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a command for linear regression of x~y1+y2+... raster would be handy and optional output of residuals (maybe optional with piecewise regressions, robust linear regressions etc.). To ease this code snippet:
library(raster)
library(RStoolbox)
lsat88 <- brick("p224r63_1988.grd")
lsat11 <- brick("p224r63_2011.grd")
lsat88.ndvi <- spectralIndices(lsat88, red="B3_sre",nir="B4_sre",indices="ndvi")
lsat11.ndvi <- spectralIndices(lsat11, red="B3_sre",nir="B4_sre",indices="ndvi")
lm.lsat <- lm(lsat88.ndvi[]~lsat11.ndvi[], na.action = NULL) # can also be done as multivariate regression: x~y1+y2+y3 with dependent and explanatory variables
summary(lm.xy)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
yc.ndvi[] 0.248039 0.001339 185.3 <2e-16
...
Multiple R-squared: 0.1702, Adjusted R-squared: 0.1702
...
resid.lsat <- raster(lsat88)
resid.lsat[] <- lm.lsat$residuals
plot(resid.lsat,stretch="lin")
The text was updated successfully, but these errors were encountered:
a command for linear regression of x~y1+y2+... raster would be handy and optional output of residuals (maybe optional with piecewise regressions, robust linear regressions etc.). To ease this code snippet:
The text was updated successfully, but these errors were encountered: