Error in UseMethod("predict")

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Error in UseMethod("predict")

R help mailing list-2
Dear R-experts,

Here below my reproducible R code. I get an error message (end of code) I can't solve.
Many thanks for your help.

##########################
#Data
y=c(34000,45000,19000,48900,65000,67000,78000,90000,51000,32000,54000,85000,38000,76345,87654,90990,78654,67894,56789,65432,18998,78987,67543,45678,76543,67876)
x=c(345,543,543,456,432,378,543,579,432,254,346,564,611,543,542,632,345,468,476,487,453,356,490,499,567,532)

Dataset=data.frame(y,x)

#Plot
plot(x,y)

#Robust GAM
library(robustgam)
true.family <- poisson()
fit=robustgam(x,y, sp=4356,family=true.family,smooth.basis='ps',K=3)
x.new <- seq(range(x)[1], range(x)[2])
robustfit.new <- pred.robustgam(fit, data.frame(X=x.new))$predict.values
lines(x.new, robustfit.new, col="green", lwd=2)

# To find the « sp » to include in the fit function here above
robustfit.gic<-robustgam.GIC.optim(x,y,family=true.family,p=3,c=1.6,show.msg=FALSE,smooth.basis="tp", method="L-BFGS-B")

## CROSS VALIDATION REPLICATIONS MSE ROBUST GAM
install.packages("ISLR")
library(ISLR)

# Create a list to store the results
lst<-list()

# This statement does the repetitions (looping)
for(i in 1 :1000)
{

n=dim(Dataset)[1]
p=0.667
sam=sample(1 :n,floor(p*n),replace=FALSE)
Training =Dataset [sam,]
Testing = Dataset [-sam,]

fit18<-robustgam(x,y, sp=4356,family=true.family,smooth.basis='ps',K=3)

ypred=predict(fit18,newdata=Testing)
y=Dataset[-sam,]$y
MSE = mean((y-ypred)^2)
MSE
lst[i]<-MSE
}
mean(unlist(lst))
####################################
 

______________________________________________
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https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: Error in UseMethod("predict")

Eric Berger
Hi Sacha,
I never used these packages before but I installed them and tried your
code. I have a few observations that may help.

1. the statement
    ypred = predict(fit18,newdata=Testing)
    is wrong. Checkout the help page (?robustgam)  which shows in the
Examples section at the bottom to use something like
    ypred = pred.robustgam( fit18, data.frame(X=Testing)

2. your logic is wrong. You define the vectors x and y at the top. They
should remain untouched during your program.
    However in the loop you redefine y and then use the redefined y as an
argument to robustgam() the next time through
    the loop. This looks like a serious error.

HTH,
Eric


On Sun, Jan 17, 2021 at 12:20 PM varin sacha via R-help <
[hidden email]> wrote:

> Dear R-experts,
>
> Here below my reproducible R code. I get an error message (end of code) I
> can't solve.
> Many thanks for your help.
>
> ##########################
> #Data
>
> y=c(34000,45000,19000,48900,65000,67000,78000,90000,51000,32000,54000,85000,38000,76345,87654,90990,78654,67894,56789,65432,18998,78987,67543,45678,76543,67876)
>
> x=c(345,543,543,456,432,378,543,579,432,254,346,564,611,543,542,632,345,468,476,487,453,356,490,499,567,532)
>
> Dataset=data.frame(y,x)
>
> #Plot
> plot(x,y)
>
> #Robust GAM
> library(robustgam)
> true.family <- poisson()
> fit=robustgam(x,y, sp=4356,family=true.family,smooth.basis='ps',K=3)
> x.new <- seq(range(x)[1], range(x)[2])
> robustfit.new <- pred.robustgam(fit, data.frame(X=x.new))$predict.values
> lines(x.new, robustfit.new, col="green", lwd=2)
>
> # To find the « sp » to include in the fit function here above
> robustfit.gic<-robustgam.GIC.optim(x,y,family=true.family,p=3,c=1.6,show.msg=FALSE,smooth.basis="tp",
> method="L-BFGS-B")
>
> ## CROSS VALIDATION REPLICATIONS MSE ROBUST GAM
> install.packages("ISLR")
> library(ISLR)
>
> # Create a list to store the results
> lst<-list()
>
> # This statement does the repetitions (looping)
> for(i in 1 :1000)
> {
>
> n=dim(Dataset)[1]
> p=0.667
> sam=sample(1 :n,floor(p*n),replace=FALSE)
> Training =Dataset [sam,]
> Testing = Dataset [-sam,]
>
> fit18<-robustgam(x,y, sp=4356,family=true.family,smooth.basis='ps',K=3)
>
> ypred=predict(fit18,newdata=Testing)
> y=Dataset[-sam,]$y
> MSE = mean((y-ypred)^2)
> MSE
> lst[i]<-MSE
> }
> mean(unlist(lst))
> ####################################
>
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: Error in UseMethod("predict")

