+
+
+
+
+
+
+
+
+
+
+
+
Compare PLS and RF
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Collect variable importance
+
+
Number of variables
+
+
+
+
```r
+```r
+```r
+```r
+pls_rf_sr_long = melt(pls_rf_sr[pls_rf_sr$Resample == \Mean\, c(1, 2, 6, 13)], id.vars = c(\ptype\, \resp\))
+
+
+
+
+
+
Error in melt(pls_rf_sr[pls_rf_sr$Resample == \Mean\, c(1, 2, 6, 13)], :
+ object 'pls_rf_sr' not found
+
+
+
+
+
+
+
Variable importance for PLS
+
+
+
+
+
+
+
+
+
+
Variable importance for RF
+
+
+
+
+
+
+
+
+
+
Trophic levels
+
+
+
+
+
+
+
+
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).
+
The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
+
+
+
+
---
title: "500 Analyse Biodiv-RS"
output: html_notebook
---

```{r, include = FALSE}
# Set up working environment and defaults --------------------------------------
require(envimaR)
root_folder = path.expand("~/plygrnd/hySpecVisKili/")
source(file.path(root_folder, "hySpecVisKili/src/000_setup_windows.R"))

all_models = readRDS(file.path(envrmt$path_120_compile_analysis_sr, 
                               "models_sr.rds"))

# Collect model performance
gam_sr = modelPerformance(all_models[["gam"]])
pls_sr = modelPerformance(all_models[["pls"]])
rf_sr = modelPerformance(all_models[["rf"]])

summary(gam_sr)
summary(pls_sr)
summary(rf_sr)

# Get trophic levels
tl = read.table(file.path(path_meta, "trophic_levels.csv"), header = TRUE, sep = ";")
gam_sr = merge(gam_sr, tl, by.x = "resp", by.y = "Species")
pls_sr = merge(pls_sr, tl, by.x = "resp", by.y = "Species")
rf_sr = merge(rf_sr, tl, by.x = "resp", by.y = "Species")

# Arrange levels and species names
pls_sr$Level = factor(pls_sr$Level, levels(pls_sr$Level)[c(1, 5, 4, 3, 6, 2)] )
pls_sr$resp = as.character(pls_sr$resp)
pls_sr$resp = substr(pls_sr$resp, 3, nchar(pls_sr$resp))
pls_sr$resp = gsub("(^[[:alpha:]])", "\\U\\1", pls_sr$resp, perl=TRUE)
pls_sr$resp = factor(pls_sr$resp, unique(pls_sr$resp[order(pls_sr$Level, pls_sr$resp)]))


rf_sr$Level = factor(rf_sr$Level, levels(rf_sr$Level)[c(1, 5, 4, 3, 6, 2)] )
rf_sr$resp = as.character(rf_sr$resp)
rf_sr$resp = substr(rf_sr$resp, 3, nchar(rf_sr$resp))
rf_sr$resp = gsub("(^[[:alpha:]])", "\\U\\1", rf_sr$resp, perl=TRUE)
rf_sr$resp = factor(rf_sr$resp, unique(rf_sr$resp[order(rf_sr$Level, rf_sr$resp)]))
```

# Compare PLS and RF
```{r, echo=FALSE}
models_sr = rbind(pls_sr[, -4], rf_sr[, -4])
models_sr$mptype = paste0(models_sr$mtype, "_", models_sr$ptype)
models_sr$mptype = factor(models_sr$mptype, levels = c("pls_elsp", "rf_elsp",
                                                       "pls_elui", "rf_elui",
                                                       "pls_kmra", "rf_kmra",
                                                       "pls_spec", "rf_spec"))

ggplot(data = models_sr[models_sr$mtype == "pls" & 
                          (models_sr$ptype == "elui" | models_sr$ptype == "spec"),], 
       aes(x = resp, y = RMSE_normSD, fill = mptype)) + 
  geom_boxplot() +
  geom_hline(yintercept=c(0.5,1), linetype="dashed", color = "black") + 
  scale_fill_brewer(palette="Dark2") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 
  labs(list(x = "Species groups", y = "RMSEn", fill = "Model set"))



