library(haven)
dat <- read_dta("ZAIR71FL.DTA")
View(dat)
names(dat)
## [1] "caseid" "v000" "v001" "v002" "v003" "v004"
## [7] "v005" "v006" "v007" "v008" "v008a" "v009"
## [13] "v010" "v011" "v012" "v013" "v014" "v015"
## [19] "v016" "v017" "v018" "v019" "v019a" "v020"
## [25] "v021" "v022" "v023" "v024" "v025" "v026"
## [31] "v027" "v028" "v029" "v030" "v031" "v032"
## [37] "v034" "v040" "v042" "v044" "v045a" "v045b"
## [43] "v045c" "v046" "v101" "v102" "v103" "v104"
## [49] "v105" "v105a" "v106" "v107" "v113" "v115"
## [55] "v116" "v119" "v120" "v121" "v122" "v123"
## [61] "v124" "v125" "v127" "v128" "v129" "v130"
## [67] "v131" "v133" "v134" "v135" "v136" "v137"
## [73] "v138" "v139" "v140" "v141" "v149" "v150"
## [79] "v151" "v152" "v153" "awfactt" "awfactu" "awfactr"
## [85] "awfacte" "awfactw" "v155" "v156" "v157" "v158"
## [91] "v159" "v160" "v161" "v166" "v167" "v168"
## [97] "v169a" "v169b" "v170" "v171a" "v171b" "v190"
## [103] "v191" "v190a" "v191a" "ml101" "bidx_01" "bidx_02"
## [109] "bidx_03" "bidx_04" "bidx_05" "bidx_06" "bidx_07" "bidx_08"
## [115] "bidx_09" "bidx_10" "bidx_11" "bidx_12" "bidx_13" "bidx_14"
## [121] "bidx_15" "bidx_16" "bidx_17" "bidx_18" "bidx_19" "bidx_20"
## [127] "bord_01" "bord_02" "bord_03" "bord_04" "bord_05" "bord_06"
## [133] "bord_07" "bord_08" "bord_09" "bord_10" "bord_11" "bord_12"
## [139] "bord_13" "bord_14" "bord_15" "bord_16" "bord_17" "bord_18"
## [145] "bord_19" "bord_20" "b0_01" "b0_02" "b0_03" "b0_04"
## [151] "b0_05" "b0_06" "b0_07" "b0_08" "b0_09" "b0_10"
## [157] "b0_11" "b0_12" "b0_13" "b0_14" "b0_15" "b0_16"
## [163] "b0_17" "b0_18" "b0_19" "b0_20" "b1_01" "b1_02"
## [169] "b1_03" "b1_04" "b1_05" "b1_06" "b1_07" "b1_08"
## [175] "b1_09" "b1_10" "b1_11" "b1_12" "b1_13" "b1_14"
## [181] "b1_15" "b1_16" "b1_17" "b1_18" "b1_19" "b1_20"
## [187] "b2_01" "b2_02" "b2_03" "b2_04" "b2_05" "b2_06"
## [193] "b2_07" "b2_08" "b2_09" "b2_10" "b2_11" "b2_12"
## [199] "b2_13" "b2_14" "b2_15" "b2_16" "b2_17" "b2_18"
## [205] "b2_19" "b2_20" "b3_01" "b3_02" "b3_03" "b3_04"
## [211] "b3_05" "b3_06" "b3_07" "b3_08" "b3_09" "b3_10"
## [217] "b3_11" "b3_12" "b3_13" "b3_14" "b3_15" "b3_16"
## [223] "b3_17" "b3_18" "b3_19" "b3_20" "b4_01" "b4_02"
## [229] "b4_03" "b4_04" "b4_05" "b4_06" "b4_07" "b4_08"
## [235] "b4_09" "b4_10" "b4_11" "b4_12" "b4_13" "b4_14"
## [241] "b4_15" "b4_16" "b4_17" "b4_18" "b4_19" "b4_20"
## [247] "b5_01" "b5_02" "b5_03" "b5_04" "b5_05" "b5_06"
## [253] "b5_07" "b5_08" "b5_09" "b5_10" "b5_11" "b5_12"
## [259] "b5_13" "b5_14" "b5_15" "b5_16" "b5_17" "b5_18"
## [265] "b5_19" "b5_20" "b6_01" "b6_02" "b6_03" "b6_04"
## [271] "b6_05" "b6_06" "b6_07" "b6_08" "b6_09" "b6_10"
## [277] "b6_11" "b6_12" "b6_13" "b6_14" "b6_15" "b6_16"
## [283] "b6_17" "b6_18" "b6_19" "b6_20" "b7_01" "b7_02"
## [289] "b7_03" "b7_04" "b7_05" "b7_06" "b7_07" "b7_08"
## [295] "b7_09" "b7_10" "b7_11" "b7_12" "b7_13" "b7_14"
## [301] "b7_15" "b7_16" "b7_17" "b7_18" "b7_19" "b7_20"
## [307] "b8_01" "b8_02" "b8_03" "b8_04" "b8_05" "b8_06"
## [313] "b8_07" "b8_08" "b8_09" "b8_10" "b8_11" "b8_12"
## [319] "b8_13" "b8_14" "b8_15" "b8_16" "b8_17" "b8_18"
## [325] "b8_19" "b8_20" "b9_01" "b9_02" "b9_03" "b9_04"
## [331] "b9_05" "b9_06" "b9_07" "b9_08" "b9_09" "b9_10"
## [337] "b9_11" "b9_12" "b9_13" "b9_14" "b9_15" "b9_16"
## [343] "b9_17" "b9_18" "b9_19" "b9_20" "b10_01" "b10_02"
## [349] "b10_03" "b10_04" "b10_05" "b10_06" "b10_07" "b10_08"
## [355] "b10_09" "b10_10" "b10_11" "b10_12" "b10_13" "b10_14"
## [361] "b10_15" "b10_16" "b10_17" "b10_18" "b10_19" "b10_20"
## [367] "b11_01" "b11_02" "b11_03" "b11_04" "b11_05" "b11_06"
## [373] "b11_07" "b11_08" "b11_09" "b11_10" "b11_11" "b11_12"
## [379] "b11_13" "b11_14" "b11_15" "b11_16" "b11_17" "b11_18"
## [385] "b11_19" "b11_20" "b12_01" "b12_02" "b12_03" "b12_04"
## [391] "b12_05" "b12_06" "b12_07" "b12_08" "b12_09" "b12_10"
## [397] "b12_11" "b12_12" "b12_13" "b12_14" "b12_15" "b12_16"
## [403] "b12_17" "b12_18" "b12_19" "b12_20" "b13_01" "b13_02"
## [409] "b13_03" "b13_04" "b13_05" "b13_06" "b13_07" "b13_08"
## [415] "b13_09" "b13_10" "b13_11" "b13_12" "b13_13" "b13_14"
## [421] "b13_15" "b13_16" "b13_17" "b13_18" "b13_19" "b13_20"
## [427] "b15_01" "b15_02" "b15_03" "b15_04" "b15_05" "b15_06"
## [433] "b15_07" "b15_08" "b15_09" "b15_10" "b15_11" "b15_12"
## [439] "b15_13" "b15_14" "b15_15" "b15_16" "b15_17" "b15_18"
## [445] "b15_19" "b15_20" "b16_01" "b16_02" "b16_03" "b16_04"
## [451] "b16_05" "b16_06" "b16_07" "b16_08" "b16_09" "b16_10"
## [457] "b16_11" "b16_12" "b16_13" "b16_14" "b16_15" "b16_16"
## [463] "b16_17" "b16_18" "b16_19" "b16_20" "b17_01" "b17_02"
## [469] "b17_03" "b17_04" "b17_05" "b17_06" "b17_07" "b17_08"
## [475] "b17_09" "b17_10" "b17_11" "b17_12" "b17_13" "b17_14"
## [481] "b17_15" "b17_16" "b17_17" "b17_18" "b17_19" "b17_20"
## [487] "b18_01" "b18_02" "b18_03" "b18_04" "b18_05" "b18_06"
## [493] "b18_07" "b18_08" "b18_09" "b18_10" "b18_11" "b18_12"
## [499] "b18_13" "b18_14" "b18_15" "b18_16" "b18_17" "b18_18"
## [505] "b18_19" "b18_20" "b19_01" "b19_02" "b19_03" "b19_04"
## [511] "b19_05" "b19_06" "b19_07" "b19_08" "b19_09" "b19_10"
## [517] "b19_11" "b19_12" "b19_13" "b19_14" "b19_15" "b19_16"
## [523] "b19_17" "b19_18" "b19_19" "b19_20" "b20_01" "b20_02"
## [529] "b20_03" "b20_04" "b20_05" "b20_06" "b20_07" "b20_08"
## [535] "b20_09" "b20_10" "b20_11" "b20_12" "b20_13" "b20_14"
## [541] "b20_15" "b20_16" "b20_17" "b20_18" "b20_19" "b20_20"
## [547] "v201" "v202" "v203" "v204" "v205" "v206"
## [553] "v207" "v208" "v209" "v210" "v211" "v212"
## [559] "v213" "v214" "v215" "v216" "v217" "v218"
## [565] "v219" "v220" "v221" "v222" "v223" "v224"
## [571] "v225" "v226" "v227" "v228" "v229" "v230"
## [577] "v231" "v232" "v233" "v234" "v235" "v237"
## [583] "v238" "v239" "v240" "v241" "v242" "v243"
## [589] "v244" "v301" "v302" "v302a" "v304a_01" "v304a_02"
## [595] "v304a_03" "v304a_04" "v304a_05" "v304a_06" "v304a_07" "v304a_08"
## [601] "v304a_09" "v304a_10" "v304a_11" "v304a_12" "v304a_13" "v304a_14"
## [607] "v304a_15" "v304a_16" "v304a_17" "v304a_18" "v304a_19" "v304a_20"
## [613] "v304_01" "v304_02" "v304_03" "v304_04" "v304_05" "v304_06"
## [619] "v304_07" "v304_08" "v304_09" "v304_10" "v304_11" "v304_12"
## [625] "v304_13" "v304_14" "v304_15" "v304_16" "v304_17" "v304_18"
## [631] "v304_19" "v304_20" "v305_01" "v305_02" "v305_03" "v305_04"
## [637] "v305_05" "v305_06" "v305_07" "v305_08" "v305_09" "v305_10"
## [643] "v305_11" "v305_12" "v305_13" "v305_14" "v305_15" "v305_16"
## [649] "v305_17" "v305_18" "v305_19" "v305_20" "v307_01" "v307_02"
## [655] "v307_03" "v307_04" "v307_05" "v307_06" "v307_07" "v307_08"
## [661] "v307_09" "v307_10" "v307_11" "v307_12" "v307_13" "v307_14"
## [667] "v307_15" "v307_16" "v307_17" "v307_18" "v307_19" "v307_20"
## [673] "v310" "v311" "v312" "v313" "v315" "v316"
## [679] "v317" "v318" "v319" "v320" "v321" "v322"
## [685] "v323" "v323a" "v325a" "v326" "v327" "v337"
## [691] "v359" "v360" "v361" "v362" "v363" "v364"
## [697] "v367" "v372" "v372a" "v375a" "v376" "v376a"
## [703] "v379" "v380" "v384a" "v384b" "v384c" "v384d"
## [709] "v393" "v393a" "v394" "v395" "v3a00a" "v3a00b"
## [715] "v3a00c" "v3a00d" "v3a00e" "v3a00f" "v3a00g" "v3a00h"
## [721] "v3a00i" "v3a00j" "v3a00k" "v3a00l" "v3a00m" "v3a00n"
## [727] "v3a00o" "v3a00p" "v3a00q" "v3a00r" "v3a00s" "v3a00t"
## [733] "v3a00u" "v3a00v" "v3a00w" "v3a00x" "v3a00y" "v3a00z"
## [739] "v3a01" "v3a02" "v3a03" "v3a04" "v3a05" "v3a06"
## [745] "v3a07" "v3a08a" "v3a08b" "v3a08c" "v3a08d" "v3a08e"
