R Under development (unstable) (2025-01-20 r87609 ucrt) -- "Unsuffered Consequences"
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> library(testthat)
> library(survPen)
>
> test_check("survPen")
Error in NR.beta(build, beta.ini, detail.beta = detail.beta, max.it.beta = max.it.beta, :
message NR.beta: Ran out of iterations (51), and did not converge
LU decomposition failed to invert penalty matrix, trying QR
set detail.rho=TRUE for details
Error in qr.solve(qr.S) : singular matrix 'a' in solve
LU and QR decompositions failed to invert penalty matrix, trying Cholesky
set detail.rho=TRUE for details
LU decomposition failed to invert penalty matrix, trying QR
set detail.rho=TRUE for details
Error in qr.solve(qr.S) : singular matrix 'a' in solve
LU and QR decompositions failed to invert penalty matrix, trying Cholesky
set detail.rho=TRUE for details
LU decomposition failed to invert penalty matrix, trying QR
set detail.rho=TRUE for details
Error in qr.solve(qr.S) : singular matrix 'a' in solve
LU and QR decompositions failed to invert penalty matrix, trying Cholesky
set detail.rho=TRUE for details
LU decomposition failed to invert penalty matrix, trying QR
set detail.rho=TRUE for details
Error in qr.solve(qr.S) : singular matrix 'a' in solve
LU and QR decompositions failed to invert penalty matrix, trying Cholesky
set detail.rho=TRUE for details
LU decomposition failed to invert penalty matrix, trying QR
set detail.rho=TRUE for details
Error in qr.solve(qr.S) : singular matrix 'a' in solve
LU and QR decompositions failed to invert penalty matrix, trying Cholesky
set detail.rho=TRUE for details
LU decomposition failed to invert penalty matrix, trying QR
set detail.rho=TRUE for details
Error in qr.solve(qr.S) : singular matrix 'a' in solve
LU and QR decompositions failed to invert penalty matrix, trying Cholesky
set detail.rho=TRUE for details
LU decomposition failed to invert penalty matrix, trying QR
set detail.rho=TRUE for details
Error in qr.solve(qr.S) : singular matrix 'a' in solve
LU and QR decompositions failed to invert penalty matrix, trying Cholesky
set detail.rho=TRUE for details
LU decomposition failed to invert penalty matrix, trying QR
set detail.rho=TRUE for details
Error in qr.solve(qr.S) : singular matrix 'a' in solve
LU and QR decompositions failed to invert penalty matrix, trying Cholesky
set detail.rho=TRUE for details
LU decomposition failed to invert penalty matrix, trying QR
set detail.rho=TRUE for details
Error in qr.solve(qr.S) : singular matrix 'a' in solve
LU and QR decompositions failed to invert penalty matrix, trying Cholesky
set detail.rho=TRUE for details
Error in NR.rho(build, rho.ini = rho.ini, data = data, formula = formula, :
message NR.rho: Ran out of iterations (31), and did not converge
_______________________________________________________________________________________
Beginning smoothing parameter estimation via LAML optimization
______________________________________________________________________________________
--------------------
Initial calculation
-------------------
---------------------------------------------------------------------------------------
Beginning regression parameter estimation
iter beta: 1
betaold= -0.7706 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
beta= -0.4041 -0.0948 0.1093 -0.008 -0.0599 -0.0831 -0.0166 -0.0329 -0.0033 -0.0287 5e-04 0.0446 0.0655 0.0903 -0.0278 -0.0101 -0.0303 -0.0281 -0.0348 0.0679 0.008 -0.0227 -0.1136 -0.109 -0.1148 0.09 -0.1155 0.2607 -0.3215 -0.5056
