Description Usage Arguments Details Value Author(s) References Examples
View source: R/gboot_variogram.R
Perform a boostrap based on error from the fitted model of the variogram.
1 | gboot_variogram(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). |
The algorithm for the bootstrap variogram is the same presented for Davison and Hinkley (1997) for the non linear regression. We can write the variogram as \hat γ(h) = γ_{mod}(h)+ε, where γ_{mod}(h) is the fitted model. The steps of the algorithm are:
Set h^*=h;
Sample with replacement ε^* from ε - \bar ε;
The new variogram will be γ^*(h^*) = γ_{mod}(h)+ε^*;
Calculate and save the statistics of interest;
Return to step 2 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
DAVISON, A.C.; HINKLEY, D. V. Bootstrap Methods and their Application. [s.l.] Cambridge University Press, 1997. p. 582
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | # 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_variogram(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
## Not run:
# 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_variogram(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
## End(Not run)
|
Loading required package: geoR
--------------------------------------------------------------
Analysis of Geostatistical Data
For an Introduction to geoR go to http://www.leg.ufpr.br/geoR
geoR version 1.7-5.2.1 (built on 2016-05-02) is now loaded
--------------------------------------------------------------
Loading required package: tidyr
Loading required package: dplyr
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: ggplot2
Warning message:
no DISPLAY variable so Tk is not available
variog: computing omnidirectional variogram
variofit: covariance model used is spherical
variofit: weights used: npairs
variofit: minimisation function used: optim
Parameters confidence interval (95%):
Parameter Lower Estimate Upper
1 Nugget 2.1125 2.4997 3.0046
2 Sill 4.0451 4.1215 4.2388
3 Contribution 1.1950 1.6217 2.0159
4 Range 68.8660 78.8282 127.7923
5 Practical Range 68.8660 78.8282 127.7923
variog: computing omnidirectional variogram
variofit: covariance model used is gaussian
variofit: weights used: npairs
variofit: minimisation function used: optim
Parameters confidence interval (95%):
Parameter Lower Estimate Upper
1 Nugget 0.0018 0.0029 0.0038
2 Sill 0.0064 0.0064 0.0070
3 Contribution 0.0029 0.0036 0.0047
4 Range 2.0676 4.3071 3.7444
5 Practical Range 3.5786 7.4548 6.4808
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