On 9/17/19 1:35 PM, varin sacha wrote:
Many thanks David, it perfectly works.
Now, one last think.
If I want my R code here below to run let's say B=500 times and at the end I
want to get the average for the MSE_GAM and for the MSE_MARS. How can I do that
?
The `replicate` function is designed for that purpose.
--
David.
library(mgcv)
library(earth)
n<-2000
x<-runif(n, 0, 5)
z <- runif(n, 0, 5)
a <- runif(n, 0, 5)
y_model<- 0.1*x^3 - 0.5 * z^2 - a + 10
y_obs <- c( rnorm(n*0.5, y_model, 0.1), rnorm(n*0.5, y_model, 0.5) )
gam_model<- gam(y_obs~s(x)+s(z)+s(a))
mars_model<-earth(y_obs~x+z+a)
MSE_GAM<-mean((gam_model$fitted.values - y_model)^2)
MSE_MARS<-mean((mars_model$fitted.values - y_model)^2)
MSE_GAM
MSE_MARS
Le mardi 17 septembre 2019 à 22:27:54 UTC+2, David Winsemius
<dwinsem...@comcast.net> a écrit :
On 9/17/19 12:48 PM, varin sacha via R-help wrote:
Dear R-helpers,
Doing dput(x) and dput(y_obs), the 2 vectors are not the same length (1800 for
y_obs and 2000 for x)
How can I solve the problem ?
Here is the reproducible R code
# # # # # # # # # #
library(mgcv)
library(earth)
n<-2000
x<-runif(n, 0, 5)
y_model<- 0.1*x^3 - 0.5 * x^2 - x + 10
# y_obs<-rnorm(n*0.9, y_model, 0.1)+rnorm(n*0.1, y_model, 0.5) # maybe not
exactly your goal?
You didn't lay out any goals for analysis, so let me guess what was
intended:
I suspect that you were hoping to model a mixture composed of 90% from
one distribution and 10% from another. If I'm right about that guess
then you would instead wat to join the samples from each distribution:
y_obs<-c( rnorm(n*0.9, y_model, 0.1), rnorm(n*0.1, y_model, 0.5) )
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