abs((beta-betaold)/betaold)= 0.47562 Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf
ll.pen.old= -3914.765
ll.pen= -3166.088
ll.pen-ll.pen.old= 748.6772
iter beta: 2
betaold= -0.4041 -0.0948 0.1093 -0.008 -0.0599 -0.0831 -0.0166 -0.0329 -0.0033 -0.0287 5e-04 0.0446 0.0655 0.0903 -0.0278 -0.0101 -0.0303 -0.0281 -0.0348 0.0679 0.008 -0.0227 -0.1136 -0.109 -0.1148 0.09 -0.1155 0.2607 -0.3215 -0.5056
beta= -0.1259 -0.1733 0.1897 -0.0113 -0.1137 -0.1619 -0.0277 -0.0591 -0.0027 -0.0511 0.0047 0.0835 0.1193 0.1606 -0.0495 -0.0153 -0.054 -0.0498 -0.0631 0.1228 0.0183 -0.0397 -0.1962 -0.1851 -0.1939 0.1194 -0.1435 0.4043 -0.5097 -0.723
abs((beta-betaold)/betaold)= 0.68838 0.82842 0.73635 0.42145 0.8987 0.94951 0.66919 0.7974 0.18864 0.78119 7.56148 0.87161 0.82234 0.77835 0.7847 0.51293 0.78185 0.7738 0.81639 0.80897 1.29265 0.74713 0.72722 0.69828 0.68886 0.32644 0.24204 0.55077 0.58517 0.42995
ll.pen.old= -3166.088
ll.pen= -3082.278
ll.pen-ll.pen.old= 83.80951
iter beta: 3
betaold= -0.1259 -0.1733 0.1897 -0.0113 -0.1137 -0.1619 -0.0277 -0.0591 -0.0027 -0.0511 0.0047 0.0835 0.1193 0.1606 -0.0495 -0.0153 -0.054 -0.0498 -0.0631 0.1228 0.0183 -0.0397 -0.1962 -0.1851 -0.1939 0.1194 -0.1435 0.4043 -0.5097 -0.723
beta= -0.06 -0.1935 0.2067 -0.0109 -0.1288 -0.1863 -0.0294 -0.0652 -0.0012 -0.0561 0.0071 0.0937 0.1324 0.1764 -0.0544 -0.0154 -0.0592 -0.0544 -0.0699 0.1358 0.0222 -0.0431 -0.2177 -0.2065 -0.2176 0.1202 -0.1364 0.4269 -0.541 -0.7609
abs((beta-betaold)/betaold)= 0.52348 0.11639 0.08919 0.03718 0.13313 0.1505 0.06058 0.10165 0.54041 0.09694 0.50713 0.12156 0.10915 0.0988 0.09751 0.00276 0.09708 0.09408 0.10745 0.10602 0.21266 0.08579 0.10996 0.11578 0.12241 0.00684 0.04931 0.056 0.06147 0.05236
ll.pen.old= -3082.278
ll.pen= -3080.206
ll.pen-ll.pen.old= 2.07206
iter beta: 4
betaold= -0.06 -0.1935 0.2067 -0.0109 -0.1288 -0.1863 -0.0294 -0.0652 -0.0012 -0.0561 0.0071 0.0937 0.1324 0.1764 -0.0544 -0.0154 -0.0592 -0.0544 -0.0699 0.1358 0.0222 -0.0431 -0.2177 -0.2065 -0.2176 0.1202 -0.1364 0.4269 -0.541 -0.7609
beta= -0.0575 -0.1942 0.2072 -0.0108 -0.1294 -0.1874 -0.0294 -0.0653 -0.0011 -0.0562 0.0072 0.094 0.1328 0.1769 -0.0545 -0.0153 -0.0593 -0.0546 -0.0701 0.1363 0.0224 -0.0431 -0.2187 -0.2075 -0.2188 0.1201 -0.1358 0.4274 -0.5416 -0.762
abs((beta-betaold)/betaold)= 0.04163 0.00351 0.00238 0.00643 0.00474 0.00607 0.00021 0.00272 0.08621 0.00243 0.01942 0.00385 0.00325 0.00279 0.00245 0.00355 0.00244 0.00224 0.00307 0.0031 0.00843 0.00173 0.00453 0.0051 0.0058 0.00029 0.00461 0.00111 0.00108 0.00147
ll.pen.old= -3080.206
ll.pen= -3080.204
ll.pen-ll.pen.old= 0.00212
iter beta: 5
betaold= -0.0575 -0.1942 0.2072 -0.0108 -0.1294 -0.1874 -0.0294 -0.0653 -0.0011 -0.0562 0.0072 0.094 0.1328 0.1769 -0.0545 -0.0153 -0.0593 -0.0546 -0.0701 0.1363 0.0224 -0.0431 -0.2187 -0.2075 -0.2188 0.1201 -0.1358 0.4274 -0.5416 -0.762
beta= -0.0575 -0.1942 0.2072 -0.0108 -0.1294 -0.1874 -0.0294 -0.0653 -0.0011 -0.0562 0.0072 0.094 0.1328 0.1769 -0.0545 -0.0153 -0.0593 -0.0546 -0.0701 0.1363 0.0224 -0.0431 -0.2187 -0.2075 -0.2188 0.1201 -0.1358 0.4274 -0.5416 -0.762
abs((beta-betaold)/betaold)= 6e-05 0 0 1e-05 1e-05 1e-05 0 0 0.00015 0 3e-05 0 0 0 0 1e-05 0 0 0 0 1e-05 0 1e-05 1e-05 1e-05 0 1e-05 0 0 0
ll.pen.old= -3080.204
ll.pen= -3080.204
ll.pen-ll.pen.old= 0
iter beta: 6
