This method helps you to visualize the output of krig() (which outputs
objects of class "krig_lst"). It is similar to utils::str() but a little
cleaner. version of str().
# S3 method for krig_lst summary(object, ...)
| object | The result of |
|---|---|
| ... | Arguments passed on to
|
Prints a cleaner version of str() and returns its input invisibly.
#>summary(result)#> var: c #> df #> Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1150 obs. of 3 variables: #> $ x: num 10 30 50 70 90 110 130 150 170 190 ... #> $ y: num 10 10 10 10 10 10 10 10 10 10 ... #> $ z: num 2.13 2.12 2.1 2.09 2.07 ... #> #> df.poly #> Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1150 obs. of 3 variables: #> $ gx: num 10 30 50 70 90 110 130 150 170 190 ... #> $ gy: num 10 10 10 10 10 10 10 10 10 10 ... #> $ z : num 2.13 2.12 2.1 2.09 2.07 ... #> #> lambda #> 'numeric' #> num 1 #> #> vg #> 'variogram' #> List of 20 #> $ u : num [1:9] 60.9 86.5 103 122.7 146.1 ... #> $ v : num [1:9] 0.284 0.422 0.882 0.543 0.211 ... #> $ n : num [1:9] 7 9 10 10 18 19 36 34 38 #> $ sd : num [1:9] 0.414 0.48 0.633 0.501 0.405 ... #> $ bins.lim : num [1:31] 1.00e-12 2.00 2.38 2.84 3.38 ... #> $ ind.bin : logi [1:30] FALSE FALSE FALSE FALSE FALSE FALSE ... #> $ var.mark : num 0.317 #> $ beta.ols : num 1.36e-09 #> $ output.type : chr "bin" #> $ max.dist : num 320 #> $ estimator.type : chr "classical" #> $ n.data : int 30 #> $ lambda : num 1 #> $ trend : chr "cte" #> $ pairs.min : num 5 #> $ nugget.tolerance: num 1e-12 #> $ direction : chr "omnidirectional" #> $ tolerance : chr "none" #> $ uvec : num [1:30] 1 2.19 2.61 3.11 3.7 ... #> $ call : language variog(geodata = geodata, breaks = breaks, trend = trend, pairs.min = 5) #> #> vm #> 'variomodel', variofit' #> List of 17 #> $ nugget : num 0.352 #> $ cov.pars : num [1:2] 0 160 #> $ cov.model : chr "exponential" #> $ kappa : num 0.5 #> $ value : num 4.64 #> $ trend : chr "cte" #> $ beta.ols : num 1.36e-09 #> $ practicalRange : num 480 #> $ max.dist : num 320 #> $ minimisation.function: chr "optim" #> $ weights : chr "npairs" #> $ method : chr "WLS" #> $ fix.nugget : logi FALSE #> $ fix.kappa : logi TRUE #> $ lambda : num 1 #> $ message : chr "optim convergence code: 0" #> $ call : language variofit(vario = vg, ini.cov.pars = c(initialVal, startRange), cov.model = varModels[i], nugget = initialVal) #> #> var: p #> df #> Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1150 obs. of 3 variables: #> $ x: num 10 30 50 70 90 110 130 150 170 190 ... #> $ y: num 10 10 10 10 10 10 10 10 10 10 ... #> $ z: num 6.37 6.35 6.33 6.31 6.29 ... #> #> df.poly #> Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1150 obs. of 3 variables: #> $ gx: num 10 30 50 70 90 110 130 150 170 190 ... #> $ gy: num 10 10 10 10 10 10 10 10 10 10 ... #> $ z : num 6.37 6.35 6.33 6.31 6.29 ... #> #> lambda #> 'numeric' #> num 1 #> #> vg #> 'variogram' #> List of 20 #> $ u : num [1:9] 60.9 86.5 103 122.7 146.1 ... #> $ v : num [1:9] 0.396 0.4 0.11 0.402 0.385 ... #> $ n : num [1:9] 7 9 10 10 18 19 36 34 38 #> $ sd : num [1:9] 0.592 0.414 0.133 0.409 0.488 ... #> $ bins.lim : num [1:31] 1.00e-12 2.00 2.38 2.84 3.38 ... #> $ ind.bin : logi [1:30] FALSE FALSE FALSE FALSE FALSE FALSE ... #> $ var.mark : num 0.267 #> $ beta.ols : num -3.14e-09 #> $ output.type : chr "bin" #> $ max.dist : num 320 #> $ estimator.type : chr "classical" #> $ n.data : int 30 #> $ lambda : num 1 #> $ trend : chr "cte" #> $ pairs.min : num 5 #> $ nugget.tolerance: num 1e-12 #> $ direction : chr "omnidirectional" #> $ tolerance : chr "none" #> $ uvec : num [1:30] 1 2.19 2.61 3.11 3.7 ... #> $ call : language variog(geodata = geodata, breaks = breaks, trend = trend, pairs.min = 5) #> #> vm #> 'variomodel', variofit' #> List of 17 #> $ nugget : num 0.305 #> $ cov.pars : num [1:2] 0 160 #> $ cov.model : chr "exponential" #> $ kappa : num 0.5 #> $ value : num 0.818 #> $ trend : chr "cte" #> $ beta.ols : num -3.14e-09 #> $ practicalRange : num 479 #> $ max.dist : num 320 #> $ minimisation.function: chr "optim" #> $ weights : chr "npairs" #> $ method : chr "WLS" #> $ fix.nugget : logi FALSE #> $ fix.kappa : logi TRUE #> $ lambda : num 1 #> $ message : chr "optim convergence code: 0" #> $ call : language variofit(vario = vg, ini.cov.pars = c(initialVal, startRange), cov.model = varModels[i], nugget = initialVal) #>str(result)#> List of 2 #> $ c:List of 5 #> ..$ df :Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1150 obs. of 3 variables: #> .. ..$ x: num [1:1150] 10 30 50 70 90 110 130 150 170 190 ... #> .. ..$ y: num [1:1150] 10 10 10 10 10 10 10 10 10 10 ... #> .. ..$ z: num [1:1150] 2.13 2.12 2.1 2.09 2.07 ... #> .. ..- attr(*, "out.attrs")=List of 2 #> .. .. ..$ dim : Named int [1:2] 50 23 #> .. .. .. ..- attr(*, "names")= chr [1:2] "gx" "gy" #> .. .. ..$ dimnames:List of 2 #> .. .. .. ..$ gx: chr [1:50] "gx= 10" "gx= 30" "gx= 50" "gx= 70" ... #> .. .. .. ..$ gy: chr [1:23] "gy= 10" "gy= 30" "gy= 50" "gy= 70" ... #> ..$ df.poly:Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1150 obs. of 3 variables: #> .. ..$ gx: num [1:1150] 10 30 50 70 90 110 130 150 170 190 ... #> .. ..$ gy: num [1:1150] 10 10 10 10 10 10 10 10 10 10 ... #> .. ..$ z : num [1:1150] 2.13 2.12 2.1 2.09 2.07 ... #> .. ..- attr(*, "out.attrs")=List of 2 #> .. .. ..$ dim : Named int [1:2] 50 23 #> .. .. .. ..- attr(*, "names")= chr [1:2] "gx" "gy" #> .. .. ..$ dimnames:List of 2 #> .. .. .. ..$ gx: chr [1:50] "gx= 10" "gx= 30" "gx= 50" "gx= 70" ... #> .. .. .. ..$ gy: chr [1:23] "gy= 10" "gy= 30" "gy= 50" "gy= 70" ... #> ..$ lambda : num 1 #> ..$ vg :List of 20 #> .. ..$ u : num [1:9] 60.9 86.5 103 122.7 146.1 ... #> .. ..$ v : num [1:9] 0.284 0.422 0.882 0.543 0.211 ... #> .. ..$ n : num [1:9] 7 9 10 10 18 19 36 34 38 #> .. ..$ sd : num [1:9] 0.414 0.48 0.633 0.501 0.405 ... #> .. ..$ bins.lim : num [1:31] 1.00e-12 2.00 2.38 2.84 3.38 ... #> .. ..$ ind.bin : logi [1:30] FALSE FALSE FALSE FALSE FALSE FALSE ... #> .. ..$ var.mark : num 0.317 #> .. ..$ beta.ols : num 1.36e-09 #> .. ..$ output.type : chr "bin" #> .. ..$ max.dist : num 320 #> .. ..$ estimator.type : chr "classical" #> .. ..$ n.data : int 30 #> .. ..$ lambda : num 1 #> .. ..$ trend : chr "cte" #> .. ..$ pairs.min : num 5 #> .. ..$ nugget.tolerance: num 1e-12 #> .. ..$ direction : chr "omnidirectional" #> .. ..$ tolerance : chr "none" #> .. ..$ uvec : num [1:30] 1 2.19 2.61 3.11 3.7 ... #> .. ..$ call : language variog(geodata = geodata, breaks = breaks, trend = trend, pairs.min = 5) #> .. ..- attr(*, "class")= chr "variogram" #> ..$ vm :List of 17 #> .. ..$ nugget : num 0.352 #> .. ..$ cov.pars : num [1:2] 0 160 #> .. ..$ cov.model : chr "exponential" #> .. ..$ kappa : num 0.5 #> .. ..$ value : num 4.64 #> .. ..$ trend : chr "cte" #> .. ..$ beta.ols : num 1.36e-09 #> .. ..$ practicalRange : num 480 #> .. ..$ max.dist : num 320 #> .. ..