BCE-package: The Bayesian Compositional Estimator Package

Description Details Author(s) References See Also Examples

Description

Functions that estimate sample (taxonomic) composition from biomarker data

bce1 estimates probability distributions of a sample composition based on an input ratio matrix, A, containing biomarker ratios in (field) samples, and an input data matrix, B, containing the biomarker ratios for several taxonomic groups

tlsce estimates the total least squares solution

This version differs from the previous version <2 in that a different MCMC method is used that shows better convergence properties and has a more sound underlying statistical model.

Details

Package: BCE
Type: Package
License: GNU Public License 2 or above

Author(s)

Karel Van den Meersche (Maintainer)

Karline Soetaert

References

Van den Meersche, K., K. Soetaert and J.J. Middelburg (2008) A Bayesian compositional estimator for microbial taxonomy based on biomarkers, Limnology and Oceanography Methods 6, 190-199

See Also

bce the new function, with better convergence properties

BCE the original function (versions < 2)

tlsce total least squares compositional estimator

Examples

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9
## Not run: 
## show examples (see respective help pages for details)
example(bce)
example(tlsce)

## show package vignette
browseURL(paste(system.file(package = "BCE"), "/doc", sep = ""))

## End(Not run)

Example output

Loading required package: FME
Loading required package: deSolve
Loading required package: rootSolve
Loading required package: coda
Loading required package: limSolve
Loading required package: Matrix

bce> ##====================================
bce> 
bce> # example using bceInput data
bce> # !!! should be weighted to correspond better to example of BCE!!!
bce> A <- t(bceInput$Rat)

bce> B <- t(bceInput$Dat)

bce> result <- bce1(A,B,niter=1000)
number of accepted runs: 0 out of 1000 (0%) 

bce> ## the number of accepted runs is zero;
bce> ## try different starting values
bce> 
bce> result <- bce1(A,B,niter=1000,initX=matrix(1/ncol(A),ncol(A),ncol(B)))
number of accepted runs: 1 out of 1000 (0.1%) 

bce> ## number of accepted runs is still low;
bce> ## smaller jumps
bce> 
bce> result <- bce1(A,B,niter=1000,initX=matrix(1/ncol(A),ncol(A),ncol(B)),jmpA=.01,jmpX=.01)
number of accepted runs: 597 out of 1000 (59.7%) 

bce> Sum <-summary(result)

bce> ## did the algorithm converge?
bce> plot(result$SS,type="l")

bce> ## no
bce> 
bce> 
bce> ## more runs, using the output of previous run as input.
bce> result <- bce1(A,B,niter=1e4,jmpA=.01,jmpX=.01,updatecov=1e3,
bce+                initX=Sum$lastX,initA=Sum$lastA,
bce+                jmpCovar=Sum$covar*2.4^2/ncol(result$pars),
bce+                )
number of accepted runs: 2395 out of 10000 (23.95%) 

bce> Sum <-summary(result)

bce> ## we inspect the output:
bce> plot(result$SS,type="l")

bce> plot(result,ask=TRUE)

bce> ## looks already pretty good; to get a better result, repeat one more
bce> ## time with a  longer run. Uncomment the following paragraph and run.
bce> ## go get some coffee, this might take a while (~30s).
bce> 
bce> ## result <- bce1(A,B,niter=1e5,jmpA=.01,jmpX=.01,updatecov=1e3,
bce> ##                outputlength=1e3,burninlength=.35e5,
bce> ##                initX=Sum$lastX,initA=Sum$lastA,
bce> ##                jmpCovar=Sum$covar*2.4^2/ncol(result$pars),
bce> ##                )
bce> ## Sum <-summary(result)
bce> ## plot(result$SS,type="l")
bce> ## plot(result,ask=TRUE)
bce> 
bce> # show results as mean with ranges
bce> print(Sum$meanX)
            sample1   sample2   sample3   sample4   sample5
species1 0.03445203 0.3201708 0.2882386 0.2172723 0.1383430
species2 0.06336986 0.1148435 0.1437103 0.4412226 0.3413541
species3 0.13719536 0.1077509 0.1022851 0.1020856 0.1252964
species4 0.69078933 0.2121559 0.2494796 0.1147912 0.1177304
species5 0.07419342 0.2450790 0.2162863 0.1246284 0.2772760

bce> # plot estimated means and ranges (lbX=lower, ubX=upper bound)
bce> xlim <- range(c(Sum$lbX,Sum$ubX))