betaold= -0.0575 -0.1942 0.2072 -0.0108 -0.1294 -0.1874 -0.0294 -0.0653 -0.0011 -0.0562 0.0072 0.094 0.1328 0.1769 -0.0545 -0.0153 -0.0593 -0.0546 -0.0701 0.1363 0.0224 -0.0431 -0.2187 -0.2075 -0.2188 0.1201 -0.1358 0.4274 -0.5416 -0.762
beta= -0.0575 -0.1942 0.2072 -0.0108 -0.1294 -0.1874 -0.0294 -0.0653 -0.0011 -0.0562 0.0072 0.094 0.1328 0.1769 -0.0545 -0.0153 -0.0593 -0.0546 -0.0701 0.1363 0.0224 -0.0431 -0.2187 -0.2075 -0.2188 0.1201 -0.1358 0.4274 -0.5416 -0.762
abs((beta-betaold)/betaold)= 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ll.pen.old= -3080.204
ll.pen= -3080.204
ll.pen-ll.pen.old= 0
Beta optimization ok, 6 iterations
--------------------------------------------------------------------------------------
new step = 38 5.12
new step corrected = 5 0.673
Smoothing parameter selection, iteration 1
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Beginning regression parameter estimation
iter beta: 1
betaold= -0.0575 -0.1942 0.2072 -0.0108 -0.1294 -0.1874 -0.0294 -0.0653 -0.0011 -0.0562 0.0072 0.094 0.1328 0.1769 -0.0545 -0.0153 -0.0593 -0.0546 -0.0701 0.1363 0.0224 -0.0431 -0.2187 -0.2075 -0.2188 0.1201 -0.1358 0.4274 -0.5416 -0.762
beta= -0.0022 -0.0597 0.1909 -0.0102 -0.1186 -0.1717 -0.027 -0.0599 -0.0013 -0.0523 0.0062 0.0865 0.1226 0.163 -0.0503 -0.0143 -0.0548 -0.0498 -0.0646 0.1251 0.0202 -0.0395 -0.1241 -0.1439 -0.176 0.084 -0.0996 0.3926 -0.5018 -0.7619
abs((beta-betaold)/betaold)= 0.96153 0.69234 0.07835 0.05556 0.08341 0.08364 0.08054 0.0829 0.11538 0.07 0.14472 0.07975 0.07691 0.07875 0.07713 0.06691 0.0767 0.08634 0.07886 0.08223 0.09682 0.08365 0.43249 0.3067 0.19591 0.30041 0.26678 0.0815 0.07341 0.00013
ll.pen.old= -3091.581
ll.pen= -3084.894
ll.pen-ll.pen.old= 6.68782
iter beta: 2
betaold= -0.0022 -0.0597 0.1909 -0.0102 -0.1186 -0.1717 -0.027 -0.0599 -0.0013 -0.0523 0.0062 0.0865 0.1226 0.163 -0.0503 -0.0143 -0.0548 -0.0498 -0.0646 0.1251 0.0202 -0.0395 -0.1241 -0.1439 -0.176 0.084 -0.0996 0.3926 -0.5018 -0.7619
beta= -0.0021 -0.0595 0.1908 -0.0102 -0.1186 -0.1718 -0.027 -0.0599 -0.0012 -0.0523 0.0062 0.0865 0.1226 0.1629 -0.0503 -0.0143 -0.0548 -0.0498 -0.0646 0.125 0.0202 -0.0395 -0.1238 -0.1433 -0.175 0.0823 -0.097 0.389 -0.4977 -0.763
abs((beta-betaold)/betaold)= 0.04134 0.00361 0.00048 0.00299 0.00018 0.00041 0.00082 0.00029 0.02356 0.00029 0.00348 9e-05 0.00013 0.00031 0.00044 0.00165 0.00034 0.00026 0.00016 2e-04 0.00127 4e-04 0.00287 0.00397 0.00571 0.02057 0.02602 0.00906 0.00819 0.00137
ll.pen.old= -3084.894
ll.pen= -3084.89
ll.pen-ll.pen.old= 0.00383
iter beta: 3
betaold= -0.0021 -0.0595 0.1908 -0.0102 -0.1186 -0.1718 -0.027 -0.0599 -0.0012 -0.0523 0.0062 0.0865 0.1226 0.1629 -0.0503 -0.0143 -0.0548 -0.0498 -0.0646 0.125 0.0202 -0.0395 -0.1238 -0.1433 -0.175 0.0823 -0.097 0.389 -0.4977 -0.763
beta= -0.0021 -0.0595 0.1908 -0.0102 -0.1186 -0.1718 -0.027 -0.0599 -0.0012 -0.0523 0.0062 0.0865 0.1226 0.1629 -0.0503 -0.0143 -0.0548 -0.0498 -0.0646 0.125 0.0202 -0.0395 -0.1238 -0.1433 -0.175 0.0823 -0.097 0.389 -0.4977 -0.763
abs((beta-betaold)/betaold)= 3e-05 1e-05 0 0 0 0 0 0 2e-05 0 0 0 0 0 0 0 0 0 0 0 0 0 1e-05 2e-05 2e-05 7e-05 7e-05 2e-05 2e-05 0
ll.pen.old= -3084.89
ll.pen= -3084.89
ll.pen-ll.pen.old= 0
Beta optimization ok, 3 iterations
--------------------------------------------------------------------------------------
_______________________________________________________________________________________
iter LAML : 1