## [751] "v3a08f" "v3a08g" "v3a08h" "v3a08i" "v3a08j" "v3a08k"
## [757] "v3a08l" "v3a08m" "v3a08n" "v3a08o" "v3a08p" "v3a08q"
## [763] "v3a08r" "v3a08s" "v3a08t" "v3a08u" "v3a08v" "v3a08w"
## [769] "v3a08aa" "v3a08ab" "v3a08ac" "v3a08ad" "v3a08x" "v3a08z"
## [775] "v3a09a" "v3a09b" "midx_1" "midx_2" "midx_3" "midx_4"
## [781] "midx_5" "midx_6" "m1_1" "m1_2" "m1_3" "m1_4"
## [787] "m1_5" "m1_6" "m1a_1" "m1a_2" "m1a_3" "m1a_4"
## [793] "m1a_5" "m1a_6" "m1b_1" "m1b_2" "m1b_3" "m1b_4"
## [799] "m1b_5" "m1b_6" "m1c_1" "m1c_2" "m1c_3" "m1c_4"
## [805] "m1c_5" "m1c_6" "m1d_1" "m1d_2" "m1d_3" "m1d_4"
## [811] "m1d_5" "m1d_6" "m1e_1" "m1e_2" "m1e_3" "m1e_4"
## [817] "m1e_5" "m1e_6" "m2a_1" "m2a_2" "m2a_3" "m2a_4"
## [823] "m2a_5" "m2a_6" "m2b_1" "m2b_2" "m2b_3" "m2b_4"
## [829] "m2b_5" "m2b_6" "m2c_1" "m2c_2" "m2c_3" "m2c_4"
## [835] "m2c_5" "m2c_6" "m2d_1" "m2d_2" "m2d_3" "m2d_4"
## [841] "m2d_5" "m2d_6" "m2e_1" "m2e_2" "m2e_3" "m2e_4"
## [847] "m2e_5" "m2e_6" "m2f_1" "m2f_2" "m2f_3" "m2f_4"
## [853] "m2f_5" "m2f_6" "m2g_1" "m2g_2" "m2g_3" "m2g_4"
## [859] "m2g_5" "m2g_6" "m2h_1" "m2h_2" "m2h_3" "m2h_4"
## [865] "m2h_5" "m2h_6" "m2i_1" "m2i_2" "m2i_3" "m2i_4"
## [871] "m2i_5" "m2i_6" "m2j_1" "m2j_2" "m2j_3" "m2j_4"
## [877] "m2j_5" "m2j_6" "m2k_1" "m2k_2" "m2k_3" "m2k_4"
## [883] "m2k_5" "m2k_6" "m2l_1" "m2l_2" "m2l_3" "m2l_4"
## [889] "m2l_5" "m2l_6" "m2m_1" "m2m_2" "m2m_3" "m2m_4"
## [895] "m2m_5" "m2m_6" "m2n_1" "m2n_2" "m2n_3" "m2n_4"
## [901] "m2n_5" "m2n_6" "m3a_1" "m3a_2" "m3a_3" "m3a_4"
## [907] "m3a_5" "m3a_6" "m3b_1" "m3b_2" "m3b_3" "m3b_4"
## [913] "m3b_5" "m3b_6" "m3c_1" "m3c_2" "m3c_3" "m3c_4"
## [919] "m3c_5" "m3c_6" "m3d_1" "m3d_2" "m3d_3" "m3d_4"
## [925] "m3d_5" "m3d_6" "m3e_1" "m3e_2" "m3e_3" "m3e_4"
## [931] "m3e_5" "m3e_6" "m3f_1" "m3f_2" "m3f_3" "m3f_4"
## [937] "m3f_5" "m3f_6" "m3g_1" "m3g_2" "m3g_3" "m3g_4"
## [943] "m3g_5" "m3g_6" "m3h_1" "m3h_2" "m3h_3" "m3h_4"
## [949] "m3h_5" "m3h_6" "m3i_1" "m3i_2" "m3i_3" "m3i_4"
## [955] "m3i_5" "m3i_6" "m3j_1" "m3j_2" "m3j_3" "m3j_4"
## [961] "m3j_5" "m3j_6" "m3k_1" "m3k_2" "m3k_3" "m3k_4"
## [967] "m3k_5" "m3k_6" "m3l_1" "m3l_2" "m3l_3" "m3l_4"
## [973] "m3l_5" "m3l_6" "m3m_1" "m3m_2" "m3m_3" "m3m_4"
## [979] "m3m_5" "m3m_6" "m3n_1" "m3n_2" "m3n_3" "m3n_4"
## [985] "m3n_5" "m3n_6" "m4_1" "m4_2" "m4_3" "m4_4"
## [991] "m4_5" "m4_6" "m5_1" "m5_2" "m5_3" "m5_4"
## [997] "m5_5" "m5_6" "m6_1" "m6_2" "m6_3" "m6_4"
## [1003] "m6_5" "m6_6" "m7_1" "m7_2" "m7_3" "m7_4"
## [1009] "m7_5" "m7_6" "m8_1" "m8_2" "m8_3" "m8_4"
## [1015] "m8_5" "m8_6" "m9_1" "m9_2" "m9_3" "m9_4"
## [1021] "m9_5" "m9_6" "m10_1" "m10_2" "m10_3" "m10_4"
## [1027] "m10_5" "m10_6" "m11_1" "m11_2" "m11_3" "m11_4"
## [1033] "m11_5" "m11_6" "m13_1" "m13_2" "m13_3" "m13_4"
## [1039] "m13_5" "m13_6" "m14_1" "m14_2" "m14_3" "m14_4"
## [1045] "m14_5" "m14_6" "m15_1" "m15_2" "m15_3" "m15_4"
## [1051] "m15_5" "m15_6" "m17_1" "m17_2" "m17_3" "m17_4"
## [1057] "m17_5" "m17_6" "m17a_1" "m17a_2" "m17a_3" "m17a_4"
## [1063] "m17a_5" "m17a_6" "m18_1" "m18_2" "m18_3" "m18_4"
## [1069] "m18_5" "m18_6" "m19_1" "m19_2" "m19_3" "m19_4"
## [1075] "m19_5" "m19_6" "m19a_1" "m19a_2" "m19a_3" "m19a_4"
## [1081] "m19a_5" "m19a_6" "m27_1" "m27_2" "m27_3" "m27_4"
## [1087] "m27_5" "m27_6" "m28_1" "m28_2" "m28_3" "m28_4"
## [1093] "m28_5" "m28_6" "m29_1" "m29_2" "m29_3" "m29_4"
## [1099] "m29_5" "m29_6" "m34_1" "m34_2" "m34_3" "m34_4"
## [1105] "m34_5" "m34_6" "m35_1" "m35_2" "m35_3" "m35_4"
## [1111] "m35_5" "m35_6" "m36_1" "m36_2" "m36_3" "m36_4"
## [1117] "m36_5" "m36_6" "m38_1" "m38_2" "m38_3" "m38_4"
## [1123] "m38_5" "m38_6" "m39a_1" "m39a_2" "m39a_3" "m39a_4"
## [1129] "m39a_5" "m39a_6" "m39_1" "m39_2" "m39_3" "m39_4"
## [1135] "m39_5" "m39_6" "m42a_1" "m42a_2" "m42a_3" "m42a_4"
## [1141] "m42a_5" "m42a_6" "m42b_1" "m42b_2" "m42b_3" "m42b_4"
## [1147] "m42b_5" "m42b_6" "m42c_1" "m42c_2" "m42c_3" "m42c_4"
## [1153] "m42c_5" "m42c_6" "m42d_1" "m42d_2" "m42d_3" "m42d_4"
## [1159] "m42d_5" "m42d_6" "m42e_1" "m42e_2" "m42e_3" "m42e_4"
## [1165] "m42e_5" "m42e_6" "m43_1" "m43_2" "m43_3" "m43_4"
## [1171] "m43_5" "m43_6" "m44_1" "m44_2" "m44_3" "m44_4"
## [1177] "m44_5" "m44_6" "m45_1" "m45_2" "m45_3" "m45_4"
## [1183] "m45_5" "m45_6" "m46_1" "m46_2" "m46_3" "m46_4"
## [1189] "m46_5" "m46_6" "m47_1" "m47_2" "m47_3" "m47_4"
## [1195] "m47_5" "m47_6" "m48_1" "m48_2" "m48_3" "m48_4"
## [1201] "m48_5" "m48_6" "m49a_1" "m49a_2" "m49a_3" "m49a_4"
## [1207] "m49a_5" "m49a_6" "m49b_1" "m49b_2" "m49b_3" "m49b_4"
## [1213] "m49b_5" "m49b_6" "m49c_1" "m49c_2" "m49c_3" "m49c_4"
## [1219] "m49c_5" "m49c_6" "m49d_1" "m49d_2" "m49d_3" "m49d_4"
## [1225] "m49d_5" "m49d_6" "m49e_1" "m49e_2" "m49e_3" "m49e_4"
## [1231] "m49e_5" "m49e_6" "m49f_1" "m49f_2" "m49f_3" "m49f_4"
## [1237] "m49f_5" "m49f_6" "m49g_1" "m49g_2" "m49g_3" "m49g_4"
## [1243] "m49g_5" "m49g_6" "m49x_1" "m49x_2" "m49x_3" "m49x_4"
## [1249] "m49x_5" "m49x_6" "m49z_1" "m49z_2" "m49z_3" "m49z_4"
## [1255] "m49z_5" "m49z_6" "m49y_1" "m49y_2" "m49y_3" "m49y_4"
## [1261] "m49y_5" "m49y_6" "m54_1" "m54_2" "m54_3" "m54_4"
## [1267] "m54_5" "m54_6" "m55_1" "m55_2" "m55_3" "m55_4"
## [1273] "m55_5" "m55_6" "m55a_1" "m55a_2" "m55a_3" "m55a_4"
## [1279] "m55a_5" "m55a_6" "m55b_1" "m55b_2" "m55b_3" "m55b_4"
## [1285] "m55b_5" "m55b_6" "m55c_1" "m55c_2" "m55c_3" "m55c_4"
## [1291] "m55c_5" "m55c_6" "m55d_1" "m55d_2" "m55d_3" "m55d_4"
## [1297] "m55d_5" "m55d_6" "m55e_1" "m55e_2" "m55e_3" "m55e_4"
## [1303] "m55e_5" "m55e_6" "m55f_1" "m55f_2" "m55f_3" "m55f_4"
## [1309] "m55f_5" "m55f_6" "m55g_1" "m55g_2" "m55g_3" "m55g_4"
## [1315] "m55g_5" "m55g_6" "m55h_1" "m55h_2" "m55h_3" "m55h_4"
## [1321] "m55h_5" "m55h_6" "m55i_1" "m55i_2" "m55i_3" "m55i_4"
## [1327] "m55i_5" "m55i_6" "m55j_1" "m55j_2" "m55j_3" "m55j_4"
## [1333] "m55j_5" "m55j_6" "m55k_1" "m55k_2" "m55k_3" "m55k_4"
## [1339] "m55k_5" "m55k_6" "m55l_1" "m55l_2" "m55l_3" "m55l_4"
## [1345] "m55l_5" "m55l_6" "m55m_1" "m55m_2" "m55m_3" "m55m_4"
## [1351] "m55m_5" "m55m_6" "m55n_1" "m55n_2" "m55n_3" "m55n_4"
## [1357] "m55n_5" "m55n_6" "m55o_1" "m55o_2" "m55o_3" "m55o_4"
## [1363] "m55o_5" "m55o_6" "m55x_1" "m55x_2" "m55x_3" "m55x_4"
## [1369] "m55x_5" "m55x_6" "m55z_1" "m55z_2" "m55z_3" "m55z_4"
## [1375] "m55z_5" "m55z_6" "m57a_1" "m57a_2" "m57a_3" "m57a_4"
## [1381] "m57a_5" "m57a_6" "m57b_1" "m57b_2" "m57b_3" "m57b_4"
## [1387] "m57b_5" "m57b_6" "m57c_1" "m57c_2" "m57c_3" "m57c_4"
## [1393] "m57c_5" "m57c_6" "m57d_1" "m57d_2" "m57d_3" "m57d_4"
## [1399] "m57d_5" "m57d_6" "m57e_1" "m57e_2" "m57e_3" "m57e_4"
## [1405] "m57e_5" "m57e_6" "m57f_1" "m57f_2" "m57f_3" "m57f_4"
## [1411] "m57f_5" "m57f_6" "m57g_1" "m57g_2" "m57g_3" "m57g_4"
## [1417] "m57g_5" "m57g_6" "m57h_1" "m57h_2" "m57h_3" "m57h_4"
## [1423] "m57h_5" "m57h_6" "m57i_1" "m57i_2" "m57i_3" "m57i_4"
## [1429] "m57i_5" "m57i_6" "m57j_1" "m57j_2" "m57j_3" "m57j_4"
## [1435] "m57j_5" "m57j_6" "m57k_1" "m57k_2" "m57k_3" "m57k_4"
## [1441] "m57k_5" "m57k_6" "m57l_1" "m57l_2" "m57l_3" "m57l_4"
## [1447] "m57l_5" "m57l_6" "m57m_1" "m57m_2" "m57m_3" "m57m_4"
## [1453] "m57m_5" "m57m_6" "m57n_1" "m57n_2" "m57n_3" "m57n_4"
## [1459] "m57n_5" "m57n_6" "m57o_1" "m57o_2" "m57o_3" "m57o_4"
## [1465] "m57o_5" "m57o_6" "m57p_1" "m57p_2" "m57p_3" "m57p_4"
## [1471] "m57p_5" "m57p_6" "m57q_1" "m57q_2" "m57q_3" "m57q_4"
## [1477] "m57q_5" "m57q_6" "m57r_1" "m57r_2" "m57r_3" "m57r_4"
## [1483] "m57r_5" "m57r_6" "m57s_1" "m57s_2" "m57s_3" "m57s_4"
## [1489] "m57s_5" "m57s_6" "m57t_1" "m57t_2" "m57t_3" "m57t_4"
