Updated with ABAmodel main parameters

area-based.2.model.calibration.Rmd

@@ -131,9 +131,9 @@ subsample <- 1:nrow(plots)
 # model calibration
 model.ABA <- lidaRtRee::ABAmodel(plots[subsample,variable], metrics[subsample,], transform="boxcox", nmax=4, xy = plots[subsample, c("X", "Y")])
 # renames outputs with variable name
-row.names(model.ABA$stats) <- names(model.ABA$model) <- variable
+row.names(model.ABA$stats) <- variable
 # display selected linear regression model 
-model.ABA$model[[variable]]
+model.ABA$model
 # display calibration and validation statistics
 model.ABA$stats
 ```
@@ -149,7 +149,7 @@ round(cor(cbind(model.ABA$values$residual, plots[subsample, c("G.m2.ha","N.ha","
 cor.test(model.ABA$values$residual, plots[subsample, variable])
 # plot predicted  VS field values
 par(mfrow=c(1,2))
-lidaRtRee::ABAmodelPlot(model.ABA, variable)
+lidaRtRee::ABAmodelPlot(model.ABA, main = variable)
 plot(plots[subsample, c("G.m2.ha")], model.ABA$values$residual, ylab = "Prediction errors", xlab = "Field values")
 abline(h = 0, lty = 2)
 ```
@@ -164,7 +164,7 @@ model.ABA.point.metrics$stats
 # cor.test(model.ABA.point.metrics$values$residual, plots[subsample, variable])
 par(mfrow=c(1,2))
 # plot predicted  VS field values
-lidaRtRee::ABAmodelPlot(model.ABA.point.metrics, variable,
+lidaRtRee::ABAmodelPlot(model.ABA.point.metrics, main = variable,
                         col = ifelse(plots$stratum == "public", "green", "blue"))
 legend("topleft", c("public", "private"), col = c("green", "blue"), pch = 1)
 plot(plots[subsample, c("G.m2.ha")],
@@ -207,7 +207,7 @@ knitr::kable(table.output)
 par(mfrow = c(1,3))
 for (i in names(models.ABA))
 {
-  lidaRtRee::ABAmodelPlot(models.ABA[[i]], i)
+  lidaRtRee::ABAmodelPlot(models.ABA[[i]], main = i)
 }
 rm(models.ABA, model.stats)
 ``` 
@@ -241,7 +241,7 @@ for (i in levels(plots[, strat]))
 # backup list of models for later use
 model.ABA.stratified.boxcox <- model.ABA.stratified
 # combine list of models into single object
-model.ABA.stratified <- lidaRtRee::ABAmodelCombineStrata(model.ABA.stratified, plots$plotID)
+model.ABA.stratified <- lidaRtRee::ABAmodelCombineStrata(model.ABA.stratified, plots$plotId)
 # model.ABA.stratified$stats
 ```
 
@@ -256,8 +256,8 @@ table.output <- cbind(model.stats[, c("n", "formula")],
 names(table.output) <- c("n", "metrics", "adj-R2.%", "CV-R2.%", "CV-RMSE.%", "CV-RMSE")
 knitr::kable(table.output)
 par(mfrow=c(1,2))
-lidaRtRee::ABAmodelPlot(model.ABA, paste0(variable, ", not stratified"))
-lidaRtRee::ABAmodelPlot(model.ABA.stratified, paste0(variable, ", stratified"))
+lidaRtRee::ABAmodelPlot(model.ABA, main = paste0(variable, ", not stratified"))
+lidaRtRee::ABAmodelPlot(model.ABA.stratified, main = paste0(variable, ", stratified"))
 ```
 
 ## Stratified models with stratum-specific variable tranformations
@@ -267,7 +267,7 @@ In case one wants to apply different variable transformations, or use different
 * public ownership, all metrics, Box-Cox transformation of basal area values (calibrated in the previous paragraph),
 + private ownership, only point cloud metrics, no data transformation.
 
-```{r stratifiedmodelCalibrationTransforation, include=TRUE, warning = FALSE}
+```{r stratifiedmodelCalibrationTransformation, include=TRUE, warning = FALSE}
 # create list of models for no transformation
 model.ABA.stratified.none <- list()
 # calibrate each stratum model
@@ -295,8 +295,8 @@ names(table.output) <- c("n", "metrics", "transform", "adj-R2.%", "CV-R2.%", "CV
 knitr::kable(table.output)
 # graphics
 par(mfrow=c(1,2))
-lidaRtRee::ABAmodelPlot(model.ABA, paste0(variable, ", not stratified"))
-lidaRtRee::ABAmodelPlot(model.ABA.stratified.mixed, paste0(variable, ", stratified"))
+lidaRtRee::ABAmodelPlot(model.ABA, main = paste0(variable, ", not stratified"))
+lidaRtRee::ABAmodelPlot(model.ABA.stratified.mixed, main = paste0(variable, ", stratified"))
 ```
 
 # Save data before next tutorial