gboot_block {geotoolsR} | R Documentation |
Block bootstrap
Description
Performs a bootstrap based on subdivision of data in blocks
Usage
gboot_block(data,var,model,B,L1,L2)
Arguments
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). |
L1 |
number of cuts in the vertical (L1xL2 blocks). |
L2 |
number of cuts in the horizontal (L1xL2 blocks). |
Details
The algorithm for the block bootstrap is an adaptation of the time series bootstrap. Consider that your data presents the second order stationarity, so, we can subdivide them into small blocks. The steps of the algorithm are:
Subdivide the data into L1xL2 blocks;
Realocate each block with probability
;
Calculate the new variogram from the new data;
Calculate and save the statistics of interest;
Return to step 2 and repeat the process at least 1000 times.
Value
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.
Author(s)
Diogo Francisco Rossoni dfrossoni@uem.br
Vinicius Basseto Felix felix_prot@hotmail.com
References
DAVISON, A.C.; HINKLEY, D. V. Bootstrap Methods and their Application. [s.l.] Cambridge University Press, 1997. p. 582
Examples
# 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_block(data,var,mod,B=10, L1=2, L2=2)
## 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<- boot<- gboot_block(data,var,mod,B=10, L1=2, L2=2)
## For better Confidence interval, try B=1000
gboot_CI(boot,digits = 4) ## Bootstrap Confidence Interval
gboot_plot(boot) ## Bootstrap Variogram plot