mlspatial:Spatial Machine Learning
workflow
olddir <- getwd()
setwd(tempdir())
setwd(olddir)
oldopt <- options(digits = 4)
options(oldopt)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(mlspatial)
library(dplyr)
library(ggplot2)
library(tmap)
library(sf)
library(spdep)
library(randomForest)
library(xgboost)
library(e1071)
library(caret)
library(tidyr)
# Quick thematic map with country labels
plot(africa_shp)
qtm(africa_shp, text = "NAME") # Replace with actual field name
tm_shape(africa_shp) +
tm_polygons() +
tm_text("NAME", size = 0.5) + # Replace with correct column
tm_title ("Africa Countries")
ggplot(africa_shp) +
geom_sf(fill = "lightgray", color = "black") +
geom_sf_text(aes(label = NAME), size = 2) + # Replace as needed
ggtitle("Africa countries") +
theme_minimal()
# Join data
mapdata <- join_data(africa_shp, panc_incidence, by = "NAME")
## OR Joining/ merging my data and shapefiles
mapdata <- inner_join(africa_shp, panc_incidence, by = "NAME")
## OR mapdata <- left_join(nat, codata, by = "DISTRICT_N")
str(mapdata)
#> Classes 'sf' and 'data.frame': 53 obs. of 26 variables:
#> $ OBJECTID : int 2 3 5 6 7 8 9 10 11 12 ...
#> $ FIPS_CNTRY: chr "UV" "CV" "GA" "GH" ...
#> $ ISO_2DIGIT: chr "BF" "CV" "GM" "GH" ...
#> $ ISO_3DIGIT: chr "BFA" "CPV" "GMB" "GHA" ...
#> $ NAME : chr "Burkina Faso" "Cabo Verde" "Gambia" "Ghana" ...
#> $ COUNTRYAFF: chr "Burkina Faso" "Cabo Verde" "Gambia" "Ghana" ...
#> $ CONTINENT : chr "Africa" "Africa" "Africa" "Africa" ...
#> $ TOTPOP : int 20107509 560899 2051363 27499924 12413867 1792338 4689021 17885245 3758571 33986655 ...
#> $ incidence : num 330.4 53.4 31.4 856.3 163.1 ...
#> $ female : num 1683 362 140 4566 375 ...
#> $ male : num 1869 211 197 4640 1378 ...
#> $ ageb : num 669.7 93.7 68.7 2047 336.7 ...
#> $ agec : num 2878 480 268 7147 1414 ...
#> $ agea : num 4.597 0.265 0.718 11.888 2.13 ...
#> $ fageb : num 250.3 40.2 23.1 782 59.1 ...
#> $ fagec : num 1429 322 116 3775 315 ...
#> $ fagea : num 3.413 0.146 0.548 8.816 1.228 ...
#> $ mageb : num 419.5 53.5 45.6 1265 277.6 ...
#> $ magec : num 1448 158 152 3372 1100 ...
#> $ magea : num 1.184 0.12 0.17 3.073 0.902 ...
#> $ yra : num 182.4 30.2 16.6 524.7 73.1 ...
#> $ yrb : num 187.2 34.1 17.1 552.6 74.9 ...
#> $ yrc : num 193.1 35 18 578.5 76.9 ...
#> $ yrd : num 198.5 35.9 18.3 602.7 78.6 ...
#> $ yre : num 204.3 36.5 18.7 621.5 79.4 ...
#> $ geometry :sfc_MULTIPOLYGON of length 53; first list element: List of 1
#> ..$ :List of 1
#> .. ..$ : num [1:317, 1:2] 102188 90385 80645 74151 70224 ...
#> ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
#> - attr(*, "sf_column")= chr "geometry"
#> - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
#> ..- attr(*, "names")= chr [1:25] "OBJECTID" "FIPS_CNTRY" "ISO_2DIGIT" "ISO_3DIGIT" ...
#Visualize Pancreatic cancer Incidence by countries
#Basic map with labels
# quantile map
p1 <- tm_shape(mapdata) +
tm_fill("incidence", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Incidence")) + tm_borders(fill_alpha = .3) + tm_compass() +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE)
p2 <- tm_shape(mapdata) +
tm_fill("female", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Female")) + tm_borders(fill_alpha = .3) + tm_compass() +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE)
p3 <- tm_shape(mapdata) +
tm_fill("male", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Male")) + tm_borders(fill_alpha = .3) + tm_compass() +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE)
p4 <- tm_shape(mapdata) +
tm_fill("ageb", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Age:20-54yrs")) + tm_borders(fill_alpha = .3) + tm_compass() +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE)
p5 <- tm_shape(mapdata) +
tm_fill("agec", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Age:55+yrs")) + tm_borders(fill_alpha = .3) + tm_compass() +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE)
p6 <- tm_shape(mapdata) +
tm_fill("agea", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Age:<20yrs")) + tm_borders(fill_alpha = .3) + tm_compass() +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE)
p7 <- tm_shape(mapdata) +
tm_fill("fageb", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Female:20-54yrs")) + tm_borders(fill_alpha = .3) + tm_compass() +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE)
p8 <- tm_shape(mapdata) +
tm_fill("fagec", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Female:55+yrs")) + tm_borders(fill_alpha = .3) + tm_compass() +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE)
