Print values of a km object

Description

Show method for km object. Printing the main features of a kriging model.

Usage

   ## S4 method for signature 'km'
show(object)

Arguments

Author(s)

O. Roustant, D. Ginsbourger, Ecole des Mines de St-Etienne.

See Also

km

Examples

 
# A 2D example - Branin-Hoo function

# a 16-points factorial design, and the corresponding response
d <- 2; n <- 16
fact.design <- expand.grid(seq(0,1,length=4), seq(0,1,length=4))
fact.design <- data.frame(fact.design); names(fact.design)<-c("x1", "x2")
branin.resp <- data.frame(branin(fact.design)); names(branin.resp) <- "y" 

# kriging model 1 : power-exponential covariance structure, no trend, 
#                   no nugget effect
m1 <- km(y~1, design=fact.design, response=branin.resp, covtype="powexp")
m1    # equivalently : show(m1)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(DiceKriging)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DiceKriging/show.km.Rd_%03d_medium.png", width=480, height=480)
> ### Name: show
> ### Title: Print values of a km object
> ### Aliases: show,km-method
> ### Keywords: methods
> 
> ### ** Examples
>  
> # A 2D example - Branin-Hoo function
> 
> # a 16-points factorial design, and the corresponding response
> d <- 2; n <- 16
> fact.design <- expand.grid(seq(0,1,length=4), seq(0,1,length=4))
> fact.design <- data.frame(fact.design); names(fact.design)<-c("x1", "x2")
> branin.resp <- data.frame(branin(fact.design)); names(branin.resp) <- "y" 
> 
> # kriging model 1 : power-exponential covariance structure, no trend, 
> #                   no nugget effect
> m1 <- km(y~1, design=fact.design, response=branin.resp, covtype="powexp")

optimisation start
------------------
* estimation method   : MLE 
* optimisation method : BFGS 
* analytical gradient : used
* trend model : ~1
* covariance model : 
  - type :  powexp 
  - nugget : NO
  - parameters lower bounds :  1e-10 1e-10 1e-10 1e-10 
  - parameters upper bounds :  2 2 2 2 
  - best initial criterion value(s) :  -86.92648 

N = 4, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate     0  f=       86.926  |proj g|=       1.7997
At iterate     1  f =       83.868  |proj g|=       0.86542
At iterate     2  f =       82.624  |proj g|=        1.0709
At iterate     3  f =       81.461  |proj g|=        1.9937
At iterate     4  f =       81.186  |proj g|=        1.9532
At iterate     5  f =       80.073  |proj g|=        1.2917
At iterate     6  f =        78.73  |proj g|=        1.2901
At iterate     7  f =       78.579  |proj g|=        1.9985
At iterate     8  f =       78.416  |proj g|=          1.29
At iterate     9  f =       78.412  |proj g|=        1.2899
At iterate    10  f =       78.412  |proj g|=        1.9171
At iterate    11  f =       78.403  |proj g|=        1.9979
At iterate    12  f =       78.388  |proj g|=        1.9981
At iterate    13  f =        78.34  |proj g|=        1.9985
At iterate    14  f =       78.243  |proj g|=        1.9985
At iterate    15  f =       78.146  |proj g|=             2
At iterate    16  f =       78.134  |proj g|=        1.9999
At iterate    17  f =       78.131  |proj g|=        1.9998
At iterate    18  f =        78.13  |proj g|=        1.9997
At iterate    19  f =        78.13  |proj g|=        1.9996
At iterate    20  f =       78.129  |proj g|=         1.999
At iterate    21  f =       78.127  |proj g|=        1.9976
At iterate    22  f =       78.122  |proj g|=        1.5264
At iterate    23  f =       78.117  |proj g|=       0.44886
At iterate    24  f =       78.115  |proj g|=       0.32438
At iterate    25  f =       78.114  |proj g|=       0.25733
At iterate    26  f =       78.114  |proj g|=       0.16023
At iterate    27  f =       78.113  |proj g|=       0.19211
At iterate    28  f =       78.113  |proj g|=      0.028136
At iterate    29  f =       78.113  |proj g|=     0.0044078
At iterate    30  f =       78.113  |proj g|=    0.00037973

iterations 30
function evaluations 42
segments explored during Cauchy searches 35
BFGS updates skipped 0
active bounds at final generalized Cauchy point 1
norm of the final projected gradient 0.000379729
final function value 78.1131

F = 78.1131
final  value 78.113116 
converged
> m1    # equivalently : show(m1)

Call:
km(formula = y ~ 1, design = fact.design, response = branin.resp, 
    covtype = "powexp")

Trend  coeff.:
               Estimate
 (Intercept)   396.7327

Covar. type  : powexp 
Covar. coeff.:
               Estimate                Estimate
   theta(x1)     0.8589        p(x1)     1.9870
   theta(x2)     2.0000        p(x2)     1.9972

Variance estimate: 161649.3
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>