Commit b8c26736 authored by agebhard's avatar agebhard

cleanup

parent 1e672c36
No preview for this file type
......@@ -30,3 +30,5 @@ library(baykrig)
leman.bk <- bk.grid(point = leman.pt, at = "cadpbm", prior=leman.prior,var.mod.obj = leman.sph, xsw=min(leman.bank$x),ysw=min(leman.bank$y), xne=max(leman.bank$x), yne=max(leman.bank$y), nx=5, ny=5, trend=1, rsearch = 10, extrap = F,border=leman.bank, duplicate="mean")
library(baykrig)
leman.bk <- bk.grid(point = leman.pt, at = "cadpbm", prior=leman.prior,var.mod.obj = leman.sph, xsw=min(leman.bank$x),ysw=min(leman.bank$y), xne=max(leman.bank$x), yne=max(leman.bank$y), nx=5, ny=5, trend=1, rsearch = 10, extrap = T,border=leman.bank, duplicate="mean")
ls()
q()
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R : Copyright 2001, The R Development Core Team
Version 1.2.2 (2001-02-26)
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.
Type `demo()' for some demos, `help()' for on-line help, or
`help.start()' for a HTML browser interface to help.
Type `q()' to quit R.
[Previously saved workspace restored]
> ls()
[1] "avex.prior" "avex12" "avex12.bk" "avex12.ev" "avex12.krige"
[6] "avex12.ok" "avex12.pr" "avex12.pt" "avex12.sph" "avex15"
[11] "avex16" "last.warning" "leman.78" "leman.83" "leman.88"
[16] "leman.bank" "leman.ev" "leman.pr" "leman.prior" "leman.pt"
[21] "leman.sph"
> library(baykrig)
Loading required package: sgeostat
Loading required package: mva
Loading required package: tripack
Warning messages:
1: Package `baykrig' found more than once,
using the one found in `/home/users/agebhard/R-Lib/alpha-dec-osf' in: library(baykrig)
2: Package `sgeostat' found more than once,
using the one found in `/home/users/agebhard/R-Lib/alpha-dec-osf' in: library(package, char = TRUE, logical = TRUE, warn.conflicts = warn.conflicts,
> glsfit
function (formula, x, covmat = NULL, method = "direct", duplicate = "error",
dupfun = NULL)
{
if (method != "direct" && method != "gqr" && method != "ols")
stop("method should be one of \"gqr\", \"ols\" or \"direct\"")
method <- switch(method, direct = 2, gqr = 1, ols = 0)
if (!is.data.frame(x))
stop("x is not a data frame!")
p <- dim(x)[2]
if (is.null(formula))
stop("formula not given!")
ft <- terms(formula)
vars <- attr(ft, "variables")
fmat <- model.matrix.default(formula, data = x)
namz <- as.character(vars[2])
ntrend <- dim(fmat)[2]
z <- x[, namz]
xy <- paste(x[, -match(namz, colnames(x))][, 1], x[, -match(namz,
colnames(x))][, 2], sep = ",")
idup <- match(xy, xy)
if (duplicate == "user" && !is.function(dupfun))
stop("duplicate=\"user\" requires dupfun to be set to a function")
if (duplicate != "error") {
centre <- function(x) {
switch(duplicate, mean = mean(x), median = median(x),
user = dupfun(x))
}
if (duplicate != "strip") {
z <- unlist(lapply(split(z, idup), centre))
ord <- !duplicated(xy)
fmat <- fmat[ord, ]
}
else {
ord <- (hist(idup, plot = F, freq = T, breaks = seq(0.5,
max(idup) + 0.5, 1))$counts == 1)
fmat <- fmat[ord, ]
z <- z[ord]
}
}
else if (any(duplicated(fmat)))
stop("duplicate data points")
n <- dim(fmat)[1]
if (is.null(n)) {
n <- length(fmat)
if (is.null(n))
stop("error: n not known!")
