Commit 0aa4e991 authored by Monnet Jean-Matthieu's avatar Monnet Jean-Matthieu
Browse files

changed figure size

parent 28cdb6f0
......@@ -142,7 +142,7 @@ The function computes values predicted in leave-one-out cross-validation, by usi
In this example, only tree metrics are selected in the basal area prediction model. The model seems to fail to predict large values. The prediction errors are positively correlated with basal area because large values are under-estimated.
```{r modelPlot, include=TRUE}
```{r modelPlot, include=TRUE, fig.height = 4.5, fig.width = 8}
# check correlation between errors and other variables
round(cor(cbind(model.ABA$values$residual, plots[subsample, c("G.m2.ha","N.ha","")], terrain.stats[subsample, 1:3])), 2)[1,]
# significance of correlation value
......@@ -155,7 +155,7 @@ abline(h = 0, lty = 2)
In case only point cloud metrics are used as potential inputs, the errors are hardly better distributed. Coloring points by ownership shows that plots located in private forests have the largest basal area values which tend to be under-estimated.
```{r point.metricsOnly, include=TRUE}
```{r point.metricsOnly, include=TRUE, fig.height = 4.5, fig.width = 8}
model.ABA.point.metrics <- lidaRtRee::ABAmodel(plots[subsample,variable], point.metrics[subsample,], transform="boxcox", nmax=4, xy = plots[subsample, c("X", "Y")])
# renames outputs
row.names(model.ABA.point.metrics$stats) <- names(model.ABA.point.metrics$model) <- variable
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