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Moscow2.R.txt
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Moscow2.R.txt
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library(yaImpute)
library(randomForest)
library(sp)
library(fastICA)
##################################################################################
cat("\n")
cat("IMPORTANT: Make sure Example ACS files are in the same directory")
cat("\n")
# setwd("github/yaImpute")
########################################################
# Page 10
data("MoscowMtStJoe")
x <- MoscowMtStJoe[, c("EASTING", "NORTHING", "ELEVMEAN", "SLPMEAN", "ASPMEAN", "INTMEAN", "HTMEAN", "CCMEAN")]
x[, 5] <- (1 - cos((x[, 5] - 30) * pi/180))/2
names(x)[5] = "TrASP"
y <- MoscowMtStJoe[, c(1, 9, 12, 14, 18)]
mal <- yai(x = x, y = y, method = "mahalanobis")
msn <- yai(x = x, y = y, method = "msn")
gnn <- yai(x = x, y = y, method = "gnn")
ica <- yai(x = x, y = y, method = "ica")
y2 <- cbind(whatsMax(y[, 1:4]), y[, 5])
names(y2) <- c("MajorSpecies", "BasalAreaMajorSp", "TotalBA")
rf <- yai(x = x, y = y2, method = "randomForest")
head(y2)
#############################################
# Page 11
levels(y2$MajorSpecies)
plot(rf, vars = yvars(rf))
#############################################
# Page 12
rfImp <- impute(rf, ancillaryData = y)
rmsd <- compare.yai(mal, msn, gnn, rfImp, ica)
apply(rmsd, 2, mean, na.rm = TRUE)
plot(rmsd)
#############################################
# Page 13
library(sp)
xfiles <- list(CCMEAN = "canopy.asc", ELEVMEAN = "dem.asc",
HTMEAN = "heights.asc", INTMEAN = "intense.asc",
SLPMEAN = "slope.asc", TrASP = "trasp.asc", EASTING = "utme.asc",
NORTHING = "utmn.asc")
outfiles <- list(ABGR_BA = "rf_abgr.asc", PIPO_BA = "rf_pipo.asc",PSME_BA = "rf_psme.asc", THPL_BA = "rf_thpl.asc",Total_BA = "rf_totBA.asc")
AsciiGridImpute(rf, xfiles, outfiles, ancillaryData = y)
# The R package sp by Pebesma and Bivand (2005) contains functions designed to read and
# manipulate ASCII grid data and are used to plot part of the total image of example X- and
# Y -variables (Figures 4 and 5).
canopy <- read.asciigrid("canopy.asc")[100:450, 400:700]
TrAsp <- read.asciigrid("trasp.asc")[100:450, 400:700]
par(mfcol = c(1, 2), plt = c(0.05, 0.95, 0.05, 0.85))
image(canopy, col = hcl(h = 140, l = seq(100, 0, -10)))
title("LiDAR mean canopy cover")
image(TrAsp, col = hcl(h = 140, l = seq(100, 0, -10)))
title("Transformed aspect")
totBA <- read.asciigrid("rf_totBA.asc")[100:450, 400:700]
psme <- read.asciigrid("rf_psme.asc")[100:450, 400:700]
par(mfcol = c(1, 2), plt = c(0.05, 0.95, 0.05, 0.85))
image(totBA, col = hcl(h = 140, l = seq(100, 0, -10)))
title("Total basal area")
image(psme, col = hcl(h = 140, l = seq(100, 0, -10)))
title("Douglas fir basal area")