000,linetype=variable,
colour=variable,fill=variable)) +
geom_line(size=1)+
theme_bw() +
scale_x_continuous(breaks=seq(1,52,3),
labels=levels(fall$week)[seq(1,52,3)],) +
opts(title="Fall Cumulative") +
labs(y="Number of fish X 1,000",x="WEEK")+
geom_line(data=fallavg,size=2)
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