bce> # first the mean
bce> dotchart(x=t(Sum$meanX),xlim=xlim,                                                          
bce+          main="Taxonomic composition",
bce+          sub="using bce",pch=16)

bce> # then ranges
bce> nr <- nrow(Sum$meanX)

bce> nc <- ncol(Sum$meanX)

bce> for (i in 1:nr) 
bce+   {ip <-(nr-i)*(nc+2)+1
bce+    cc <- ip : (ip+nc-1)
bce+    segments(t(Sum$lbX[i,]),cc,t(Sum$ubX[i,]),cc)
bce+   }

tlsce> A <- t(bceInput$Rat)

tlsce> B <- t(bceInput$Dat)

tlsce> tlsce(A,B)
$X
           sample1    sample2   sample3   sample4   sample5
species1 0.0000000 0.36739459 0.5247678 0.2243613 0.1940198
species2 0.0000000 0.00000000 0.0000000 0.3019997 0.0000000
species3 0.0000000 0.02169081 0.0000000 0.0000000 0.0000000
species4 0.7826177 0.34311315 0.2246568 0.1753205 0.2943653
species5 0.2173823 0.26780144 0.2505754 0.2983185 0.5116149

$A_fit
            species1   species2   species3   species4  species5
biomarker1 0.0000000 0.00000000 0.00000000 0.00000000 0.3120027
biomarker2 0.0000000 0.00000000 0.00000000 0.00000000 0.2450860
biomarker3 0.4213464 0.23308917 2.52898327 1.21352173 0.3044179
biomarker4 0.1900306 0.09612558 0.24963488 0.40726891 1.1221068
biomarker5 1.4678069 0.34531022 0.06323953 0.07593617 0.0000000
biomarker6 0.0000000 0.57903990 0.03360601 0.18730850 0.0000000
biomarker7 0.0000000 0.00000000 0.00000000 1.32118872 0.0000000
biomarker8 0.0000000 0.00000000 0.00000000 0.23776652 0.0000000

$B_fit
              sample1    sample2    sample3    sample4    sample5
biomarker1 0.06782386 0.08355477 0.07818019 0.09307617 0.15962521
biomarker2 0.05327735 0.06563437 0.06141251 0.07311367 0.12538962
biomarker3 1.01589865 0.70755492 0.57001456 0.46849537 0.59471300
biomarker4 0.56266201 0.51547212 0.47239000 0.47781320 0.73084207
biomarker5 0.05942899 0.56669072 0.78731742 0.44691581 0.30713655
biomarker6 0.14659095 0.06499695 0.04208012 0.20770892 0.05513713
biomarker7 1.03398570 0.45331723 0.29681399 0.23163141 0.38891218
biomarker8 0.18608029 0.08158082 0.05341586 0.04168534 0.06999023

$SS
    total         A         B 
0.5934600 0.2887776 0.3046824 

$fit
$par
 [1] 0.42134644 0.19003056 1.46780686 0.23308917 0.09612558 0.34531022
 [7] 0.57903990 2.52898327 0.24963488 0.06323953 0.03360601 1.21352173
[13] 0.40726891 0.07593617 0.18730850 1.32118872 0.23776652 0.31200268
[19] 0.24508595 0.30441787 1.12210682