p9 <- tm_shape(mapdata) +
tm_fill("fagea", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Female:<20yrs")) + tm_borders(fill_alpha = .3) + tm_compass() +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE)
p10 <- tm_shape(mapdata) +
tm_fill("mageb", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Male:20-54yrs")) + tm_borders(fill_alpha = .3) + tm_compass() +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE)
p11 <- tm_shape(mapdata) +
tm_fill("magec", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Male:55+yrs")) + tm_borders(fill_alpha = .3) + tm_compass() +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE)
p12 <- tm_shape(mapdata) +
tm_fill("magea", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Male:<20yrs")) + tm_borders(fill_alpha = .3) + tm_compass() +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE)
current.mode <- tmap_mode("plot")
tmap_arrange(p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12,
widths = c(.75, .75))
## Machine Learning Model building
# 1. Random Forest Regression
# Train Random Forest
set.seed(123)
rf_model <- randomForest(incidence ~ female + male + agea + ageb + agec + fagea + fageb + fagec +
magea + mageb + magec + yrb + yrc + yrd + yre, data = mapdata, ntree = 500,
importance = TRUE)
# View model results
print(rf_model)
#>
#> Call:
#> randomForest(formula = incidence ~ female + male + agea + ageb + agec + fagea + fageb + fagec + magea + mageb + magec + yrb + yrc + yrd + yre, data = mapdata, ntree = 500, importance = TRUE)
#> Type of random forest: regression
#> Number of trees: 500
#> No. of variables tried at each split: 5
#>
#> Mean of squared residuals: 76299.33
#> % Var explained: 91.35
varImpPlot(rf_model)
#Plot Variable Importance
library(ggplot2)
importance_df <- data.frame(
Variable = rownames(importance(rf_model)),
Importance = importance(rf_model)[, "IncNodePurity"])
ggplot(importance_df, aes(x = reorder(Variable, Importance), y = Importance)) +
geom_bar(stat = "identity", fill = "steelblue") +
coord_flip() +
labs(title = "Variable Importance (IncNodePurity)", x = "Variable", y = "Importance")
# 2. Gradient Boosting Machine (XGBoost)
# Prepare the data
x_vars <- model.matrix(incidence ~ female + male + agea + ageb + agec + fagea + fageb + fagec +
magea + mageb + magec + yrb + yrc + yrd + yre, data = mapdata)[,-1]
y <- mapdata$incidence
# Convert to DMatrix
dtrain <- xgb.DMatrix(data = x_vars, label = y)
# Train model
xgb_model <- xgboost(data = dtrain,
objective = "reg:squarederror",
nrounds = 100,
max_depth = 4,
eta = 0.1,
verbose = 0)
# Feature importance
xgb.importance(model = xgb_model)
#> Feature Gain Cover Frequency
#> <char> <num> <num> <num>
#> 1: female 9.762372e-01 0.41045424 0.471518987
#> 2: ageb 1.425639e-02 0.21332584 0.151898734
#> 3: agec 6.055607e-03 0.13157222 0.145569620
#> 4: male 3.411915e-03 0.12804660 0.136075949
#> 5: mageb 2.492252e-05 0.01400031 0.011075949
#> 6: magea 7.191460e-06 0.01808799 0.012658228
#> 7: fagec 6.206994e-06 0.03454090 0.030063291
#> 8: fageb 2.428280e-07 0.01108783 0.007911392
#> 9: magec 1.919227e-07 0.01829237 0.015822785
#> 10: agea 1.379456e-07 0.02059169 0.017405063
# 3. Support Vector Regression (SVR)
# Train SVR model
svr_model <- svm(incidence ~ female + male + agea + ageb + agec + fagea + fageb + fagec +
magea + mageb + magec + yrb + yrc + yrd + yre, data = mapdata,
type = "eps-regression",
kernel = "radial")
# Summary and predictions
summary(svr_model)
#>
#> Call:
#> svm(formula = incidence ~ female + male + agea + ageb + agec + fagea +
#> fageb + fagec + magea + mageb + magec + yrb + yrc + yrd + yre,
#> data = mapdata, type = "eps-regression", kernel = "radial")
#>
#>
#> Parameters:
#> SVM-Type: eps-regression
#> SVM-Kernel: radial
#> cost: 1
#> gamma: 0.06666667
#> epsilon: 0.1
#>
#>
#> Number of Support Vectors: 7
#mapdata$pred_svr <- predict(svr_model)
# Model Evaluation (predictions):
# evaluate each model step-by-step:
# 1. Random Forest Evaluation
rf_preds <- predict(rf_model, newdata = mapdata)
rf_metrics <- postResample(pred = rf_preds, obs = mapdata$incidence)
print(rf_metrics)
#> RMSE Rsquared MAE
#> 98.2197100 0.9976235 30.2558297
# 2. XGBoost Evaluation
xgb_preds <- predict(xgb_model, newdata = x_vars)
xgb_metrics <- postResample(pred = xgb_preds, obs = mapdata$incidence)
print(xgb_metrics)
#> RMSE Rsquared MAE
#> 2.2921957 0.9999979 0.8058498
# 3. SVR Evaluation
svr_preds <- predict(svr_model, newdata = mapdata)
