3.2_caa_assessment.Rmd

---
title: "Catch-at-age: assessment plots"
date: "`r Sys.Date()`"
output:
  html_document:
    toc: true
    toc_depth: 3
    toc_float:
      collapsed: FALSE
    code_folding: show
    number_sections: TRUE
---
    
# SET-UP
```{r  markdown, echo=F}
library(knitr)
opts_chunk$set(echo = T, collapse = T, fig.align = "center", fig.width = 9, fig.height = 6)
options(width = 140)
```    

## settings
```{r settings, message = F}
source('0.0_settings.R')
```

## load all data
```{r data, message = F}
load(paste0(dir.rdat, "caa.Rdata"))
```

# CAA plots {.tabset}
## RAW
```{r caan_raw, message = F, fig.height = 3,fig.width = 10}
ggplot(caa,aes(x=year,y=age))+
  geom_point(alpha=0.8,aes(size=caan))+ 
  scale_size(range = c(1,12))+
  scale_y_continuous(breaks=min(caa$age):max(caa$age))
```

## SPAY
```{r caan_spay, message = F, fig.height = 3,fig.width = 10}
d <- reshape2::dcast(caa,age~year,value.var = 'caan')
d <- cbind(age=d[,1],spay(d[,-1]))
d <- reshape2::melt(d,id.vars='age',variable.name='year',value.name='caan')
d$year <- as.numeric(as.character(d$year))
ggplot(d,aes(x=year,y=age))+
  geom_point(alpha=0.8,aes(size=caan))+ 
  scale_size(range = c(1,12))+
  scale_y_continuous(breaks=min(caa$age):max(caa$age))
```

## SPYA
```{r caan_spya, message = F, fig.height = 3,fig.width = 10}
d <- reshape2::dcast(caa,age~year,value.var = 'caan')
d <- cbind(age=d[,1],spya(d[,-1]))
d <- reshape2::melt(d,id.vars='age',variable.name='year',value.name='caan')
d$year <- as.numeric(as.character(d$year))
ggplot(d,aes(x=year,y=age))+
  geom_point(alpha=0.8,aes(size=caan))+ 
  scale_size(range = c(1,12))+
  scale_y_continuous(breaks=min(caa$age):max(caa$age))
```

# WAA plots {.tabset}
## Model predictions
```{r waa_fit, message = F, fig.height = 11,fig.width = 10}
caa$waa.cv <- 0.01 # need to calculate this!! this is absurdly small value so all predictions will match observed
caa[caa$caaw==0,'caaw'] <- NA # should be correct in get.caa function
waa.fit <- with(caa, armatrix.fit(year=caa$year,age=caa$age,x=caa$waa,cv=caa$waa.cv))
waa.fit

caa$waa.cv <- with(caa,waa.sd/waa) # need to calculate this!! this is absurdly small value so all predictions will match observed
waa.fit <- armatrix.fit(year=caa$year,age=caa$age,x=caa$waa,cv=caa$waa.cv)
waa.fit
```

## Predicted
```{r waa_predobs, message = F, fig.height = 10,fig.width = 10}
armatrix.predobs(waa.fit,scale='free',ncol=3)
```

## Predicted
```{r waa_pred, message = F, fig.height = 10,fig.width = 10}
armatrix.pred(waa.fit,scale='free',ncol=3)
```

## Resid bubble
```{r waa_res, message = F, fig.height = 3,fig.width = 10}
armatrix.res(waa.fit)
```

## Resid dot
```{r waa_res2, message = F, fig.height = 10,fig.width = 10}
armatrix.res2(waa.fit)
```

## effects
```{r waa_effects, message = F, fig.height = 10,fig.width = 10}
armatrix.effects(waa.fit) # empty plot because of ggplotGrob function within (used to align plots)
```

## cvs
```{r waa_cv, message = F, fig.height = 10,fig.width = 10}
armatrix.cvs(waa.fit,scale='free',ncol=3)
```