rho.old= -1 -1
rho= 4 -0.3268
val.old= 3136.102
val= 3117.18
val-val.old= -18.92107
gradient= -1 -6.5
_______________________________________________________________________________________
Smoothing parameter selection, iteration 2
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Beginning regression parameter estimation
iter beta: 1
betaold= -0.0021 -0.0595 0.1908 -0.0102 -0.1186 -0.1718 -0.027 -0.0599 -0.0012 -0.0523 0.0062 0.0865 0.1226 0.1629 -0.0503 -0.0143 -0.0548 -0.0498 -0.0646 0.125 0.0202 -0.0395 -0.1238 -0.1433 -0.175 0.0823 -0.097 0.389 -0.4977 -0.763
beta= 4e-04 -0.0134 0.053 -0.003 -0.0324 -0.0473 -0.0075 -0.0161 -5e-04 -0.0147 0.0014 0.0238 0.0339 0.0455 -0.0139 -0.0042 -0.0154 -0.0138 -0.0178 0.0344 0.0055 -0.0108 -0.0432 -0.0747 -0.1235 0.0573 -0.0683 0.3672 -0.4792 -0.7636
abs((beta-betaold)/betaold)= 1.19772 0.77523 0.72239 0.70513 0.72652 0.72482 0.72236 0.7306 0.61705 0.71958 0.77267 0.72468 0.72364 0.72071 0.72374 0.70305 0.71839 0.72223 0.72484 0.72502 0.73059 0.72787 0.65087 0.47903 0.29406 0.30359 0.29559 0.056 0.03727 0.00086
ll.pen.old= -3116.202
ll.pen= -3094.394
ll.pen-ll.pen.old= 21.80833
iter beta: 2
betaold= 4e-04 -0.0134 0.053 -0.003 -0.0324 -0.0473 -0.0075 -0.0161 -5e-04 -0.0147 0.0014 0.0238 0.0339 0.0455 -0.0139 -0.0042 -0.0154 -0.0138 -0.0178 0.0344 0.0055 -0.0108 -0.0432 -0.0747 -0.1235 0.0573 -0.0683 0.3672 -0.4792 -0.7636
beta= 5e-04 -0.0134 0.0513 -0.0025 -0.0328 -0.0485 -0.007 -0.0159 0 -0.0143 0.0019 0.0239 0.0335 0.0444 -0.0135 -0.0037 -0.0151 -0.0135 -0.0175 0.0339 0.0059 -0.0104 -0.0432 -0.0746 -0.1233 0.0567 -0.0672 0.3657 -0.4771 -0.7663
abs((beta-betaold)/betaold)= 0.12448 0.00176 0.03223 0.16256 0.01016 0.02659 0.0603 0.01724 1.06174 0.02344 0.35705 0.00169 0.01217 0.02429 0.02503 0.1161 0.02166 0.02568 0.0143 0.01319 0.08733 0.03733 0.00084 0.00127 0.00183 0.01023 0.01612 0.00426 0.00438 0.00347
ll.pen.old= -3094.394
ll.pen= -3094.385
ll.pen-ll.pen.old= 0.00877
iter beta: 3
betaold= 5e-04 -0.0134 0.0513 -0.0025 -0.0328 -0.0485 -0.007 -0.0159 0 -0.0143 0.0019 0.0239 0.0335 0.0444 -0.0135 -0.0037 -0.0151 -0.0135 -0.0175 0.0339 0.0059 -0.0104 -0.0432 -0.0746 -0.1233 0.0567 -0.0672 0.3657 -0.4771 -0.7663
beta= 5e-04 -0.0134 0.0513 -0.0025 -0.0328 -0.0485 -0.007 -0.0159 0 -0.0143 0.0019 0.0239 0.0335 0.0444 -0.0135 -0.0037 -0.0151 -0.0135 -0.0175 0.0339 0.0059 -0.0104 -0.0432 -0.0746 -0.1233 0.0567 -0.0672 0.3657 -0.4771 -0.7663
abs((beta-betaold)/betaold)= 0 0 2e-05 0.00013 1e-05 2e-05 4e-05 1e-05 0.01129 2e-05 0.00018 0 1e-05 2e-05 2e-05 9e-05 2e-05 2e-05 1e-05 1e-05 5e-05 3e-05 0 0 1e-05 3e-05 5e-05 1e-05 1e-05 1e-05
ll.pen.old= -3094.385
ll.pen= -3094.385
ll.pen-ll.pen.old= 0
iter beta: 4
betaold= 5e-04 -0.0134 0.0513 -0.0025 -0.0328 -0.0485 -0.007 -0.0159 0 -0.0143 0.0019 0.0239 0.0335 0.0444 -0.0135 -0.0037 -0.0151 -0.0135 -0.0175 0.0339 0.0059 -0.0104 -0.0432 -0.0746 -0.1233 0.0567 -0.0672 0.3657 -0.4771 -0.7663
beta= 5e-04 -0.0134 0.0513 -0.0025 -0.0328 -0.0485 -0.007 -0.0159 0 -0.0143 0.0019 0.0239 0.0335 0.0444 -0.0135 -0.0037 -0.0151 -0.0135 -0.0175 0.0339 0.0059 -0.0104 -0.0432 -0.0746 -0.1233 0.0567 -0.0672 0.3657 -0.4771 -0.7663
abs((beta-betaold)/betaold)= 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ll.pen.old= -3094.385
ll.pen= -3094.385
ll.pen-ll.pen.old= 0
Beta optimization ok, 4 iterations
--------------------------------------------------------------------------------------