svr_metrics <- postResample(pred = svr_preds, obs = mapdata$incidence)
print(svr_metrics)
#> RMSE Rsquared MAE
#> 445.9372342 0.9178101 127.7105534
# Compare All Models
model_eval <- data.frame(
Model = c("Random Forest", "XGBoost", "SVR"),
RMSE = c(rf_metrics["RMSE"], xgb_metrics["RMSE"], svr_metrics["RMSE"]),
MAE = c(rf_metrics["MAE"], xgb_metrics["MAE"], svr_metrics["MAE"]),
Rsquared = c(rf_metrics["Rsquared"], xgb_metrics["Rsquared"], svr_metrics["Rsquared"]))
print(model_eval)
#> Model RMSE MAE Rsquared
#> 1 Random Forest 98.219710 30.2558297 0.9976235
#> 2 XGBoost 2.292196 0.8058498 0.9999979
#> 3 SVR 445.937234 127.7105534 0.9178101
#Choosing the Best Model
#Lowest RMSE and MAE = most accurate predictions
#Highest R² = best variance explanation
#Sort and interpret:
model_eval[order(model_eval$RMSE), ] # Best = Top row
#> Model RMSE MAE Rsquared
#> 2 XGBoost 2.292196 0.8058498 0.9999979
#> 1 Random Forest 98.219710 30.2558297 0.9976235
#> 3 SVR 445.937234 127.7105534 0.9178101
#### Models Predicted plots
# Create a data frame from your table
model_preds <- data.frame(rf_preds, xgb_preds, svr_preds)
# Add observation ID
model_preds$ID <- 1:nrow(model_preds)
# Reshape for plotting
model_long <- model_preds %>%
tidyr::pivot_longer(cols = c("rf_preds", "xgb_preds", "svr_preds"), names_to = "Model", values_to = "Predicted")
# Plot
ggplot(model_long, aes(x = ID, y = Predicted, color = Model)) +
geom_line(linewidth = 0.5) +
labs(title = "Model Predictions Over Observations",
x = "Observation", y = "Predicted Incidence") +
theme_minimal()
## plot actual vs predicted
oldpar <- par(mfrow = c(1, 3)) # 3 plots side-by-side
# Random Forest
plot(mapdata$incidence, mapdata$rf_pred,
xlab = "Observed", ylab = "Predicted",
main = "Random Forest", pch = 19, col = "steelblue")
abline(0, 1, col = "red", lwd = 2)
# XGBoost
plot(mapdata$incidence, mapdata$xgb_pred,
xlab = "Observed", ylab = "Predicted",
main = "XGBoost", pch = 19, col = "darkgreen")
abline(0, 1, col = "red", lwd = 2)
# SVR
plot(mapdata$incidence, mapdata$svr_pred,
xlab = "Observed", ylab = "Predicted",
main = "SVR", pch = 19, col = "purple")
abline(0, 1, col = "red", lwd = 2)
par(oldpar)
## Actual vs predicted plot with correlations
library(ggplot2)
library(ggpubr) # For stat_cor
mapdata$rf_pred <- predict(rf_model)
mapdata$xgb_pred <- predict(xgb_model, newdata = x_vars)
mapdata$svr_pred <- predict(svr_model, newdata = mapdata)
# Random Forest Plot
p1 <- ggplot(mapdata, aes(x = incidence, y = rf_pred)) +
geom_point(color = "steelblue", alpha = 0.6) +
geom_abline(slope = 1, intercept = 0, color = "red", linetype = "dashed") +
stat_cor(method = "pearson", aes(label = paste0("R² = ")), label.x = 0) +
labs(title = "Random Forest", x = "Observed Incidence", y = "Predicted Incidence") +
theme_minimal()
# XGBoost Plot
p2 <- ggplot(mapdata, aes(x = incidence, y = xgb_pred)) +
geom_point(color = "darkgreen", alpha = 0.6) +
geom_abline(slope = 1, intercept = 0, color = "red", linetype = "dashed") +
stat_cor(method = "pearson", aes(label = paste0("R² = ")), label.x = 0) +
labs(title = "XGBoost", x = "Observed Incidence", y = "Predicted Incidence") +
theme_minimal()
# SVR Plot
p3 <- ggplot(mapdata, aes(x = incidence, y = svr_pred)) +
geom_point(color = "purple", alpha = 0.6) +
geom_abline(slope = 1, intercept = 0, color = "red", linetype = "dashed") +
stat_cor(method = "pearson", aes(label = paste0("R² = ")), label.x = 0) +
labs(title = "SVR", x = "Observed Incidence", y = "Predicted Incidence") +
theme_minimal()
combined_plot <- ggarrange(p1, p2, p3, ncol = 3, nrow = 1, common.legend = FALSE)
## CROSS-VALIDATION
#Step 1: Prepare common setup
# Set seed for reproducibility
set.seed(123)
# Define 5-fold cross-validation
cv_control <- trainControl(method = "cv", number = 5)
# 1. Random Forest
library(randomForest)
rf_cv <- train(
incidence ~ female + male + agea + ageb + agec + fagea + fageb + fagec +
magea + mageb + magec + yrb + yrc + yrd + yre,
data = mapdata,
method = "rf",
trControl = cv_control,
tuneLength = 3,
importance = TRUE
)
print(rf_cv)
#> Random Forest
#>
#> 53 samples
#> 16 predictors
#>
#> No pre-processing
#> Resampling: Cross-Validated (5 fold)
#> Summary of sample sizes: 41, 45, 42, 42, 42
#> Resampling results across tuning parameters:
#>
#> mtry RMSE Rsquared MAE
#> 2 214.1022 0.9875411 82.55215
#> 8 231.4409 0.9836142 86.88933
#> 15 239.2705 0.9825693 90.05875
#>
#> RMSE was used to select the optimal model using the smallest value.
#> The final value used for the model was mtry = 2.