$ minimisation.function: chr "optim" #> .. ..$ weights : chr "npairs" #> .. ..$ method : chr "WLS" #> .. ..$ fix.nugget : logi FALSE #> .. ..$ fix.kappa : logi TRUE #> .. ..$ lambda : num 1 #> .. ..$ message : chr "optim convergence code: 0" #> .. ..$ call : language variofit(vario = vg, ini.cov.pars = c(initialVal, startRange), cov.model = varModels[i], nugget = initialVal) #> .. ..- attr(*, "class")= chr [1:2] "variomodel" "variofit" #> $ p:List of 5 #> ..$ df :Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1150 obs. of 3 variables: #> .. ..$ x: num [1:1150] 10 30 50 70 90 110 130 150 170 190 ... #> .. ..$ y: num [1:1150] 10 10 10 10 10 10 10 10 10 10 ... #> .. ..$ z: num [1:1150] 6.37 6.35 6.33 6.31 6.29 ... #> .. ..- attr(*, "out.attrs")=List of 2 #> .. .. ..$ dim : Named int [1:2] 50 23 #> .. .. .. ..- attr(*, "names")= chr [1:2] "gx" "gy" #> .. .. ..$ dimnames:List of 2 #> .. .. .. ..$ gx: chr [1:50] "gx= 10" "gx= 30" "gx= 50" "gx= 70" ... #> .. .. .. ..$ gy: chr [1:23] "gy= 10" "gy= 30" "gy= 50" "gy= 70" ... #> ..$ df.poly:Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1150 obs. of 3 variables: #> .. ..$ gx: num [1:1150] 10 30 50 70 90 110 130 150 170 190 ... #> .. ..$ gy: num [1:1150] 10 10 10 10 10 10 10 10 10 10 ... #> .. ..$ z : num [1:1150] 6.37 6.35 6.33 6.31 6.29 ... #> .. ..- attr(*, "out.attrs")=List of 2 #> .. .. ..$ dim : Named int [1:2] 50 23 #> .. .. .. ..- attr(*, "names")= chr [1:2] "gx" "gy" #> .. .. ..$ dimnames:List of 2 #> .. .. .. ..$ gx: chr [1:50] "gx= 10" "gx= 30" "gx= 50" "gx= 70" ... #> .. .. .. ..$ gy: chr [1:23] "gy= 10" "gy= 30" "gy= 50" "gy= 70" ... #> ..$ lambda : num 1 #> ..$ vg :List of 20 #> .. ..$ u : num [1:9] 60.9 86.5 103 122.7 146.1 ... #> .. ..$ v : num [1:9] 0.396 0.4 0.11 0.402 0.385 ... #> .. ..$ n : num [1:9] 7 9 10 10 18 19 36 34 38 #> .. ..$ sd : num [1:9] 0.592 0.414 0.133 0.409 0.488 ... #> .. ..$ bins.lim : num [1:31] 1.00e-12 2.00 2.38 2.84 3.38 ... #> .. ..$ ind.bin : logi [1:30] FALSE FALSE FALSE FALSE FALSE FALSE ... #> .. ..$ var.mark : num 0.267 #> .. ..$ beta.ols : num -3.14e-09 #> .. ..$ output.type : chr "bin" #> .. ..$ max.dist : num 320 #> .. ..$ estimator.type : chr "classical" #> .. ..$ n.data : int 30 #> .. ..$ lambda : num 1 #> .. ..$ trend : chr "cte" #> .. ..$ pairs.min : num 5 #> .. ..$ nugget.tolerance: num 1e-12 #> .. ..$ direction : chr "omnidirectional" #> .. ..$ tolerance : chr "none" #> .. ..$ uvec : num [1:30] 1 2.19 2.61 3.11 3.7 ... #> .. ..$ call : language variog(geodata = geodata, breaks = breaks, trend = trend, pairs.min = 5) #> .. ..- attr(*, "class")= chr "variogram" #> ..$ vm :List of 17 #> .. ..$ nugget : num 0.305 #> .. ..$ cov.pars : num [1:2] 0 160 #> .. ..$ cov.model : chr "exponential" #> .. ..$ kappa : num 0.5 #> .. ..$ value : num 0.818 #> .. ..$ trend : chr "cte" #> .. ..$ beta.ols : num -3.14e-09 #> .. ..$ practicalRange : num 479 #> .. ..$ max.dist : num 320 #> .. ..$ minimisation.function: chr "optim" #> .. ..$ weights : chr "npairs" #> .. ..$ method : chr "WLS" #> .. ..$ fix.nugget : logi FALSE #> .. ..$ fix.kappa : logi TRUE #> .. ..$ lambda : num 1 #> .. ..$ message : chr "optim convergence code: 0" #> .. ..$ call : language variofit(vario = vg, ini.cov.pars = c(initialVal, startRange), cov.model = varModels[i], nugget = initialVal) #> .. ..- attr(*, "class")= chr [1:2] "variomodel" "variofit" #> - attr(*, "class")= chr [1:2] "krig_lst" "list"