I want to beautify the image of the code output.Here's my code.
library(randomForest)
library(raster)
library(sp)
library(AUC)
library(pROC)
presence <- read.csv("C:/Users/k/Desktop/ten/00.csv")
squads <- tibble::tribble(
~X2020, ~aspect, ~BC1, ~BC13, ~BC17, ~BC18, ~BC19, ~BC2, ~BC5, ~BC7, ~srad5, ~dataType,
15.27190781, 142.8058472, 16.4628334, 120L, 157L, 258L, 157L, 10.91166687, 22.95999908, 13.06399918, 13457L, "presence",
39.73490524, 170.2971497, 26.58716583, 449L, 748L, 757L, 1255L, 7.725000381, 31.77199936, 9.944000244, 17011L, "presence",
4.887162209, 97.70574951, 23.4968338, 693L, 51L, 645L, 95L, 7.373666763, 29.53199959, 11.32799911, 18173L, "presence",
2.965255737, 16.85839844, 24.80566597, 331L, 48L, 234L, 92L, 12.31400013, 33.62799835, 17.97599792, 14490L, "presence",
21.24339485, 17.64453888, 17.13266754, 229L, 50L, 220L, 54L, 15.11933327, 26.50399971, 18.43600082, 15431L, "presence"
)
head(squads)
absence <- read.csv("C:/Users/k/Desktop/ten/AA.csv")
squads <- tibble::tribble(
~X2020, ~aspect, ~BC1, ~BC13, ~BC17, ~BC18, ~BC19, ~BC2, ~BC5, ~BC7, ~srad5, ~dataType,
19.54455757, 91.97493744, 10.2998333, 115L, 107L, 332L, 107L, 11.96033287, 30.40799904, 41.0039978, 19866L, "absence",
1.624001265, 22.43912125, -0.858333349, 48L, 18L, 107L, 23L, 14.0340004, 14.67599964, 33.35200119, 21928L, "absence",
7.476354599, 244.379776, 23.47533417, 509L, 35L, 926L, 43L, 10.94933414, 34.34400177, 25.08000183, 23329L, "absence",
0.000172869, 173.6598053, 23.48366737, 23L, 14L, 54L, 22L, 15.34866619, 39.49599838, 33.09599686, 15337L, "absence",
5.378329277, 292.49646, 20.9829998, 70L, 19L, 160L, 40L, 13.43599987, 37.2159996, 34.39199829, 24567L, "absence"
)
presence$dataType <- "presence"
absence$dataType <- "absence"
data <- rbind(presence, absence)
data$dataType <- factor(data$dataType, levels = c("absence", "presence"))
summary(data)
set.seed(123)
train_index <- sample(1:nrow(data), size = 0.7 * nrow(data))
train <- data[train_index, ]
test <- data[-train_index, ]
test$dataType <- factor(test$dataType, levels = levels(train$dataType))
set.seed(123)
rf_model <- randomForest(
dataType ~ .,
data = train,
ntree = 1000,
mtry = sqrt(ncol(train) - 1),
importance = TRUE
)
print(rf_model)
varImpPlot(rf_model)
test_pred <- predict(rf_model, test, type = "prob")
test_labels <- test$dataType
auc_value <- roc(test_labels, test_pred[, "presence"])
print(paste("AUC:", auc_value$auc))
plot(roc(test_labels, test_pred[, "presence"]), col = "blue", main = "ROC Curve")
setwd("C:/Users/zm/Desktop/RFxuexi/ten")
BC1 <- raster("BC1.tif")
BC2 <- raster("BC2.tif")
BC5 <- raster("BC5.tif")
BC7 <- raster("BC7.tif")
BC13 <- raster("BC13.tif")
BC17 <- raster("BC17.tif")
BC18 <- raster("BC18.tif")
BC19 <- raster("BC19.tif")
aspect <- raster("aspect.tif")
srad5 <- raster("srad5.tif")
X2020 <- raster("X2020.tif")
env_stack <- stack(BC1, BC2, BC5, BC7, BC13, BC17, BC18, BC19, aspect, srad5, X2020)
names(env_stack) <- c("BC1", "BC2", "BC5", "BC7", "BC13", "BC17", "BC18", "BC19", "aspect", "srad5", "X2020")
pred_map <- predict(env_stack, rf_model, type = "prob", index = 2)
output_pred_map <- "C:/Users/k/Desktop/RF"
writeRaster(pred_map, filename = output_pred_map, format = "GTiff", overwrite = TRUE)
cat("k ", output_pred_map)