Prelim GBM tests with data from Elith et al (2008)

a-b · Jun 27, 2013 · c57a7a5 · c57a7a5
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diff --git a/R/gbm_ecotest.R b/R/gbm_ecotest.R
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+library(gbm)
+source("brt.functions.R")
+
+ecology.data <- read.csv("../smalldata/gbm_test/ecology_model.csv")
+angaus.tc5.lr01 <- gbm.step(data=ecology.data, gbm.x = 3:14, gbm.y = 2, family = "bernoulli", tree.complexity = 5, learning.rate = 0.01, bag.fraction = 0.5)
+angaus.tc5.lr005 <- gbm.step(data=ecology.data, gbm.x = 3:14, gbm.y = 2, family = "bernoulli", tree.complexity = 5, learning.rate = 0.005, bag.fraction = 0.5)
+gbm.plot(angaus.tc5.lr005, n.plots = 12, write.title = F)
+
+ecology.int <- gbm.interactions(angaus.tc5.lr005)
+print(ecology.int$rank.list)
+print(ecology.int$interactions)
+
+ecology_eval.data <- read.csv("../smalldata/gbm_test/ecology_eval.csv", as.is = T)
+ecology_eval.data$Method <- factor(ecology_eval.data$Method, levels = levels(ecology.data$Method))
+ecology.preds <- predict.gbm(angaus.tc5.lr005, ecology_eval.data, n.trees = angaus.tc5.lr005$gbm.call$best.trees, type = "response")
+# gbm.predict.grids(angaus.tc5.lr005, ecology_eval.data, want.grids = F, sp.name = "preds")
+dev <- calc.deviance(ecology_eval.data$Angaus_obs, ecology.preds, calc.mean = T)
+print(paste("Deviance is ",dev,sep=""))