[Soiltexture-commits] r36 - in pkg: . soiltexture soiltexture/R soiltexture/inst/doc soiltexture/inst/doc2 soiltexture/man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Thu Aug 19 11:36:44 CEST 2010
Author: jmoeys
Date: 2010-08-19 11:36:43 +0200 (Thu, 19 Aug 2010)
New Revision: 36
Modified:
pkg/soiltexture/DESCRIPTION
pkg/soiltexture/R/soiltexture.R
pkg/soiltexture/inst/doc/soiltexture_vignette.pdf
pkg/soiltexture/inst/doc2/soiltexture_vignette.R
pkg/soiltexture/inst/doc2/soiltexture_vignette.Rnw
pkg/soiltexture/inst/doc2/soiltexture_vignette.tex
pkg/soiltexture/man/TT.text.transf.Xm.Rd
pkg/soiltexture/man/soiltexture-package.Rd
pkg/soiltexture_1.0.tar.gz
pkg/soiltexture_1.0.zip
Log:
Commit latest changes by Wei. Compiled package.
Modified: pkg/soiltexture/DESCRIPTION
===================================================================
--- pkg/soiltexture/DESCRIPTION 2010-08-16 16:08:15 UTC (rev 35)
+++ pkg/soiltexture/DESCRIPTION 2010-08-19 09:36:43 UTC (rev 36)
@@ -1,8 +1,8 @@
Package: soiltexture
Version: 1.0
-Date: 2010-08-16
+Date: 2010-08-19
Title: Functions for soil texture plot, classification and transformation
-Author: Julien MOEYS <jules_m78-soiltexture at yahoo.fr>
+Author: Julien MOEYS <jules_m78-soiltexture at yahoo.fr>, contributions from Wei Shangguan.
Maintainer: Julien MOEYS <jules_m78-soiltexture at yahoo.fr>
Depends: R (>= 2.11.1), sp, MASS, drc
Suggests: plotrix
Modified: pkg/soiltexture/R/soiltexture.R
===================================================================
--- pkg/soiltexture/R/soiltexture.R 2010-08-16 16:08:15 UTC (rev 35)
+++ pkg/soiltexture/R/soiltexture.R 2010-08-19 09:36:43 UTC (rev 36)
@@ -2617,21 +2617,21 @@
TT.text.transf.Xm <- function(# Transformations of a soil texture data table between 2 particle size systems (X classes), various methods.
### using various Particle Size Distribution (PSD) models including Anderson (AD), Fredlund4P (F4P), Fredlund3P (F3P),
### modified logistic growth (ML), Offset-Nonrenormalized Lognormal (ONL), Offset-Renormalized Lognormal (ORL),
-### Skaggs (S), van Genuchten type(VG),van Genuchten modified, Weibull (W), Logarithm(L),
-### Logistic growth (LG), Simple Lognormal (SL),Shiozawa and Compbell (SC).
+### Skaggs (S), van Genuchten type(VG),van Genuchten modified(VGM), Weibull (W), Logarithm(L),
+### Logistic growth (LG), Simple Lognormal (SL),Shiozawa and Compbell (SC).
### The performance of PSD models is influenced by many aspects like soil texture class,
### number and position (or closeness) of observation points, clay content etc.
-### The latter four PSD models perform worse than the former ten.
-### The AD, F4P, S, and W model is recommended for most of texture classes.
+### The latter four PSD models perform worse than the former ten.
+### The AD, F4P, S, and W model is recommended for most of texture classes.