R help mailing list-2
Dear Eric,

Many thanks, I correct your 2 points and now I get another error message (Error in splineDesign(knots, x, ord, derivs, outer.ok = outer.ok, sparse = sparse) :
  empty 'derivs').
I have googleized and found some hints like (outer.ok=TRUE) but no one seems to work.

https://r.789695.n4.nabble.com/mgcv-gam-predict-problem-td3411006.html 

Any idea to make my code work would be appreciated.

Here below my new R code :

##########################
#Data
y=c(34000,45000,19000,48900,65000,67000,78000,90000,51000,32000,54000,85000,38000,76345,87654,90990,78654,67894,56789,65432,18998,78987,67543,45678,76543,67876)
x=c(345,543,543,456,432,378,543,579,432,254,346,564,611,543,542,632,345,468,476,487,453,356,490,499,567,532)

Dataset=data.frame(y,x)

#Plot
plot(x,y)

#Robust GAM
library(robustgam)
true.family <- poisson()
fit=robustgam(x,y, sp=2424,family=true.family,smooth.basis='ps',K=3)
x.new <- seq(range(x)[1], range(x)[2])
robustfit.new <- pred.robustgam(fit, data.frame(X=x.new))$predict.values
lines(x.new, robustfit.new, col="green", lwd=2)

# To find the « sp » to include in the fit function here above
robustfit.gic<-robustgam.GIC.optim(x,y,family=true.family,p=3,c=1.6,show.msg=FALSE,smooth.basis="ps", method="L-BFGS-B")

## CROSS VALIDATION REPLICATIONS MSE ROBUST GAM
install.packages("ISLR")
library(ISLR)

# Create a list to store the results
lst<-list()

# This statement does the repetitions (looping)
for(i in 1 :1000)
{

n=dim(Dataset)[1]
p=0.667
sam=sample(1 :n,floor(p*n),replace=FALSE)
Training =Dataset [sam,]
Testing = Dataset [-sam,]

fit18<-robustgam(x,y, sp=4356,family=true.family,smooth.basis='ps',K=3)

ypred=pred.robustgam(fit18,data.frame(X=Testing))
MSE = mean((y-ypred)^2)
MSE
lst[i]<-MSE
}
mean(unlist(lst))
####################################



 Le dimanche 17 janvier 2021 à 11:41:49 UTC+1, Eric Berger <[hidden email]> a écrit :


Hi Sacha,
I never used these packages before but I installed them and tried your code. I have a few observations that may help.

1. the statement
    ypred = predict(fit18,newdata=Testing)
    is wrong. Checkout the help page (?robustgam)  which shows in the Examples section at the bottom to use something like
    ypred = pred.robustgam( fit18, data.frame(X=Testing)

2. your logic is wrong. You define the vectors x and y at the top. They should remain untouched during your program.
    However in the loop you redefine y and then use the redefined y as an argument to robustgam() the next time through
    the loop. This looks like a serious error.

