Nothing
library( "micEconNP" )
## taken from the documentation of crs(), slightly modified
set.seed(42)
## Example - simulated data
n <- 250
num.eval <- 50
x1 <- runif(n)
x2 <- runif(n)
z <- round( runif( n, min = 0, max = 3 ) )
dgp <- cos( 2 * pi * x1 ) + sin( 2 * pi * x2 ) + z/5
z <- factor(z)
y <- dgp + rnorm( n, sd = 0.5 )
## Estimate a model with specified degree and bandwidth
model <- crs( y ~ x1 + x2 + z, degree = c(5,5), segments= c(1,1), lambda = c(0.1),
cv = "none", kernel = TRUE, deriv = 1, basis = "additive" )
summary( model )
zGrad <- crsGradFactor( model, "z" )
round( zGrad, 2 )
all.equal( rowSums( zGrad, na.rm = TRUE )[ z != 0 ],
model$deriv.mat[ z != 0 , 3 ] )
## Estimate a model with data-driven degree, segments, and bandwidth
model2 <- crs( y ~ x1 + x2 + z, deriv = 1, basis = "additive", nmulti = 1 )
summary( model2 )
zGrad2 <- crsGradFactor( model2, "z" )
round( zGrad2, 2 )
all.equal( rowSums( zGrad2, na.rm = TRUE )[ z != 0 ],
model2$deriv.mat[ z != 0 , 3 ] )
## test crsCv()
cv2 <- crsCv( model2 )
round( c( cv2 ), 3 )
round( attr( cv2, "err" ), 3 )
all.equal( cv2, model2$cv.min, check.attributes = FALSE )
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