## [1495] "m57t_5" "m57t_6" "m57u_1" "m57u_2" "m57u_3" "m57u_4"
## [1501] "m57u_5" "m57u_6" "m57v_1" "m57v_2" "m57v_3" "m57v_4"
## [1507] "m57v_5" "m57v_6" "m57x_1" "m57x_2" "m57x_3" "m57x_4"
## [1513] "m57x_5" "m57x_6" "m60_1" "m60_2" "m60_3" "m60_4"
## [1519] "m60_5" "m60_6" "m61_1" "m61_2" "m61_3" "m61_4"
## [1525] "m61_5" "m61_6" "m62_1" "m62_2" "m62_3" "m62_4"
## [1531] "m62_5" "m62_6" "m63_1" "m63_2" "m63_3" "m63_4"
## [1537] "m63_5" "m63_6" "m64_1" "m64_2" "m64_3" "m64_4"
## [1543] "m64_5" "m64_6" "m65a_1" "m65a_2" "m65a_3" "m65a_4"
## [1549] "m65a_5" "m65a_6" "m65b_1" "m65b_2" "m65b_3" "m65b_4"
## [1555] "m65b_5" "m65b_6" "m65c_1" "m65c_2" "m65c_3" "m65c_4"
## [1561] "m65c_5" "m65c_6" "m65d_1" "m65d_2" "m65d_3" "m65d_4"
## [1567] "m65d_5" "m65d_6" "m65e_1" "m65e_2" "m65e_3" "m65e_4"
## [1573] "m65e_5" "m65e_6" "m65f_1" "m65f_2" "m65f_3" "m65f_4"
## [1579] "m65f_5" "m65f_6" "m65g_1" "m65g_2" "m65g_3" "m65g_4"
## [1585] "m65g_5" "m65g_6" "m65h_1" "m65h_2" "m65h_3" "m65h_4"
## [1591] "m65h_5" "m65h_6" "m65i_1" "m65i_2" "m65i_3" "m65i_4"
## [1597] "m65i_5" "m65i_6" "m65j_1" "m65j_2" "m65j_3" "m65j_4"
## [1603] "m65j_5" "m65j_6" "m65k_1" "m65k_2" "m65k_3" "m65k_4"
## [1609] "m65k_5" "m65k_6" "m65l_1" "m65l_2" "m65l_3" "m65l_4"
## [1615] "m65l_5" "m65l_6" "m65x_1" "m65x_2" "m65x_3" "m65x_4"
## [1621] "m65x_5" "m65x_6" "m66_1" "m66_2" "m66_3" "m66_4"
## [1627] "m66_5" "m66_6" "m67_1" "m67_2" "m67_3" "m67_4"
## [1633] "m67_5" "m67_6" "m68_1" "m68_2" "m68_3" "m68_4"
## [1639] "m68_5" "m68_6" "m69_1" "m69_2" "m69_3" "m69_4"
## [1645] "m69_5" "m69_6" "m70_1" "m70_2" "m70_3" "m70_4"
## [1651] "m70_5" "m70_6" "m71_1" "m71_2" "m71_3" "m71_4"
## [1657] "m71_5" "m71_6" "m72_1" "m72_2" "m72_3" "m72_4"
## [1663] "m72_5" "m72_6" "m73_1" "m73_2" "m73_3" "m73_4"
## [1669] "m73_5" "m73_6" "m74_1" "m74_2" "m74_3" "m74_4"
## [1675] "m74_5" "m74_6" "m75_1" "m75_2" "m75_3" "m75_4"
## [1681] "m75_5" "m75_6" "m76_1" "m76_2" "m76_3" "m76_4"
## [1687] "m76_5" "m76_6" "m77_1" "m77_2" "m77_3" "m77_4"
## [1693] "m77_5" "m77_6" "m78a_1" "m78a_2" "m78a_3" "m78a_4"
## [1699] "m78a_5" "m78a_6" "m78b_1" "m78b_2" "m78b_3" "m78b_4"
## [1705] "m78b_5" "m78b_6" "m78c_1" "m78c_2" "m78c_3" "m78c_4"
## [1711] "m78c_5" "m78c_6" "m78d_1" "m78d_2" "m78d_3" "m78d_4"
## [1717] "m78d_5" "m78d_6" "m78e_1" "m78e_2" "m78e_3" "m78e_4"
## [1723] "m78e_5" "m78e_6" "m78f_1" "m78f_2" "m78f_3" "m78f_4"
## [1729] "m78f_5" "m78f_6" "m78g_1" "m78g_2" "m78g_3" "m78g_4"
## [1735] "m78g_5" "m78g_6" "m78h_1" "m78h_2" "m78h_3" "m78h_4"
## [1741] "m78h_5" "m78h_6" "m78i_1" "m78i_2" "m78i_3" "m78i_4"
## [1747] "m78i_5" "m78i_6" "m78j_1" "m78j_2" "m78j_3" "m78j_4"
## [1753] "m78j_5" "m78j_6" "v401" "v404" "v405" "v406"
## [1759] "v407" "v408" "v409" "v409a" "v410" "v410a"
## [1765] "v411" "v411a" "v412" "v412a" "v412b" "v412c"
## [1771] "v413" "v413a" "v413b" "v413c" "v413d" "v414a"
## [1777] "v414b" "v414c" "v414d" "v414e" "v414f" "v414g"
## [1783] "v414h" "v414i" "v414j" "v414k" "v414l" "v414m"
## [1789] "v414n" "v414o" "v414p" "v414q" "v414r" "v414s"
## [1795] "v414t" "v414u" "v414v" "v414w" "v415" "v416"
## [1801] "v417" "v418" "v418a" "v419" "v420" "v421"
## [1807] "v426" "v437" "v438" "v439" "v440" "v441"
## [1813] "v442" "v443" "v444" "v444a" "v445" "v446"
## [1819] "v447" "v447a" "v452a" "v452b" "v452c" "v453"
## [1825] "v454" "v455" "v456" "v457" "v458" "v459"
## [1831] "v460" "v461" "v462" "v463a" "v463b" "v463c"
## [1837] "v463d" "v463e" "v463f" "v463g" "v463h" "v463i"
## [1843] "v463j" "v463k" "v463l" "v463x" "v463z" "v463aa"
## [1849] "v463ab" "v464" "v465" "v466" "v467a" "v467b"
## [1855] "v467c" "v467d" "v467e" "v467f" "v467g" "v467h"
## [1861] "v467i" "v467j" "v467k" "v467l" "v467m" "v468"
## [1867] "v469e" "v469f" "v469x" "v471a" "v471b" "v471c"
## [1873] "v471d" "v471e" "v471f" "v471g" "v472a" "v472b"
## [1879] "v472c" "v472d" "v472e" "v472f" "v472g" "v472h"
## [1885] "v472i" "v472j" "v472k" "v472l" "v472m" "v472n"
## [1891] "v472o" "v472p" "v472q" "v472r" "v472s" "v472t"
## [1897] "v472u" "v473a" "v473b" "v474" "v474a" "v474b"
## [1903] "v474c" "v474d" "v474e" "v474f" "v474g" "v474h"
## [1909] "v474i" "v474j" "v474x" "v474z" "v475" "v476"
## [1915] "v477" "v478" "v479" "v480" "v481" "v481a"
## [1921] "v481b" "v481c" "v481d" "v481e" "v481f" "v481g"
## [1927] "v481h" "v481x" "v482a" "v482b" "v482c" "hidx_1"
## [1933] "hidx_2" "hidx_3" "hidx_4" "hidx_5" "hidx_6" "h1_1"
## [1939] "h1_2" "h1_3" "h1_4" "h1_5" "h1_6" "h1a_1"
## [1945] "h1a_2" "h1a_3" "h1a_4" "h1a_5" "h1a_6" "h2_1"
## [1951] "h2_2" "h2_3" "h2_4" "h2_5" "h2_6" "h2d_1"
## [1957] "h2d_2" "h2d_3" "h2d_4" "h2d_5" "h2d_6" "h2m_1"
## [1963] "h2m_2" "h2m_3" "h2m_4" "h2m_5" "h2m_6" "h2y_1"
## [1969] "h2y_2" "h2y_3" "h2y_4" "h2y_5" "h2y_6" "h3_1"
## [1975] "h3_2" "h3_3" "h3_4" "h3_5" "h3_6" "h3d_1"
## [1981] "h3d_2" "h3d_3" "h3d_4" "h3d_5" "h3d_6" "h3m_1"
## [1987] "h3m_2" "h3m_3" "h3m_4" "h3m_5" "h3m_6" "h3y_1"
## [1993] "h3y_2" "h3y_3" "h3y_4" "h3y_5" "h3y_6" "h4_1"
## [1999] "h4_2" "h4_3" "h4_4" "h4_5" "h4_6" "h4d_1"
## [2005] "h4d_2" "h4d_3" "h4d_4" "h4d_5" "h4d_6" "h4m_1"
## [2011] "h4m_2" "h4m_3" "h4m_4" "h4m_5" "h4m_6" "h4y_1"
## [2017] "h4y_2" "h4y_3" "h4y_4" "h4y_5" "h4y_6" "h5_1"
## [2023] "h5_2" "h5_3" "h5_4" "h5_5" "h5_6" "h5d_1"
## [2029] "h5d_2" "h5d_3" "h5d_4" "h5d_5" "h5d_6" "h5m_1"
## [2035] "h5m_2" "h5m_3" "h5m_4" "h5m_5" "h5m_6" "h5y_1"
## [2041] "h5y_2" "h5y_3" "h5y_4" "h5y_5" "h5y_6" "h6_1"
## [2047] "h6_2" "h6_3" "h6_4" "h6_5" "h6_6" "h6d_1"
## [2053] "h6d_2" "h6d_3" "h6d_4" "h6d_5" "h6d_6" "h6m_1"
## [2059] "h6m_2" "h6m_3" "h6m_4" "h6m_5" "h6m_6" "h6y_1"
## [2065] "h6y_2" "h6y_3" "h6y_4" "h6y_5" "h6y_6" "h7_1"
## [2071] "h7_2" "h7_3" "h7_4" "h7_5" "h7_6" "h7d_1"
## [2077] "h7d_2" "h7d_3" "h7d_4" "h7d_5" "h7d_6" "h7m_1"
## [2083] "h7m_2" "h7m_3" "h7m_4" "h7m_5" "h7m_6" "h7y_1"
## [2089] "h7y_2" "h7y_3" "h7y_4" "h7y_5" "h7y_6" "h8_1"
## [2095] "h8_2" "h8_3" "h8_4" "h8_5" "h8_6" "h8d_1"
## [2101] "h8d_2" "h8d_3" "h8d_4" "h8d_5" "h8d_6" "h8m_1"
## [2107] "h8m_2" "h8m_3" "h8m_4" "h8m_5" "h8m_6" "h8y_1"
## [2113] "h8y_2" "h8y_3" "h8y_4" "h8y_5" "h8y_6" "h9_1"
## [2119] "h9_2" "h9_3" "h9_4" "h9_5" "h9_6" "h9d_1"
## [2125] "h9d_2" "h9d_3" "h9d_4" "h9d_5" "h9d_6" "h9m_1"
## [2131] "h9m_2" "h9m_3" "h9m_4" "h9m_5" "h9m_6" "h9y_1"
## [2137] "h9y_2" "h9y_3" "h9y_4" "h9y_5" "h9y_6" "h9a_1"
## [2143] "h9a_2" "h9a_3" "h9a_4" "h9a_5" "h9a_6" "h9ad_1"
## [2149] "h9ad_2" "h9ad_3" "h9ad_4" "h9ad_5" "h9ad_6" "h9am_1"
## [2155] "h9am_2" "h9am_3" "h9am_4" "h9am_5" "h9am_6" "h9ay_1"
## [2161] "h9ay_2" "h9ay_3" "h9ay_4" "h9ay_5" "h9ay_6" "h0_1"
## [2167] "h0_2" "h0_3" "h0_4" "h0_5" "h0_6" "h0d_1"
## [2173] "h0d_2" "h0d_3" "h0d_4" "h0d_5" "h0d_6" "h0m_1"
## [2179] "h0m_2" "h0m_3" "h0m_4" "h0m_5" "h0m_6" "h0y_1"
## [2185] "h0y_2" "h0y_3" "h0y_4" "h0y_5" "h0y_6" "h10_1"
## [2191] "h10_2" "h10_3" "h10_4" "h10_5" "h10_6" "h33_1"
## [2197] "h33_2" "h33_3" "h33_4" "h33_5" "h33_6" "h33d_1"
## [2203] "h33d_2" "h33d_3" "h33d_4" "h33d_5" "h33d_6" "h33m_1"
## [2209] "h33m_2" "h33m_3" "h33m_4" "h33m_5" "h33m_6" "h33y_1"
## [2215] "h33y_2" "h33y_3" "h33y_4" "h33y_5" "h33y_6" "h35_1"
## [2221] "h35_2" "h35_3" "h35_4" "h35_5" "h35_6" "h36a_1"
## [2227] "h36a_2" "h36a_3" "h36a_4" "h36a_5" "h36a_6" "h36b_1"
## [2233] "h36b_2" "h36b_3" "h36b_4" "h36b_5" "h36b_6" "h36c_1"
## [2239] "h36c_2" "h36c_3" "h36c_4" "h36c_5" "h36c_6" "h36d_1"
## [2245] "h36d_2" "h36d_3" "h36d_4" "h36d_5" "h36d_6" "h36e_1"
## [2251] "h36e_2" "h36e_3" "h36e_4" "h36e_5" "h36e_6" "h36f_1"