$hessian
               [,1]          [,2]         [,3]          [,4]          [,5]
 [1,]  2.561320e+00  0.1651321911  0.083430047  8.936733e-02 -1.167770e-03
 [2,]  1.651322e-01  2.6734164410  0.367058559 -1.167759e-03  4.609390e-02
 [3,]  8.343005e-02  0.3670585587  2.262195471 -6.309703e-03  2.078047e-02
 [4,]  8.936733e-02 -0.0011677592 -0.006309703  2.120345e+00 -8.354491e-04
 [5,] -1.167770e-03  0.0460939005  0.020780466 -8.354491e-04  2.072268e+00
 [6,] -6.309708e-03  0.0207804673  0.016861134 -8.185894e-03  3.223670e-02
 [7,]  1.663401e-02  0.0473794641  0.034004738  2.312236e-02  7.394069e-02
 [8,]  1.452505e-03  0.0031272196  0.002310961 -3.421719e-10 -2.318084e-10
 [9,]  3.127215e-03  0.0094383581  0.005101986 -4.235526e-10 -1.360328e-09
[10,]  2.310956e-03  0.0051019839  0.003691620 -1.821776e-09 -1.756700e-09
[11,] -5.495352e-05  0.0003715646  0.000308663  1.549790e-09  5.377116e-10
[12,]  3.365259e-01  0.1066787931  0.057628311  6.984040e-02 -8.157381e-04
[13,]  1.066788e-01  0.4212162464  0.229701577 -8.157446e-04  3.736230e-02
[14,]  5.762830e-02  0.2297015384  0.167168203 -4.890153e-03  1.679883e-02
[15,] -1.214199e-02  0.0362745288  0.030180390  1.309440e-02  3.835931e-02
[16,] -1.517868e-01  0.1010860325  0.096748129 -4.720922e-02  6.554996e-03
[17,] -2.833738e-02  0.0141062084  0.015348026 -8.560917e-03  2.779164e-04
[18,]  5.504858e-02 -0.0989258599  0.087271571  5.948149e-03 -3.391825e-02
[19,]  4.288875e-02 -0.0789948718  0.067878210  4.656852e-03 -2.685975e-02
[20,]  4.490367e-01  0.1072539319  0.051199095  1.188328e-01 -1.456682e-03
[21,]  1.072540e-01  0.4657675830  0.253006200 -1.456690e-03  6.262120e-02
               [,6]          [,7]          [,8]          [,9]         [,10]
 [1,] -6.309708e-03  1.663401e-02  1.452505e-03  3.127215e-03  2.310956e-03
 [2,]  2.078047e-02  4.737946e-02  3.127220e-03  9.438358e-03  5.101984e-03
 [3,]  1.686113e-02  3.400474e-02  2.310961e-03  5.101986e-03  3.691620e-03
 [4,] -8.185894e-03  2.312236e-02 -3.421719e-10 -4.235526e-10 -1.821776e-09
 [5,]  3.223670e-02  7.394069e-02 -2.318084e-10 -1.360328e-09 -1.756700e-09
 [6,]  2.024475e+00  5.001313e-02  3.692749e-11 -2.917271e-10 -9.048561e-10
 [7,]  5.001313e-02  2.116462e+00 -2.620160e-10 -1.389465e-09 -2.927940e-09
 [8,]  3.692749e-11 -2.620160e-10  2.001084e+00  2.915374e-03  1.747592e-03
 [9,] -2.917271e-10 -1.389465e-09  2.915374e-03  2.008027e+00  4.708400e-03
[10,] -9.048561e-10 -2.927940e-09  1.747592e-03  4.708400e-03  2.002818e+00
[11,]  3.859625e-10  1.354197e-09 -1.170485e-04 -2.893648e-04 -1.654466e-04
[12,] -4.890181e-03  1.309439e-02  7.136634e-04  1.162089e-03  1.120730e-03
[13,]  1.679881e-02  3.835930e-02  1.162094e-03  4.004503e-03  1.926795e-03
[14,]  1.340950e-02  2.712939e-02  1.120733e-03  1.926811e-03  1.773209e-03
[15,]  2.712940e-02  6.306976e-02  2.195990e-05  5.474539e-04  4.065276e-04
[16,]  1.018835e-02  5.464998e-03  5.896188e-04  7.111406e-04  8.742117e-04
[17,]  1.457319e-03  8.683504e-05 -1.723947e-04 -6.338485e-04 -2.921551e-04
[18,]  1.193460e-02  1.956902e-02  9.305444e-04 -1.374096e-03  1.341500e-03
[19,]  9.284769e-03  1.515706e-02  7.236247e-04 -1.099468e-03  1.041933e-03
[20,] -8.349545e-03  2.221264e-02  1.121241e-03  2.450394e-03  1.785338e-03
[21,]  2.818663e-02  6.432303e-02  2.450406e-03  7.347314e-03  3.994769e-03
              [,11]         [,12]         [,13]         [,14]         [,15]
 [1,] -5.495352e-05  3.365259e-01  0.1066788261  0.0576282989 -0.0121419890
 [2,]  3.715646e-04  1.066788e-01  0.4212162464  0.2297015384  0.0362745288
 [3,]  3.086630e-04  5.762831e-02  0.2297015772  0.1671682034  0.0301803903
 [4,]  1.549790e-09  6.984040e-02 -0.0008157446 -0.0048901530  0.0130943974
 [5,]  5.377116e-10 -8.157381e-04  0.0373623037  0.0167988335  0.0383593129
 [6,]  3.859625e-10 -4.890181e-03  0.0167988066  0.0134094981  0.0271294048
 [7,]  1.354197e-09  1.309439e-02  0.0383592992  0.0271293924  0.0630697577
 [8,] -1.170485e-04  7.136634e-04  0.0011620944  0.0011207326  0.0000219599
 [9,] -2.893648e-04  1.162089e-03  0.0040045026  0.0019268112  0.0005474539
[10,] -1.654466e-04  1.120730e-03  0.0019267953  0.0017732093  0.0004065276
[11,]  2.000939e+00  2.197657e-05  0.0005474976  0.0004065327  0.0146427522
[12,]  2.197657e-05  3.205328e+00  0.3751095006  0.0332317258 -0.0726950096
[13,]  5.474976e-04  3.751095e-01  3.5198627231  0.2392290541  0.0882242857
[14,]  4.065327e-04  3.323173e-02  0.2392290541  3.3766277138  0.0326909387
[15,]  1.464275e-02 -7.269501e-02  0.0882242857  0.0326909387  3.7779847263
[16,] -1.701446e-03 -5.465976e-01  0.4575240886  0.0452865846 -0.1437937030
[17,] -2.744455e-04 -8.870060e-02  0.1126848076  0.0158285431 -0.0171838973
[18,]  1.454123e-04  7.544427e-02 -0.1161887163  0.0807629047  0.0314516578
[19,]  1.150617e-04  5.924766e-02 -0.0917223295  0.0628000323  0.0236810617
[20,] -4.718635e-05  6.366439e-01  0.1321963946  0.0402400951 -0.0241578851
[21,]  2.513351e-04  1.321964e-01  0.6230151416  0.2308528266  0.0700098332
              [,16]         [,17]         [,18]         [,19]         [,20]
 [1,] -0.1517868396 -2.833738e-02  0.0550485776  0.0428887513  4.490367e-01
 [2,]  0.1010860325  1.410621e-02 -0.0989258599 -0.0789948718  1.072539e-01
 [3,]  0.0967481290  1.534803e-02  0.0872715715  0.0678782102  5.119909e-02
 [4,] -0.0472092230 -8.560917e-03  0.0059481490  0.0046568522  1.188328e-01
 [5,]  0.0065549964  2.779164e-04 -0.0339182491 -0.0268597469 -1.456682e-03
 [6,]  0.0101883465  1.457319e-03  0.0119346025  0.0092847687 -8.349545e-03
 [7,]  0.0054649982  8.683504e-05  0.0195690153  0.0151570610  2.221264e-02
 [8,]  0.0005896188 -1.723947e-04  0.0009305444  0.0007236247  1.121241e-03
 [9,]  0.0007111406 -6.338485e-04 -0.0013740959 -0.0010994685  2.450394e-03
[10,]  0.0008742117 -2.921551e-04  0.0013414996  0.0010419331  1.785338e-03
[11,] -0.0017014456 -2.744455e-04  0.0001454123  0.0001150617 -4.718635e-05
[12,] -0.5465975911 -8.870060e-02  0.0754442681  0.0592476605  6.366439e-01
[13,]  0.4575240886  1.126848e-01 -0.1161887163 -0.0917223295  1.321964e-01
[14,]  0.0452865846  1.582854e-02  0.0807629047  0.0628000323  4.024010e-02
[15,] -0.1437937030 -1.718390e-02  0.0314516578  0.0236810617 -2.415789e-02
[16,]  2.7520406176 -1.831918e-01  0.1009046818  0.0794318533 -3.246192e-01
[17,] -0.1831918177  3.774486e+00  0.0199721401  0.0153455017 -6.672246e-02
[18,]  0.1009046818  1.997214e-02  3.0212934117 -0.0353009674  7.184994e-02
[19,]  0.0794318533  1.534550e-02 -0.0353009674  3.0366933945  5.683182e-02
[20,] -0.3246191787 -6.672246e-02  0.0718499399  0.0568318236  2.701141e+00
[21,]  0.1771645036  7.285593e-03 -0.1437438118 -0.1109389198  1.931741e-01
              [,21]
 [1,]  0.1072539897
 [2,]  0.4657675830
 [3,]  0.2530061998
 [4,] -0.0014566900
 [5,]  0.0626211962
 [6,]  0.0281866293
 [7,]  0.0643230311
 [8,]  0.0024504055
 [9,]  0.0073473136
[10,]  0.0039947687
[11,]  0.0002513351
[12,]  0.1321964186
[13,]  0.6230151416
[14,]  0.2308528266
[15,]  0.0700098332
[16,]  0.1771645036
[17,]  0.0072855934
[18,] -0.1437438118
[19,] -0.1109389198
[20,]  0.1931741402
[21,]  2.8048510810

