gboot_cross | R Documentation |
Performs a boostrap based on error from the cross-validation
gboot_cross(data,var,model,B)
data |
object of the class geodata. |
var |
object of the class variogram. |
model |
object of the class variomodel. |
B |
number of the bootstrap that will be performed (default B=1000). |
We can define the error of prediction by \epsilon({s_i})=Z({s_i})-\hat Z({s_i})
,
where \hat Z({s_i})
are obtained from cross-validation. The steps of the algorithm are:
Set {s_i}^*={s_i}
;
Obtain \hat Z({s_i})
from \hat Z({s_i})=\sum\limits_{j \ne i}^{n - 1}{{\lambda _j}Z({s_j})}
;
Calculate \epsilon({s_i})=Z({s_i})-\hat Z({s_i})
Sample with replacement \epsilon^*(s_i)
from \epsilon (s_i) - \bar \epsilon (s_i)
;
The new data will be Z^*({s_i})=\hat Z({s_i})+ \epsilon^*(s_i)
;
Calculate the new variogram;
Calculate and save the statistics of interest;
Return to step 4 and repeat the process at least 1000 times.
variogram_boot gives the variogram of each bootstrap.
variogram_or gives the original variogram.
pars_boot gives the estimatives of the nugget, sill, contribution, range and practical range for each bootstrap.
pars_or gives the original estimatives of the nugget, sill, contribution, range and practical range.
Invalid arguments will return an error message.
Diogo Francisco Rossoni dfrossoni@uem.br
Vinicius Basseto Felix felix_prot@hotmail.com
# Example 1
## transforming the data.frame in an object of class geodata
data<- as.geodata(soilmoisture)
points(data) ## data visualization
var<- variog(data, max.dist = 140) ## Obtaining the variogram
plot(var)
## Fitting the model
mod<- variofit(var,ini.cov.pars = c(2,80),nugget = 2,cov.model = "sph")
lines(mod, col=2, lwd=2) ##fitted model
## Bootstrap procedure
boot<- gboot_cross(data,var,mod,B=10)
## For better Confidence interval, try B=1000
gboot_CI(boot,digits = 4) ## Bootstrap Confidence Interval
gboot_plot(boot) ## Bootstrap Variogram plot
# Example 2
## transforming the data.frame in an object of class geodata
data<- as.geodata(NVDI)
points(data) ## data visualization
var<- variog(data, max.dist = 18) ## Obtaining the variogram
plot(var)
## Fitting the model
mod<- variofit(var,ini.cov.pars = c(0.003,6),nugget = 0.003,cov.model = "gaus")
lines(mod, col=2, lwd=2) ##fitted model
## Bootstrap procedure
boot<- gboot_cross(data,var,mod,B=10)
## For better Confidence interval, try B=1000
gboot_CI(boot,digits = 4) ## Bootstrap Confidence Interval
gboot_plot(boot) ## Bootstrap Variogram plot
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