}
if (is.null(covmat) || method == 0)
covmat <- diag(rep(1, n))
ldc <- dim(covmat)[1]
cat(paste("ldc is ", ldc, "\n"))
lwork <- glsfit.workquery(n, ntrend, method)
cat(paste("optimales lwork:", lwork, "\n"))
ans <- .Fortran("glsfit", fmat = as.double(fmat), fmat2 = as.double(fmat),
n = as.integer(n), ntrend = as.integer(ntrend), ldf = as.integer(n),
y = as.double(z), covmat = as.double(covmat), ldc = as.integer(ldc),
beta = double(ntrend), errbeta = double(1), dev = double(n),
errdev = double(1), covbta = double(ntrend * ntrend),
ldcovbta = as.integer(ntrend), sgsqr = double(1), chlup = double(n *
n), ldchlup = as.integer(n), cminv = double(n * n),
ldcinv = as.integer(n), cwork = double(n * n), ldcwork = as.integer(n),
cwork2 = double(n * n), ldcwork2 = as.integer(n), forwarderr = double(n),
backwarderr = double(n), work = double(lwork), lwork = as.integer(lwork),
ipvt = integer(ntrend), ipiv = integer(n), iwork = integer(3 *
n), info = integer(1), method = as.integer(method))
tlab <- attr(ft, "term.labels")
if (attr(ft, "intercept") == 1)
tlab <- c("(Intercept)", tlab)
ret <- list(est = ans$beta, estrelerr = ans$errbeta, cov = matrix(ans$covbta,
ntrend, ntrend), residuals = ans$dev, resrelerr = ans$errdev,
data = x, formula = formula, call = match.call(), covmat = ans$covmat,
wsqr = ans$chlup, sigma.squared = ans$sgsqr)
names(ret$est) <- tlab
rownames(ret$cov) <- tlab
colnames(ret$cov) <- tlab
ret$predict <- z - ret$residuals
class(ret) <- "glsfit"
ret
}
> ls()
[1] "avex.prior" "avex12" "avex12.bk" "avex12.ev" "avex12.krige"
[6] "avex12.ok" "avex12.pr" "avex12.pt" "avex12.sph" "avex15"
[11] "avex16" "last.warning" "leman.78" "leman.83" "leman.88"
[16] "leman.bank" "leman.ev" "leman.pr" "leman.prior" "leman.pt"
[21] "leman.sph"
> glsfit(z~x+y+1,avex16)
Error in eval(expr, envir, enclos) : Object "z" not found
> str(avex16)
`data.frame': 16 obs. of 3 variables:
$ easting : num 2578562 2576998 2576268 2577428 2576325 ...
$ northing: num 5729964 5729756 5730764 5731521 5729924 ...
$ height : num 325 399 195 202 335 203 225 200 345 320 ...