# 2. XGBoost
library(xgboost)
xgb_cv <- train(
incidence ~ female + male + agea + ageb + agec + fagea + fageb + fagec +
magea + mageb + magec + yrb + yrc + yrd + yre,
data = mapdata,
method = "xgbTree",
trControl = cv_control,
tuneLength = 3
)
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
#> [09:19:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
print(xgb_cv)
#> eXtreme Gradient Boosting
#>
#> 53 samples
#> 16 predictors
#>
#> No pre-processing
#> Resampling: Cross-Validated (5 fold)
#> Summary of sample sizes: 43, 43, 42, 43, 41
#> Resampling results across tuning parameters:
#>
#> eta max_depth colsample_bytree subsample nrounds RMSE Rsquared
#> 0.3 1 0.6 0.50 50 77.51211 0.9825285
#> 0.3 1 0.6 0.50 100 74.53665 0.9848370
#> 0.3 1 0.6 0.50 150 74.12358 0.9856897
#> 0.3 1 0.6 0.75 50 87.75916 0.9804207
#> 0.3 1 0.6 0.75 100 85.75756 0.9801767
#> 0.3 1 0.6 0.75 150 85.03767 0.9810229
#> 0.3 1 0.6 1.00 50 108.10833 0.9623482
#> 0.3 1 0.6 1.00 100 110.16070 0.9535319
#> 0.3 1 0.6 1.00 150 111.31761 0.9516319
#> 0.3 1 0.8 0.50 50 72.75480 0.9726405
#> 0.3 1 0.8 0.50 100 66.34459 0.9740674
#> 0.3 1 0.8 0.50 150 65.37974 0.9750195
#> 0.3 1 0.8 0.75 50 103.75480 0.9728968
#> 0.3 1 0.8 0.75 100 104.71482 0.9697262
#> 0.3 1 0.8 0.75 150 106.92944 0.9666806
#> 0.3 1 0.8 1.00 50 85.97447 0.9655598
#> 0.3 1 0.8 1.00 100 86.83885 0.9575015
#> 0.3 1 0.8 1.00 150 86.85652 0.9557451
#> 0.3 2 0.6 0.50 50 94.00716 0.9842634
#> 0.3 2 0.6 0.50 100 92.24585 0.9854197
#> 0.3 2 0.6 0.50 150 92.44745 0.9854256
#> 0.3 2 0.6 0.75 50 68.03277 0.9812312
#> 0.3 2 0.6 0.75 100 67.78304 0.9814041
#> 0.3 2 0.6 0.75 150 67.81383 0.9813999
#> 0.3 2 0.6 1.00 50 99.36000 0.9709040
#> 0.3 2 0.6 1.00 100 98.55775 0.9714097
#> 0.3 2 0.6 1.00 150 98.53653 0.9714456
#> 0.3 2 0.8 0.50 50 87.25126 0.9782998
#> 0.3 2 0.8 0.50 100 85.04951 0.9777886
#> 0.3 2 0.8 0.50 150 84.72495 0.9778720
#> 0.3 2 0.8 0.75 50 79.05042 0.9853749
#> 0.3 2 0.8 0.75 100 78.56149 0.9857103
#> 0.3 2 0.8 0.75 150 78.48532 0.9857411
#> 0.3 2 0.8 1.00 50 103.38290 0.9827400
#> 0.3 2 0.8 1.00 100 102.86457 0.9832881
#> 0.3 2 0.8 1.00 150 102.77863 0.9833378
#> 0.3 3 0.6 0.50 50 79.92382 0.9871462
#> 0.3 3 0.6 0.50 100 70.76064 0.9873868
#> 0.3 3 0.6 0.50 150 70.44796 0.9875159
#> 0.3 3 0.6 0.75 50 77.02424 0.9820164
#> 0.3 3 0.6 0.75 100 76.72561 0.9823722
#> 0.3 3 0.6 0.75 150 76.69185 0.9823914
#> 0.3 3 0.6 1.00 50 116.69622 0.9864377
#> 0.3 3 0.6 1.00 100 116.53618 0.9864894
#> 0.3 3 0.6 1.00 150 116.52377 0.9864951
#> 0.3 3 0.8 0.50 50 61.89082 0.9885897
#> 0.3 3 0.8 0.50 100 58.47174 0.9895862
#> 0.3 3 0.8 0.50 150 58.42756 0.9897027
#> 0.3 3 0.8 0.75 50 58.14062 0.9783710
#> 0.3 3 0.8 0.75 100 57.26210 0.9785542
#> 0.3 3 0.8 0.75 150 57.26729 0.9785667
#> 0.3 3 0.8 1.00 50 89.04480 0.9754549
#> 0.3 3 0.8 1.00 100 88.94853 0.9756043
#> 0.3 3 0.8 1.00 150 88.95190 0.9756110
#> 0.4 1 0.6 0.50 50 140.70223 0.9663413
#> 0.4 1 0.6 0.50 100 133.59046 0.9713471
#> 0.4 1 0.6 0.50 150 132.51984 0.9706645
#> 0.4 1 0.6 0.75 50 121.39408 0.9703187
#> 0.4 1 0.6 0.75 100 120.30878 0.9691842
#> 0.4 1 0.6 0.75 150 120.08749 0.9683910
#> 0.4 1 0.6 1.00 50 118.90278 0.9496329
#> 0.4 1 0.6 1.00 100 113.22443 0.9513211
#> 0.4 1 0.6 1.00 150 113.67348 0.9494586
#> 0.4 1 0.8 0.50 50 105.04239 0.9694201
#> 0.4 1 0.8 0.50 100 107.82358 0.9698624
#> 0.4 1 0.8 0.50 150 108.38966 0.9703541