$residuals
 [1] -0.0303464435 -0.1200305597 -0.0638068633 -0.0010891675 -0.0151255825
 [6] -0.0063102169 -0.0150398951 -0.0008933799 -0.0024438696 -0.0014417753
[11]  0.0001018530 -0.1435217263 -0.4012689050 -0.0639361728  0.0026914986
[16] -0.0931887234 -0.1157665218  0.0419973236  0.0249140457 -0.0694178661
[21] -0.2321068215 -0.0347402472 -0.0236457314 -0.1443577612 -0.3668251719
[26] -0.0046735702  0.0824883868 -0.1070399456 -0.0895607348  0.0081778815
[31]  0.0003077380 -0.0411887970 -0.1126685807 -0.0664751074  0.0046954445
[36] -0.0240697048 -0.0189216917  0.0182723589  0.0107212650 -0.0106306047
[41] -0.0621722138 -0.0329590821 -0.0178277119 -0.0119417745 -0.0203168612
[46]  0.0329903321  0.0216115272 -0.0036076330 -0.0500838007 -0.0208953463
[51] -0.0498018145  0.0041636036 -0.0484434190  0.0643871128  0.0407335329
[56] -0.0454986417 -0.1792108363 -0.0896888480 -0.1723760956  0.0026687933
[61] -0.0887399339

