Displaying 4 results from an estimated 4 matches for "ckertype".
2016 Apr 22
1
npudens(np) Error missing value where TRUE/FALSE needed
...ere
TRUE/FALSE needed
I suppose some if-statement doesn?t evaluate to a TRUE or
FALSE somewhere in the npudens function.
But as I am not an expert in writing functions, this error
message doesn?t really help.Changing it to the following also doesn't help: kerz <- npudens(bws=(bw_cx[i,]),ckertype="epanechnikov",,okertype="liracine",tdat=tdata,edat=dat)
Am I doing something wrong here?
Were some changes made to this function and
do I need to alter some arguments to these changes?
Or might this be a bug?
Thanks!
Carolien
[[alternative HTML versi...
2011 Jul 18
0
np package, estimating the standard errors of Klein and Spady's estimator
...that I am using:
library(np)
N<-100
X<-matrix(c(rnorm(N,1,1), rnorm(N,0,1)), ncol=2)
BETA <-matrix(1,2,1)
Z<-X%*%BETA
L<-rlogis(N,location=0, scale=1)
Y <-as.vector(X%*%BETA+L>=0)*1
KS <- npindexbw (xdat=X, ydat=Y, bandwidth.compute=TRUE,
method="kleinspady", ckertype="epanechnikov" )
KSi <- npindex(KS, errors=TRUE)
se(KSi)
But then I get as a result a vector Nx1, which I do not understand what it
is, and if I let errors=FALSE then I get a NA as a result. So, how can I get
the standard error of the estimated coefficient?
Thank you
Dimitris
--...
2011 Jul 20
0
np package, KleinSpady estimator, error when I estimate the bootstrapped standard errors
Dear all,
I am using np package in order to estimate a model with Klein and Spady
estimator. To estimate the model I use
KS <- npindexbw (xdat=X, ydat=Y, bandwidth.compute=TRUE,
method="kleinspady", optim.maxit=10^3, ckertype="epanechnikov", ckerorder=2)
and to estimate beta hats standard errors I use
KSi <- npindex(KS, gradients=T, boot.num=300)
vcov(KSi)
This is fine so far, but if I want to estimate the bootstrapped standard
errors on estimates by se(KSi) then the result is NA and if I include the
arg...
2011 Jul 25
0
error in optimization when I include constant term in Klein and Spady (np package)
...ry(np)
N<-250
q<-2
BETA<-matrix(1,3,1)
X<-matrix(c(rnorm(N,0,1), rnorm(N,1,1)), ncol=q)
X<-cbind(X,1)
L<-rlogis(N,location=0, scale=1)
Y <-as.vector(X%*%BETA+L>=0)*1
KS <- npindexbw(xdat=X, ydat=Y, bandwidth.compute=TRUE,
method="kleinspady", optim.maxit=10^3, ckertype="epanechnikov", ckerorder=2)
Thank you
Dimitris
--
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