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Put plots in their own file and fixed a bunch of errors.
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| Original file line number | Diff line number | Diff line change |
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| # Make a few plots from the results | ||
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| # To get back to normal space | ||
| reverse_scale <- function(x, center = NULL, scale = NULL) { | ||
| if (!is.null(attr(x, "scaled:scale"))) { | ||
| x <- x * attr(x, "scaled:scale") | ||
| } else { x <- x * scale } | ||
| if (!is.null(attr(x, "scaled:center"))) { | ||
| x <- x + attr(x, "scaled:center") | ||
| } else { x <- x + center } | ||
| x | ||
| } | ||
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| makeCovarianceMap <- function(input.season = input.season, cmisst = cmisst) { | ||
| # Covariance Map | ||
| myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")), space="Lab") | ||
| season <- switch(input.season, | ||
| win = 2, | ||
| spr = 3, | ||
| sum = 4, | ||
| aut = 5) | ||
| myTitle <- switch(input.season, | ||
| win = "Winter", | ||
| spr = "Spring", | ||
| sum = "Summer", | ||
| aut = "Autumn") | ||
| covMap<-cmisst[[season]] | ||
| lmt<-max(abs(covMap), na.rm=TRUE) | ||
| limits<-c(-lmt, lmt) | ||
| extent <- cmisst[[6]] # min, max of lat, long | ||
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| gg <- ggplot() + ggtitle(myTitle) + | ||
| geom_raster(data = melt(covMap), aes(x = Var1, y = Var2, fill=value)) + | ||
| geom_sf(data=land, color="black", fill="grey", linewidth=0.25) + | ||
| xlim(extent[3], extent[4]) + ylim(extent[1], extent[2]) + | ||
| scale_fill_gradientn(colours = myPalette(100),limits=limits,name="Covariance", na.value = "white") + | ||
| theme_classic() + theme(panel.border = element_rect(colour = "grey", fill=NA)) + | ||
| labs(x = "Longitude", y = "Latitude") | ||
| gg | ||
| } | ||
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| makeBiplot <- function(input.season = input.season, cmisst = cmisst) { | ||
| # Biplot with response | ||
| index <- cmisst[[1]] | ||
| season <- switch(input.season, | ||
| win = 1, | ||
| spr = 2, | ||
| sum = 3, | ||
| aut = 4) | ||
| index$ind <- index[,season] | ||
| myTitle <- switch(input.season, | ||
| win = "Winter", | ||
| spr = "Spring", | ||
| sum = "Summer", | ||
| aut = "Autumn") | ||
| plot(index$ind, index$val, pch=20, cex=2, xlab=paste(myTitle, "CMISST Index"), | ||
| ylab="Scaled (Z-score) Response", main=myTitle) | ||
| lm1 <- lm(index$val~index$ind) | ||
| abline(lm1) | ||
| text(bquote(~ R^2 == .(round(summary(lm1)$adj.r.squared, 2))), | ||
| x = par("usr")[1]*0.8, y=par("usr")[4]*0.80, cex=1.6, col="blue") | ||
| if (input.loocv) { | ||
| mae <- cmisst[[7]] | ||
| text(paste("MAE =", round(mae[season,"mae"], 2)), | ||
| x = par("usr")[1]*0.75, y=par("usr")[4]*0.60, cex=1.6, col="blue") | ||
| } | ||
| } | ||
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| makeTimeSeriesPlot <- function(input.season = input.season, cmisst = cmisst, | ||
| ylab="", yaxis_scaler=1) { | ||
| # Time series plot in normal space | ||
| response.tmp <- response | ||
| response.tmp$year <- response.tmp$year - as.numeric(input.lag) | ||
| response.tmp <- response.tmp[response.tmp$year %in% seq(input.years[1], input.years[2], 1), c('year', input.stock)] | ||
| colnames(response.tmp) <- c('year','val') | ||
| if(input.log) response.tmp$val <- log(response.tmp$val) | ||
| response.tmp$val.scl <- scale(response.tmp$val) | ||
| #reverse_scale(response.tmp$val.scl) | ||
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| index <- cmisst[[1]] | ||
| season <- switch(input.season, | ||
| win = 2, spr = 3, sum = 4, aut = 5) | ||
| index$ind <- index[,season] | ||
| index$counts <- reverse_scale(index$val, attr(response.tmp$val.scl, "scaled:center"), attr(response.tmp$val.scl, "scaled:scale")) | ||
| if (input.log) index$counts <- exp(index$counts) | ||
| myTitle <- switch(input.season, | ||
| win = "Winter", spr = "Spring", sum = "Summer", aut = "Autumn") | ||
| lm1 <- lm(index$val~index$ind) | ||