$info
[1] 1

$message
[1] "Relative error in the sum of squares is at most `ftol'."

$iterations
[1] 12

$rsstrace
 [1] 1.1852456 0.7757644 0.6903279 0.6154719 0.5971540 0.5940798 0.5935597
 [8] 0.5934757 0.5934624 0.5934604 0.5934600 0.5934600 0.5934600

$ssr
[1] 0.59346

$diag
     diag1      diag2      diag3      diag4      diag5      diag6      diag7 
0.47681962 0.23752279 1.56105207 0.24125487 0.09932342 0.34741621 0.59565976 
     diag8      diag9     diag10     diag11     diag12     diag13     diag14 
2.54648475 0.27284062 0.06512500 0.03468434 1.55642253 1.01119030 0.10653174 
    diag15     diag16     diag17     diag18     diag19     diag20     diag21 
0.28162183 1.54978773 0.33430910 0.46104919 0.35308761 0.35374576 1.32880167 

$ms
[1] 0.009728852

$var_ms_unscaled
[1] NA

$var_ms_unweighted
[1] NA

$var_ms
[1] NA

$rank
[1] 21

$df.residual
[1] 40

attr(,"class")
[1] "modFit"


tlsce> ## weighting Wa inversely proportional to A
tlsce> tlsce(A,B,Wa=1/A)
$X
            sample1   sample2    sample3    sample4    sample5
species1 0.00000000 0.3316064 0.48024422 0.21454415 0.17824956
species2 0.00000000 0.0000000 0.00000000 0.28217109 0.00000000
species3 0.05720514 0.0560328 0.02346464 0.01805893 0.04097266
species4 0.59250216 0.2654179 0.17729545 0.14255547 0.21723831
species5 0.35029270 0.3469429 0.31899569 0.34267038 0.56353947

$A_fit
             species1   species2   species3    species4  species5
biomarker1 0.00000000 0.00000000 0.00000000 0.000000000 0.3402311
biomarker2 0.00000000 0.00000000 0.00000000 0.000000000 0.2645218
biomarker3 0.39806496 0.23221154 2.63861113 1.238611578 0.2422926
biomarker4 0.07078116 0.08112961 0.25022076 0.006014673 1.2094134
biomarker5 1.57849416 0.33998549 0.06185610 0.012014071 0.0000000
biomarker6 0.00000000 0.57017478 0.03371573 0.190947872 0.0000000
biomarker7 0.00000000 0.00000000 0.00000000 1.510810954 0.0000000
biomarker8 0.00000000 0.00000000 0.00000000 0.124760432 0.0000000