## [2257] "h36f_2" "h36f_3" "h36f_4" "h36f_5" "h36f_6" "h40_1"
## [2263] "h40_2" "h40_3" "h40_4" "h40_5" "h40_6" "h40d_1"
## [2269] "h40d_2" "h40d_3" "h40d_4" "h40d_5" "h40d_6" "h40m_1"
## [2275] "h40m_2" "h40m_3" "h40m_4" "h40m_5" "h40m_6" "h40y_1"
## [2281] "h40y_2" "h40y_3" "h40y_4" "h40y_5" "h40y_6" "h41a_1"
## [2287] "h41a_2" "h41a_3" "h41a_4" "h41a_5" "h41a_6" "h41b_1"
## [2293] "h41b_2" "h41b_3" "h41b_4" "h41b_5" "h41b_6" "h50_1"
## [2299] "h50_2" "h50_3" "h50_4" "h50_5" "h50_6" "h50d_1"
## [2305] "h50d_2" "h50d_3" "h50d_4" "h50d_5" "h50d_6" "h50m_1"
## [2311] "h50m_2" "h50m_3" "h50m_4" "h50m_5" "h50m_6" "h50y_1"
## [2317] "h50y_2" "h50y_3" "h50y_4" "h50y_5" "h50y_6" "h51_1"
## [2323] "h51_2" "h51_3" "h51_4" "h51_5" "h51_6" "h51d_1"
## [2329] "h51d_2" "h51d_3" "h51d_4" "h51d_5" "h51d_6" "h51m_1"
## [2335] "h51m_2" "h51m_3" "h51m_4" "h51m_5" "h51m_6" "h51y_1"
## [2341] "h51y_2" "h51y_3" "h51y_4" "h51y_5" "h51y_6" "h52_1"
## [2347] "h52_2" "h52_3" "h52_4" "h52_5" "h52_6" "h52d_1"
## [2353] "h52d_2" "h52d_3" "h52d_4" "h52d_5" "h52d_6" "h52m_1"
## [2359] "h52m_2" "h52m_3" "h52m_4" "h52m_5" "h52m_6" "h52y_1"
## [2365] "h52y_2" "h52y_3" "h52y_4" "h52y_5" "h52y_6" "h53_1"
## [2371] "h53_2" "h53_3" "h53_4" "h53_5" "h53_6" "h53d_1"
## [2377] "h53d_2" "h53d_3" "h53d_4" "h53d_5" "h53d_6" "h53m_1"
## [2383] "h53m_2" "h53m_3" "h53m_4" "h53m_5" "h53m_6" "h53y_1"
## [2389] "h53y_2" "h53y_3" "h53y_4" "h53y_5" "h53y_6" "h54_1"
## [2395] "h54_2" "h54_3" "h54_4" "h54_5" "h54_6" "h54d_1"
## [2401] "h54d_2" "h54d_3" "h54d_4" "h54d_5" "h54d_6" "h54m_1"
## [2407] "h54m_2" "h54m_3" "h54m_4" "h54m_5" "h54m_6" "h54y_1"
## [2413] "h54y_2" "h54y_3" "h54y_4" "h54y_5" "h54y_6" "h55_1"
## [2419] "h55_2" "h55_3" "h55_4" "h55_5" "h55_6" "h55d_1"
## [2425] "h55d_2" "h55d_3" "h55d_4" "h55d_5" "h55d_6" "h55m_1"
## [2431] "h55m_2" "h55m_3" "h55m_4" "h55m_5" "h55m_6" "h55y_1"
## [2437] "h55y_2" "h55y_3" "h55y_4" "h55y_5" "h55y_6" "h56_1"
## [2443] "h56_2" "h56_3" "h56_4" "h56_5" "h56_6" "h56d_1"
## [2449] "h56d_2" "h56d_3" "h56d_4" "h56d_5" "h56d_6" "h56m_1"
## [2455] "h56m_2" "h56m_3" "h56m_4" "h56m_5" "h56m_6" "h56y_1"
## [2461] "h56y_2" "h56y_3" "h56y_4" "h56y_5" "h56y_6" "h57_1"
## [2467] "h57_2" "h57_3" "h57_4" "h57_5" "h57_6" "h57d_1"
## [2473] "h57d_2" "h57d_3" "h57d_4" "h57d_5" "h57d_6" "h57m_1"
## [2479] "h57m_2" "h57m_3" "h57m_4" "h57m_5" "h57m_6" "h57y_1"
## [2485] "h57y_2" "h57y_3" "h57y_4" "h57y_5" "h57y_6" "h58_1"
## [2491] "h58_2" "h58_3" "h58_4" "h58_5" "h58_6" "h58d_1"
## [2497] "h58d_2" "h58d_3" "h58d_4" "h58d_5" "h58d_6" "h58m_1"
## [2503] "h58m_2" "h58m_3" "h58m_4" "h58m_5" "h58m_6" "h58y_1"
## [2509] "h58y_2" "h58y_3" "h58y_4" "h58y_5" "h58y_6" "h59_1"
## [2515] "h59_2" "h59_3" "h59_4" "h59_5" "h59_6" "h59d_1"
## [2521] "h59d_2" "h59d_3" "h59d_4" "h59d_5" "h59d_6" "h59m_1"
## [2527] "h59m_2" "h59m_3" "h59m_4" "h59m_5" "h59m_6" "h59y_1"
## [2533] "h59y_2" "h59y_3" "h59y_4" "h59y_5" "h59y_6" "h60_1"
## [2539] "h60_2" "h60_3" "h60_4" "h60_5" "h60_6" "h60d_1"
## [2545] "h60d_2" "h60d_3" "h60d_4" "h60d_5" "h60d_6" "h60m_1"
## [2551] "h60m_2" "h60m_3" "h60m_4" "h60m_5" "h60m_6" "h60y_1"
## [2557] "h60y_2" "h60y_3" "h60y_4" "h60y_5" "h60y_6" "h61_1"
## [2563] "h61_2" "h61_3" "h61_4" "h61_5" "h61_6" "h61d_1"
## [2569] "h61d_2" "h61d_3" "h61d_4" "h61d_5" "h61d_6" "h61m_1"
## [2575] "h61m_2" "h61m_3" "h61m_4" "h61m_5" "h61m_6" "h61y_1"
## [2581] "h61y_2" "h61y_3" "h61y_4" "h61y_5" "h61y_6" "h62_1"
## [2587] "h62_2" "h62_3" "h62_4" "h62_5" "h62_6" "h62d_1"
## [2593] "h62d_2" "h62d_3" "h62d_4" "h62d_5" "h62d_6" "h62m_1"
## [2599] "h62m_2" "h62m_3" "h62m_4" "h62m_5" "h62m_6" "h62y_1"
## [2605] "h62y_2" "h62y_3" "h62y_4" "h62y_5" "h62y_6" "h63_1"
## [2611] "h63_2" "h63_3" "h63_4" "h63_5" "h63_6" "h63d_1"
## [2617] "h63d_2" "h63d_3" "h63d_4" "h63d_5" "h63d_6" "h63m_1"
## [2623] "h63m_2" "h63m_3" "h63m_4" "h63m_5" "h63m_6" "h63y_1"
## [2629] "h63y_2" "h63y_3" "h63y_4" "h63y_5" "h63y_6" "h64_1"
## [2635] "h64_2" "h64_3" "h64_4" "h64_5" "h64_6" "h64d_1"
## [2641] "h64d_2" "h64d_3" "h64d_4" "h64d_5" "h64d_6" "h64m_1"
## [2647] "h64m_2" "h64m_3" "h64m_4" "h64m_5" "h64m_6" "h64y_1"
## [2653] "h64y_2" "h64y_3" "h64y_4" "h64y_5" "h64y_6" "h65_1"
## [2659] "h65_2" "h65_3" "h65_4" "h65_5" "h65_6" "h65d_1"
## [2665] "h65d_2" "h65d_3" "h65d_4" "h65d_5" "h65d_6" "h65m_1"
## [2671] "h65m_2" "h65m_3" "h65m_4" "h65m_5" "h65m_6" "h65y_1"
## [2677] "h65y_2" "h65y_3" "h65y_4" "h65y_5" "h65y_6" "h66_1"
## [2683] "h66_2" "h66_3" "h66_4" "h66_5" "h66_6" "h66d_1"
## [2689] "h66d_2" "h66d_3" "h66d_4" "h66d_5" "h66d_6" "h66m_1"
## [2695] "h66m_2" "h66m_3" "h66m_4" "h66m_5" "h66m_6" "h66y_1"
## [2701] "h66y_2" "h66y_3" "h66y_4" "h66y_5" "h66y_6" "h80a_1"
## [2707] "h80a_2" "h80a_3" "h80a_4" "h80a_5" "h80a_6" "h80b_1"
## [2713] "h80b_2" "h80b_3" "h80b_4" "h80b_5" "h80b_6" "h80c_1"
## [2719] "h80c_2" "h80c_3" "h80c_4" "h80c_5" "h80c_6" "h80d_1"
## [2725] "h80d_2" "h80d_3" "h80d_4" "h80d_5" "h80d_6" "h80e_1"
## [2731] "h80e_2" "h80e_3" "h80e_4" "h80e_5" "h80e_6" "h80f_1"
## [2737] "h80f_2" "h80f_3" "h80f_4" "h80f_5" "h80f_6" "h80g_1"
## [2743] "h80g_2" "h80g_3" "h80g_4" "h80g_5" "h80g_6" "hidxa_1"
## [2749] "hidxa_2" "hidxa_3" "hidxa_4" "hidxa_5" "hidxa_6" "h11_1"
## [2755] "h11_2" "h11_3" "h11_4" "h11_5" "h11_6" "h11b_1"
## [2761] "h11b_2" "h11b_3" "h11b_4" "h11b_5" "h11b_6" "h12a_1"
## [2767] "h12a_2" "h12a_3" "h12a_4" "h12a_5" "h12a_6" "h12b_1"
## [2773] "h12b_2" "h12b_3" "h12b_4" "h12b_5" "h12b_6" "h12c_1"
## [2779] "h12c_2" "h12c_3" "h12c_4" "h12c_5" "h12c_6" "h12d_1"
## [2785] "h12d_2" "h12d_3" "h12d_4" "h12d_5" "h12d_6" "h12e_1"
## [2791] "h12e_2" "h12e_3" "h12e_4" "h12e_5" "h12e_6" "h12f_1"
## [2797] "h12f_2" "h12f_3" "h12f_4" "h12f_5" "h12f_6" "h12g_1"
## [2803] "h12g_2" "h12g_3" "h12g_4" "h12g_5" "h12g_6" "h12h_1"
## [2809] "h12h_2" "h12h_3" "h12h_4" "h12h_5" "h12h_6" "h12i_1"
## [2815] "h12i_2" "h12i_3" "h12i_4" "h12i_5" "h12i_6" "h12j_1"
## [2821] "h12j_2" "h12j_3" "h12j_4" "h12j_5" "h12j_6" "h12k_1"
## [2827] "h12k_2" "h12k_3" "h12k_4" "h12k_5" "h12k_6" "h12l_1"
## [2833] "h12l_2" "h12l_3" "h12l_4" "h12l_5" "h12l_6" "h12m_1"
## [2839] "h12m_2" "h12m_3" "h12m_4" "h12m_5" "h12m_6" "h12n_1"
## [2845] "h12n_2" "h12n_3" "h12n_4" "h12n_5" "h12n_6" "h12o_1"
## [2851] "h12o_2" "h12o_3" "h12o_4" "h12o_5" "h12o_6" "h12p_1"
## [2857] "h12p_2" "h12p_3" "h12p_4" "h12p_5" "h12p_6" "h12q_1"
## [2863] "h12q_2" "h12q_3" "h12q_4" "h12q_5" "h12q_6" "h12r_1"
## [2869] "h12r_2" "h12r_3" "h12r_4" "h12r_5" "h12r_6" "h12s_1"
## [2875] "h12s_2" "h12s_3" "h12s_4" "h12s_5" "h12s_6" "h12t_1"
## [2881] "h12t_2" "h12t_3" "h12t_4" "h12t_5" "h12t_6" "h12u_1"
## [2887] "h12u_2" "h12u_3" "h12u_4" "h12u_5" "h12u_6" "h12v_1"
## [2893] "h12v_2" "h12v_3" "h12v_4" "h12v_5" "h12v_6" "h12w_1"
## [2899] "h12w_2" "h12w_3" "h12w_4" "h12w_5" "h12w_6" "h12x_1"
## [2905] "h12x_2" "h12x_3" "h12x_4" "h12x_5" "h12x_6" "h12y_1"
## [2911] "h12y_2" "h12y_3" "h12y_4" "h12y_5" "h12y_6" "h12z_1"
## [2917] "h12z_2" "h12z_3" "h12z_4" "h12z_5" "h12z_6" "h13_1"
## [2923] "h13_2" "h13_3" "h13_4" "h13_5" "h13_6" "h13b_1"
## [2929] "h13b_2" "h13b_3" "h13b_4" "h13b_5" "h13b_6" "h14_1"
## [2935] "h14_2" "h14_3" "h14_4" "h14_5" "h14_6" "h15_1"
## [2941] "h15_2" "h15_3" "h15_4" "h15_5" "h15_6" "h15a_1"
## [2947] "h15a_2" "h15a_3" "h15a_4" "h15a_5" "h15a_6" "h15b_1"
## [2953] "h15b_2" "h15b_3" "h15b_4" "h15b_5" "h15b_6" "h15c_1"
## [2959] "h15c_2" "h15c_3" "h15c_4" "h15c_5" "h15c_6" "h15d_1"
## [2965] "h15d_2" "h15d_3" "h15d_4" "h15d_5" "h15d_6" "h15e_1"
