Displaying 5 results from an estimated 5 matches for "bootmedianse".
2018 May 22
2
Bootstrap and average median squared error
...ake the
results reproducible.
Rui Barradas
On 5/22/2018 10:00 AM, Rui Barradas wrote:
> Hello,
>
> If you want to bootstrap a statistic, I suggest you use base package boot.
> You would need the data in a data.frame, see how you could do it.
>
>
> library(boot)
>
> bootMedianSE <- function(data, indices){
> ??? d <- data[indices, ]
> ??? fit <- rq(crp ~ bmi + glucose, tau = 0.5, data = d)
> ??? ypred <- predict(fit)
> ??? y <- d$crp
> ??? median(y - ypred)^2
> }
>
> dat <- data.frame(crp, bmi, glucose)
> nboot <- 100
&...
2018 May 22
1
Bootstrap and average median squared error
Hello,
Right!
I copied from the OP's question without thinking about it.
Corrected would be
bootMedianSE <- function(data, indices){
d <- data[indices, ]
fit <- rq(crp ~ bmi + glucose, tau = 0.5, data = d)
ypred <- predict(fit)
y <- d$crp
median((y - ypred)^2)
}
Sorry,
rui Barradas
On 5/22/2018 11:32 AM, Daniel Nordlund wrote:
> On 5/22/2018 2:32 AM, Rui B...
2018 May 22
0
Bootstrap and average median squared error
Hello,
If you want to bootstrap a statistic, I suggest you use base package boot.
You would need the data in a data.frame, see how you could do it.
library(boot)
bootMedianSE <- function(data, indices){
d <- data[indices, ]
fit <- rq(crp ~ bmi + glucose, tau = 0.5, data = d)
ypred <- predict(fit)
y <- d$crp
median(y - ypred)^2
}
dat <- data.frame(crp, bmi, glucose)
nboot <- 100
medse <- boot(dat, bootMedianSE, R = nboot...
2018 May 21
2
Bootstrap and average median squared error
Dear R-experts,
I am trying to bootstrap (and average) the median squared error evaluation metric for a robust regression. I can't get it. What is going wrong ?
Here is the reproducible example.
#############################
install.packages( "quantreg" )
library(quantreg)
crp <-c(12,14,13,24,25,34,45,56,25,34,47,44,35,24,53,44,55,46,36,67)
bmi
2018 May 22
0
Bootstrap and average median squared error
On 5/22/2018 2:32 AM, Rui Barradas wrote:
> bootMedianSE <- function(data, indices){
> ???? d <- data[indices, ]
> ???? fit <- rq(crp ~ bmi + glucose, tau = 0.5, data = d)
> ???? ypred <- predict(fit)
> ???? y <- d$crp
> ???? median(y - ypred)^2
> }
since the OP is looking for the "median squared error", s...