Soil data of North Bavaria, Germany

Description

Soil physical and chemical data collected on a field in the Weissenstaedter Becken, Germany

Usage

data(soil)

Format

This data frame contains the following columns:

x.coord

x coordinates given in cm

y.coord

y coordinates given in cm

nr

number of the samples, which were taken in this order

moisture

moisture content [Kg/Kg * 100%]

NO3.N

nitrate nitrogen [mg/Kg]

Total.N

total nitrogen [mg/Kg]

NH4.N

ammonium nitrogen [mg/Kg]

DOC

dissolved organic carbon [mg/Kg]

N20N

nitrous oxide [mg/Kg dried substance]

Details

For technical reasons some of the data were obtained as differences of two measurements (which are not available anymore). Therefore, some of the data have negative values.

Source

The data were collected by Wolfgang Falk, Soil Physics Group, University of Bayreuth, Germany.

References

Falk, W. (2000) Kleinskalige raeumliche Variabilitaet von Lachgas und bodenchemischen Parameters [Small Scale Spatial Variability of Nitrous Oxide and Pedo-Chemical Parameters], Master thesis, University of Bayreuth, Germany.

Examples

RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again




################################################################
## ##
## a geostatistical analysis that demonstrates ##
## features of the package `RandomFields' ##
## ##
################################################################


data(soil)
str(soil)
soil <- RFspatialPointsDataFrame(
 coords = soil[ , c("x.coord", "y.coord")],
 data = soil[ , c("moisture", "NO3.N", "Total.N", "NH4.N", "DOC", "N20N")],
 RFparams=list(vdim=6, n=1)
)
data <- soil["moisture"]


## plot the data first
colour <- rainbow(100)
plot(data, col=colour)


## fit by eye
gui.model <- RFgui(data) 
 

## fit by ML
model <- ~1 + RMwhittle(scale=NA, var=NA, nu=NA) + RMnugget(var=NA)
(fit <- RFfit(model, data=data))
plot(fit, method=c("ml", "plain", "sqrt.nr", "sd.inv"),
     model = gui.model, col=1:8)

## Kriging ...
x <- seq(min(data@coords[, 1]), max(data@coords[, 1]), l=121)
k <- RFinterpolate(fit, x=x, y=x, data=data)
plot(x=k, col=colour)
plot(x=k, y=data, col=colour)

## what is the probability that at no point of the
## grid given by x and y the moisture is greater than 24 percent?

cs <- RFsimulate(model=fit, x=x, y=x, data=data, n=50)
plot(cs, col=colour)
plot(cs, y=data, col=colour)
Print(mean(apply(as.array(cs) <= 24, 3, all))) ## about 40 percent ...

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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'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(RandomFields)
Loading required package: sp
Loading required package: RandomFieldsUtils
This is RandomFieldsUtils Version: 0.2.1
This is RandomFields Version: 3.1.16

Attaching package: 'RandomFields'

The following object is masked from 'package:RandomFieldsUtils':

    RFoptions

The following objects are masked from 'package:base':

    abs, acosh, asin, asinh, atan, atan2, atanh, cos, cosh, exp, expm1,
    floor, gamma, lgamma, log, log1p, log2, logb, max, min, round, sin,
    sinh, sqrt, tan, tanh, trunc