## [2971] "h15e_2" "h15e_3" "h15e_4" "h15e_5" "h15e_6" "h15f_1"
## [2977] "h15f_2" "h15f_3" "h15f_4" "h15f_5" "h15f_6" "h15g_1"
## [2983] "h15g_2" "h15g_3" "h15g_4" "h15g_5" "h15g_6" "h15h_1"
## [2989] "h15h_2" "h15h_3" "h15h_4" "h15h_5" "h15h_6" "h15i_1"
## [2995] "h15i_2" "h15i_3" "h15i_4" "h15i_5" "h15i_6" "h15j_1"
## [3001] "h15j_2" "h15j_3" "h15j_4" "h15j_5" "h15j_6" "h15k_1"
## [3007] "h15k_2" "h15k_3" "h15k_4" "h15k_5" "h15k_6" "h15l_1"
## [3013] "h15l_2" "h15l_3" "h15l_4" "h15l_5" "h15l_6" "h15m_1"
## [3019] "h15m_2" "h15m_3" "h15m_4" "h15m_5" "h15m_6" "h20_1"
## [3025] "h20_2" "h20_3" "h20_4" "h20_5" "h20_6" "h21a_1"
## [3031] "h21a_2" "h21a_3" "h21a_4" "h21a_5" "h21a_6" "h21_1"
## [3037] "h21_2" "h21_3" "h21_4" "h21_5" "h21_6" "h22_1"
## [3043] "h22_2" "h22_3" "h22_4" "h22_5" "h22_6" "h31_1"
## [3049] "h31_2" "h31_3" "h31_4" "h31_5" "h31_6" "h31b_1"
## [3055] "h31b_2" "h31b_3" "h31b_4" "h31b_5" "h31b_6" "h31c_1"
## [3061] "h31c_2" "h31c_3" "h31c_4" "h31c_5" "h31c_6" "h31d_1"
## [3067] "h31d_2" "h31d_3" "h31d_4" "h31d_5" "h31d_6" "h31e_1"
## [3073] "h31e_2" "h31e_3" "h31e_4" "h31e_5" "h31e_6" "h32a_1"
## [3079] "h32a_2" "h32a_3" "h32a_4" "h32a_5" "h32a_6" "h32b_1"
## [3085] "h32b_2" "h32b_3" "h32b_4" "h32b_5" "h32b_6" "h32c_1"
## [3091] "h32c_2" "h32c_3" "h32c_4" "h32c_5" "h32c_6" "h32d_1"
## [3097] "h32d_2" "h32d_3" "h32d_4" "h32d_5" "h32d_6" "h32e_1"
## [3103] "h32e_2" "h32e_3" "h32e_4" "h32e_5" "h32e_6" "h32f_1"
## [3109] "h32f_2" "h32f_3" "h32f_4" "h32f_5" "h32f_6" "h32g_1"
## [3115] "h32g_2" "h32g_3" "h32g_4" "h32g_5" "h32g_6" "h32h_1"
## [3121] "h32h_2" "h32h_3" "h32h_4" "h32h_5" "h32h_6" "h32i_1"
## [3127] "h32i_2" "h32i_3" "h32i_4" "h32i_5" "h32i_6" "h32j_1"
## [3133] "h32j_2" "h32j_3" "h32j_4" "h32j_5" "h32j_6" "h32k_1"
## [3139] "h32k_2" "h32k_3" "h32k_4" "h32k_5" "h32k_6" "h32l_1"
## [3145] "h32l_2" "h32l_3" "h32l_4" "h32l_5" "h32l_6" "h32m_1"
## [3151] "h32m_2" "h32m_3" "h32m_4" "h32m_5" "h32m_6" "h32n_1"
## [3157] "h32n_2" "h32n_3" "h32n_4" "h32n_5" "h32n_6" "h32o_1"
## [3163] "h32o_2" "h32o_3" "h32o_4" "h32o_5" "h32o_6" "h32p_1"
## [3169] "h32p_2" "h32p_3" "h32p_4" "h32p_5" "h32p_6" "h32q_1"
## [3175] "h32q_2" "h32q_3" "h32q_4" "h32q_5" "h32q_6" "h32r_1"
## [3181] "h32r_2" "h32r_3" "h32r_4" "h32r_5" "h32r_6" "h32s_1"
## [3187] "h32s_2" "h32s_3" "h32s_4" "h32s_5" "h32s_6" "h32t_1"
## [3193] "h32t_2" "h32t_3" "h32t_4" "h32t_5" "h32t_6" "h32u_1"
## [3199] "h32u_2" "h32u_3" "h32u_4" "h32u_5" "h32u_6" "h32v_1"
## [3205] "h32v_2" "h32v_3" "h32v_4" "h32v_5" "h32v_6" "h32w_1"
## [3211] "h32w_2" "h32w_3" "h32w_4" "h32w_5" "h32w_6" "h32x_1"
## [3217] "h32x_2" "h32x_3" "h32x_4" "h32x_5" "h32x_6" "h32y_1"
## [3223] "h32y_2" "h32y_3" "h32y_4" "h32y_5" "h32y_6" "h32z_1"
## [3229] "h32z_2" "h32z_3" "h32z_4" "h32z_5" "h32z_6" "h34_1"
## [3235] "h34_2" "h34_3" "h34_4" "h34_5" "h34_6" "h37a_1"
## [3241] "h37a_2" "h37a_3" "h37a_4" "h37a_5" "h37a_6" "h37b_1"
## [3247] "h37b_2" "h37b_3" "h37b_4" "h37b_5" "h37b_6" "h37c_1"
## [3253] "h37c_2" "h37c_3" "h37c_4" "h37c_5" "h37c_6" "h37d_1"
## [3259] "h37d_2" "h37d_3" "h37d_4" "h37d_5" "h37d_6" "h37da_1"
## [3265] "h37da_2" "h37da_3" "h37da_4" "h37da_5" "h37da_6" "h37e_1"
## [3271] "h37e_2" "h37e_3" "h37e_4" "h37e_5" "h37e_6" "h37aa_1"
## [3277] "h37aa_2" "h37aa_3" "h37aa_4" "h37aa_5" "h37aa_6" "h37ab_1"
## [3283] "h37ab_2" "h37ab_3" "h37ab_4" "h37ab_5" "h37ab_6" "h37f_1"
## [3289] "h37f_2" "h37f_3" "h37f_4" "h37f_5" "h37f_6" "h37g_1"
## [3295] "h37g_2" "h37g_3" "h37g_4" "h37g_5" "h37g_6" "h37h_1"
## [3301] "h37h_2" "h37h_3" "h37h_4" "h37h_5" "h37h_6" "h37i_1"
## [3307] "h37i_2" "h37i_3" "h37i_4" "h37i_5" "h37i_6" "h37j_1"
## [3313] "h37j_2" "h37j_3" "h37j_4" "h37j_5" "h37j_6" "h37k_1"
## [3319] "h37k_2" "h37k_3" "h37k_4" "h37k_5" "h37k_6" "h37l_1"
## [3325] "h37l_2" "h37l_3" "h37l_4" "h37l_5" "h37l_6" "h37m_1"
## [3331] "h37m_2" "h37m_3" "h37m_4" "h37m_5" "h37m_6" "h37n_1"
## [3337] "h37n_2" "h37n_3" "h37n_4" "h37n_5" "h37n_6" "h37o_1"
## [3343] "h37o_2" "h37o_3" "h37o_4" "h37o_5" "h37o_6" "h37p_1"
## [3349] "h37p_2" "h37p_3" "h37p_4" "h37p_5" "h37p_6" "h37x_1"
## [3355] "h37x_2" "h37x_3" "h37x_4" "h37x_5" "h37x_6" "h37y_1"
## [3361] "h37y_2" "h37y_3" "h37y_4" "h37y_5" "h37y_6" "h37z_1"
## [3367] "h37z_2" "h37z_3" "h37z_4" "h37z_5" "h37z_6" "h38_1"
## [3373] "h38_2" "h38_3" "h38_4" "h38_5" "h38_6" "h39_1"
## [3379] "h39_2" "h39_3" "h39_4" "h39_5" "h39_6" "h42_1"
## [3385] "h42_2" "h42_3" "h42_4" "h42_5" "h42_6" "h43_1"
## [3391] "h43_2" "h43_3" "h43_4" "h43_5" "h43_6" "h44a_1"
## [3397] "h44a_2" "h44a_3" "h44a_4" "h44a_5" "h44a_6" "h44b_1"
## [3403] "h44b_2" "h44b_3" "h44b_4" "h44b_5" "h44b_6" "h44c_1"
## [3409] "h44c_2" "h44c_3" "h44c_4" "h44c_5" "h44c_6" "h45_1"
## [3415] "h45_2" "h45_3" "h45_4" "h45_5" "h45_6" "h46a_1"
## [3421] "h46a_2" "h46a_3" "h46a_4" "h46a_5" "h46a_6" "h46b_1"
## [3427] "h46b_2" "h46b_3" "h46b_4" "h46b_5" "h46b_6" "h47_1"
## [3433] "h47_2" "h47_3" "h47_4" "h47_5" "h47_6" "hwidx_1"
## [3439] "hwidx_2" "hwidx_3" "hwidx_4" "hwidx_5" "hwidx_6" "hw1_1"
## [3445] "hw1_2" "hw1_3" "hw1_4" "hw1_5" "hw1_6" "hw2_1"
## [3451] "hw2_2" "hw2_3" "hw2_4" "hw2_5" "hw2_6" "hw3_1"
## [3457] "hw3_2" "hw3_3" "hw3_4" "hw3_5" "hw3_6" "hw4_1"
## [3463] "hw4_2" "hw4_3" "hw4_4" "hw4_5" "hw4_6" "hw5_1"
## [3469] "hw5_2" "hw5_3" "hw5_4" "hw5_5" "hw5_6" "hw6_1"
## [3475] "hw6_2" "hw6_3" "hw6_4" "hw6_5" "hw6_6" "hw7_1"
## [3481] "hw7_2" "hw7_3" "hw7_4" "hw7_5" "hw7_6" "hw8_1"
## [3487] "hw8_2" "hw8_3" "hw8_4" "hw8_5" "hw8_6" "hw9_1"
## [3493] "hw9_2" "hw9_3" "hw9_4" "hw9_5" "hw9_6" "hw10_1"
## [3499] "hw10_2" "hw10_3" "hw10_4" "hw10_5" "hw10_6" "hw11_1"
## [3505] "hw11_2" "hw11_3" "hw11_4" "hw11_5" "hw11_6" "hw12_1"
## [3511] "hw12_2" "hw12_3" "hw12_4" "hw12_5" "hw12_6" "hw13_1"
## [3517] "hw13_2" "hw13_3" "hw13_4" "hw13_5" "hw13_6" "hw15_1"
## [3523] "hw15_2" "hw15_3" "hw15_4" "hw15_5" "hw15_6" "hw16_1"
## [3529] "hw16_2" "hw16_3" "hw16_4" "hw16_5" "hw16_6" "hw17_1"
## [3535] "hw17_2" "hw17_3" "hw17_4" "hw17_5" "hw17_6" "hw18_1"
## [3541] "hw18_2" "hw18_3" "hw18_4" "hw18_5" "hw18_6" "hw19_1"
## [3547] "hw19_2" "hw19_3" "hw19_4" "hw19_5" "hw19_6" "hw51_1"
## [3553] "hw51_2" "hw51_3" "hw51_4" "hw51_5" "hw51_6" "hw52_1"
## [3559] "hw52_2" "hw52_3" "hw52_4" "hw52_5" "hw52_6" "hw53_1"
## [3565] "hw53_2" "hw53_3" "hw53_4" "hw53_5" "hw53_6" "hw55_1"
## [3571] "hw55_2" "hw55_3" "hw55_4" "hw55_5" "hw55_6" "hw56_1"
## [3577] "hw56_2" "hw56_3" "hw56_4" "hw56_5" "hw56_6" "hw57_1"
## [3583] "hw57_2" "hw57_3" "hw57_4" "hw57_5" "hw57_6" "hw58_1"
## [3589] "hw58_2" "hw58_3" "hw58_4" "hw58_5" "hw58_6" "hw70_1"
## [3595] "hw70_2" "hw70_3" "hw70_4" "hw70_5" "hw70_6" "hw71_1"
## [3601] "hw71_2" "hw71_3" "hw71_4" "hw71_5" "hw71_6" "hw72_1"
## [3607] "hw72_2" "hw72_3" "hw72_4" "hw72_5" "hw72_6" "hw73_1"
## [3613] "hw73_2" "hw73_3" "hw73_4" "hw73_5" "hw73_6" "v501"
## [3619] "v502" "v503" "v504" "v505" "v506" "v507"
## [3625] "v508" "v509" "v510" "v511" "v512" "v513"
## [3631] "v525" "v527" "v528" "v529" "v530" "v531"
## [3637] "v532" "v535" "v536" "v537" "v538" "v539"
## [3643] "v540" "v541" "v602" "v603" "v604" "v605"
## [3649] "v613" "v614" "v616" "v621" "v623" "v624"
## [3655] "v625" "v626" "v625a" "v626a" "v627" "v628"
## [3661] "v629" "v631" "v632" "v632a" "v633a" "v633b"
## [3667] "v633c" "v633d" "v633e" "v633f" "v633g" "v634"
## [3673] "v701" "v702" "v704" "v704a" "v705" "v714"
## [3679] "v714a" "v715" "v716" "v717" "v719" "v721"
## [3685] "v729" "v730" "v731" "v732" "v739" "v740"
## [3691] "v741" "v743a" "v743b" "v743c" "v743d" "v743e"