| preds<-predict(lm1, newdata = index, interval = "confidence") | ||
| preds<-reverse_scale(preds, attr(response.tmp$val.scl, "scaled:center"), attr(response.tmp$val.scl, "scaled:scale")) | ||
| if (input.log) preds<-exp(preds) | ||
| # Use prediction interval for predicted points | ||
| preds_new<-predict(lm1, newdata = index, interval = "prediction") | ||
| preds_new<-reverse_scale(preds_new, attr(response.tmp$val.scl, "scaled:center"), attr(response.tmp$val.scl, "scaled:scale")) | ||
| if (input.log) preds_new<- exp(preds_new) | ||
| # replace just the ones that were not used during fitting | ||
| preds[index$year %in% input.years.pred,]<-preds_new[index$year %in% input.years.pred,] | ||
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| preds<-data.frame(preds) | ||
| # unlag the year to show the plot in return year | ||
| index$year_return <- index$year + input.lag | ||
| preds$year_return <- index$year_return | ||
| # Plot for SOEM talk in 2024 | ||
| ggplot() + | ||
| geom_line(data = index, aes(x=year_return, y=counts/yaxis_scaler)) + | ||
| geom_point(data = index, aes(x=year_return, y=counts/yaxis_scaler)) + | ||
| theme_classic() + | ||
| ylab(label = ylab) + xlab("Response Year") + | ||
| geom_line(data=preds, aes(x=year_return, y=fit/yaxis_scaler), color="deepskyblue2", linewidth=1.3) + | ||
| geom_point(data=preds, aes(x=year_return, y=fit/yaxis_scaler), color="deepskyblue2") + | ||
| geom_ribbon(data=preds, aes(x=year_return, ymin = lwr/yaxis_scaler, ymax = upr/yaxis_scaler), fill = "deepskyblue2", alpha = 0.2) | ||
| } | ||
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| makeIndexPlot <- function(cmisst = cmisst) { | ||
| # Output: Index time series | ||
| index <- cmisst[[1]] | ||
| plot(index$year, index$win.cov, type='b', pch=20, col="red4", | ||
| xlab="", ylab="CMISST Index", | ||
| ylim=c(min(index[,c("win.cov","spr.cov","sum.cov","aut.cov")], na.rm=TRUE), | ||
| max(index[,c("win.cov","spr.cov","sum.cov","aut.cov")], na.rm=TRUE))) | ||
| points(index$year, index$spr.cov, type='b', pch=20, col="blue") | ||
| points(index$year, index$sum.cov, type='b', pch=20, col="green3") | ||
| points(index$year, index$aut.cov, type='b', pch=20, col="purple") | ||
| legend("topleft", legend = c("Win","Spr","Sum","Aut"), bty='n', | ||
| col = c("red4","blue","green3","purple"), pch = 20, lty=1) | ||
| } | ||
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| makeLOOplot <- function(cmisst = cmisst, season = "spr") { | ||
| # Output: Observed and predicted time series from the LOO | ||
| index <- cmisst[[1]] # This gets us the whole time series | ||
| plot(index$year, index$val, type='b', pch=20, cex=2, col="black", xlab="", ylab="Scaled Response", main = input.stock) | ||
| abline(0,0, lty=2) | ||
| index <- cmisst[[7]] # this is just the loo results | ||
| index2<-index[index$season==season & index$model=="cmisst",] | ||
| lines(index2$year, index2$pred, lwd=3, col="deepskyblue2") | ||
| text(labels = paste("LOO MAE CMISST =", round(mean(index2$mae),2)), | ||
| x = par("usr")[1]+9, y=par("usr")[4]*0.80, cex=1.0, col="deepskyblue2") | ||
| } | ||
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| makeTable <- function(cmisst = cmisst) { | ||
| # Time series plot in normal space | ||
| response.tmp <- response | ||
| response.tmp$year <- response.tmp$year - as.numeric(input.lag) | ||
| response.tmp <- response.tmp[response.tmp$year %in% seq(input.years[1], input.years[2], 1), c('year', input.stock)] | ||
| colnames(response.tmp) <- c('year','val') | ||
| if(input.log) response.tmp$val <- log(response.tmp$val) | ||
| response.tmp$val.scl <- scale(response.tmp$val) | ||
| index <- cmisst[[1]] | ||
| index$response <- reverse_scale(index$val, attr(response.tmp$val.scl, "scaled:center"), attr(response.tmp$val.scl, "scaled:scale")) | ||
| if (input.log) index$response <- exp(index$response) | ||
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| # Output: Table | ||
| out<-cmisst[[1]] | ||
| out$year <- as.integer(out$year)#out <- out[,c(5,6,1:4)] | ||
| out$response <- index$response | ||
| out | ||
| } |
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