$B_fit
              sample1    sample2    sample3    sample4    sample5
biomarker1 0.11918046 0.11804077 0.10853224 0.11658711 0.19173364
biomarker2 0.09266006 0.09177397 0.08438131 0.09064378 0.14906848
biomarker3 0.96969550 0.69266102 0.54997296 0.45817374 0.58468118
biomarker4 0.44152630 0.45868589 0.42672761 0.45788440 0.70572772
biomarker5 0.01065685 0.53009347 0.76164417 0.43742048 0.28651021
biomarker6 0.11506574 0.05257016 0.03464532 0.18871637 0.04286262
biomarker7 0.89515875 0.40099623 0.26785990 0.21537436 0.32820601
biomarker8 0.07392083 0.03311365 0.02211946 0.01778528 0.02710275

$SS
    total         A         B 
0.8357650 0.2281971 0.6075679 

$fit
$par
 [1] 0.398064957 0.070781156 1.578494158 0.232211542 0.081129615 0.339985492
 [7] 0.570174778 2.638611127 0.250220761 0.061856103 0.033715726 1.238611578
[13] 0.006014673 0.012014071 0.190947872 1.510810954 0.124760432 0.340231066
[19] 0.264521801 0.242292649 1.209413387

$hessian
              [,1]          [,2]        [,3]          [,4]          [,5]
 [1,] 1.315496e+01   0.158800381 0.126940045  0.0039032406  7.355217e-03
 [2,] 1.588004e-01 408.660168570 0.295297953  0.0073552140  3.235365e-02
 [3,] 1.269400e-01   0.295297953 1.238401161  0.0069966532  1.547563e-02
 [4,] 3.903241e-03   0.007355214 0.006996653 37.1640921549  1.376275e-02
 [5,] 7.355217e-03   0.032353647 0.015475632  0.0137627532  3.048947e+02
 [6,] 6.996662e-03   0.015475648 0.012825554  0.0109770516  2.927524e-02
 [7,] 1.619972e-02   0.035167779 0.029613402  0.0253239940  6.644840e-02
 [8,] 6.280412e-03   0.012497240 0.010762862  0.0003660395  8.075616e-04
 [9,] 1.249726e-02   0.037753190 0.022969082  0.0008075537  3.670505e-03
[10,] 1.076287e-02   0.022969075 0.018668738  0.0006707298  1.713786e-03
[11,] 2.764979e-04   0.001800762 0.002297497  0.0015487524  3.890896e-03
[12,] 3.809179e-02   0.078398128 0.065708290  0.0026315996  5.078527e-03
[13,] 7.839812e-02   0.241912498 0.145071727  0.0050785313  2.245914e-02
[14,] 6.570831e-02   0.145071747 0.114768610  0.0047320149  1.070027e-02
[15,] 7.164069e-03   0.028414393 0.023975009  0.0109519890  2.431228e-02
[16,] 4.920665e-02   0.113316378 0.085335704  0.0023064277  6.348841e-03
[17,] 2.248008e-03   0.003183939 0.003554888 -0.0003709653 -2.297651e-03
[18,] 6.779171e-02  -0.113702854 0.087311612  0.0090525859 -3.565578e-02
[19,] 5.230461e-02  -0.089772227 0.067109946  0.0069666408 -2.808110e-02
[20,] 6.696163e-02   0.136866744 0.115496573  0.0063810632  1.248566e-02
[21,] 1.368667e-01   0.425452548 0.253838107  0.0124856385  5.538417e-02
               [,6]         [,7]          [,8]          [,9]         [,10]
 [1,]  0.0069966625  0.016199716  0.0062804118  0.0124972550  1.076287e-02
 [2,]  0.0154756482  0.035167779  0.0124972396  0.0377531898  2.296908e-02
 [3,]  0.0128255540  0.029613402  0.0107628621  0.0229690824  1.866874e-02
 [4,]  0.0109770516  0.025323994  0.0003660395  0.0008075537  6.707298e-04
 [5,]  0.0292752410  0.066448401  0.0008075616  0.0036705049  1.713786e-03
 [6,] 17.4240005156  0.047714173  0.0006707354  0.0017137922  1.258042e-03
 [7,]  0.0477141728  6.397213067  0.0015487529  0.0038908910  2.896654e-03
 [8,]  0.0006707354  0.001548753  0.3364054488  0.0623792798  1.364268e-02
 [9,]  0.0017137922  0.003890891  0.0623792798 32.9037271798  4.099308e-02
[10,]  0.0012580417  0.002896654  0.0136426835  0.0409930801  5.237270e+02
[11,]  0.0028966584  0.006671877  0.0015741269  0.0119702246  1.154486e-02
[12,]  0.0047320228  0.010951992 -0.0023076954 -0.0150977743 -1.674546e-03
[13,]  0.0107002779  0.024312277 -0.0150977810 -0.0390773637 -7.072872e-03
[14,]  0.0087032026  0.020086952 -0.0016745436 -0.0070728655  7.235290e-02
[15,]  0.0200869556  0.046376480  0.0003853838 -0.0007391000 -4.901740e-03
[16,]  0.0043824441  0.010075006 -0.0032078511 -0.0162590151  1.236689e-03
[17,] -0.0008630337 -0.001940848 -0.0177568113 -0.0509122697 -1.252867e-02