ggplot(data = models_sr[models_sr$mtype == "rf" & 
                          (models_sr$ptype == "elui" | models_sr$ptype == "spec"),], 
       aes(x = resp, y = RMSE_normSD, fill = mptype)) + 
  geom_boxplot() +
  geom_hline(yintercept=c(0.5,1), linetype="dashed", color = "black") + 
  scale_fill_brewer(palette="Dark2") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 
  labs(list(x = "Species groups", y = "RMSEn", fill = "Model set"))


ggplot(data = models_sr[models_sr$mtype == "rf" & models_sr$ptype == "spec",], 
       aes(x = resp, y = RMSE_normSD, fill = Level)) + 
  geom_boxplot() +
  geom_hline(yintercept=c(0.5,1), linetype="dashed", color = "black") + 
  scale_fill_brewer(palette="Dark2") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 
  labs(list(x = "Species richness", y = "RMSEn", fill = "Trophic level"))
```

```{r, echo=FALSE}
pls_rf_sr = merge(pls_sr, rf_sr, by = c("ptype", "resp", "Resample"), all.y = TRUE)
colnames(pls_rf_sr)[grep("\\.x", colnames(pls_rf_sr))] = 
  gsub("\\.x", "_pls", colnames(pls_rf_sr)[grep("\\.x", colnames(pls_rf_sr))])
colnames(pls_rf_sr)[grep("\\.y", colnames(pls_rf_sr))] = 
  gsub("\\.y", "_rf", colnames(pls_rf_sr)[grep("\\.y", colnames(pls_rf_sr))])
# nrow(pls_rf_sr)