#> 0.4 1 0.8 0.75 50 153.50867 0.9427469
#> 0.4 1 0.8 0.75 100 148.30620 0.9487484
#> 0.4 1 0.8 0.75 150 149.49092 0.9492826
#> 0.4 1 0.8 1.00 50 97.42608 0.9576658
#> 0.4 1 0.8 1.00 100 91.74249 0.9596515
#> 0.4 1 0.8 1.00 150 90.99246 0.9592085
#> 0.4 2 0.6 0.50 50 93.35311 0.9708654
#> 0.4 2 0.6 0.50 100 95.07506 0.9707180
#> 0.4 2 0.6 0.50 150 94.89844 0.9707940
#> 0.4 2 0.6 0.75 50 71.25586 0.9781923
#> 0.4 2 0.6 0.75 100 71.37091 0.9784772
#> 0.4 2 0.6 0.75 150 71.30138 0.9784914
#> 0.4 2 0.6 1.00 50 106.94425 0.9783419
#> 0.4 2 0.6 1.00 100 106.57502 0.9786253
#> 0.4 2 0.6 1.00 150 106.53421 0.9786394
#> 0.4 2 0.8 0.50 50 69.66801 0.9876778
#> 0.4 2 0.8 0.50 100 68.00072 0.9880788
#> 0.4 2 0.8 0.50 150 68.19230 0.9879329
#> 0.4 2 0.8 0.75 50 70.73793 0.9700478
#> 0.4 2 0.8 0.75 100 70.12544 0.9706356
#> 0.4 2 0.8 0.75 150 70.11536 0.9706220
#> 0.4 2 0.8 1.00 50 79.88233 0.9844111
#> 0.4 2 0.8 1.00 100 79.76636 0.9844580
#> 0.4 2 0.8 1.00 150 79.72954 0.9844831
#> 0.4 3 0.6 0.50 50 59.00555 0.9937262
#> 0.4 3 0.6 0.50 100 59.19193 0.9938993
#> 0.4 3 0.6 0.50 150 59.22514 0.9938975
#> 0.4 3 0.6 0.75 50 100.17948 0.9764393
#> 0.4 3 0.6 0.75 100 100.03388 0.9766898
#> 0.4 3 0.6 0.75 150 100.02630 0.9766957
#> 0.4 3 0.6 1.00 50 106.97402 0.9792226
#> 0.4 3 0.6 1.00 100 106.88879 0.9792971
#> 0.4 3 0.6 1.00 150 106.88569 0.9792982
#> 0.4 3 0.8 0.50 50 120.04486 0.9765378
#> 0.4 3 0.8 0.50 100 120.12232 0.9765760
#> 0.4 3 0.8 0.50 150 120.10263 0.9765777
#> 0.4 3 0.8 0.75 50 86.76026 0.9681896
#> 0.4 3 0.8 0.75 100 86.62827 0.9687068
#> 0.4 3 0.8 0.75 150 86.62227 0.9687191
#> 0.4 3 0.8 1.00 50 70.25068 0.9843500
#> 0.4 3 0.8 1.00 100 70.28841 0.9843700
#> 0.4 3 0.8 1.00 150 70.28776 0.9843709
#> MAE
#> 54.65428
#> 52.06881
#> 50.60871
#> 60.06351
#> 56.05069
#> 54.11307
#> 65.59206
#> 64.50375
#> 64.40123
#> 46.90761
#> 41.90154
#> 41.52556
#> 62.63757
#> 61.17063
#> 61.29549
#> 53.13176
#> 51.18509
#> 50.40242
#> 49.96162
#> 48.67661
#> 48.47087
#> 41.44950
#> 41.17389
#> 41.23609
#> 55.43944
#> 54.77878
#> 54.79039
#> 47.32302
#> 45.59611
#> 45.62411
#> 44.50286
#> 44.19403
#> 44.08855
#> 54.21665
#> 53.49812
#> 53.40870
#> 43.92885
#> 40.14532
#> 40.07339
#> 42.48043
#> 42.33643
#> 42.31961
#> 54.61403
#> 54.60299
#> 54.59477
#> 37.92887
#> 36.13997
#> 36.09046
#> 34.22140
#> 33.83544
#> 33.82400
#> 49.67038
#> 49.60288
#> 49.60279
#> 83.12435
#> 77.55708
#> 76.74397
#> 67.75398
#> 65.06029
#> 64.83385
#> 72.54725
#> 67.23458
#> 67.18036
#> 67.66800
#> 65.94107
#> 65.24107
#> 91.32890
#> 83.03416
#> 82.64282
#> 61.12489
#> 56.48147
#> 55.81566
#> 59.70785
#> 61.30165
#> 61.03857
#> 45.36963
#> 45.13388
#> 45.10589
#> 58.85405
#> 58.47487
#> 58.46973
#> 43.66037
#> 42.89484
#> 43.34947
#> 42.51137
#> 42.45496
#> 42.47265
#> 45.44857
#> 45.41403
#> 45.43604
#> 36.85656
#> 36.79573
#> 36.83167
#> 56.73538
#> 56.51237
#> 56.50071
#> 53.35098
#> 53.32043
#> 53.31983
#> 61.47282
#> 61.62869
#> 61.62023
#> 54.06670
#> 53.93185
#> 53.91606
#> 43.49995
#> 43.58945
#> 43.58928
#>
#> Tuning parameter 'gamma' was held constant at a value of 0
#> Tuning
#> parameter 'min_child_weight' was held constant at a value of 1
#> RMSE was used to select the optimal model using the smallest value.
#> The final values used for the model were nrounds = 100, max_depth = 3, eta
#> = 0.3, gamma = 0, colsample_bytree = 0.8, min_child_weight = 1 and subsample
#> = 0.75.