_______________________________________________________________________________________
iter LAML : 2
rho.old= 4 -0.3268
rho= 5.6866 2.4985
val.old= 3117.18
val= 3107.78
val-val.old= -9.4008
gradient= -0.47 -0.34
_______________________________________________________________________________________
Smoothing parameter selection, iteration 3
---------------------------------------------------------------------------------------
Beginning regression parameter estimation
iter beta: 1
betaold= 5e-04 -0.0134 0.0513 -0.0025 -0.0328 -0.0485 -0.007 -0.0159 0 -0.0143 0.0019 0.0239 0.0335 0.0444 -0.0135 -0.0037 -0.0151 -0.0135 -0.0175 0.0339 0.0059 -0.0104 -0.0432 -0.0746 -0.1233 0.0567 -0.0672 0.3657 -0.4771 -0.7663
beta= 0 -2e-04 0.0338 -0.0016 -0.0217 -0.0324 -0.0045 -0.0103 2e-04 -0.0094 0.0012 0.0159 0.022 0.0292 -0.0089 -0.0026 -0.0101 -0.009 -0.0116 0.0225 0.004 -0.0067 -8e-04 -0.0019 -0.007 0.0031 -0.0197 0.2771 -0.4569 -0.7728
abs((beta-betaold)/betaold)= 0.92919 0.98643 0.3405 0.37205 0.33718 0.33265 0.35832 0.35165 4.69025 0.34001 0.38029 0.33327 0.34383 0.34135 0.34429 0.31945 0.33148 0.33298 0.33993 0.33621 0.33258 0.35045 0.98124 0.97386 0.94293 0.94484 0.70721 0.24219 0.04235 0.00849
ll.pen.old= -3195.972
ll.pen= -3099.934
ll.pen-ll.pen.old= 96.03724
iter beta: 2
betaold= 0 -2e-04 0.0338 -0.0016 -0.0217 -0.0324 -0.0045 -0.0103 2e-04 -0.0094 0.0012 0.0159 0.022 0.0292 -0.0089 -0.0026 -0.0101 -0.009 -0.0116 0.0225 0.004 -0.0067 -8e-04 -0.0019 -0.007 0.0031 -0.0197 0.2771 -0.4569 -0.7728
beta= 0 -2e-04 0.0338 -0.0016 -0.0217 -0.0324 -0.0045 -0.0103 2e-04 -0.0094 0.0012 0.0159 0.0219 0.0292 -0.0089 -0.0025 -0.0101 -0.009 -0.0116 0.0225 0.004 -0.0067 -8e-04 -0.0019 -0.007 0.0029 -0.0188 0.2749 -0.4549 -0.7739
abs((beta-betaold)/betaold)= 0.01719 0.01628 0.00057 0.00378 0.00017 0.00042 0.00095 0.00043 0.02397 0.00065 0.00611 0.00013 0.00027 0.00049 0.00029 0.00308 0.00071 0.00066 0.00017 0.00027 0.00154 0.00021 0.00633 0.00686 0.00388 0.07562 0.04228 0.00781 0.00431 0.00139
ll.pen.old= -3099.934
ll.pen= -3099.933
ll.pen-ll.pen.old= 0.00194
iter beta: 3
betaold= 0 -2e-04 0.0338 -0.0016 -0.0217 -0.0324 -0.0045 -0.0103 2e-04 -0.0094 0.0012 0.0159 0.0219 0.0292 -0.0089 -0.0025 -0.0101 -0.009 -0.0116 0.0225 0.004 -0.0067 -8e-04 -0.0019 -0.007 0.0029 -0.0188 0.2749 -0.4549 -0.7739
beta= 0 -2e-04 0.0338 -0.0016 -0.0217 -0.0324 -0.0045 -0.0103 2e-04 -0.0094 0.0012 0.0159 0.0219 0.0292 -0.0089 -0.0025 -0.0101 -0.009 -0.0116 0.0225 0.004 -0.0067 -8e-04 -0.0019 -0.007 0.0029 -0.0188 0.2749 -0.4549 -0.7739
abs((beta-betaold)/betaold)= 1e-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4e-05 3e-05 1e-05 1e-05 0
ll.pen.old= -3099.933
ll.pen= -3099.933
ll.pen-ll.pen.old= 0
Beta optimization ok, 3 iterations
--------------------------------------------------------------------------------------
_______________________________________________________________________________________
iter LAML : 3
rho.old= 5.6866 2.4985
rho= 9.7713 3.0058
val.old= 3107.78
val= 3106.854
val-val.old= -0.92612
gradient= 0.65 -0.11
_______________________________________________________________________________________
Smoothing parameter selection, iteration 4
---------------------------------------------------------------------------------------
Beginning regression parameter estimation
iter beta: 1