ptypes = c("elui", "kmra", "spec", "elsp")
perf_check = lapply(ptypes, function(pt){
  subdf = pls_rf_sr[!is.na(pls_rf_sr$RMSE_pls) & 
                      pls_rf_sr$ptype == pt &
                      pls_rf_sr$Resample == "Mean", ]
  rownames(subdf[subdf$RMSE_pls < subdf$RMSE_rf, ])
})
names(perf_check) = ptypes
```

# Check performance of PLS and RF
```{r, echo = FALSE}
for(i in seq(length(perf_check))){
  rmse_perf = sort(round(1-pls_rf_sr[as.numeric(perf_check[[i]]), "RMSE_pls"] / 
                           pls_rf_sr[as.numeric(perf_check[[i]]), "RMSE_rf"],2))
  var_rf_prct = sort(round(pls_rf_sr[as.numeric(perf_check[[i]]), "nvars_rf"] / 
                             pls_rf_sr[as.numeric(perf_check[[i]]), "nvars_pls"],2))
  level_pls = sort(table(pls_rf_sr[as.numeric(perf_check[[i]]), "Level_pls"]))
  print(names(perf_check[i]))
  print(pls_rf_sr[as.numeric(perf_check[[i]]),])
  cat("RMSE (1 - PLS/RF):", rmse_perf, "\n")
  cat("Var number (RF/PLS):", var_rf_prct, "\n")
  cat("Levels with PLS is better:", level_pls, "\n")
  cat("\n\n")
}
```

# Collect variable importance
## Number of variables
```{r}
pls_rf_sr_long = melt(pls_rf_sr[pls_rf_sr$Resample == "Mean", c(1, 2, 6, 13)], id.vars = c("ptype", "resp"))
ggplot(data = pls_rf_sr_long, aes(x = variable, y = value, fill = ptype)) +
  geom_boxplot() + 
  labs(list(x = "Models", y = "Number of variables" ,
            fill = "Predictor Set")) +
  theme_bw()
```


# Variable importance for PLS
```{r, echo=FALSE}
var_imp <- compVarImp(all_models[["pls"]][["spec"]]@model[[1]], scale = FALSE)
# plotVarImp(var_imp)
plotVarImpHeatmap(var_imp, xlab = "Species", ylab = "Band")
```

# Variable importance for RF
```{r, echo=FALSE}
var_imp <- compVarImp(all_models[["rf"]][["spec"]]@model[[1]], scale = FALSE)
# plotVarImp(var_imp)
plotVarImpHeatmap(var_imp, xlab = "Species", ylab = "Band")
```


# Trophic levels
```{r}
var_imp_levels = var_imp
for(i in seq(length(var_imp_levels))){
  var_imp_levels[[i]]$RESPONSE = tl$Level[grep(var_imp_levels[[i]]$RESPONSE[1], tl$Species)]
}
plotVarImpHeatmap(var_imp_levels, xlab = "Species", ylab = "Band")
```




When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file).

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Compare PLS and RF
+
+
+
+
+
+
+
+
+
+
Collect variable importance
+
+
Number of variables
+
+
+
+
+
+
+
+
+
+
+
Variable importance for RF
+
+
+
+
+
+
+
+
+
+
Trophic levels
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
rr t = do.call(, var_imp) t = t[t$mean>=0.6,] t\(RESPONSE = gsub(\_rf_elui_res\, \\, t\)RESPONSE) tt = table(t\(VARIABLE, t\)RESPONSE) pca_tt <- prcomp(tt, scale = TRUE, center = TRUE) pca_var <- as.data.frame(pca_tt\(rotation) pca_obs <- as.data.frame(pca_tt\)x) ggplot(pca_var, aes(PC1, PC2))+ geom_point()+ geom_point(data = pca_obs, aes(PC1, PC2), color = ) ggbiplot(pca_tt, labels = rownames(pca_tt$x), choices = 1:2)
+
biplot(pca_tt)
+
+
+
+
+
+
+
+