# 3. Support Vector Regression (SVR)
library(e1071)
library(kernlab)
svr_cv <- train(
incidence ~ female + male + agea + ageb + agec + fagea + fageb + fagec +
magea + mageb + magec + yrb + yrc + yrd + yre,
data = mapdata,
method = "svmRadial",
trControl = cv_control,
preProcess = c("center", "scale"), # SVR often benefits from scaling
tuneLength = 3
)
print(svr_cv)
#> Support Vector Machines with Radial Basis Function Kernel
#>
#> 53 samples
#> 16 predictors
#>
#> Pre-processing: centered (15), scaled (15)
#> Resampling: Cross-Validated (5 fold)
#> Summary of sample sizes: 42, 42, 44, 42, 42
#> Resampling results across tuning parameters:
#>
#> C RMSE Rsquared MAE
#> 0.25 650.5027 0.6279956 301.1569
#> 0.50 631.0990 0.6401412 288.2435
#> 1.00 616.1446 0.6475419 276.9980
#>
#> Tuning parameter 'sigma' was held constant at a value of 5.615089
#> RMSE was used to select the optimal model using the smallest value.
#> The final values used for the model were sigma = 5.615089 and C = 1.
# Compare All Models (from CV)
results <- resamples(list(
RF = rf_cv,
XGB = xgb_cv,
SVR = svr_cv
))
# Summary of RMSE, MAE, Rsquared
summary(results)
#>
#> Call:
#> summary.resamples(object = results)
#>
#> Models: RF, XGB, SVR
#> Number of resamples: 5
#>
#> MAE
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> RF 20.31325 21.17305 41.25341 82.55215 147.75263 182.26841 0
#> XGB 14.50613 19.26326 26.05017 33.83544 53.93394 55.42368 0
#> SVR 78.46538 133.65823 165.17365 276.99798 478.34369 529.34896 0
#>
#> RMSE
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> RF 31.72226 36.05807 91.66574 214.1022 396.83212 514.2326 0
#> XGB 18.12370 29.80374 36.72109 57.2621 96.40236 105.2596 0
#> SVR 93.38999 187.69338 239.85619 616.1446 1264.83400 1294.9496 0
#>
#> Rsquared
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> RF 0.9632935 0.9818386 0.9957268 0.9875411 0.9976922 0.9991540 0
#> XGB 0.9414156 0.9547185 0.9972071 0.9785542 0.9995794 0.9998504 0
#> SVR 0.2589199 0.3684605 0.7853384 0.6475419 0.8635981 0.9613925 0
# Boxplot of performance
bwplot(results)
## Spatial maps of predicted values of each model
# 1. Random Forest Spatial Map
mapdata$pred_rf <- predict(rf_model, newdata = mapdata)
tm_shape(mapdata) +
tm_fill("pred_rf", fill.scale =tm_scale_intervals(values = "brewer.greens", style = "quantile"),
fill.legend = tm_legend(title = "Inci_pred_rf")) + tm_borders(fill_alpha = .2) +
tm_compass() + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"),
frame = TRUE, component.autoscale = FALSE)
# 2. XGBoost Spatial Map
# Ensure matrix used in training
mapdata$pred_xgb <- predict(xgb_model, newdata = x_vars)
tm_shape(mapdata) +
tm_fill("pred_xgb", fill.scale =tm_scale_intervals(values = "brewer.purples", style = "quantile"),
fill.legend = tm_legend(title = "Inci_pred_xgb")) + tm_borders(fill_alpha = .2) +
tm_compass() + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"),
frame = TRUE, component.autoscale = FALSE)
# 3. Support Vector Regression (SVR) Spatial Map
mapdata$pred_svr <- predict(svr_model, newdata = mapdata)
tm_shape(mapdata) +
tm_fill("pred_svr", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Inci_pred_svr")) + tm_borders(fill_alpha = .2) +
tm_compass() + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"),
frame = TRUE, component.autoscale = FALSE)
# Compare Side by Side
tmap_arrange(
tm_shape(mapdata) +
tm_fill("pred_rf", fill.scale =tm_scale_intervals(values = "brewer.greens", style = "quantile"),
fill.legend = tm_legend(title = "Inci_pred_rf")) + tm_borders(fill_alpha = .2) +
tm_compass() + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"),
frame = TRUE, component.autoscale = FALSE),
tm_shape(mapdata) +
tm_fill("pred_xgb", fill.scale =tm_scale_intervals(values = "brewer.purples", style = "quantile"),
fill.legend = tm_legend(title = "Inci_pred_xgb")) + tm_borders(fill_alpha = .2) +
tm_compass() + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"),
frame = TRUE, component.autoscale = FALSE),
tm_shape(mapdata) +
tm_fill("pred_svr", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Inci_pred_svr")) + tm_borders(fill_alpha = .2) +
tm_compass() + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"),
frame = TRUE, component.autoscale = FALSE),
nrow = 1
)
# Predicted Residuals
# we've already trained all 3 models and have `mapdata$incidence` as your actual values.
### Step 1: Generate predictions and residuals for each model
# Random Forest
rf_preds <- predict(rf_model, newdata = mapdata)
rf_resid <- mapdata$incidence - rf_preds
# XGBoost
xgb_preds <- predict(xgb_model, newdata = x_vars) # x_vars = model.matrix(...)