betaold= 0 -2e-04 0.0338 -0.0016 -0.0217 -0.0324 -0.0045 -0.0103 2e-04 -0.0094 0.0012 0.0159 0.0219 0.0292 -0.0089 -0.0025 -0.0101 -0.009 -0.0116 0.0225 0.004 -0.0067 -8e-04 -0.0019 -0.007 0.0029 -0.0188 0.2749 -0.4549 -0.7739
beta= 1e-04 -4e-04 0.024 -0.0011 -0.0156 -0.0233 -0.0032 -0.0074 1e-04 -0.0067 9e-04 0.0114 0.0157 0.0208 -0.0063 -0.0018 -0.0072 -0.0064 -0.0083 0.016 0.0029 -0.0048 -0.0019 -0.0045 -0.0149 0.0088 -0.0245 0.3137 -0.4458 -0.7703
abs((beta-betaold)/betaold)= 1.01333 1.37957 0.28894 0.28583 0.28375 0.28218 0.28624 0.28464 0.14201 0.28966 0.2302 0.28594 0.28533 0.28705 0.28806 0.30276 0.28882 0.29074 0.28573 0.28766 0.27198 0.29011 1.31238 1.34906 1.12025 2.02855 0.29917 0.14093 0.01999 0.00458
ll.pen.old= -3099.833
ll.pen= -3099.471
ll.pen-ll.pen.old= 0.36155
iter beta: 2
betaold= 1e-04 -4e-04 0.024 -0.0011 -0.0156 -0.0233 -0.0032 -0.0074 1e-04 -0.0067 9e-04 0.0114 0.0157 0.0208 -0.0063 -0.0018 -0.0072 -0.0064 -0.0083 0.016 0.0029 -0.0048 -0.0019 -0.0045 -0.0149 0.0088 -0.0245 0.3137 -0.4458 -0.7703
beta= 1e-04 -4e-04 0.024 -0.0011 -0.0156 -0.0233 -0.0032 -0.0074 1e-04 -0.0067 9e-04 0.0114 0.0157 0.0208 -0.0063 -0.0018 -0.0072 -0.0064 -0.0083 0.016 0.0029 -0.0048 -0.0019 -0.0045 -0.0149 0.0087 -0.0245 0.3135 -0.446 -0.7704
abs((beta-betaold)/betaold)= 0.00047 5e-05 0.00016 0.00111 5e-05 0.00014 0.00034 8e-05 0.00756 0.00015 0.00124 0 8e-05 0.00013 0.00014 7e-04 0.00015 0.00015 8e-05 7e-05 0.00037 0.00019 0.00019 0.00025 0.00012 0.00349 0.00065 0.00052 0.00035 0.00011
ll.pen.old= -3099.471
ll.pen= -3099.471
ll.pen-ll.pen.old= 1e-05
iter beta: 3
betaold= 1e-04 -4e-04 0.024 -0.0011 -0.0156 -0.0233 -0.0032 -0.0074 1e-04 -0.0067 9e-04 0.0114 0.0157 0.0208 -0.0063 -0.0018 -0.0072 -0.0064 -0.0083 0.016 0.0029 -0.0048 -0.0019 -0.0045 -0.0149 0.0087 -0.0245 0.3135 -0.446 -0.7704
beta= 1e-04 -4e-04 0.024 -0.0011 -0.0156 -0.0233 -0.0032 -0.0074 1e-04 -0.0067 9e-04 0.0114 0.0157 0.0208 -0.0063 -0.0018 -0.0072 -0.0064 -0.0083 0.016 0.0029 -0.0048 -0.0019 -0.0045 -0.0149 0.0087 -0.0245 0.3135 -0.446 -0.7704
abs((beta-betaold)/betaold)= 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ll.pen.old= -3099.471
ll.pen= -3099.471
ll.pen-ll.pen.old= 0
Beta optimization ok, 3 iterations
--------------------------------------------------------------------------------------
_______________________________________________________________________________________
iter LAML : 4
rho.old= 9.7713 3.0058
rho= 8.9551 3.3915
val.old= 3106.854
val= 3106.547
val-val.old= -0.30637
gradient= 0.066 -0.033
_______________________________________________________________________________________
Smoothing parameter selection, iteration 5
---------------------------------------------------------------------------------------
Beginning regression parameter estimation
iter beta: 1
betaold= 1e-04 -4e-04 0.024 -0.0011 -0.0156 -0.0233 -0.0032 -0.0074 1e-04 -0.0067 9e-04 0.0114 0.0157 0.0208 -0.0063 -0.0018 -0.0072 -0.0064 -0.0083 0.016 0.0029 -0.0048 -0.0019 -0.0045 -0.0149 0.0087 -0.0245 0.3135 -0.446 -0.7704
beta= 1e-04 -5e-04 0.0191 -9e-04 -0.0124 -0.0186 -0.0026 -0.0059 1e-04 -0.0053 7e-04 0.0091 0.0125 0.0166 -0.005 -0.0014 -0.0057 -0.0051 -0.0066 0.0128 0.0023 -0.0038 -0.0021 -0.0051 -0.0164 0.0098 -0.0253 0.3173 -0.4458 -0.7702
abs((beta-betaold)/betaold)= 0.09933 0.12498 0.2031 0.21162 0.20041 0.19911 0.20471 0.20246 0.1125 0.20249 0.18303 0.20109 0.20161 0.20226 0.2028 0.20923 0.20221 0.20295 0.20199 0.20213 0.19577 0.20391 0.12002 0.12048 0.1045 0.12619 0.03458 0.01219 0.00032 0.00021