HTH,
Eric


On Sun, Jan 17, 2021 at 12:20 PM varin sacha via R-help <[hidden email]> wrote:

> Dear R-experts,
>
> Here below my reproducible R code. I get an error message (end of code) I can't solve.
> Many thanks for your help.
>
> ##########################
> #Data
> y=c(34000,45000,19000,48900,65000,67000,78000,90000,51000,32000,54000,85000,38000,76345,87654,90990,78654,67894,56789,65432,18998,78987,67543,45678,76543,67876)
> x=c(345,543,543,456,432,378,543,579,432,254,346,564,611,543,542,632,345,468,476,487,453,356,490,499,567,532)
>
> Dataset=data.frame(y,x)
>
> #Plot
> plot(x,y)
>
> #Robust GAM
> library(robustgam)
> true.family <- poisson()
> fit=robustgam(x,y, sp=4356,family=true.family,smooth.basis='ps',K=3)
> x.new <- seq(range(x)[1], range(x)[2])
> robustfit.new <- pred.robustgam(fit, data.frame(X=x.new))$predict.values
> lines(x.new, robustfit.new, col="green", lwd=2)
>
> # To find the « sp » to include in the fit function here above
> robustfit.gic<-robustgam.GIC.optim(x,y,family=true.family,p=3,c=1.6,show.msg=FALSE,smooth.basis="tp", method="L-BFGS-B")
>
> ## CROSS VALIDATION REPLICATIONS MSE ROBUST GAM
> install.packages("ISLR")
> library(ISLR)
>
> # Create a list to store the results
> lst<-list()
>
> # This statement does the repetitions (looping)
> for(i in 1 :1000)
> {
>
> n=dim(Dataset)[1]
> p=0.667
> sam=sample(1 :n,floor(p*n),replace=FALSE)
> Training =Dataset [sam,]
> Testing = Dataset [-sam,]
>
> fit18<-robustgam(x,y, sp=4356,family=true.family,smooth.basis='ps',K=3)
>
> ypred=predict(fit18,newdata=Testing)
> y=Dataset[-sam,]$y
> MSE = mean((y-ypred)^2)
> MSE
> lst[i]<-MSE
> }
> mean(unlist(lst))
> ####################################
>  
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: Error in UseMethod("predict")

Eric Berger
Hi Sacha,
I took a quick look. Sorry, I don't see immediately what is causing the
problem.
Maybe someone else can help.


On Sun, Jan 17, 2021 at 3:22 PM varin sacha <[hidden email]> wrote:

> Dear Eric,
>
> Many thanks, I correct your 2 points and now I get another error message
> (Error in splineDesign(knots, x, ord, derivs, outer.ok = outer.ok, sparse =
> sparse) :
>   empty 'derivs').
> I have googleized and found some hints like (outer.ok=TRUE) but no one
> seems to work.
>
> https://r.789695.n4.nabble.com/mgcv-gam-predict-problem-td3411006.html
>
> Any idea to make my code work would be appreciated.
>
> Here below my new R code :
>
> ##########################
> #Data
>
> y=c(34000,45000,19000,48900,65000,67000,78000,90000,51000,32000,54000,85000,38000,76345,87654,90990,78654,67894,56789,65432,18998,78987,67543,45678,76543,67876)
>
> x=c(345,543,543,456,432,378,543,579,432,254,346,564,611,543,542,632,345,468,476,487,453,356,490,499,567,532)
>
> Dataset=data.frame(y,x)
>
> #Plot
> plot(x,y)
>
> #Robust GAM
> library(robustgam)
> true.family <- poisson()
> fit=robustgam(x,y, sp=2424,family=true.family,smooth.basis='ps',K=3)
> x.new <- seq(range(x)[1], range(x)[2])
> robustfit.new <- pred.robustgam(fit, data.frame(X=x.new))$predict.values
> lines(x.new, robustfit.new, col="green", lwd=2)
>
> # To find the « sp » to include in the fit function here above
> robustfit.gic<-robustgam.GIC.optim(x,y,family=true.family,p=3,c=1.6,show.msg=FALSE,smooth.basis="ps",
> method="L-BFGS-B")
>
> ## CROSS VALIDATION REPLICATIONS MSE ROBUST GAM
> install.packages("ISLR")
> library(ISLR)
>
> # Create a list to store the results
> lst<-list()
>
> # This statement does the repetitions (looping)
> for(i in 1 :1000)
> {
>
> n=dim(Dataset)[1]
> p=0.667
> sam=sample(1 :n,floor(p*n),replace=FALSE)
> Training =Dataset [sam,]
> Testing = Dataset [-sam,]
>
> fit18<-robustgam(x,y, sp=4356,family=true.family,smooth.basis='ps',K=3)
>
> ypred=pred.robustgam(fit18,data.frame(X=Testing))
> MSE = mean((y-ypred)^2)
> MSE
> lst[i]<-MSE
> }
> mean(unlist(lst))
> ####################################
>
>
>
>  Le dimanche 17 janvier 2021 à 11:41:49 UTC+1, Eric Berger <
> [hidden email]> a écrit :
>
>
> Hi Sacha,
> I never used these packages before but I installed them and tried your
> code. I have a few observations that may help.
>
> 1. the statement
>     ypred = predict(fit18,newdata=Testing)
>     is wrong. Checkout the help page (?robustgam)  which shows in the
> Examples section at the bottom to use something like
>     ypred = pred.robustgam( fit18, data.frame(X=Testing)
>
> 2. your logic is wrong. You define the vectors x and y at the top. They
> should remain untouched during your program.
>     However in the loop you redefine y and then use the redefined y as an
> argument to robustgam() the next time through
>     the loop. This looks like a serious error.
>
> HTH,
> Eric
>
>
> On Sun, Jan 17, 2021 at 12:20 PM varin sacha via R-help <
> [hidden email]> wrote:
> > Dear R-experts,
> >
> > Here below my reproducible R code. I get an error message (end of code)
> I can't solve.
> > Many thanks for your help.
> >
> > ##########################
> > #Data
> >
> y=c(34000,45000,19000,48900,65000,67000,78000,90000,51000,32000,54000,85000,38000,76345,87654,90990,78654,67894,56789,65432,18998,78987,67543,45678,76543,67876)
> >
> x=c(345,543,543,456,432,378,543,579,432,254,346,564,611,543,542,632,345,468,476,487,453,356,490,499,567,532)
> >
> > Dataset=data.frame(y,x)
> >
> > #Plot
> > plot(x,y)
> >
> > #Robust GAM
> > library(robustgam)
> > true.family <- poisson()
> > fit=robustgam(x,y, sp=4356,family=true.family,smooth.basis='ps',K=3)
> > x.new <- seq(range(x)[1], range(x)[2])
> > robustfit.new <- pred.robustgam(fit, data.frame(X=x.new))$predict.values
> > lines(x.new, robustfit.new, col="green", lwd=2)
> >
> > # To find the « sp » to include in the fit function here above
> >
> robustfit.gic<-robustgam.GIC.optim(x,y,family=true.family,p=3,c=1.6,show.msg=FALSE,smooth.basis="tp",
> method="L-BFGS-B")
> >
> > ## CROSS VALIDATION REPLICATIONS MSE ROBUST GAM
> > install.packages("ISLR")
> > library(ISLR)
> >
> > # Create a list to store the results
> > lst<-list()
> >
> > # This statement does the repetitions (looping)
> > for(i in 1 :1000)
> > {
> >
> > n=dim(Dataset)[1]
> > p=0.667
> > sam=sample(1 :n,floor(p*n),replace=FALSE)
> > Training =Dataset [sam,]
> > Testing = Dataset [-sam,]
> >
> > fit18<-robustgam(x,y, sp=4356,family=true.family,smooth.basis='ps',K=3)
> >
> > ypred=predict(fit18,newdata=Testing)
> > y=Dataset[-sam,]$y
> > MSE = mean((y-ypred)^2)
> > MSE
> > lst[i]<-MSE
> > }
> > mean(unlist(lst))
> > ####################################
> >
> >
> > ______________________________________________
> > [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: Error in UseMethod("predict")

R help mailing list-2
Eric,

No problem. Let's see if somebody else has a solution. I have changed the smooth basis from P-splines ('ps') to thin plate ('tp'). It still does not work, but this time I get another error message.