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RandomFields/soil.Rd_%03d_medium.png", width=480, height=480)
> ### Name: soil
> ### Title: Soil data of North Bavaria, Germany
> ### Aliases: soil
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
> RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
> ##                   RFoptions(seed=NA) to make them all random again
> ## Don't show: 
> StartExample()
> ## End(Don't show)
> 
> 
> ################################################################
> ## ##
> ## a geostatistical analysis that demonstrates ##
> ## features of the package `RandomFields' ##
> ## ##
> ################################################################
> 
> 
> data(soil)
> str(soil)
'data.frame':	201 obs. of  9 variables:
 $ x.coord : num  -90 -60 -30 0 30 60 90 -120 -90 -60 ...
 $ y.coord : num  -150 -150 -150 -150 -150 -150 -150 -120 -120 -120 ...
 $ nr      : int  1 2 3 4 5 6 7 8 9 10 ...
 $ moisture: num  7.23 14.63 12.67 13.73 7.4 ...
 $ NO3.N   : num  3.77 7.83 11.49 14.48 5.67 ...
 $ Total.N : num  34.5 44.2 50.6 52.2 42.2 ...
 $ NH4.N   : num  4.46 5.14 7.28 6.45 3.45 ...
 $ DOC     : num  107 135 152 136 135 ...
 $ N20N    : num  0.000496 0.000404 0.00031 0.000461 0.000518 ...
> soil <- RFspatialPointsDataFrame(
+  coords = soil[ , c("x.coord", "y.coord")],
+  data = soil[ , c("moisture", "NO3.N", "Total.N", "NH4.N", "DOC", "N20N")],
+  RFparams=list(vdim=6, n=1)
+ )
> data <- soil["moisture"]
> ## Don't show: 
> if (RFoptions()$internal$examples_red) {
+   warning("data have been reduced !")
+   All <- 1:7 
+   data(soil)
+   soil <- RFspatialPointsDataFrame(
+      coords = soil[All, c("x.coord", "y.coord")],
+      data = soil[All, c("moisture", "NO3.N", "Total.N",
+       "NH4.N", "DOC", "N20N")],
+       RFparams=list(vdim=6, n=1)
+   )
+   data <- soil["moisture"]
+ }
Warning message:
data have been reduced ! 
> ## End(Don't show)
> 
> ## plot the data first
> colour <- rainbow(100)
> plot(data, col=colour)
> 
> 
> ## fit by eye
> gui.model <- RFgui(data) 
Warning message:
In RFgui(data) : 'RFgui' can be used only in an interactive mode
> ## Don't show: 
> #if (!interactive()) RFgui.model <- RMexp()
> ## End(Don't show) 
> 
> ## fit by ML
> model <- ~1 + RMwhittle(scale=NA, var=NA, nu=NA) + RMnugget(var=NA)
> (fit <- RFfit(model, data=data))
The modus_operandi='normal' is save, but slow. If you like the MLE running
faster (at the price of being less precise!) choose modus='easygoing' or
even modus='sloppy'.
.........++$

Internal variables:
       whittle.var  whittle.s whittle.nu   nugget.var  cst
value 5.169524e+00   3.144890  0.1000000 5.168337e+00 11.7
sd             Inf        Inf  0.2282568          Inf    -
lower 5.169524e-04   1.825742  0.1000000 5.169524e-04    -
upper 1.033905e+02 540.000000 10.0000000 1.033905e+02    -

User's variables:
      whittle.var whittle.s whittle.nu nugget.var  cst
value    5.169524   3.14489        0.1   5.168337 11.7
sd       9.342639       Inf        Inf   1.520771    -

      #variab loglikelihood           AIC 
      5.00000     -18.10833      46.21666 

Note that the (internal) fitted variable 'whittle.nu' is close to or on the effective lower boundary.
Hence the gradient of the likelihood function might not be zero and none of the
reported 'sd' values might be reliable. 

> plot(fit, method=c("ml", "plain", "sqrt.nr", "sd.inv"),
+      model = gui.model, col=1:8)
> 
> ## Kriging ...
> x <- seq(min(data@coords[, 1]), max(data@coords[, 1]), l=121)
> k <- RFinterpolate(fit, x=x, y=x, data=data)
> plot(x=k, col=colour)
Warning message:
In FUN(newX[, i], ...) : range of data is a single value
> plot(x=k, y=data, col=colour)
> 
> ## what is the probability that at no point of the
> ## grid given by x and y the moisture is greater than 24 percent?
> cs <- RFsimulate(model=fit, x=x, y=x, data=data, n=50)
number of simulations reduced
..
New output format of RFsimulate: S4 object of class 'RFsp';
for a bare, but faster array format use 'RFoptions(spConform=FALSE)'.
> plot(cs, col=colour)
> plot(cs, y=data, col=colour)
> Print(mean(apply(as.array(cs) <= 24, 3, all))) ## about 40 percent ...


mean(apply(as.array(cs) <= 24, 3, all)) [1] = 1
> 
> ## Don't show: 
> FinalizeExample()
> ## End(Don't show)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>