## [3697] "v743f" "v744a" "v744b" "v744c" "v744d" "v744e"
## [3703] "v745a" "v745b" "v745c" "v745d" "v746" "v750"
## [3709] "v751" "v754bp" "v754cp" "v754dp" "v754jp" "v754wp"
## [3715] "v756" "v761" "v761b" "v761c" "v762" "v762a"
## [3721] "v762aa" "v762ab" "v762ac" "v762ad" "v762ae" "v762af"
## [3727] "v762ag" "v762ah" "v762ai" "v762aj" "v762ak" "v762al"
## [3733] "v762am" "v762an" "v762ao" "v762ap" "v762aq" "v762ar"
## [3739] "v762as" "v762at" "v762au" "v762av" "v762aw" "v762ax"
## [3745] "v762az" "v762ba" "v762bb" "v762bc" "v762bd" "v762be"
## [3751] "v762bf" "v762bg" "v762bh" "v762bi" "v762bj" "v762bk"
## [3757] "v762bl" "v762bm" "v762bn" "v762bo" "v762bp" "v762bq"
## [3763] "v762br" "v762bs" "v762bt" "v762bu" "v762bv" "v762bw"
## [3769] "v762bx" "v762bz" "v763a" "v763b" "v763c" "v763d"
## [3775] "v763e" "v763f" "v763g" "v766a" "v766b" "v767a"
## [3781] "v767b" "v767c" "v768a" "v768b" "v768c" "v769"
## [3787] "v769a" "v770" "v770a" "v770b" "v770c" "v770d"
## [3793] "v770e" "v770f" "v770g" "v770h" "v770i" "v770j"
## [3799] "v770k" "v770l" "v770m" "v770n" "v770o" "v770p"
## [3805] "v770q" "v770r" "v770s" "v770t" "v770u" "v770v"
## [3811] "v770w" "v770x" "v774a" "v774b" "v774c" "v775"
## [3817] "v777" "v777a" "v778" "v779" "v780" "v781"
## [3823] "v783" "v784a" "v784b" "v784c" "v784d" "v784e"
## [3829] "v784f" "v784g" "v784h" "v784i" "v784j" "v784k"
## [3835] "v784l" "v784m" "v784n" "v784o" "v784p" "v784q"
## [3841] "v784r" "v784s" "v784t" "v784u" "v784v" "v784x"
## [3847] "v785" "v791a" "v820" "v821a" "v821b" "v821c"
## [3853] "v822" "v823" "v824" "v825" "v826" "v826a"
## [3859] "v827" "v828" "v829" "v830" "v831" "v832b"
## [3865] "v832c" "v833a" "v833b" "v833c" "v834a" "v834b"
## [3871] "v834c" "v835a" "v835b" "v835c" "v836" "v837"
## [3877] "v838a" "v838b" "v838c" "v839" "v839a" "v840"
## [3883] "v840a" "v841" "v841a" "v842" "v843" "v844"
## [3889] "v845" "v846" "v847" "v848" "v849" "v850a"
## [3895] "v850b" "v851a" "v851b" "v851c" "v851d" "v851e"
## [3901] "v851f" "v851g" "v851h" "v851i" "v851j" "v851k"
## [3907] "v851l" "v852a" "v852b" "v852c" "v853a" "v853b"
## [3913] "v853c" "v854a" "v854b" "v855" "v856" "v857a"
## [3919] "v857b" "v857c" "v857d" "v858" "v801" "v802"
## [3925] "v803" "v804" "v805" "v806" "v811" "v812"
## [3931] "v813" "v814" "v815a" "v815b" "v815c" "vcol_1"
## [3937] "vcol_2" "vcol_3" "vcol_4" "vcol_5" "vcol_6" "vcol_7"
## [3943] "vcol_8" "vcol_9" "vcal_1" "vcal_2" "vcal_3" "vcal_4"
## [3949] "vcal_5" "vcal_6" "vcal_7" "vcal_8" "vcal_9" "mmidx_01"
## [3955] "mmidx_02" "mmidx_03" "mmidx_04" "mmidx_05" "mmidx_06" "mmidx_07"
## [3961] "mmidx_08" "mmidx_09" "mmidx_10" "mmidx_11" "mmidx_12" "mmidx_13"
## [3967] "mmidx_14" "mmidx_15" "mmidx_16" "mmidx_17" "mmidx_18" "mmidx_19"
## [3973] "mmidx_20" "mm1_01" "mm1_02" "mm1_03" "mm1_04" "mm1_05"
## [3979] "mm1_06" "mm1_07" "mm1_08" "mm1_09" "mm1_10" "mm1_11"
## [3985] "mm1_12" "mm1_13" "mm1_14" "mm1_15" "mm1_16" "mm1_17"
## [3991] "mm1_18" "mm1_19" "mm1_20" "mm2_01" "mm2_02" "mm2_03"
## [3997] "mm2_04" "mm2_05" "mm2_06" "mm2_07" "mm2_08" "mm2_09"
## [4003] "mm2_10" "mm2_11" "mm2_12" "mm2_13" "mm2_14" "mm2_15"
## [4009] "mm2_16" "mm2_17" "mm2_18" "mm2_19" "mm2_20" "mm3_01"
## [4015] "mm3_02" "mm3_03" "mm3_04" "mm3_05" "mm3_06" "mm3_07"
## [4021] "mm3_08" "mm3_09" "mm3_10" "mm3_11" "mm3_12" "mm3_13"
## [4027] "mm3_14" "mm3_15" "mm3_16" "mm3_17" "mm3_18" "mm3_19"
## [4033] "mm3_20" "mm4_01" "mm4_02" "mm4_03" "mm4_04" "mm4_05"
## [4039] "mm4_06" "mm4_07" "mm4_08" "mm4_09" "mm4_10" "mm4_11"
## [4045] "mm4_12" "mm4_13" "mm4_14" "mm4_15" "mm4_16" "mm4_17"
## [4051] "mm4_18" "mm4_19" "mm4_20" "mm5_01" "mm5_02" "mm5_03"
## [4057] "mm5_04" "mm5_05" "mm5_06" "mm5_07" "mm5_08" "mm5_09"
## [4063] "mm5_10" "mm5_11" "mm5_12" "mm5_13" "mm5_14" "mm5_15"
## [4069] "mm5_16" "mm5_17" "mm5_18" "mm5_19" "mm5_20" "mm6_01"
## [4075] "mm6_02" "mm6_03" "mm6_04" "mm6_05" "mm6_06" "mm6_07"
## [4081] "mm6_08" "mm6_09" "mm6_10" "mm6_11" "mm6_12" "mm6_13"
## [4087] "mm6_14" "mm6_15" "mm6_16" "mm6_17" "mm6_18" "mm6_19"
## [4093] "mm6_20" "mm7_01" "mm7_02" "mm7_03" "mm7_04" "mm7_05"
## [4099] "mm7_06" "mm7_07" "mm7_08" "mm7_09" "mm7_10" "mm7_11"
## [4105] "mm7_12" "mm7_13" "mm7_14" "mm7_15" "mm7_16" "mm7_17"
## [4111] "mm7_18" "mm7_19" "mm7_20" "mm8_01" "mm8_02" "mm8_03"
## [4117] "mm8_04" "mm8_05" "mm8_06" "mm8_07" "mm8_08" "mm8_09"
## [4123] "mm8_10" "mm8_11" "mm8_12" "mm8_13" "mm8_14" "mm8_15"
## [4129] "mm8_16" "mm8_17" "mm8_18" "mm8_19" "mm8_20" "mm9_01"
## [4135] "mm9_02" "mm9_03" "mm9_04" "mm9_05" "mm9_06" "mm9_07"
## [4141] "mm9_08" "mm9_09" "mm9_10" "mm9_11" "mm9_12" "mm9_13"
## [4147] "mm9_14" "mm9_15" "mm9_16" "mm9_17" "mm9_18" "mm9_19"
## [4153] "mm9_20" "mm10_01" "mm10_02" "mm10_03" "mm10_04" "mm10_05"
## [4159] "mm10_06" "mm10_07" "mm10_08" "mm10_09" "mm10_10" "mm10_11"
## [4165] "mm10_12" "mm10_13" "mm10_14" "mm10_15" "mm10_16" "mm10_17"
## [4171] "mm10_18" "mm10_19" "mm10_20" "mm11_01" "mm11_02" "mm11_03"
## [4177] "mm11_04" "mm11_05" "mm11_06" "mm11_07" "mm11_08" "mm11_09"
## [4183] "mm11_10" "mm11_11" "mm11_12" "mm11_13" "mm11_14" "mm11_15"
## [4189] "mm11_16" "mm11_17" "mm11_18" "mm11_19" "mm11_20" "mm12_01"
## [4195] "mm12_02" "mm12_03" "mm12_04" "mm12_05" "mm12_06" "mm12_07"
## [4201] "mm12_08" "mm12_09" "mm12_10" "mm12_11" "mm12_12" "mm12_13"
## [4207] "mm12_14" "mm12_15" "mm12_16" "mm12_17" "mm12_18" "mm12_19"
## [4213] "mm12_20" "mm13_01" "mm13_02" "mm13_03" "mm13_04" "mm13_05"
## [4219] "mm13_06" "mm13_07" "mm13_08" "mm13_09" "mm13_10" "mm13_11"
## [4225] "mm13_12" "mm13_13" "mm13_14" "mm13_15" "mm13_16" "mm13_17"
## [4231] "mm13_18" "mm13_19" "mm13_20" "mm14_01" "mm14_02" "mm14_03"
## [4237] "mm14_04" "mm14_05" "mm14_06" "mm14_07" "mm14_08" "mm14_09"
## [4243] "mm14_10" "mm14_11" "mm14_12" "mm14_13" "mm14_14" "mm14_15"
## [4249] "mm14_16" "mm14_17" "mm14_18" "mm14_19" "mm14_20" "mm15_01"
## [4255] "mm15_02" "mm15_03" "mm15_04" "mm15_05" "mm15_06" "mm15_07"
## [4261] "mm15_08" "mm15_09" "mm15_10" "mm15_11" "mm15_12" "mm15_13"
## [4267] "mm15_14" "mm15_15" "mm15_16" "mm15_17" "mm15_18" "mm15_19"
## [4273] "mm15_20" "mm16_01" "mm16_02" "mm16_03" "mm16_04" "mm16_05"
## [4279] "mm16_06" "mm16_07" "mm16_08" "mm16_09" "mm16_10" "mm16_11"
## [4285] "mm16_12" "mm16_13" "mm16_14" "mm16_15" "mm16_16" "mm16_17"
## [4291] "mm16_18" "mm16_19" "mm16_20" "mmc1" "mmc2" "mmc3"
## [4297] "mmc4_1" "mmc4_2" "mmc4_3" "mmc4_4" "mmc4_5" "mmc4_6"
## [4303] "mmc5" "d005" "d101a" "d101b" "d101c" "d101d"
## [4309] "d101e" "d101f" "d101g" "d101h" "d101i" "d101j"
## [4315] "d102" "d103a" "d103b" "d103c" "d103d" "d103e"
## [4321] "d103f" "d104" "d105a" "d105b" "d105c" "d105d"
## [4327] "d105e" "d105f" "d105g" "d105h" "d105i" "d105j"
## [4333] "d105k" "d105l" "d105m" "d105n" "d106" "d107"
## [4339] "d108" "d109" "d110a" "d110b" "d110c" "d110d"
## [4345] "d110e" "d110f" "d110g" "d110h" "d111" "d112"
## [4351] "d112a" "d113" "d114" "d115b" "d115c" "d115d"
## [4357] "d115e" "d115f" "d115g" "d115h" "d115i" "d115j"
## [4363] "d115k" "d115l" "d115m" "d115n" "d115o" "d115p"
## [4369] "d115q" "d115r" "d115s" "d115t" "d115u" "d115v"
## [4375] "d115w" "d115x" "d115y" "d115xa" "d115xb" "d115xc"
## [4381] "d115xd" "d115xe" "d115xf" "d115xg" "d115xh" "d115xi"
## [4387] "d115xj" "d115xk" "d116" "d117a" "d118a" "d118b"
## [4393] "d118c" "d118d" "d118e" "d118f" "d118g" "d118h"
## [4399] "d118i" "d118j" "d118k" "d118l" "d118m" "d118n"
## [4405] "d118o" "d118p" "d118q" "d118r" "d118s" "d118t"