xgb_resid <- mapdata$incidence - xgb_preds
# SVR
svr_preds <- predict(svr_model, newdata = mapdata)
svr_resid <- mapdata$incidence - svr_preds
### Step 2: Combine into a single data frame
residuals_df <- data.frame(
actual = mapdata$incidence,
rf_pred = rf_preds,
rf_resid = rf_resid,
xgb_pred = xgb_preds,
xgb_resid = xgb_resid,
svr_pred = svr_preds,
svr_resid = svr_resid
)
# export
library(writexl)
### Compare residual distributions
boxplot(residuals_df$rf_resid, residuals_df$xgb_resid, residuals_df$svr_resid,
names = c("RF", "XGB", "SVR"),
main = "Model Residuals",
ylab = "Prediction Error (Residual)")
## Spatial maps of residual values from each model
#Add residuals to mapdata
#You should already have these from the previous steps:
mapdata$rf_resid <- residuals_df$rf_resid
mapdata$xgb_resid <- residuals_df$xgb_resid
mapdata$svr_resid <- residuals_df$svr_resid
# Set tmap mode to plot (static map)
tmap_mode("plot")
# Create individual residual maps
map_rf <- tm_shape(mapdata) +
tm_fill("rf_resid", fill.scale =tm_scale_intervals(values = "brewer.greens", style = "quantile",
midpoint = 0), fill.legend = tm_legend(title = "Inci_rf_resid")) +
tm_borders(fill_alpha = .2) + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"),
frame = TRUE, component.autoscale = FALSE)
map_xgb <- tm_shape(mapdata) +
tm_fill("xgb_resid", fill.scale =tm_scale_intervals(values = "brewer.purples", style = "quantile",
midpoint = 0), fill.legend = tm_legend(title = "Inci_xgb_resid")) + tm_borders(fill_alpha = .2) +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"),
frame = TRUE, component.autoscale = FALSE)
map_svr <- tm_shape(mapdata) +
tm_fill("svr_resid", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"),
fill.legend = tm_legend(title = "Inci_svr_resid")) + tm_borders(fill_alpha = .2) +
tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"),
frame = TRUE, component.autoscale = FALSE)
#Step 3: Combine maps in a grid
# Combine maps in a grid layout
tmap_arrange(map_rf, map_xgb, map_svr, nrow = 1)
##Barplot and Spatial maps for RMSE and MAE
#Step 1: Calculate RMSE and MAE for each model
# Random Forest
rf_metrics <- postResample(pred = rf_preds, obs = mapdata$incidence)
# XGBoost
xgb_metrics <- postResample(pred = xgb_preds, obs = mapdata$incidence)
# SVR
svr_metrics <- postResample(pred = svr_preds, obs = mapdata$incidence)
#Step 2: Combine into a summary data frame
model_eval <- data.frame(
Model = c("Random Forest", "XGBoost", "SVR"),
RMSE = c(rf_metrics["RMSE"], xgb_metrics["RMSE"], svr_metrics["RMSE"]),
MAE = c(rf_metrics["MAE"], xgb_metrics["MAE"], svr_metrics["MAE"]),
Rsquared = c(rf_metrics["Rsquared"], xgb_metrics["Rsquared"], svr_metrics["Rsquared"])
)
print(model_eval)
#> Model RMSE MAE Rsquared
#> 1 Random Forest 98.219710 30.2558297 0.9976235
#> 2 XGBoost 2.292196 0.8058498 0.9999979
#> 3 SVR 445.937234 127.7105534 0.9178101
#Visualize MAE and RMSE
oldpar <- par(mfrow = c(1, 3))
#Barplot of RMSE
barplot(model_eval$RMSE, names.arg = model_eval$Model,
col = "skyblue", las = 1, main = "Model RMSE", ylab = "RMSE")
#Barplot of MAE
barplot(model_eval$MAE, names.arg = model_eval$Model,
col = "lightgreen", las = 1, main = "Model MAE", ylab = "MAE")
#Barplot of MAE
barplot(model_eval$Rsquared, names.arg = model_eval$Model,
col = "grey", las = 1, main = "Model Rsquared", ylab = "MAE")
par(oldpar)
#Add metrics to residual maps as captions
map_rf <- tm_shape(mapdata) +
tm_fill("rf_resid", fill.scale =tm_scale_intervals(values = "brewer.greens", midpoint = 0),
title = paste0("rf_resid\nRMSE: ", round(rf_metrics["RMSE"], 2),
"\nMAE: ", round(rf_metrics["MAE"], 2))) +
tm_borders(fill_alpha = .2) + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"))
map_xgb <- tm_shape(mapdata) +
tm_fill("xgb_resid", fill.scale =tm_scale_intervals(values = "-RdBu", midpoint = 0),
title = paste0("xgb_resid\nRMSE: ", round(xgb_metrics["RMSE"], 2),
"\nMAE: ", round(xgb_metrics["MAE"], 2))) +
tm_borders(fill_alpha = .2) + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"))
map_svr <- tm_shape(mapdata) +
tm_fill("svr_resid", fill.scale =tm_scale_intervals(values = "-RdBu", midpoint = 0),
title = paste0("svr_resid\nRMSE: ", round(svr_metrics["RMSE"], 2),
"\nMAE: ", round(svr_metrics["MAE"], 2))) +
tm_borders(fill_alpha = .2) + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"))
tmap_arrange(map_rf, map_xgb, map_svr, nrow = 1)
#Global Moran’s I, Local Moran’s I (LISA), Cluster categories (e.g., High-High, Low-Low), Maps: Moran’s I map,
#LISA clusters, High-High clusters using the predicted values from the four machine learning models
#Assumptions: Your spatial data is in mapdata (an sf object).