ll.pen.old= -3099.653
ll.pen= -3099.591
ll.pen-ll.pen.old= 0.06112
iter beta: 2
betaold= 1e-04 -5e-04 0.0191 -9e-04 -0.0124 -0.0186 -0.0026 -0.0059 1e-04 -0.0053 7e-04 0.0091 0.0125 0.0166 -0.005 -0.0014 -0.0057 -0.0051 -0.0066 0.0128 0.0023 -0.0038 -0.0021 -0.0051 -0.0164 0.0098 -0.0253 0.3173 -0.4458 -0.7702
beta= 1e-04 -5e-04 0.0191 -9e-04 -0.0124 -0.0186 -0.0026 -0.0059 1e-04 -0.0053 8e-04 0.0091 0.0125 0.0166 -0.005 -0.0014 -0.0057 -0.0051 -0.0066 0.0128 0.0023 -0.0038 -0.0021 -0.0051 -0.0164 0.0098 -0.0253 0.3173 -0.4458 -0.7702
abs((beta-betaold)/betaold)= 1e-05 0 4e-05 0.00027 1e-05 4e-05 9e-05 3e-05 0.00178 3e-05 0.00031 0 2e-05 3e-05 4e-05 0.00017 3e-05 4e-05 2e-05 2e-05 9e-05 5e-05 0 1e-05 0 4e-05 1e-05 1e-05 0 0
ll.pen.old= -3099.591
ll.pen= -3099.591
ll.pen-ll.pen.old= 0
iter beta: 3
betaold= 1e-04 -5e-04 0.0191 -9e-04 -0.0124 -0.0186 -0.0026 -0.0059 1e-04 -0.0053 8e-04 0.0091 0.0125 0.0166 -0.005 -0.0014 -0.0057 -0.0051 -0.0066 0.0128 0.0023 -0.0038 -0.0021 -0.0051 -0.0164 0.0098 -0.0253 0.3173 -0.4458 -0.7702
beta= 1e-04 -5e-04 0.0191 -9e-04 -0.0124 -0.0186 -0.0026 -0.0059 1e-04 -0.0053 8e-04 0.0091 0.0125 0.0166 -0.005 -0.0014 -0.0057 -0.0051 -0.0066 0.0128 0.0023 -0.0038 -0.0021 -0.0051 -0.0164 0.0098 -0.0253 0.3173 -0.4458 -0.7702
abs((beta-betaold)/betaold)= 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ll.pen.old= -3099.591
ll.pen= -3099.591
ll.pen-ll.pen.old= 0
Beta optimization ok, 3 iterations
--------------------------------------------------------------------------------------
_______________________________________________________________________________________
iter LAML : 5
rho.old= 8.9551 3.3915
rho= 8.8438 3.6401
val.old= 3106.547
val= 3106.539
val-val.old= -0.00848
gradient= 0.0024 -0.0077
_______________________________________________________________________________________
Smoothing parameter selection, iteration 6
---------------------------------------------------------------------------------------
Beginning regression parameter estimation
iter beta: 1
betaold= 1e-04 -5e-04 0.0191 -9e-04 -0.0124 -0.0186 -0.0026 -0.0059 1e-04 -0.0053 8e-04 0.0091 0.0125 0.0166 -0.005 -0.0014 -0.0057 -0.0051 -0.0066 0.0128 0.0023 -0.0038 -0.0021 -0.0051 -0.0164 0.0098 -0.0253 0.3173 -0.4458 -0.7702
beta= 1e-04 -5e-04 0.0174 -8e-04 -0.0113 -0.0169 -0.0023 -0.0053 1e-04 -0.0049 7e-04 0.0082 0.0114 0.0151 -0.0046 -0.0013 -0.0052 -0.0046 -0.006 0.0116 0.0021 -0.0034 -0.0021 -0.0051 -0.0165 0.0099 -0.0254 0.3175 -0.446 -0.7703
abs((beta-betaold)/betaold)= 0.00388 0.00472 0.09246 0.09684 0.09134 0.09076 0.09344 0.09236 0.0554 0.09214 0.08512 0.09157 0.09189 0.09213 0.09238 0.09483 0.09198 0.09228 0.09205 0.09202 0.08958 0.09286 0.00451 0.00465 0.00387 0.00499 0.00174 0.00046 0.00038 8e-05
ll.pen.old= -3099.678
ll.pen= -3099.669
ll.pen-ll.pen.old= 0.0083
iter beta: 2
betaold= 1e-04 -5e-04 0.0174 -8e-04 -0.0113 -0.0169 -0.0023 -0.0053 1e-04 -0.0049 7e-04 0.0082 0.0114 0.0151 -0.0046 -0.0013 -0.0052 -0.0046 -0.006 0.0116 0.0021 -0.0034 -0.0021 -0.0051 -0.0165 0.0099 -0.0254 0.3175 -0.446 -0.7703
beta= 1e-04 -5e-04 0.0174 -8e-04 -0.0113 -0.0169 -0.0023 -0.0053 1e-04 -0.0049 7e-04 0.0082 0.0114 0.0151 -0.0046 -0.0013 -0.0052 -0.0046 -0.006 0.0116 0.0021 -0.0034 -0.0021 -0.0051 -0.0165 0.0099 -0.0254 0.3175 -0.446 -0.7703
abs((beta-betaold)/betaold)= 0 0 1e-05 4e-05 0 1e-05 1e-05 0 0.00023 0 4e-05 0 0 0 0 2e-05 0 0 0 0 1e-05 1e-05 0 0 0 0 0 0 0 0