[18,]  0.0096960466  0.024341678  0.0137592458 -0.0147203100  1.249422e-02
[19,]  0.0073823761  0.018571789  0.0106665720 -0.0116209472  9.544201e-03
[20,]  0.0114953794  0.026599270  0.0093216066  0.0163509386  1.252100e-02
[21,]  0.0263276457  0.059814376  0.0163509290  0.0514117471  2.857458e-02
              [,11]         [,12]         [,13]         [,14]         [,15]
 [1,]  2.764979e-04  0.0380917945  7.839812e-02  6.570831e-02  0.0071640695
 [2,]  1.800762e-03  0.0783981282  2.419125e-01  1.450717e-01  0.0284143932
 [3,]  2.297497e-03  0.0657082896  1.450717e-01  1.147686e-01  0.0239750093
 [4,]  1.548752e-03  0.0026315996  5.078531e-03  4.732015e-03  0.0109519890
 [5,]  3.890896e-03  0.0050785265  2.245914e-02  1.070027e-02  0.0243122836
 [6,]  2.896658e-03  0.0047320228  1.070028e-02  8.703203e-03  0.0200869556
 [7,]  6.671877e-03  0.0109519918  2.431228e-02  2.008695e-02  0.0463764804
 [8,]  1.574127e-03 -0.0023076954 -1.509778e-02 -1.674544e-03  0.0003853838
 [9,]  1.197022e-02 -0.0150977743 -3.907736e-02 -7.072866e-03 -0.0007391000
[10,]  1.154486e-02 -0.0016745461 -7.072872e-03  7.235290e-02 -0.0049017400
[11,]  1.760261e+03  0.0003853967 -7.390725e-04 -4.901733e-03  0.1103708626
[12,]  3.853967e-04  1.8953728900  3.244702e-01  4.805521e-02  0.0129557450
[13,] -7.390725e-04  0.3244702310  5.555646e+04  1.625444e-01  0.0724515218
[14,] -4.901733e-03  0.0480552130  1.625444e-01  1.388970e+04  0.0356686805
[15,]  1.103709e-01  0.0129557450  7.245152e-02  3.566868e-02 56.4471165303
[16,] -1.073351e-02  0.1862810907  4.334232e-01  9.100195e-02 -0.0846566025
[17,] -8.197979e-03  0.0485231762  1.306116e-01  3.274832e-02  0.0090573517
[18,]  4.393458e-03  0.0924301402 -1.154563e-01  6.381205e-02  0.0224514423
[19,]  3.187177e-03  0.0717986757 -9.012743e-02  4.921511e-02  0.0169669621
[20,]  2.210052e-03  0.0916994111  1.817609e-01  7.200428e-02  0.0174604735
[21,]  8.360884e-03  0.1817608433  5.339889e-01  1.812521e-01  0.0684331997
             [,16]         [,17]        [,18]        [,19]        [,20]
 [1,]  0.049206647  2.248008e-03  0.067791706  0.052304608  0.066961627
 [2,]  0.113316378  3.183939e-03 -0.113702854 -0.089772227  0.136866744
 [3,]  0.085335704  3.554888e-03  0.087311612  0.067109946  0.115496573
 [4,]  0.002306428 -3.709653e-04  0.009052586  0.006966641  0.006381063
 [5,]  0.006348841 -2.297651e-03 -0.035655775 -0.028081102  0.012485658
 [6,]  0.004382444 -8.630337e-04  0.009696047  0.007382376  0.011495379
 [7,]  0.010075006 -1.940848e-03  0.024341678  0.018571789  0.026599270
 [8,] -0.003207851 -1.775681e-02  0.013759246  0.010666572  0.009321607
 [9,] -0.016259015 -5.091227e-02 -0.014720310 -0.011620947  0.016350939
[10,]  0.001236689 -1.252867e-02  0.012494219  0.009544201  0.012521003
[11,] -0.010733514 -8.197979e-03  0.004393458  0.003187177  0.002210052
[12,]  0.186281091  4.852318e-02  0.092430140  0.071798676  0.091699411
[13,]  0.433423220  1.306116e-01 -0.115456341 -0.090127427  0.181760866
[14,]  0.091001951  3.274832e-02  0.063812052  0.049215108  0.072004279
[15,] -0.084656603  9.057352e-03  0.022451442  0.016966962  0.017460474
[16,]  1.582695552 -2.340490e-02  0.065933764  0.051203216  0.115221845
[17,] -0.023404903  1.354577e+02  0.009970989  0.007496675 -0.003756779
[18,]  0.065933764  9.970989e-03 17.469273065 -0.040191405  0.111903668
[19,]  0.051203216  7.496675e-03 -0.040191405 28.962407120  0.087570464
[20,]  0.115221845 -3.756779e-03  0.111903668  0.087570464 36.370523425
[21,]  0.254856527 -1.520942e-02 -0.237385845 -0.182292587  0.336883909
             [,21]
 [1,]  0.136866742
 [2,]  0.425452548
 [3,]  0.253838107
 [4,]  0.012485638
 [5,]  0.055384171
 [6,]  0.026327646
 [7,]  0.059814376
 [8,]  0.016350929
 [9,]  0.051411747
[10,]  0.028574583
[11,]  0.008360884
[12,]  0.181760843
[13,]  0.533988906
[14,]  0.181252056
[15,]  0.068433200
[16,]  0.254856527
[17,] -0.015209422
[18,] -0.237385845
[19,] -0.182292587
[20,]  0.336883909
[21,]  3.534930353