#Predicted values for each model are already stored in: rf_preds, xgb_preds, svr_preds, mlp_preds
#STEP 1: Create Spatial Weights (if not done yet)
neighbors <- poly2nb(mapdata) #if this gives warning, use the below codes
mapdata <- st_as_sf(mapdata) # If it's not already sf
mapdata <- st_make_valid(mapdata) # Fix any invalid geometries
neighbors <- poly2nb(mapdata, snap = 1e-15) ## Try 1e-6, 1e-5, or higher if needed. You can adjust snap upward incrementally until the warnings disappear or are reduced
listw <- nb2listw(neighbors, style = "W", zero.policy = TRUE)
#STEP 2: Define a function to compute Moran’s I and cluster categories
analyze_spatial_autocorrelation <- function(mapdata, values, listw, model_name, signif_level = 0.05) {
# Standardize predicted values
mapdata$val_st <- scale(values)[, 1]
# Compute lag
mapdata$lag_val <- lag.listw(listw, mapdata$val_st, zero.policy = TRUE)
# Global Moran's I
global_moran <- moran.test(values, listw, zero.policy = TRUE)
# Local Moran's I (LISA)
lisa <- localmoran(values, listw, zero.policy = TRUE)
lisa_df <- as.data.frame(lisa)
#rename p-value column
colnames(lisa_df)[5] <- "Pr_z"
# Add to mapdata
mapdata$Ii <- lisa_df$Ii
mapdata$Z_Ii <- lisa_df$Z.I
mapdata$Pr_z <- lisa_df$Pr_z
#mapdata$Pr_z <- lisa_df[, "Pr(z > 0)"]
# Classify clusters
mapdata <- mapdata %>%
mutate(
cluster = case_when(
val_st > 0 & lag_val > 0 & Pr_z <= signif_level ~ "High-High",
val_st < 0 & lag_val < 0 & Pr_z <= signif_level ~ "Low-Low",
val_st < 0 & lag_val > 0 & Pr_z <= signif_level ~ "Low-High",
val_st > 0 & lag_val < 0 & Pr_z <= signif_level ~ "High-Low",
TRUE ~ "Not Significant"
)
)
return(list(
map = mapdata,
global_moran = global_moran
))
}
#STEP 3: Run the function for each model
rf_result <- analyze_spatial_autocorrelation(mapdata, rf_preds, listw, "Random Forest")
xgb_result <- analyze_spatial_autocorrelation(mapdata, xgb_preds, listw, "XGBoost")
svr_result <- analyze_spatial_autocorrelation(mapdata, svr_preds, listw, "SVR")
#STEP 4: Mapping LISA Clusters and High-High Areas for Random Forest
tmap_mode("plot")
# LISA Cluster Map. fill.scale =tm_scale_intervals(values = "-RdBu")
tm_rf <- tm_shape(rf_result$map) +
tm_fill(
"cluster",
fill.scale = tm_scale(values = c(
"High-High" = "red",
"Low-Low" = "blue",
"Low-High" = "lightblue",
"High-Low" = "pink",
"Not Significant" = "gray90")),
fill.legend = tm_legend(title = "LISA Clusters - RF")) +
tm_borders(fill_alpha = .2) + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"))
# Mapping LISA Clusters and High-High Areas for XGBoost
tmap_mode("plot")
# LISA Cluster Map
tm_xgb <- tm_shape(xgb_result$map) +
tm_fill("cluster",
fill.scale = tm_scale(values = c(
"High-High" = "red",
"Low-Low" = "blue",
"Low-High" = "lightblue",
"High-Low" = "pink",
"Not Significant" = "gray90")),
fill.legend = tm_legend(title = "LISA Clusters - XGBoost")) +
tm_borders(fill_alpha = .2) + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"))
#Mapping LISA Clusters and High-High Areas for SVR
tmap_mode("plot")
# LISA Cluster Map
tm_svr <- tm_shape(svr_result$map) +
tm_fill("cluster",
fill.scale = tm_scale(values = c(
"High-High" = "red",
"Low-Low" = "blue",
"Low-High" = "lightblue",
"High-Low" = "pink",
"Not Significant" = "gray90")),
fill.legend = tm_legend(title = "LISA Clusters - SVR")) +
tm_borders(fill_alpha = .2) + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"))
tmap_arrange(tm_rf, tm_xgb, tm_svr, nrow = 1)
#You can also arrange maps side-by-side using tmap_arrange().
#View Global Moran’s I Results
#These print the test statistic and p-values for global spatial autocorrelation of predictions.
rf_result$global_moran
#>
#> Moran I test under randomisation
#>
#> data: values
#> weights: listw
#> n reduced by no-neighbour observations
#>
#> Moran I statistic standard deviate = -0.24595, p-value = 0.5971
#> alternative hypothesis: greater
#> sample estimates:
#> Moran I statistic Expectation Variance
#> -0.044681255 -0.022727273 0.007967958
xgb_result$global_moran
#>
#> Moran I test under randomisation
#>
#> data: values
#> weights: listw
#> n reduced by no-neighbour observations
#>
#> Moran I statistic standard deviate = -0.32541, p-value = 0.6276
#> alternative hypothesis: greater
#> sample estimates:
#> Moran I statistic Expectation Variance
#> -0.051276885 -0.022727273 0.007697266
svr_result$global_moran
#>
#> Moran I test under randomisation
#>
#> data: values
#> weights: listw
#> n reduced by no-neighbour observations
#>
#> Moran I statistic standard deviate = -0.28034, p-value = 0.6104
#> alternative hypothesis: greater
#> sample estimates:
#> Moran I statistic Expectation Variance
#> -0.05111299 -0.02272727 0.01025262