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Type 'q()' to quit R. > 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 Error in NR.beta(build, beta.ini, detail.beta = detail.beta, max.it.beta = max.it.beta, : message NR.beta: step has been divided by two 52 times in a row, Log-likelihood could not be optimized _______________________________________________________________________________________ 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 --------------------------------------------------------------------------------------- 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 --------------------------------------------------------------------------------------- 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 1 | SKIP 0 | PASS 56 ] [ FAIL 0 | WARN 1 | SKIP 0 | PASS 56 ] > > proc.time() user system elapsed 34.03 5.31 39.34