> glsfit(height~easting+northing+1,avex16)
Error in glsfit(height ~ easting + northing + 1, avex16) :
duplicate data points
> glsfit(height~easting+northing+1,avex16,duplicate="mean")
ldc is 16
optimales lwork: 1024
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16,
duplicate = "mean")
Coefficients:
(Intercept) easting northing
1.382386e+05 6.197686e-03 -2.685778e-02
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 1.362458e+06 -1.300433e-01 -1.792534e-01
easting -1.300433e-01 7.040535e-08 -8.965151e-09
northing -1.792534e-01 -8.965151e-09 3.530710e-08
> lsfit(height~easting+northing+1,avex16)
Error in array(x, c(length(x), 1), if (!is.null(names(x))) list(names(x), :
dim<- : invalid first argument
> lm(height~easting+northing+1,avex16)
Call:
lm(formula = height ~ easting + northing + 1, data = avex16)
Coefficients:
(Intercept) easting northing
1.382e+05 6.197e-03 -2.686e-02
> dim(avex16)
[1] 16 3
> diag(1:16)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 1 0 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 2 0 0 0 0 0 0 0 0 0 0 0
[3,] 0 0 3 0 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 4 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 5 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 6 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 7 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 8 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 9 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0 10 0 0 0
[11,] 0 0 0 0 0 0 0 0 0 0 11 0 0
[12,] 0 0 0 0 0 0 0 0 0 0 0 12 0
[13,] 0 0 0 0 0 0 0 0 0 0 0 0 13
[14,] 0 0 0 0 0 0 0 0 0 0 0 0 0
[15,] 0 0 0 0 0 0 0 0 0 0 0 0 0
[16,] 0 0 0 0 0 0 0 0 0 0 0 0 0
[,14] [,15] [,16]
[1,] 0 0 0
[2,] 0 0 0
[3,] 0 0 0
[4,] 0 0 0
[5,] 0 0 0
[6,] 0 0 0
[7,] 0 0 0
[8,] 0 0 0
[9,] 0 0 0
[10,] 0 0 0
[11,] 0 0 0
[12,] 0 0 0
[13,] 0 0 0
[14,] 14 0 0
[15,] 0 15 0
[16,] 0 0 16
> lsfit(height~easting+northing+1,avex16,diag(1:16))
Error in array(x, c(length(x), 1), if (!is.null(names(x))) list(names(x), :
dim<- : invalid first argument
> glsfit(height~easting+northing+1,avex16,diag(1:16))
Error in glsfit(height ~ easting + northing + 1, avex16, diag(1:16)) :
duplicate data points
> glsfit(height~easting+northing+1,avex16,diag(1:16),duplicate="mean")
ldc is 16
optimales lwork: 1024
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16,
covmat = diag(1:16), duplicate = "mean")
Coefficients:
(Intercept) easting northing
2.300687e+05 6.430434e-03 -4.298753e-02
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 1.278846e+07 -1.126850e+00 -1.724791e+00
easting -1.126850e+00 3.167323e-07 5.418621e-08
northing -1.724791e+00 5.418621e-08 2.766062e-07
> glsfit(height~easting+northing+1,avex16,diag(c(1,1,1:14)),duplicate="mean")
ldc is 16
optimales lwork: 1024
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16,
covmat = diag(c(1, 1, 1:14)), duplicate = "mean")
Coefficients:
(Intercept) easting northing
2.597619e+05 1.482014e-02 -5.194324e-02
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 8.957013e+06 -7.433641e-01 -1.228701e+00
easting -7.433641e-01 2.391458e-07 2.217088e-08
northing -1.228701e+00 2.217088e-08 2.044379e-07
> glsfit(height~easting+northing+1,avex16,diag(c(1,1,1:12,1,1)),duplicate="mean")
ldc is 16
optimales lwork: 1024
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16,
covmat = diag(c(1, 1, 1:12, 1, 1)), duplicate = "mean")
Coefficients:
(Intercept) easting northing
1.916157e+05 -4.768449e-04 -3.317093e-02
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 7.917424e+06 -9.578960e-01 -9.507933e-01
easting -9.578960e-01 1.883042e-07 8.247598e-08
northing -9.507933e-01 8.247598e-08 1.288169e-07
> glsfit(height~easting+northing+1,avex16,duplicate="mean")
ldc is 16
optimales lwork: 1024
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16,
duplicate = "mean")
Coefficients:
(Intercept) easting northing
1.382386e+05 6.197686e-03 -2.685778e-02
