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<pre><code>#Set your working directory
setwd('~/GitHub/RWorkflow-NWFSC-2020/')
#setwd('C:Users/Eli.Holmes/Workshops/RWorkflow-NWFSC-2020/')
#-----Read in salmon data----
fil <- "data/salmon.csv"
saldat <- read.csv(fil)
colnames(saldat) <- c("year", "wild", "flow", "temp")
fil <- "steelhead.csv"
steeldat <- as.matrix(read.csv(fil))
#Create training and testing data frames
dat1 <- saldat[saldat$year<=2010,]
dat2 <- saldat[saldat$year>2010,]
# gam fit
library(mgcv)
fit1 <- gam(wild ~ s(flow), data=dat1)
fit2 <- lm(steeldat[1,1:30] ~ steeldat[2,1:30])
library(forecast)
dat <- dat1$wild
fit3 <- auto.arima(dat)
#Save data
save(fit1, fit2, fit3, file="Sim_6_5_2020_wild.RData") #save data
#Make some plots
plot(fit1)
plot(fit2)
pred1 <- predict(fit1, newdata=data.frame(dat2))
plot(dat1$year, dat1$wild, xlim=c(1980,2020), type="l")
lines(dat2$year, dat2$wild, col="blue")
lines(dat2$year, pred1, col="red", lty=2)
pred2 <- predict(fit2, newdata=data.frame(dat2))
plot(dat1$year, dat1$wild, xlim=c(1980,2020), type="l")
lines(dat2$year, dat2$wild, col="blue")
lines(dat2$year, pred2, col="red", lty=2)
pred3 <- forecast(fit3, h=9)
plot(dat1$year, dat1$wild, xlim=c(1980,2020), type="l")
lines(dat2$year, dat2$wild, col="blue")
lines(dat2$year, pred3$mean, col="red", lty=2)</code></pre>
<p>Let’s take the fitting lines and make a function.</p>
<pre><code>fitfun <- function(x, max.train.year=2010, fun="gam", response.var="wild", covariate="flow"){
subx <- subset(x, year<max.train.year)
subx$resp <- subx[[response.var]]
subx$cov <- subx[[covariate]]
if(fun=="gam") fit <- mgcv::gam(resp ~ s(cov), data=subx)
if(fun=="lm") fit <- stats::lm(resp ~ cov, data=subx)
if(fun=="auto.arima") fit <- forecast::auto.arima(subx$resp)
return(fit)
}</code></pre>
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