##########################
#Data
y=c(34000,45000,19000,48900,65000,67000,78000,90000,51000,32000,54000,85000,38000,76345,87654,90990,78654,67894,56789,65432,18998,78987,67543,45678,76543,67876)
x=c(345,543,543,456,432,378,543,579,432,254,346,564,611,543,542,632,345,468,476,487,453,356,490,499,567,532)

Dataset=data.frame(y,x)

#Plot
plot(x,y)

#Robust GAM
library(robustgam)
true.family <- poisson()
fit=robustgam(x,y, sp=2424,family=true.family,smooth.basis='tp')
x.new <- seq(range(x)[1], range(x)[2])
robustfit.new <- pred.robustgam(fit, data.frame(X=x.new))$predict.values
lines(x.new, robustfit.new, col="green", lwd=2)

# To find the « sp » to include in the fit function here above
robustfit.gic<-robustgam.GIC.optim(x,y,family=true.family,p=3,c=1.6,show.msg=FALSE,smooth.basis="ps", method="L-BFGS-B")

## CROSS VALIDATION REPLICATIONS MSE ROBUST GAM

# Create a list to store the results
lst<-list()

# This statement does the repetitions (looping)
for(i in 1 :1000)
{

n=dim(Dataset)[1]
p=0.667
sam=sample(1 :n,floor(p*n),replace=FALSE)
Training =Dataset [sam,]
Testing = Dataset [-sam,]

fit18<-robustgam(x,y, sp=2424,family=true.family,smooth.basis='tp')

ypred=pred.robustgam(fit18,data.frame(X=Testing))
MSE = mean((y-ypred)^2)
MSE
lst[i]<-MSE
}
mean(unlist(lst))
####################################





Le dimanche 17 janvier 2021 à 15:02:45 UTC+1, Eric Berger <[hidden email]> a écrit :





Hi Sacha, 
I took a quick look. Sorry, I don't see immediately what is causing the problem.
Maybe someone else can help.