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 1.362458e+06 -1.300433e-01 -1.792534e-01
easting -1.300433e-01 7.040535e-08 -8.965151e-09
northing -1.792534e-01 -8.965151e-09 3.530710e-08
> avex16
easting northing height
1 2578562 5729964 325
2 2576998 5729756 399
3 2576268 5730764 195
4 2577428 5731521 202
5 2576325 5729924 335
6 2577517 5731601 203
7 2577129 5731402 225
8 2577024 5732022 200
9 2578041 5730688 345
10 2576066 5730892 320
11 2576025 5731557 260
12 2576283 5729907 344
13 2576191 5730201 350
14 2578775 5735099 258
15 2575650 5732450 260
16 2575580 5732955 249
> glsfit(height~easting+northing+1,avex16,diag(c(rep(1,11),5,5,1,1,1)),duplicate="mean")
ldc is 16
optimales lwork: 1024
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16,
covmat = diag(c(rep(1, 11), 5, 5, 1, 1, 1)), duplicate = "mean")
Coefficients:
(Intercept) easting northing
1.149772e+05 8.811376e-03 -2.397510e-02
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 1.550463e+06 -1.503122e-01 -2.029367e-01
easting -1.503122e-01 7.266389e-08 -6.444810e-09
northing -2.029367e-01 -6.444810e-09 3.830537e-08
> glsfit(height~easting+northing+1,avex16,diag(c(rep(1,11),5,5,1,1,1)),duplicate="mean")
ldc is 16
optimales lwork: 1024
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16,
covmat = diag(c(rep(1, 11), 5, 5, 1, 1, 1)), duplicate = "mean")
Coefficients:
(Intercept) easting northing
1.149772e+05 8.811376e-03 -2.397510e-02
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 1.550463e+06 -1.503122e-01 -2.029367e-01
easting -1.503122e-01 7.266389e-08 -6.444810e-09
northing -2.029367e-01 -6.444810e-09 3.830537e-08
> glsfit(height~easting+northing+1,avex16,duplicate="mean")
ldc is 16
optimales lwork: 1024
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16,
duplicate = "mean")
Coefficients:
(Intercept) easting northing
1.382386e+05 6.197686e-03 -2.685778e-02
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 1.362458e+06 -1.300433e-01 -1.792534e-01
easting -1.300433e-01 7.040535e-08 -8.965151e-09
northing -1.792534e-01 -8.965151e-09 3.530710e-08
> glsfit(height~easting+northing+1,avex16,diag(c(rep(1,11),0.1,0.1,1,1,1)),duplicate="mean")
ldc is 16
optimales lwork: 1024
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16,
covmat = diag(c(rep(1, 11), 0.1, 0.1, 1, 1, 1)), duplicate = "mean")
Coefficients:
(Intercept) easting northing
1.987134e+05 -1.370552e-03 -3.400442e-02
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 8.623378e+05 -7.710823e-02 -1.158101e-01
easting -7.710823e-02 6.399441e-08 -1.531688e-08
northing -1.158101e-01 -1.531688e-08 2.709551e-08
> glsfit(height~easting+northing+1,avex16,diag(c(rep(1,11),0.01,0.01,1,1,1)),duplicate="mean")
ldc is 16
optimales lwork: 1024
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16,
covmat = diag(c(rep(1, 11), 0.01, 0.01, 1, 1, 1)), duplicate = "mean")
Coefficients:
(Intercept) easting northing
2.081144e+05 -1.096066e-02 -3.133160e-02
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 6.596593e+05 -6.611294e-02 -8.539635e-02
easting -6.611294e-02 5.711190e-08 -1.413987e-08
northing -8.539635e-02 -1.413987e-08 2.126031e-08
> glsfit(height~easting+northing+1,avex16[-(12,13),],duplicate="mean")
Error: syntax error
> glsfit(height~easting+northing+1,avex16[-c(12,13),],duplicate="mean")
ldc is 14
optimales lwork: 896
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16[-c(12,
13), ], duplicate = "mean")
Coefficients:
(Intercept) easting northing
1.070870e+05 9.689835e-03 -2.299367e-02
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 1.614101e+06 -1.571834e-01 -2.109486e-01
easting -1.571834e-01 7.342416e-08 -5.588004e-09
northing -2.109486e-01 -5.588004e-09 3.931778e-08
> glsfit(height~easting+northing+1,avex16,diag(c(rep(1,11),0.0001,0.0001,1,1,1)),duplicate="mean")
ldc is 16
optimales lwork: 1024
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16,
