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Carga del protocolo incial para RK
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setwd("working_dir") | ||
library(gstat) | ||
library(raster) | ||
library(sp) | ||
library(soiltexture) | ||
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datos <- read.csv("Horiz_A.csv") | ||
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datos.sp <- datos | ||
coordinates(datos.sp) = ~X+Y | ||
plot(datos.sp) | ||
summary(datos.sp) | ||
## Deben estar en una carpeta "covar" dentro de la carpeta de trabajo | ||
## Cargamos las covariables ambientales en R | ||
startdir <- getwd() | ||
setwd(paste(getwd(), "/covar_ss", sep="")) | ||
files <- list.files(pattern="sdat") | ||
stack1 <- list() | ||
for(i in 1:length(files)) { | ||
stack1[[i]] <- raster(files[i])} | ||
covariables <- do.call(stack, stack1) ### JO! | ||
setwd(startdir) | ||
covariables | ||
plot(covariables) | ||
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covariables.sp <- as(covariables, "SpatialGridDataFrame") ## 45Mb!!! | ||
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## Creamos la grilla de interpolacion | ||
halfres <- res(covariables)[1]/2 | ||
grilla <- expand.grid(x=seq(from=xmin(covariables)+halfres, to=xmax(covariables)-halfres, by=res(covariables)[1]), y=seq(from=ymin(covariables)+halfres, to=ymax(covariables)-halfres, by=res(covariables)[2])) | ||
coordinates(grilla) <- ~ x+y | ||
gridded(grilla) <- TRUE | ||
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# Extraemos los valores de las covariables | ||
datos <- cbind(datos, extract(covariables, datos.sp)) | ||
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### Análisis Descriptivo #### | ||
summary(datos) | ||
plot(datos.sp,pch=1 ,cex = datos$Arcilla/max(datos$Arcilla)*4) | ||
boxplot(datos$Arcilla) | ||
summary(datos$Arcilla) | ||
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#Algunos gráficos | ||
boxplot(datos$Arcilla) | ||
hist(datos$Arcilla) | ||
#Diagrama de tallo-hoja | ||
stem(datos$Arcilla) | ||
#La Varianza | ||
var(datos$Arcilla) | ||
# Desvio standar | ||
mean(datos$Arcilla) | ||
sd(datos$Arcilla) | ||
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plot(datos.sp) | ||
points(datos.sp[datos$Arcilla<30,]) | ||
plot(datos$Serie, datos$Arcilla, las=3, main="Contenido de Arcillas, horiz superficial", cex.axis=0.7) | ||
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#### Modelo del regresion MLR #### | ||
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Model.Arcilla.Super <- lm(Arcilla ~ Altitude_above_Channel_Network+Aspect+Channel_Network_Base_Level | ||
+LS.Factor+Profile_Curvature+Slope_Height+Slope+SRTM30_estaca+Standardized_Height+Valley_Depth+Wetness_Index, data=datos) | ||
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summary(Model.Arcilla.Super) | ||
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Model.Arcilla.Super.step <- step(Model.Arcilla.Super, direction="both") | ||
summary(Model.Arcilla.Super.step) | ||
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### | ||
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Model.Arcilla.Super2 <- lm(Arcilla ~ Altitude_above_Channel_Network+Channel_Network_Base_Level | ||
+LS.Factor+Profile_Curvature+Slope+SRTM30_estaca+Wetness_Index, data=datos) | ||
summary(Model.Arcilla.Super2) | ||
Model.Arcilla.Super.step2 <- step(Model.Arcilla.Super2, direction="both") | ||
summary(Model.Arcilla.Super.step2) | ||
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Model.Arcilla.Super.step2$residuals | ||
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Arcilla.Super.MLR <- predict(covariables, Model.Arcilla.Super.step2, progress="text") | ||
plot(Arcilla.Super.MLR) | ||
points(datos.sp) | ||
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#### Modelo de kriging #### | ||
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residuos.Arcilla.Sup <- Model.Arcilla.Super.step2$residuals | ||
datos$Arcilla.res <- residuos.Arcilla.Sup | ||
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datos.sp <- datos | ||
coordinates(datos.sp) = ~X+Y | ||
plot(datos.sp,pch=1 ,cex = datos$Arcilla.res/max(datos$Arcilla.res)*4) | ||
bubble(datos.sp, "Arcilla.res",col=c("blue", "red")) # Este graf. se ve mejor a pantalla completa | ||
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### Regression-kriging #### | ||
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dependend_var.v <- variogram(as.formula(Model.Arcilla.Super.step2$call$formula), datos.sp) | ||
dependend_var.ovgm <- fit.variogram(dependend_var.v, vgm(nugget=0, "Exp", range=sqrt(diff(datos.sp@bbox[1,])^2 + diff(datos.sp@bbox[2,])^2)/4, psill=var(Model.Arcilla.Super.step2$residuals))) | ||
plot(dependend_var.v, dependend_var.ovgm, plot.nu=T, main="Residuos Arcilla Sup") ## 350*260 | ||
dependend_var.ovgm | ||
Arcilla.Super.auto.rk <- krige(as.formula(Model.Arcilla.Super.step2$call$formula), datos.sp, covariables.sp, dependend_var.ovgm) | ||
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#### Leave One Out Cross Validation | ||
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Arcilla.Super.cross <- krige.cv(as.formula(Model.Arcilla.Super.step2$call$formula), datos.sp, dependend_var.ovgm , verbose=F) | ||
Arcilla.Super.explained_variation <- 1-var(Arcilla.Super.cross$residual, na.rm=T)/var(datos.sp$Arcilla) | ||
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summary(Arcilla.Super.cross) | ||
# mean error, ideally 0: | ||
mean(Arcilla.Super.cross$residual) | ||
# MSPE, ideally small | ||
mean(Arcilla.Super.cross$residual^2) | ||
# Mean square normalized error, ideally close to 1 | ||
mean(Arcilla.Super.cross$zscore^2) | ||
# correlation observed and predicted, ideally 1 | ||
cor(Arcilla.Super.cross$observed, Arcilla.Super.cross$observed - Arcilla.Super.cross$residual) | ||
# correlation predicted and residual, ideally 0 | ||
cor(Arcilla.Super.cross$observed - Arcilla.Super.cross$residual, Arcilla.Super.cross$residual) | ||
# explained variation | ||
Arcilla.Super.explained_variation | ||
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Arcilla.Super.rk.pred <- raster(Arcilla.Super.auto.rk["var1.pred"]) | ||
Arcilla.Super.rk.var <- raster(Arcilla.Super.auto.rk["var1.var"]) | ||
Arcilla.Super.rk.error <- qnorm(0.95)*sqrt(Arcilla.Super.rk.var)/sqrt(nrow(datos.sp)) | ||
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plot(Arcilla.Super.rk.pred, main="Arcilla por RK") | ||
points(datos.sp) | ||
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plot(Arcilla.Super.rk.pred-Arcilla.Super.rk.error, main="IC95 minimo") ## Esto sería: el valor mínimo del modelo dentro de un intervalo de confianza del 95% | ||
plot(Arcilla.Super.rk.pred+Arcilla.Super.rk.error, main="IC95 maximo") | ||
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writeRaster(Arcilla.Super.rk.pred, "Resultados/Arcilla.Super.RK.tiff", overwrite=TRUE) | ||
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