## [4411] "d118u" "d118v" "d118w" "d118x" "d118y" "d118xa"
## [4417] "d118xb" "d118xc" "d118xd" "d118xe" "d118xf" "d118xg"
## [4423] "d118xh" "d118xi" "d118xj" "d118xk" "d119a" "d119b"
## [4429] "d119c" "d119d" "d119e" "d119f" "d119g" "d119h"
## [4435] "d119i" "d119j" "d119k" "d119l" "d119m" "d119n"
## [4441] "d119o" "d119p" "d119q" "d119r" "d119s" "d119t"
## [4447] "d119u" "d119v" "d119w" "d119x" "d119y" "d119xa"
## [4453] "d119xb" "d119xc" "d119xd" "d119xe" "d119xf" "d119xg"
## [4459] "d119xh" "d119xi" "d119xj" "d119xk" "d120" "d121"
## [4465] "d122a" "d122b" "d122c" "d123" "d124" "d125"
## [4471] "d126" "d127" "d128" "d129" "d130a" "d130b"
## [4477] "d130c" "sqtype" "s105d" "s109" "s236b" "s236c"
## [4483] "s236d" "s648c" "s653b" "s653c" "s701a" "s701b"
## [4489] "s815d" "s815e" "s905" "s934a" "s934b" "s1502a"
## [4495] "s1512a" "s1512b" "s1514a" "s1515c" "s1515e" "s1516b"
## [4501] "s1520a" "s1522c" "s1522d" "s1523" "idx92_01" "idx92_02"
## [4507] "idx92_03" "idx92_04" "idx92_05" "idx92_06" "idx92_07" "idx92_08"
## [4513] "idx92_09" "idx92_10" "idx92_11" "idx92_12" "idx92_13" "idx92_14"
## [4519] "idx92_15" "idx92_16" "idx92_17" "idx92_18" "idx92_19" "idx92_20"
## [4525] "s220a_01" "s220a_02" "s220a_03" "s220a_04" "s220a_05" "s220a_06"
## [4531] "s220a_07" "s220a_08" "s220a_09" "s220a_10" "s220a_11" "s220a_12"
## [4537] "s220a_13" "s220a_14" "s220a_15" "s220a_16" "s220a_17" "s220a_18"
## [4543] "s220a_19" "s220a_20" "idx94_1" "idx94_2" "idx94_3" "idx94_4"
## [4549] "idx94_5" "idx94_6" "s413d_1" "s413d_2" "s413d_3" "s413d_4"
## [4555] "s413d_5" "s413d_6" "s413e_1" "s413e_2" "s413e_3" "s413e_4"
## [4561] "s413e_5" "s413e_6" "s431a_1" "s431a_2" "s431a_3" "s431a_4"
## [4567] "s431a_5" "s431a_6" "s431b_1" "s431b_2" "s431b_3" "s431b_4"
## [4573] "s431b_5" "s431b_6" "idx95_1" "idx95_2" "idx95_3" "idx95_4"
## [4579] "idx95_5" "idx95_6" "s505a_1" "s505a_2" "s505a_3" "s505a_4"
## [4585] "s505a_5" "s505a_6" "s505b_1" "s505b_2" "s505b_3" "s505b_4"
## [4591] "s505b_5" "s505b_6" "s506_1" "s506_2" "s506_3" "s506_4"
## [4597] "s506_5" "s506_6" "s507a_1" "s507a_2" "s507a_3" "s507a_4"
## [4603] "s507a_5" "s507a_6" "s508p4_1" "s508p4_2" "s508p4_3" "s508p4_4"
## [4609] "s508p4_5" "s508p4_6" "s508p4d_1" "s508p4d_2" "s508p4d_3" "s508p4d_4"
## [4615] "s508p4d_5" "s508p4d_6" "s508p4m_1" "s508p4m_2" "s508p4m_3" "s508p4m_4"
## [4621] "s508p4m_5" "s508p4m_6" "s508p4y_1" "s508p4y_2" "s508p4y_3" "s508p4y_4"
## [4627] "s508p4y_5" "s508p4y_6" "s525_1" "s525_2" "s525_3" "s525_4"
## [4633] "s525_5" "s525_6" "s526a_1" "s526a_2" "s526a_3" "s526a_4"
## [4639] "s526a_5" "s526a_6" "s526b_1" "s526b_2" "s526b_3" "s526b_4"
## [4645] "s526b_5" "s526b_6" "s526c_1" "s526c_2" "s526c_3" "s526c_4"
## [4651] "s526c_5" "s526c_6" "s526d_1" "s526d_2" "s526d_3" "s526d_4"
## [4657] "s526d_5" "s526d_6" "s526e_1" "s526e_2" "s526e_3" "s526e_4"
## [4663] "s526e_5" "s526e_6" "s526f_1" "s526f_2" "s526f_3" "s526f_4"
## [4669] "s526f_5" "s526f_6" "s526g_1" "s526g_2" "s526g_3" "s526g_4"
## [4675] "s526g_5" "s526g_6" "s526h_1" "s526h_2" "s526h_3" "s526h_4"
## [4681] "s526h_5" "s526h_6" "s526x_1" "s526x_2" "s526x_3" "s526x_4"
## [4687] "s526x_5" "s526x_6" "s526z_1" "s526z_2" "s526z_3" "s526z_4"
## [4693] "s526z_5" "s526z_6"
dat<-zap_labels(dat)
sub<-dat %>%
filter(bidx_01==1&b0_01==0)%>%
transmute(CASEID=caseid,
int.cmc=v008,
fbir.cmc=b3_01,
sbir.cmc=b3_02,
marr.cmc=v509,
rural=v025,
educ=v106,
age = v012,
agec=cut(v012, breaks = seq(15,50,5), include.lowest=T),
partneredu=v701,
partnerage=v730,
weight=v005/1000000,
psu=v021,
strata=v022)%>%
select(CASEID, int.cmc, fbir.cmc, sbir.cmc, marr.cmc, rural, educ, age, agec, partneredu, partnerage, weight, psu, strata)%>%
mutate(agefb = (age - (int.cmc - fbir.cmc)/12))%>%
mutate(secbi = ifelse(is.na(sbir.cmc)==T,
int.cmc - fbir.cmc,
fbir.cmc - sbir.cmc),
b2event = ifelse(is.na(sbir.cmc)==T,0,1))
##person-period model ##You must form a person-period data set
pp<-survSplit(Surv(secbi, b2event)~. ,
data = sub[sub$secbi>0,],
cut=seq(0,240, 12),
episode="year_birth")
pp$year <- pp$year_birth-1
pp<-pp[order(pp$CASEID, pp$year_birth),]
knitr::kable(head(pp[, c("CASEID", "secbi", "b2event", "year", "educ", "agefb", "rural", "partneredu")], n=20))
|
CASEID
|
secbi
|
b2event
|
year
|
educ
|
agefb
|
rural
|
partneredu
|
|
1 13 2
|
12
|
0
|
1
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
24
|
0
|
2
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
36
|
0
|
3
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
48
|
0
|
4
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
60
|
0
|
5
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
72
|
0
|
6
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
84
|
0
|
7
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
96
|
0
|
8
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
108
|
0
|
9
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
120
|
0
|
10
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
132
|
0
|
11
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
144
|
0
|
12
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
156
|
0
|
13
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
168
|
0
|
14
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
180
|
0
|
15
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
192
|
0
|
16
|
1
|
24.66667
|
1
|
1
|
|
1 13 2
|
196
|
0
|
17
|
1
|
24.66667
|
1
|
1
|
|
1 36 2
|
12
|
0
|
1
|
2
|
22.75000
|
1
|
2
|
|
1 36 2
|
24
|
0
|
2
|
2
|
22.75000
|
1
|
2
|
|
1 36 2
|
36
|
0
|
3
|
2
|
22.75000
|
1
|
2
|
options(survey.lonely.psu = "adjust")
des<-survey::svydesign(ids=~psu,
strata=~strata,
data=pp,
weight=~weight )
##basic log models
fit.0<-svyglm(b2event~as.factor(year)-1,
design=des,
family=binomial(link="cloglog"))
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
summary(fit.0)
##
## Call:
## svyglm(formula = b2event ~ as.factor(year) - 1, design = des,
## family = binomial(link = "cloglog"))
##
## Survey design:
## survey::svydesign(ids = ~psu, strata = ~strata, data = pp, weight = ~weight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## as.factor(year)1 -4.73285 0.16969 -27.891 < 2e-16 ***
## as.factor(year)2 -2.59301 0.06627 -39.126 < 2e-16 ***
## as.factor(year)3 -2.05019 0.05698 -35.982 < 2e-16 ***
## as.factor(year)4 -1.94422 0.05018 -38.746 < 2e-16 ***
## as.factor(year)5 -1.85472 0.05608 -33.072 < 2e-16 ***
## as.factor(year)6 -1.77539 0.05706 -31.115 < 2e-16 ***
## as.factor(year)7 -1.69999 0.06850 -24.819 < 2e-16 ***
## as.factor(year)8 -2.00585 0.08490 -23.625 < 2e-16 ***
## as.factor(year)9 -2.00025 0.09480 -21.099 < 2e-16 ***
## as.factor(year)10 -2.06047 0.11273 -18.278 < 2e-16 ***
## as.factor(year)11 -1.96067 0.11481 -17.078 < 2e-16 ***
## as.factor(year)12 -2.15754 0.13221 -16.319 < 2e-16 ***
## as.factor(year)13 -2.23079 0.17452 -12.782 < 2e-16 ***
## as.factor(year)14 -2.26038 0.17878 -12.644 < 2e-16 ***
## as.factor(year)15 -2.33106 0.20768 -11.225 < 2e-16 ***
## as.factor(year)16 -2.41583 0.19583 -12.336 < 2e-16 ***
## as.factor(year)17 -2.65901 0.31435 -8.459 < 2e-16 ***
## as.factor(year)18 -2.78069 0.34502 -8.060 3.54e-15 ***
## as.factor(year)19 -3.92030 0.47573 -8.241 9.06e-16 ***
## as.factor(year)20 -2.98481 0.38224 -7.809 2.24e-14 ***
## as.factor(year)21 -3.10096 0.51659 -6.003 3.18e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.000026)
##
## Number of Fisher Scoring iterations: 7
##Interaction models
fit.1<-svyglm(b2event~as.factor(year)-1+rural+as.factor(partneredu)+partnerage,
design=des,
family=binomial(link="cloglog"))