ll.pen.old= -3099.669
ll.pen= -3099.669
ll.pen-ll.pen.old= 0
iter beta: 3
betaold= 1e-04 -5e-04 0.0174 -8e-04 -0.0113 -0.0169 -0.0023 -0.0053 1e-04 -0.0049 7e-04 0.0082 0.0114 0.0151 -0.0046 -0.0013 -0.0052 -0.0046 -0.006 0.0116 0.0021 -0.0034 -0.0021 -0.0051 -0.0165 0.0099 -0.0254 0.3175 -0.446 -0.7703
beta= 1e-04 -5e-04 0.0174 -8e-04 -0.0113 -0.0169 -0.0023 -0.0053 1e-04 -0.0049 7e-04 0.0082 0.0114 0.0151 -0.0046 -0.0013 -0.0052 -0.0046 -0.006 0.0116 0.0021 -0.0034 -0.0021 -0.0051 -0.0165 0.0099 -0.0254 0.3175 -0.446 -0.7703
abs((beta-betaold)/betaold)= 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ll.pen.old= -3099.669
ll.pen= -3099.669
ll.pen-ll.pen.old= 0
Beta optimization ok, 3 iterations
--------------------------------------------------------------------------------------
_______________________________________________________________________________________
iter LAML : 6
rho.old= 8.8438 3.6401
rho= 8.8394 3.7448
val.old= 3106.539
val= 3106.538
val-val.old= -0.00044
gradient= 3.6e-06 -0.00095
_______________________________________________________________________________________
Smoothing parameter selection, iteration 7
---------------------------------------------------------------------------------------
Beginning regression parameter estimation
iter beta: 1
betaold= 1e-04 -5e-04 0.0174 -8e-04 -0.0113 -0.0169 -0.0023 -0.0053 1e-04 -0.0049 7e-04 0.0082 0.0114 0.0151 -0.0046 -0.0013 -0.0052 -0.0046 -0.006 0.0116 0.0021 -0.0034 -0.0021 -0.0051 -0.0165 0.0099 -0.0254 0.3175 -0.446 -0.7703
beta= 1e-04 -5e-04 0.0171 -8e-04 -0.0111 -0.0167 -0.0023 -0.0052 1e-04 -0.0048 7e-04 0.0081 0.0112 0.0149 -0.0045 -0.0012 -0.0051 -0.0045 -0.0059 0.0114 0.0021 -0.0034 -0.0021 -0.0051 -0.0165 0.0099 -0.0254 0.3175 -0.446 -0.7703
abs((beta-betaold)/betaold)= 2e-05 1e-05 0.01549 0.01624 0.0153 0.0152 0.01566 0.01548 0.0095 0.01543 0.01429 0.01534 0.0154 0.01543 0.01548 0.01588 0.01541 0.01546 0.01542 0.01541 0.01501 0.01556 0 3e-05 1e-05 7e-05 7e-05 0 7e-05 2e-05
ll.pen.old= -3099.682
ll.pen= -3099.682
ll.pen-ll.pen.old= 2e-04
iter beta: 2
betaold= 1e-04 -5e-04 0.0171 -8e-04 -0.0111 -0.0167 -0.0023 -0.0052 1e-04 -0.0048 7e-04 0.0081 0.0112 0.0149 -0.0045 -0.0012 -0.0051 -0.0045 -0.0059 0.0114 0.0021 -0.0034 -0.0021 -0.0051 -0.0165 0.0099 -0.0254 0.3175 -0.446 -0.7703
beta= 1e-04 -5e-04 0.0171 -8e-04 -0.0111 -0.0167 -0.0023 -0.0052 1e-04 -0.0048 7e-04 0.0081 0.0112 0.0149 -0.0045 -0.0012 -0.0051 -0.0045 -0.0059 0.0114 0.0021 -0.0034 -0.0021 -0.0051 -0.0165 0.0099 -0.0254 0.3175 -0.446 -0.7703
abs((beta-betaold)/betaold)= 0 0 0 0 0 0 0 0 1e-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ll.pen.old= -3099.682
ll.pen= -3099.682
ll.pen-ll.pen.old= 0
Beta optimization ok, 2 iterations
--------------------------------------------------------------------------------------
_______________________________________________________________________________________
iter LAML : 7
rho.old= 8.8394 3.7448
rho= 8.8394 3.7616
val.old= 3106.538
val= 3106.538
val-val.old= -1e-05
gradient= -7.7e-09 -2.1e-05
_______________________________________________________________________________________
Smoothing parameter(s) selection via LAML ok, 7 iterations
______________________________________________________________________________________
[ FAIL 0 | WARN 10 | SKIP 0 | PASS 60 ]
[ FAIL 0 | WARN 10 | SKIP 0 | PASS 60 ]
>
> proc.time()
user system elapsed
19.07 1.89 21.14