### And it will be even better to compare several different PSD models and using the results of the model
### with the minimum residual sum of squares (or other measures).
### Sometimes, the fitting will failed for the iteration is not converged or some errors happened.
-### Transformation of a soil texture data table
-### ('tri.data') from one
-### particle size system ('dat.ps.lim') into another
-### ('base.ps.lim'). No limit in the number of texture classes
-### in the input and output texture tables. See TT.text.transf
-### for transformation involving only 3 particle classes. 'tri.data'
+### Transformation of a soil texture data table
+### ('tri.data') from one
+### particle size system ('dat.ps.lim') into another
+### ('base.ps.lim'). No limit in the number of texture classes
+### in the input and output texture tables. See TT.text.transf
+### for transformation involving only 3 particle classes. 'tri.data'
### can only contain texture data.
##author<<Wei Shangguan
@@ -2645,7 +2645,7 @@
psdmodel = "AD",
omethod = "all",#see optim for available methods, the default "all" is to run all methods and
# choose the best results with minimum residual sum of squares (RSS).
- tri.sum.norm = FALSE #Weather the sum of is
+ tri.sum.norm = FALSE #Weather the sum of is
){#
TT.auto.set( set.par = FALSE )
#
@@ -2691,7 +2691,7 @@
# dat.ps.lim2 <- TT.dia2phi(dat.ps.lim)
#
# old.col.nm <- colnames( tri.data )
-
+
#
fitpsd <- function(
y,
@@ -2699,7 +2699,7 @@
xout,
psdmodel,
method)
- {
+ {
require( "drc" ) # Added 2010/08/11 by JM
#
#default max and min of initial parameters
@@ -2803,6 +2803,8 @@
logi <- S
pn <- 2
pname <- c("u","c")
+ #S model can not deal with first texture data with zero value
+ if(y[1] == 0) y[1] <- 0.0001
}
else if ( psdmodel == "VG" )
{
@@ -2852,7 +2854,7 @@
pn <- 2
pname <- c("u","o")
}
- #default lower and upper limit for drc::drm, these values should not set
+ #default lower and upper limit for drc::drm, these values should not set
#at the beginning of the function for pn is set later
lowerl <- rep(10e-9,times=pn)
upperl <- rep(10e+5,times=pn)
@@ -2860,12 +2862,12 @@
spa <- c(1,1,1,1)
#methods used in optim() of drc::drm
meth <- c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN")
-
+
mdev <- 100
for( i in 1:5 ) # The nonlinear optimization runs were carried out using at least
- # five random initial parameter estimates for all soils.
+ # five random initial parameter estimates for all soils.
#When the final solution for each soil converged to different parameter values,
- #the parameter values with the best fitting statistics (RSS) were kept.
+ #the parameter values with the best fitting statistics (RSS) were kept.
{
if( method == "all" )# using all optim methods
{
@@ -2937,18 +2939,19 @@
#when the residual sum of error (deviance) is very small, the iteration is stopped to save time
if(mdev < 0.0001) break
}
- if( psdmodel == "AD" ) #predict() has some bug for AD model
+ #predict() has some bug for PSD model to predict the target values
+ if( psdmodel == "AD" )
{
pre <- coef(ttbest)[1] + coef(ttbest)[2]*atan(coef(ttbest)[3]*log10(xout/coef(ttbest)[4]))
- }
- else if( psdmodel == "F4P" ) #predict() has some bug for F4P model
+ }
+ else if( psdmodel == "F4P" )
{
pre <- (1-(log(1+coef(ttbest)[1]/xout)/log(1+coef(ttbest)[1]/0.0001))^7)/(log(exp(1)+(coef(ttbest)[2]/xout)^coef(ttbest)[3]))^coef(ttbest)[4]
}
else if( psdmodel == "F3P" )
{
pre <- (1-(log(1+0.001/xout)/log(1+0.001/0.0001))^7)/(log(exp(1)+(coef(ttbest)[1]/xout)^coef(ttbest)[2]))^coef(ttbest)[3]
- }
+ }
else if( psdmodel == "ML" )
{
pre <- 1/(1+coef(ttbest)[1]*exp(-coef(ttbest)[2]*xout^(coef(ttbest)[3])))
@@ -2956,46 +2959,46 @@
else if( psdmodel == "ONL" )
{
t <- (-1)^(log(xout) >= coef(ttbest)[1]+1)
- pre <- (1+t*erf((log(xout)+coef(ttbest)[1])/coef(ttbest)[2]*2^0.5))/2+(coef(ttbest)[3])
+ pre <- (1+t*erf((log(xout)+coef(ttbest)[1])/coef(ttbest)[2]*2^0.5))/2+(coef(ttbest)[3])
}
- else if( psdmodel == "ORL" ) #predict() has some bug for F4P model
+ else if( psdmodel == "ORL" )
{
t <- (-1)^(log(xout) >= coef(ttbest)[1]+1)
- pre <- (1-coef(ttbest)[3])*(1+t*erf((log(xout)+coef(ttbest)[1])/coef(ttbest)[2]*2^0.5))/2+coef(ttbest)[3]
+ pre <- (1-coef(ttbest)[3])*(1+t*erf((log(xout)+coef(ttbest)[1])/coef(ttbest)[2]*2^0.5))/2+coef(ttbest)[3]
}
- else if( psdmodel == "S" ) #predict() has some bug for F4P model
+ else if( psdmodel == "S" )
{
- pre <- 1/(1+(1/y[1]-1)*exp(-coef(ttbest)[1]*((xout-r0)/r0)^coef(ttbest)[2]))
+ pre <- 1/(1+(1/y[1]-1)*exp(-coef(ttbest)[1]*((xout-r0)/r0)^coef(ttbest)[2]))
}
- else if( psdmodel == "VG" ) #predict() has some bug for F4P model
+ else if( psdmodel == "VG" )
{
- pre <- 1(1+(coef(ttbest)[1]/xout)^coef(ttbest)[2])^(1/coef(ttbest)[2]-1)
+ pre <- (1+(coef(ttbest)[1]/xout)^coef(ttbest)[2])^(1/coef(ttbest)[2]-1)
}
- else if( psdmodel == "VGM" ) #predict() has some bug for F4P model
+ else if( psdmodel == "VGM" )
{
- pre <- y[1]+(1-y[1])*(1+(coef(ttbest)[1]*xout)^(-coef(ttbest)[2]))^(1/coef(ttbest)[2]-1)
+ pre <- y[1]+(1-y[1])*(1+(coef(ttbest)[1]*xout)^(-coef(ttbest)[2]))^(1/coef(ttbest)[2]-1)
}
- else if( psdmodel == "W" ) #predict() has some bug for F4P model
+ else if( psdmodel == "W" )
{
- pre <- coef(ttbest)[3]+(1-coef(ttbest)[3])*(1-exp(-coef(ttbest)[1]*((xout-dmin)/(dmax-dmin))^coef(ttbest)[2]))
+ pre <- coef(ttbest)[3]+(1-coef(ttbest)[3])*(1-exp(-coef(ttbest)[1]*((xout-dmin)/(dmax-dmin))^coef(ttbest)[2]))
}
- else if( psdmodel == "L" ) #predict() has some bug for F4P model
+ else if( psdmodel == "L" )
{
- pre <- coef(ttbest)[1]*log(xout)+coef(ttbest)[2]
+ pre <- coef(ttbest)[1]*log(xout)+coef(ttbest)[2]
}
- else if( psdmodel == "LG" ) #predict() has some bug for F4P model
+ else if( psdmodel == "LG" )
{
- pre <- 1/(1+coef(ttbest)[1]*exp(-coef(ttbest)[2]*xout))
+ pre <- 1/(1+coef(ttbest)[1]*exp(-coef(ttbest)[2]*xout))
}
- else if( psdmodel == "SC" ) #predict() has some bug for F4P model
+ else if( psdmodel == "SC" )
{
t <- (-1)^(log(xout) >= coef(ttbest)[1]+1)
