Dear R Experts,
Can anyone teach me how to modify the FMCD algorithm in R. If I wish to replace
the C-step in FMCD by the index set.
Let I(old)={pi(1)old,pi(2)old,...,p(h)old} and I(new)={pi(1)new,
pi(2)new,..,p(h)new} the index sets that correspond to the sample items in
H(old) and H(new) res
2 + 3*X
a <- data.frame(X = X, Y = Y)
fun <- function(a){
fit <- lm(Y ~ X, data=a)
return(coef(fit))
}
result <- boot::tsboot(a, statistic = fun, R = 10, sim = "geom",
l = 10, orig.t = TRUE)
Hope this helps,
Rui Barradas
Em 17-09-2012 14:42, Hock Ann Lim escreve
Dear R experts,
I'm running the following stationary bootstrap programming to find the
parameters estimate of a linear model:
X<-runif(10,0,10)
Y<-2+3*X
a<-data.frame(X,Y)
coef<-function(fit){
fit <- lm(Y~X,data=a)
return(coef(fit))
}
result<- tsboot(a,statistic=coef(fit),R = 10,n.sim
Dear R Users,
Kindly advice me what's wrong in my programming.
I'm using the Cochrane-Orcutt two stage procedure with Prais Wisten
transformation, below is my R programming :
>Y<-c(60.8,62.5,64.6,66.1,67.7,69.1,71.7,73.5,76.2,77.3,78.8,80.2,82.6,84.3,83.3,84.1,86.4,87.6,89.1,89.3,89.1,
>,
+
89
Dear R users,
May I know is there a package that implements the Cochrane-Orcutt Prais Winsten
itterative for dealing with autocorrelation in a regression model?
I understand that gls in nlme package does it properly, my question is
will this
two methods provide the same answer for linear mod
Dear experts,
I am a beginner of R.
I'm looking for experts to guide me how to do programming in R in order to
randomly replace 5 observations in X explanatory variable with outliers drawn
from U(15,20) in sample size n=100. The replacement subject to y < 15.
The ultimate goal of my study is to
May I know how to find the R-squared for robust regression model?
Thank you.
Hock Ann
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