Description Usage Arguments Details Value See Also Examples
Convert an rdd_object to a non-parametric regression npreg
from package np
1 2 3 | as.npregbw(x, ...)
as.npreg(x, ...)
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x |
Object of class |
... |
Further arguments passed to the |
This function converts an rdd_reg object into an npreg
object from package np
Note that the output won't be the same, since npreg
does not offer a triangular kernel, but a Gaussian or Epanechinkov one.
Another reason why estimates might differ slightly is that npreg
implements a multivariate kernel, while rdd_reg
proceeds as if the kernel was univariate. A simple solution to make the multivariate kernel similar to the univariate one
is to set the bandwidth for x and Dx to a large number, so that they converge towards a constant, and one obtains back the univariate kernel.
An object of class npreg
or npregbw
as.lm
which converts rdd_reg
objects into lm
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # Estimate ususal rdd_reg:
data(house)
house_rdd <- rdd_data(y=house$y, x=house$x, cutpoint=0)
reg_nonpara <- rdd_reg_np(rdd_object=house_rdd)
## Convert to npreg:
reg_nonpara_np <- as.npreg(reg_nonpara)
reg_nonpara_np
rdd_coef(reg_nonpara_np, allCo=TRUE, allInfo=TRUE)
## Compare with result obtained with a Gaussian kernel:
bw_lm <- dnorm(house_rdd$x, sd=rddtools:::getBW(reg_nonpara))
reg_nonpara_gaus <- rdd_reg_lm(rdd_object=house_rdd, w=bw_lm)
all.equal(rdd_coef(reg_nonpara_gaus),rdd_coef(reg_nonpara_np))
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