- pre <- (1-coef(ttbest)[3])*(1+t*erf((log(xout)+coef(ttbest)[1])/coef(ttbest)[2]*2^0.5))/2+coef(ttbest)[3]*(1+t*erf((log(xout)+1.96)/1*2^0.5))/2
+ pre <- (1-coef(ttbest)[3])*(1+t*erf((log(xout)+coef(ttbest)[1])/coef(ttbest)[2]*2^0.5))/2+coef(ttbest)[3]*(1+t*erf((log(xout)+1.96)/1*2^0.5))/2
}
- else if( psdmodel == "SL" ) #predict() has some bug for F4P model
+ else if( psdmodel == "SL" )
{
t <- (-1)^(log(xout) >= coef(ttbest)[1]+1)
- pre <- (1+t*erf((log(xout)+coef(ttbest)[1])/coef(ttbest)[2]*2^0.5))/2
+ pre <- (1+t*erf((log(xout)+coef(ttbest)[1])/coef(ttbest)[2]*2^0.5))/2
}
#pre are the predicted values, coef(ttbest) are the model paremeters,
out <- c(pre[1],pre[2:length(pre)]-pre[1:length(pre)-1])*100
@@ -3003,17 +3006,22 @@
c(out,coef(ttbest),dev=mdev*10000)
}
- results <- t(apply(tri.data[1:dim(tri.data)[1],],
- 1,
- fitpsd,
+ results <- t(apply(
+ X = tri.data[1:dim(tri.data)[1],],
+
+ MARGIN = 1,
+ FUN = fitpsd,
xin = dat.ps.lim[ ps.start:ps.end ],
xout = base.ps.lim[ ps.start:length(base.ps.lim) ],
psdmodel= psdmodel,
method = omethod)
)
-# results <- t(apply(tri.data[1:5,],
-# 1,
-# fitpsd,
+
+
+# results <- t(apply(
+# X = tri.data[1:5,],
+# MARGIN = 1,
+# FUN = fitpsd,
# xin = dat.ps.lim[ ps.start:ps.end ],
# xout = base.ps.lim[ ps.start:length(base.ps.lim) ],
# psdmodel= psdmodel,
@@ -3024,28 +3032,31 @@
results
}
-# my.text4 <- data.frame(
-# "CLAY" = c(05,60,15,05,25,05,25,45,65,75,13,47),
-# "FSILT" = c(02,04,10,15,25,40,35,20,10,05,10,20),
-# "CSILT" = c(03,04,05,10,30,45,30,25,05,10,07,23),
-# "SAND" = c(90,32,70,70,20,10,10,10,20,10,70,10)
+# my.text4 <- data.frame(
+# "CLAY" = c(05,60,15,05,25,05,25,45,65,75,13,47),
+# "FSILT" = c(02,04,10,15,25,40,35,20,10,05,10,20),
+# "CSILT" = c(03,04,05,10,30,45,30,25,05,10,07,23),
+# "SAND" = c(90,32,70,70,20,10,10,10,20,10,70,10)
# ) #
-# TT.text.transf.Xm(
-# tri.data = my.text4,
-# base.ps.lim = c(0,2,20,50,2000),
-# dat.ps.lim = c(0,2,20,63,2000),
+# TT.text.transf.Xm(
+# tri.data = my.text4,
+# base.ps.lim = c(0,2,20,50,2000),
+# dat.ps.lim = c(0,2,20,63,2000),
# psdmodel = "S"
# ) #
# TT.text.transf.Xm( # JM: does not work on my PC
-# tri.data = my.text4,
-# base.ps.lim = c(0,1,50,2000),
+# tri.data = my.text4,
+# base.ps.lim = c(0,1,50,2000),
# dat.ps.lim = c(0,2,30,60,2000),
# psdmodel = "AD",
-# omethod = "Nelder-Mead"
-# )
+# omethod = "Nelder-Mead"
+# )
+
+
+
TT.deg2rad <- function(# Function to convert angle in degree to angle in radian.
### Function to convert angle in degree to angle in radian.
Modified: pkg/soiltexture/inst/doc/soiltexture_vignette.pdf
===================================================================
--- pkg/soiltexture/inst/doc/soiltexture_vignette.pdf 2010-08-16 16:08:15 UTC (rev 35)
+++ pkg/soiltexture/inst/doc/soiltexture_vignette.pdf 2010-08-19 09:36:43 UTC (rev 36)
@@ -387,8 +387,7 @@
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[TRUNCATED]
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svnlook diff /svnroot/soiltexture -r 36
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