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Small function to create PCA analysis and spatial eigenvariables
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eigenvariables.fct <- function(vars, name, proportion = 0.95){ | |
library("raster") | |
if (file.exists("./pca") == FALSE) dir.create("./pca") | |
#Running PCA: | |
# Counts not NA cels | |
non.na <- sum(!is.na(values(vars[[1]]))) | |
# Sample the study area with n-non.na and creates an environmental table | |
sr <- sampleRandom(vars, non.na) | |
# faz o PCA dessa tabela padronizada | |
pca <- prcomp(scale(sr)) | |
summary.pca <- summary(pca) | |
#Saving results: | |
capture.output(pca, file = sprintf('./pca/%s.pca.txt', name)) | |
#saving summary | |
capture.output(summary.pca, file = sprintf('./pca/%s.summary.pca.txt', name)) | |
#Plotting results | |
#GGPLOT | |
##### | |
#library(ggplot2) | |
# create data frame with scores | |
#scores <- as.data.frame(pca$x) | |
# plot of observations | |
#ggplot(data = scores, aes(x = PC1, y = PC2, label = rownames(scores))) + | |
# geom_hline(yintercept = 0, colour = "gray65") + | |
# geom_vline(xintercept = 0, colour = "gray65") + | |
# geom_text(colour = "tomato", alpha = 0.8, size = 4) + | |
# ggtitle("PCA plot of USA States - Crime Rates") | |
##### | |
# png(filename = sprintf('./pca/%s.pca.biplot.png',name), | |
# bg = "white") | |
# biplot(pca) | |
# dev.off() | |
# Creating eigenvariable in space | |
axis.nb <- which(summary.pca$importance["Cumulative Proportion",] >= proportion)[1] | |
eigenvariables <- predict(vars, pca, index = 1:axis.nb) | |
if (file.exists("./env") == FALSE) dir.create("./env") | |
writeRaster(eigenvariables,sprintf('./env/%s.eigenvariables.tif',name),overwrite=T) | |
} |
Inserida variavel 'proportion', para definirmos qual proporcao de explicaçao (logo, quantos eixos iremos salvar) da PCA. proportion=entre 0 e 1
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Small change for predicting all variables of PCA (line 37).