covmat = diag(c(rep(1, 11), 1e-04, 1e-04, 1, 1, 1)), duplicate = "mean")
Coefficients:
(Intercept) easting northing
1.312980e+05 -5.280224e-02 8.865604e-04
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 5.309554e+05 -1.164427e-01 -4.030873e-02
easting -1.164427e-01 2.925997e-08 7.166095e-09
northing -4.030873e-02 7.166095e-09 3.812732e-09
> glsfit(height~easting+northing+1,avex16,diag(c(rep(1,11),0.00000001,0.0000001,1,1,1)),duplicate="mean")
ldc is 16
optimales lwork: 1024
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16,
covmat = diag(c(rep(1, 11), 1e-08, 1e-07, 1, 1, 1)), duplicate = "mean")
Coefficients:
(Intercept) easting northing
1.247637e+05 -5.523639e-02 3.121180e-03
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 5.196318e+05 -1.189250e-01 -3.721657e-02
easting -1.189250e-01 2.721982e-08 8.516544e-09
northing -3.721657e-02 8.516544e-09 2.665931e-09
> glsfit(height~easting+northing+1,avex16,diag(c(rep(1,11),10,10,1,1,1)),duplicate="mean")
ldc is 16
optimales lwork: 1024
Call:
glsfit(formula = height ~ easting + northing + 1, x = avex16,
covmat = diag(c(rep(1, 11), 10, 10, 1, 1, 1)), duplicate = "mean")
Coefficients:
(Intercept) easting northing
1.111677e+05 9.235898e-03 -2.350143e-02
Covariance of Estimation:
(Intercept) easting northing
(Intercept) 1.581194e+06 -1.536298e-01 -2.068059e-01
easting -1.536298e-01 7.303123e-08 -6.031235e-09
northing -2.068059e-01 -6.031235e-09 3.879438e-08
> model.matrix(height~easting+northing+1,avex16)
(Intercept) easting northing
1 1 2578562 5729964
2 1 2576998 5729756
3 1 2576268 5730764
4 1 2577428 5731521
5 1 2576325 5729924
6 1 2577517 5731601
7 1 2577129 5731402
8 1 2577024 5732022
9 1 2578041 5730688
10 1 2576066 5730892
11 1 2576025 5731557
12 1 2576283 5729907
13 1 2576191 5730201
14 1 2578775 5735099
15 1 2575650 5732450
16 1 2575580 5732955
attr(,"assign")
[1] 0 1 2
> t(model.matrix(height~easting+northing+1,avex16))%*%(diag(c(rep(1, 11), 10, 10, 1, 1, 1))^-1)%*%model.matrix(height~easting+northing+1,avex16)
(Intercept) easting northing
(Intercept) Inf Inf Inf
easting Inf Inf Inf
northing Inf Inf Inf
> t(model.matrix(height~easting+northing+1,avex16))%*%(diag(c(rep(1, 11), 10, 10, 1, 1, 1)^-1))%*%model.matrix(height~easting+northing+1,avex16)
(Intercept) easting northing
(Intercept) 14.2 3.659264e+07 8.138661e+07
easting 36592635.4 9.429726e+13 2.097289e+14
northing 81386605.8 2.097289e+14 4.664634e+14
> solve(t(model.matrix(height~easting+northing+1,avex16))%*%(diag(c(rep(1, 11), 10, 10, 1, 1, 1)^-1))%*%model.matrix(height~easting+northing+1,avex16))
Error in solve.default(t(model.matrix(height ~ easting + northing + 1, :
singular matrix `a' in solve
> solve(t(model.matrix(height~easting+northing+1,avex16))%*%(diag(c(rep(1, 11), 0.1, 0.1, 1, 1, 1)^-1))%*%model.matrix(height~easting+northing+1,avex16))
Error in solve.default(t(model.matrix(height ~ easting + northing + 1, :
singular matrix `a' in solve
> solve(t(model.matrix(height~easting+northing+1,avex16))%*%(diag(c(rep(1, 11), 1, 1, 1, 1, 1)^-1))%*%model.matrix(height~easting+northing+1,avex16))
Error in solve.default(t(model.matrix(height ~ easting + northing + 1, :
singular matrix `a' in solve
> solve(t(model.matrix(height~easting+northing+1,avex16))%*%model.matrix(height~easting+northing+1,avex16))
Error in solve.default(t(model.matrix(height ~ easting + northing + 1, :
singular matrix `a' in solve
> eigen(t(model.matrix(height~easting+northing+1,avex16))%*%model.matrix(height~easting+northing+1,avex16))
$values
[1] 6.318076e+14 1.434382e+07 7.339676e-07
$vectors
northing easting (Intercept)
(Intercept) 1.591357e-07 -3.310172e-08 1.000000e+00
easting 4.100714e-01 -9.120534e-01 -9.544753e-08
northing 9.120534e-01 4.100714e-01 -1.315662e-07
>
\ No newline at end of file
R : Copyright 2001, The R Development Core Team
Version 1.2.2 (2001-02-26)
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.