> Dear Eric,
>
> Many thanks, I correct your 2 points and now I get another error message (Error in splineDesign(knots, x, ord, derivs, outer.ok = outer.ok, sparse = sparse) :
>   empty 'derivs').
> I have googleized and found some hints like (outer.ok=TRUE) but no one seems to work.
>
> https://r.789695.n4.nabble.com/mgcv-gam-predict-problem-td3411006.html 
>
> Any idea to make my code work would be appreciated.
>
> Here below my new R code :
>
> ##########################
> #Data
> y=c(34000,45000,19000,48900,65000,67000,78000,90000,51000,32000,54000,85000,38000,76345,87654,90990,78654,67894,56789,65432,18998,78987,67543,45678,76543,67876)
> x=c(345,543,543,456,432,378,543,579,432,254,346,564,611,543,542,632,345,468,476,487,453,356,490,499,567,532)
>
> Dataset=data.frame(y,x)
>
> #Plot
> plot(x,y)
>
> #Robust GAM
> library(robustgam)
> true.family <- poisson()
> fit=robustgam(x,y, sp=2424,family=true.family,smooth.basis='ps',K=3)
> x.new <- seq(range(x)[1], range(x)[2])
> robustfit.new <- pred.robustgam(fit, data.frame(X=x.new))$predict.values
> lines(x.new, robustfit.new, col="green", lwd=2)
>
> # To find the « sp » to include in the fit function here above
> robustfit.gic<-robustgam.GIC.optim(x,y,family=true.family,p=3,c=1.6,show.msg=FALSE,smooth.basis="ps", method="L-BFGS-B")
>
> ## CROSS VALIDATION REPLICATIONS MSE ROBUST GAM
> install.packages("ISLR")
> library(ISLR)
>
> # Create a list to store the results
> lst<-list()
>
> # This statement does the repetitions (looping)
> for(i in 1 :1000)
> {
>
> n=dim(Dataset)[1]
> p=0.667
> sam=sample(1 :n,floor(p*n),replace=FALSE)
> Training =Dataset [sam,]
> Testing = Dataset [-sam,]
>
> fit18<-robustgam(x,y, sp=4356,family=true.family,smooth.basis='ps',K=3)
>
> ypred=pred.robustgam(fit18,data.frame(X=Testing))
> MSE = mean((y-ypred)^2)
> MSE
> lst[i]<-MSE
> }
> mean(unlist(lst))
> ####################################
>
>
>
>  Le dimanche 17 janvier 2021 à 11:41:49 UTC+1, Eric Berger <[hidden email]> a écrit :
>
>
> Hi Sacha,
> I never used these packages before but I installed them and tried your code. I have a few observations that may help.
>
> 1. the statement
>     ypred = predict(fit18,newdata=Testing)
>     is wrong. Checkout the help page (?robustgam)  which shows in the Examples section at the bottom to use something like
>     ypred = pred.robustgam( fit18, data.frame(X=Testing)
>
> 2. your logic is wrong. You define the vectors x and y at the top. They should remain untouched during your program.
>     However in the loop you redefine y and then use the redefined y as an argument to robustgam() the next time through
>     the loop. This looks like a serious error.
>
> HTH,
> Eric
>
>
> On Sun, Jan 17, 2021 at 12:20 PM varin sacha via R-help <[hidden email]> wrote:
>> Dear R-experts,
>>
>> Here below my reproducible R code. I get an error message (end of code) I can't solve.
>> Many thanks for your help.
>>
>> ##########################
>> #Data
>> y=c(34000,45000,19000,48900,65000,67000,78000,90000,51000,32000,54000,85000,38000,76345,87654,90990,78654,67894,56789,65432,18998,78987,67543,45678,76543,67876)
>> x=c(345,543,543,456,432,378,543,579,432,254,346,564,611,543,542,632,345,468,476,487,453,356,490,499,567,532)
>>
>> Dataset=data.frame(y,x)
>>
>> #Plot
>> plot(x,y)
>>
>> #Robust GAM
>> library(robustgam)
>> true.family <- poisson()
>> fit=robustgam(x,y, sp=4356,family=true.family,smooth.basis='ps',K=3)
>> x.new <- seq(range(x)[1], range(x)[2])
>> robustfit.new <- pred.robustgam(fit, data.frame(X=x.new))$predict.values
>> lines(x.new, robustfit.new, col="green", lwd=2)
>>
>> # To find the « sp » to include in the fit function here above
>> robustfit.gic<-robustgam.GIC.optim(x,y,family=true.family,p=3,c=1.6,show.msg=FALSE,smooth.basis="tp", method="L-BFGS-B")
>>
>> ## CROSS VALIDATION REPLICATIONS MSE ROBUST GAM
>> install.packages("ISLR")
>> library(ISLR)
>>
>> # Create a list to store the results
>> lst<-list()
>>
>> # This statement does the repetitions (looping)
>> for(i in 1 :1000)
>> {
>>
>> n=dim(Dataset)[1]
>> p=0.667
>> sam=sample(1 :n,floor(p*n),replace=FALSE)
>> Training =Dataset [sam,]
>> Testing = Dataset [-sam,]
>>
>> fit18<-robustgam(x,y, sp=4356,family=true.family,smooth.basis='ps',K=3)
>>
>> ypred=predict(fit18,newdata=Testing)
>> y=Dataset[-sam,]$y
>> MSE = mean((y-ypred)^2)
>> MSE
>> lst[i]<-MSE
>> }
>> mean(unlist(lst))
>> ####################################
>>  
>>
>> ______________________________________________
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>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>

______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.