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).
+
The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
+
+
+
+
LS0tDQp0aXRsZTogIjUxMCBBbmFseXNlIEJpb2Rpdi1SUyINCm91dHB1dDogaHRtbF9ub3RlYm9vaw0KLS0tDQoNCmBgYHtyLCBpbmNsdWRlID0gRkFMU0V9DQpyb290X2ZvbGRlciA9IHBhdGguZXhwYW5kKCJ+L3BseWdybmQvaHlTcGVjVmlzS2lsaS8iKQ0Kc291cmNlKGZpbGUucGF0aChyb290X2ZvbGRlciwgImh5U3BlY1Zpc0tpbGkvc3JjLzAwMF9zZXR1cF93aW5kb3dzLlIiKSkNCg0KYWxsX21vZGVsc19yZXMgPSByZWFkUkRTKGZpbGUucGF0aChlbnZybXQkcGF0aF8yMjBfY29tcGlsZV9hbmFseXNpc19zcl9lbGV2X3JlcywgDQogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIm1vZGVsc19zcl9lbGV2X3Jlcy5yZHMiKSkNCg0KDQojIENvbGxlY3QgbW9kZWwgcGVyZm9ybWFuY2UNCm1vZGVsc19yZXMgPSBtb2RlbFBlcmZvcm1hbmNlKGFsbF9tb2RlbHNfcmVzW1sicmYiXV0pDQoNCm1vZGVsc19yZXMkcmVzbW9kZWwgPSBtb2RlbHNfcmVzJHJlc3ANCm1vZGVsc19yZXMkcmVzcCA9IGdzdWIoIl9wbHNfZWx1aV9yZXMiLCAiIiwgbW9kZWxzX3JlcyRyZXNwKQ0KbW9kZWxzX3JlcyRyZXNwID0gZ3N1YigiX3JmX2VsdWlfcmVzIiwgIiIsIG1vZGVsc19yZXMkcmVzcCkNCg0KcmZfcGxzX3JlcyA9IG1vZGVsc19yZXNbbW9kZWxzX3JlcyRwdHlwZSA9PSAicGxzX2VsdWlfcmVzIixdDQpyZl9yZl9yZXMgPSBtb2RlbHNfcmVzW21vZGVsc19yZXMkcHR5cGUgPT0gInJmX2VsdWlfcmVzIixdDQoNCnN1bW1hcnkocmZfcGxzX3JlcykNCnN1bW1hcnkocmZfcmZfcmVzKQ0KDQojIEdldCB0cm9waGljIGxldmVscw0KdGwgPSByZWFkLnRhYmxlKGZpbGUucGF0aChwYXRoX21ldGEsICJ0cm9waGljX2xldmVscy5jc3YiKSwgaGVhZGVyID0gVFJVRSwgc2VwID0gIjsiKQ0KcmZfcGxzX3JlcyA9IG1lcmdlKHJmX3Bsc19yZXMsIHRsLCBieS54ID0gInJlc3AiLCBieS55ID0gIlNwZWNpZXMiKQ0KcmZfcmZfcmVzID0gbWVyZ2UocmZfcmZfcmVzLCB0bCwgYnkueCA9ICJyZXNwIiwgYnkueSA9ICJTcGVjaWVzIikNCmBgYA0KDQojIENvbXBhcmUgUExTIGFuZCBSRg0KYGBge3IsIGVjaG89RkFMU0V9DQptb2RlbHNfcmVzJG1wdHlwZSA9IHBhc3RlMChtb2RlbHNfcmVzJG10eXBlLCAiXyIsIG1vZGVsc19yZXMkcHR5cGUpDQptb2RlbHNfcmVzJG1wdHlwZSA9IGZhY3Rvcihtb2RlbHNfcmVzJG1wdHlwZSkNCg0KDQpnZ3Bsb3QoZGF0YSA9IG1vZGVsc19yZXMsIGFlcyh4ID0gcmVzcCwgeSA9IFJNU0Vfbm9ybVNELCBmaWxsID0gbXB0eXBlKSkgKyANCiAgZ2VvbV9ib3hwbG90KCkgKw0KICB0aGVtZV9idygpICsgDQogIHRoZW1lKGF4aXMudGV4dC54ID0gZWxlbWVudF90ZXh0KGFuZ2xlID0gNDUsIGhqdXN0ID0gMSkpICsgDQogIGxhYnMobGlzdCh4ID0gIlNwZWNpZXMgZ3JvdXBzIiwgeSA9ICJSTVNFbiIsIGZpbGwgPSAiTW9kZWwgc2V0IikpDQpgYGANCg0KIyBDb2xsZWN0IHZhcmlhYmxlIGltcG9ydGFuY2UNCiMjIE51bWJlciBvZiB2YXJpYWJsZXMNCmBgYHtyfQ0KcmZfcGxzX3Jlc19sb25nID0gbWVsdChtb2RlbHNfcmVzW21vZGVsc19yZXMkUmVzYW1wbGUgPT0gIk1lYW4iLCBjKDIsMyw1KV0sIGlkLnZhcnMgPSBjKCJwdHlwZSIsICJyZXNwIikpDQpnZ3Bsb3QoZGF0YSA9IHJmX3Bsc19yZXNfbG9uZywgYWVzKHggPSB2YXJpYWJsZSwgeSA9IHZhbHVlLCBmaWxsID0gcHR5cGUpKSArDQogIGdlb21fYm94cGxvdCgpICsgDQogIGxhYnMobGlzdCh4ID0gIk1vZGVscyIsIHkgPSAiTnVtYmVyIG9mIHZhcmlhYmxlcyIgLA0KICAgICAgICAgICAgZmlsbCA9ICJQcmVkaWN0b3IgU2V0IikpICsNCiAgdGhlbWVfYncoKQ0KYGBgDQoNCg0KIyBWYXJpYWJsZSBpbXBvcnRhbmNlIGZvciBSRg0KYGBge3J9DQp2YXJfaW1wIDwtIGNvbXBWYXJJbXAoYWxsX21vZGVsc19yZXMkcmYkcmZfZWx1aV9yZXNAbW9kZWxbWzFdXSwgc2NhbGUgPSBGQUxTRSkNCiMgcGxvdFZhckltcCh2YXJfaW1wKQ0KcGxvdFZhckltcEhlYXRtYXAodmFyX2ltcCwgeGxhYiA9ICJTcGVjaWVzIiwgeWxhYiA9ICJCYW5kIikNCmBgYA0KDQoNCiMgVHJvcGhpYyBsZXZlbHMNCmBgYHtyfQ0KdmFyX2ltcF9sZXZlbHMgPSB2YXJfaW1wDQpmb3IoaSBpbiBzZXEobGVuZ3RoKHZhcl9pbXBfbGV2ZWxzKSkpew0KICBhY3Rfc3BlY2llcyA9IGdzdWIoIl9yZl9lbHVpX3JlcyIsICIiLCB2YXJfaW1wX2xldmVsc1tbaV1dJFJFU1BPTlNFWzFdKQ0KICB2YXJfaW1wX2xldmVsc1tbaV1dJFJFU1BPTlNFID0gdGwkTGV2ZWxbZ3JlcChhY3Rfc3BlY2llcywgdGwkU3BlY2llcyldDQp9DQpwbG90VmFySW1wSGVhdG1hcCh2YXJfaW1wX2xldmVscywgeGxhYiA9ICJTcGVjaWVzIiwgeWxhYiA9ICJCYW5kIikNCmBgYA0KDQpgYGB7cn0NCnQgPSBkby5jYWxsKCJyYmluZCIsIHZhcl9pbXApDQp0ID0gdFt0JG1lYW4+PTAuNixdDQp0JFJFU1BPTlNFID0gZ3N1YigiX3JmX2VsdWlfcmVzIiwgIiIsIHQkUkVTUE9OU0UpDQp0dCA9IHRhYmxlKHQkVkFSSUFCTEUsIHQkUkVTUE9OU0UpDQpwY2FfdHQgPC0gcHJjb21wKHR0LCBzY2FsZSA9IFRSVUUsIGNlbnRlciA9IFRSVUUpDQpwY2FfdmFyIDwtIGFzLmRhdGEuZnJhbWUocGNhX3R0JHJvdGF0aW9uKQ0KcGNhX29icyA8LSBhcy5kYXRhLmZyYW1lKHBjYV90dCR4KQ0KZ2dwbG90KHBjYV92YXIsIGFlcyhQQzEsIFBDMikpKw0KICBnZW9tX3BvaW50KCkrDQpnZW9tX3BvaW50KGRhdGEgPSBwY2Ffb2JzLCBhZXMoUEMxLCBQQzIpLCBjb2xvciA9ICJyZWQiKQ0KZ2diaXBsb3QocGNhX3R0LCBsYWJlbHMgPSByb3duYW1lcyhwY2FfdHQkeCksIGNob2ljZXMgPSAxOjIpDQpiaXBsb3QocGNhX3R0KQ0KYGBgDQoNCldoZW4geW91IHNhdmUgdGhlIG5vdGVib29rLCBhbiBIVE1MIGZpbGUgY29udGFpbmluZyB0aGUgY29kZSBhbmQgb3V0cHV0IHdpbGwgYmUgc2F2ZWQgYWxvbmdzaWRlIGl0IChjbGljayB0aGUgKlByZXZpZXcqIGJ1dHRvbiBvciBwcmVzcyAqQ3RybCtTaGlmdCtLKiB0byBwcmV2aWV3IHRoZSBIVE1MIGZpbGUpLg0KDQpUaGUgcHJldmlldyBzaG93cyB5b3UgYSByZW5kZXJlZCBIVE1MIGNvcHkgb2YgdGhlIGNvbnRlbnRzIG9mIHRoZSBlZGl0b3IuIENvbnNlcXVlbnRseSwgdW5saWtlICpLbml0KiwgKlByZXZpZXcqIGRvZXMgbm90IHJ1biBhbnkgUiBjb2RlIGNodW5rcy4gSW5zdGVhZCwgdGhlIG91dHB1dCBvZiB0aGUgY2h1bmsgd2hlbiBpdCB3YXMgbGFzdCBydW4gaW4gdGhlIGVkaXRvciBpcyBkaXNwbGF5ZWQuDQo=
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Compare PLS and RF
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Check performance of PLS and RF
+
+
+
+
+
+
+
Collect variable importance
+
+
Number of variables
+
+
+
+
pls_rf_res_long = melt(pls_rf_res[pls_rf_res$Resample == "Mean", c(1, 2, 6, 13)], id.vars = c("ptype", "resp"))
+ggplot(data = pls_rf_res_long, aes(x = variable, y = value, fill = ptype)) +
+ geom_boxplot() +
+ labs(list(x = "Models", y = "Number of variables" ,
+ fill = "Predictor Set")) +
+ theme_bw()
+
+
+
+
+
+
+
Variable importance for PLS
+
+
+
+
+
+
+
Variable importance for RF
+
+
+
+
+
+
+
Trophic levels
+
+
+
+
var_imp_levels = var_imp
+for(i in seq(length(var_imp_levels))){
+ var_imp_levels[[i]]$RESPONSE = tl$Level[grep(var_imp_levels[[i]]$RESPONSE[1], tl$Species)]
+}
+plotVarImpHeatmap(var_imp_levels, xlab = "Species", ylab = "Band")
+
+
+
+
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).
+
The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
+
+
+
+
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
+
+
+
+
+
+
+
+
+
+
+
+
Lable colors in the first tow boxplots have the following meaning: blue = LiDAR is better than hyperspectral observations, green = hyperspectral is better, black = prediction using elevation is better.
+
best_model_gam$best_model_table
+
##
+## gam_elev gam_elui
+## 10 17
+
best_model_pls_incl_gam$best_model_table