$residuals
 [1] -0.0180689440 -0.0111593663 -0.1242835882 -0.0009118192 -0.0016001807
 [6] -0.0029070572 -0.0109481883 -0.0437172900 -0.0122567173 -0.0009442096
[11] -0.0002332161 -0.1575809143 -0.0024454390 -0.0011725832 -0.0049887988
[16] -0.2303020795 -0.0226264894  0.0388952925  0.0202896269 -0.0310325493
[21] -0.3588914460  0.0166163557  0.0157369787 -0.1905609052 -0.4879608780
[26] -0.0534457138  0.0509631750 -0.2458668908 -0.2017202010  0.0426638814
[31]  0.0264473374 -0.0560827016 -0.1694548175 -0.1030723548 -0.0077313434
[36] -0.0763907032 -0.0673888644  0.0486244089  0.0336900696 -0.0306721964
[41] -0.1078346016 -0.0586323246 -0.0252625183 -0.0408958570 -0.0516132625
[46]  0.0565012704  0.0391416388 -0.0139292689 -0.0700125996 -0.0303906756
[51] -0.0687943607 -0.0120934511 -0.0723434735  0.0964955404  0.0644123908
[56] -0.0555304557 -0.2043251909 -0.1103151872 -0.1846506124 -0.0580373716
[61] -0.1316274140

$info
[1] 1

$message
[1] "Relative error in the sum of squares is at most `ftol'."

$iterations
[1] 7

$rsstrace
[1] 1.1852456 0.8421130 0.8359591 0.8357774 0.8357659 0.8357651 0.8357650
[8] 0.8357650

$ssr
[1] 0.835765

$diag
   diag1    diag2    diag3    diag4    diag5    diag6    diag7    diag8 
1.020899 1.014766 1.249979 1.000992 1.002057 1.003558 1.020019 1.101723 
   diag9   diag10   diag11   diag12   diag13   diag14   diag15   diag16 
1.029206 1.001585 1.000375 1.205744 1.002794 1.001202 1.014488 1.397125 
  diag17   diag18   diag19   diag20   diag21 
1.028299 1.042713 1.025559 1.033233 1.623961 

$ms
[1] 0.01370107

$var_ms_unscaled
[1] NA

$var_ms_unweighted
[1] NA

$var_ms
[1] NA

$rank
[1] 21

$df.residual
[1] 40

attr(,"class")
[1] "modFit"

sh: 0: Can't open /dev/null

BCE documentation built on May 2, 2019, 1:29 p.m.