Type `demo()' for some demos, `help()' for on-line help, or
`help.start()' for a HTML browser interface to help.
Type `q()' to quit R.
[Previously saved workspace restored]
>
> ls()
[1] "avex.prior" "avex12" "avex12.bk" "avex12.ev" "avex12.ok"
[6] "avex12.pr" "avex12.pt" "avex12.sph" "avex15" "avex16"
[11] "last.warning"
> avex.prior
$formula
height ~ 1
$variables
list(height)
$ntr
[1] 1
$n
[1] 2
$mu
$mu[[1]]
(Intercept)
302.0066
$mu[[2]]
(Intercept)
317.046
$phi
$phi[[1]]
(Intercept)
(Intercept) 490.3698
$phi[[2]]
(Intercept)
(Intercept) 602.7327
$phiinv
$phiinv[[1]]
[,1]
[1,] 0.002039277
$phiinv[[2]]
[,1]
[1,] 0.00165911
$lon
[1] 2576866 2577858
$lat
[1] 5731294 5734192
$type
[1] "empirical" "empirical"
$info
[1] 16 15
$call
$call[[1]]
empirical.prior(x = avex16, formula = height ~ 1, var.mod = avex12.sph,
namx = "easting", namy = "northing", duplicate = "mean")
$call[[2]]
empirical.prior(x = avex15, formula = height ~ 1, var.mod = avex12.sph,
prior = avex.prior, namx = "easting", namy = "northing",
duplicate = "mean")
attr(,"class")
[1] "bk.prior"
> library(baykrig)
Loading required package: sgeostat
Loading required package: mva
Loading required package: tripack
Warning messages:
1: Package `sgeostat' found more than once,
using the one found in `/home/users/agebhard/R-Lib/i386-unknown-linux' in: library(package, char = TRUE, logical = TRUE, warn.conflicts = warn.conflicts,
2: Package `tripack' found more than once,
using the one found in `/home/users/agebhard/R-Lib/i386-unknown-linux' in: library(package, char = TRUE, logical = TRUE, warn.conflicts = warn.conflicts,
> avex.prior
Prior Knowledge Object used for Bayesian Kriging
Number of priors: 2
prior 1
type is empirical
based on a additional dataset of size 16 15
prior guess for regression parameter:
(Intercept)
302.0066
prior covariance for regression parameter:
(Intercept)
(Intercept) 490.3698
prior 2
type is empirical
based on a additional dataset of size 16 15
prior guess for regression parameter:
(Intercept)
317.046
prior covariance for regression parameter:
(Intercept)
(Intercept) 602.7327
> sqrt(avex.prior$phi)
Error in sqrt(avex.prior$phi) : Non-numeric argument to mathematical function
> sqrt(avex.prior$phi[[1]])
(Intercept)