+
##
+## pls_eldr pls_elsp pls_esld pls_lidr pls_spec pls_splr
+## 2 2 2 1 3 17
+
best_model_pls_single$best_model_table
+
##
+## pls_kmra pls_lidr pls_spec
+## 1 9 17
+
best_model_pls_single_incl_gam$best_model_table
+
##
+## gam_elev gam_elui pls_lidr pls_spec
+## 1 3 8 15
+
best_model_rf_incl_gam$best_model_table
+
##
+## rf_eldr rf_elsp rf_esld rf_spec rf_splr
+## 1 3 7 3 13
+
best_model_rf_single$best_model_table
+
##
+## rf_lidr rf_spec
+## 9 18
+
best_model_rf_single_incl_gam$best_model_table
+
##
+## gam_elev gam_elui rf_lidr rf_spec
+## 1 1 8 17
+
lbl_color <- best_model_pls_single_incl_gam$best_model[, c("resp", "ptype")]
+lbl_color$color = "black"
+lbl_color$color[lbl_color$ptype == "lidr"] = "blue"
+lbl_color$color[lbl_color$ptype == "spec"] = "darkgreen"
+
+ggplot(
+ data = model_sr_m[model_sr_m$mptype %in% pls_models_single| model_sr_m$mptype %in% gam_models, ],
+ aes(x = resp, y = RMSE_normSD, fill = mptype)
+) +
+ geom_boxplot() +
+ geom_hline(yintercept = c(0.5, 1), linetype = "dashed", color = "black") +
+ scale_fill_brewer(palette = "Dark2") +
+ theme_bw() +
+ theme(axis.text.x = element_text(angle = 45, hjust = 1, colour = lbl_color$color)) +
+ labs(list(x = "Species groups", y = "RMSEn", fill = "Model set"))
+
## Warning: Vectorized input to `element_text()` is not officially supported.
+## Results may be unexpected or may change in future versions of ggplot2.
+
+
lbl_color <- best_model_rf_single_incl_gam$best_model[, c("resp", "ptype")]
+lbl_color$color = "black"
+lbl_color$color[lbl_color$ptype == "lidr"] = "blue"
+lbl_color$color[lbl_color$ptype == "spec"] = "darkgreen"
+
+ggplot(
+ data = model_sr_m[model_sr_m$mptype %in% rf_models_single| model_sr_m$mptype %in% gam_models, ],
+ aes(x = resp, y = RMSE_normSD, fill = mptype)
+) +
+ geom_boxplot() +
+ geom_hline(yintercept = c(0.5, 1), linetype = "dashed", color = "black") +
+ scale_fill_brewer(palette = "Dark2") +
+ theme_bw() +
+ theme(axis.text.x = element_text(angle = 45, hjust = 1, colour = lbl_color$color)) +
+ labs(list(x = "Species groups", y = "RMSEn", fill = "Model set"))
+
## Warning: Vectorized input to `element_text()` is not officially supported.
+## Results may be unexpected or may change in future versions of ggplot2.
+
+
ggplot(data = rbind(model_sr_m[(model_sr_m$mptype == "gam_elui" | model_sr_m$mptype == "gam_elev" | model_sr_m$mptype == "pls_spec" | model_sr_m$mptype == "pls_lidr" | model_sr_m$mptype == "pls_elui" | model_sr_m$mptype == "rf_spec" | model_sr_m$mptype == "rf_lidr" | model_sr_m$mptype == "rf_elui") & model_sr_m$Resample != "Mean", ]), aes(x = resp, y = RMSE_normSD, fill = mptype)) +
+ geom_boxplot() +
+ geom_hline(yintercept = c(0.5, 1), linetype = "dashed", color = "black") +
+ scale_fill_brewer(palette = "Dark2") +
+ theme_bw() +
+ theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+ labs(list(x = "Species groups", y = "RMSEn", fill = "Model set"))
+
+
+
+
+
+