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
summary(fit.1)
##
## Call:
## svyglm(formula = b2event ~ as.factor(year) - 1 + rural + as.factor(partneredu) +
## partnerage, design = des, family = binomial(link = "cloglog"))
##
## Survey design:
## survey::svydesign(ids = ~psu, strata = ~strata, data = pp, weight = ~weight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## as.factor(year)1 -4.908192 0.399056 -12.300 < 2e-16 ***
## as.factor(year)2 -2.732109 0.312194 -8.751 < 2e-16 ***
## as.factor(year)3 -2.037476 0.295256 -6.901 1.28e-11 ***
## as.factor(year)4 -1.947793 0.285902 -6.813 2.28e-11 ***
## as.factor(year)5 -1.868783 0.294278 -6.350 4.16e-10 ***
## as.factor(year)6 -1.738694 0.292766 -5.939 4.79e-09 ***
## as.factor(year)7 -1.621521 0.293016 -5.534 4.63e-08 ***
## as.factor(year)8 -1.946245 0.308604 -6.307 5.43e-10 ***
## as.factor(year)9 -1.855534 0.315975 -5.872 7.02e-09 ***
## as.factor(year)10 -1.981299 0.333192 -5.946 4.59e-09 ***
## as.factor(year)11 -1.857938 0.341823 -5.435 7.88e-08 ***
## as.factor(year)12 -2.226836 0.353793 -6.294 5.86e-10 ***
## as.factor(year)13 -2.110471 0.409274 -5.157 3.39e-07 ***
## as.factor(year)14 -2.200178 0.343422 -6.407 2.95e-10 ***
## as.factor(year)15 -2.306683 0.429086 -5.376 1.08e-07 ***
## as.factor(year)16 -2.455384 0.447310 -5.489 5.90e-08 ***
## as.factor(year)17 -2.546821 0.585627 -4.349 1.60e-05 ***
## as.factor(year)18 -3.173675 0.590285 -5.377 1.08e-07 ***
## as.factor(year)19 -3.872114 0.660841 -5.859 7.56e-09 ***
## as.factor(year)20 -4.127426 0.783516 -5.268 1.91e-07 ***
## as.factor(year)21 -4.360893 0.928015 -4.699 3.22e-06 ***
## rural 0.196898 0.061649 3.194 0.00148 **
## as.factor(partneredu)1 0.179323 0.177747 1.009 0.31343
## as.factor(partneredu)2 0.061896 0.176883 0.350 0.72651
## as.factor(partneredu)3 0.134403 0.191122 0.703 0.48218
## as.factor(partneredu)8 0.134713 0.553534 0.243 0.80780
## partnerage -0.004413 0.003421 -1.290 0.19761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.071103)
##
## Number of Fisher Scoring iterations: 7
fit.2<-svyglm(b2event~as.factor(year)-1+rural+as.factor(partneredu)+partnerage+as.factor(partneredu)*partnerage,
design=des,
family=binomial(link="cloglog"))
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
summary(fit.2)
##
## Call:
## svyglm(formula = b2event ~ as.factor(year) - 1 + rural + as.factor(partneredu) +
## partnerage + as.factor(partneredu) * partnerage, design = des,
## family = binomial(link = "cloglog"))
##
## Survey design:
## survey::svydesign(ids = ~psu, strata = ~strata, data = pp, weight = ~weight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## as.factor(year)1 -5.211298 0.895990 -5.816 9.69e-09 ***
## as.factor(year)2 -3.035063 0.853590 -3.556 0.000406 ***
## as.factor(year)3 -2.339763 0.851119 -2.749 0.006153 **
## as.factor(year)4 -2.249733 0.832703 -2.702 0.007089 **
## as.factor(year)5 -2.169545 0.843421 -2.572 0.010336 *
## as.factor(year)6 -2.038948 0.845055 -2.413 0.016123 *
## as.factor(year)7 -1.921595 0.846153 -2.271 0.023494 *
## as.factor(year)8 -2.247050 0.851107 -2.640 0.008498 **
## as.factor(year)9 -2.157354 0.854865 -2.524 0.011867 *
## as.factor(year)10 -2.284000 0.863921 -2.644 0.008408 **
## as.factor(year)11 -2.159901 0.866946 -2.491 0.012987 *
## as.factor(year)12 -2.527163 0.870275 -2.904 0.003818 **
## as.factor(year)13 -2.411031 0.918692 -2.624 0.008896 **
## as.factor(year)14 -2.501753 0.754035 -3.318 0.000961 ***
## as.factor(year)15 -2.607626 0.909052 -2.869 0.004266 **
## as.factor(year)16 -2.755735 0.905528 -3.043 0.002441 **
## as.factor(year)17 -2.846917 0.988815 -2.879 0.004127 **
## as.factor(year)18 -3.473739 0.982299 -3.536 0.000436 ***
## as.factor(year)19 -4.171728 1.022707 -4.079 5.12e-05 ***
## as.factor(year)20 -4.424442 1.114850 -3.969 8.08e-05 ***
## as.factor(year)21 -4.657207 1.217147 -3.826 0.000143 ***
## rural 0.192480 0.061079 3.151 0.001704 **
## as.factor(partneredu)1 0.765521 0.947176 0.808 0.419280
## as.factor(partneredu)2 0.397319 0.843362 0.471 0.637728
## as.factor(partneredu)3 0.227167 0.917789 0.248 0.804592
## as.factor(partneredu)8 -0.557761 2.316804 -0.241 0.809833
## partnerage 0.001954 0.015663 0.125 0.900774
## as.factor(partneredu)1:partnerage -0.012307 0.018015 -0.683 0.494770
## as.factor(partneredu)2:partnerage -0.007065 0.016125 -0.438 0.661428
## as.factor(partneredu)3:partnerage -0.001226 0.018164 -0.067 0.946214
## as.factor(partneredu)8:partnerage 0.016968 0.044267 0.383 0.701618
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.070974)
##
## Number of Fisher Scoring iterations: 7
#Plot the hazard function on the probability scale
haz<-1/(1+exp(-coef(fit.0)))
time<-seq(1,21,1)
plot(haz~time, type="l", ylab="h(t)")
title(main="Discrete Time Hazard Function for second birth")
### Plot the survival function estimate
St<-NA
time<-1:length(haz)
St[1]<-1-haz[1]
for(i in 2:length(haz)){
St[i]<-St[i-1]* (1-haz[i])
}
St<-c(1, St)
time<-c(0, time)
plot(y=St,x=time, type="l",
main="Survival function for second birth interval")
## The first of the three models before time specifications were added tested the event birth number 2 with years, followed by the addition of rural, partners age and education, then with the latter variables plus an interaction term of partners age*education. Consistently across the core two models, partners age and education this includes the interaction term, had no significance on whether there was a risk of a second birth.
## In terms of having the rural indicator, living in an area that was rural compared to urban increased the risk of a second birth by at least 54%. This was seen in both models where rural was present as a variable.
## Lastly, the role of age had been considered in all three models, with the results indicating that each additional year of age resulted in a much decreased risk of a second birth. In this case, the peak chances were lowest 1 to 2 years prior the 1st birth with them having about a .008 to .005 percent chance of experiencing another birth, the second peak of which occurred typically from the years 18 to 21 where they had as high as .005% to .001% chances of having a second birth. The ages in between were largely consistent for risk of a second birth.
##Fit the discrete time hazard model to your outcome
##Consider both the general model and other time specifications
##Include all main effects in the model
##Test for an interaction between at least two of the predictors
## Basic discrete time model
#Linear term for time
fit.0<-svyglm(b2event~1,
design=des ,
family=binomial(link="cloglog"))
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
summary(fit.0)
##
## Call:
## svyglm(formula = b2event ~ 1, design = des, family = binomial(link = "cloglog"))
##
## Survey design:
## survey::svydesign(ids = ~psu, strata = ~strata, data = pp, weight = ~weight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.22706 0.01918 -116.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.000026)
##
## Number of Fisher Scoring iterations: 5
1/(1+exp(-coef(fit.0)))
## (Intercept)
## 0.09734632
1/(1+exp(-coef(fit.1)))
## as.factor(year)1 as.factor(year)2 as.factor(year)3
## 0.007331681 0.061105051 0.115323942
## as.factor(year)4 as.factor(year)5 as.factor(year)6
## 0.124794221 0.133682655 0.149478921
## as.factor(year)7 as.factor(year)8 as.factor(year)9
## 0.164995152 0.124963396 0.135224491
## as.factor(year)10 as.factor(year)11 as.factor(year)12
## 0.121180391 0.134943553 0.097366339
## as.factor(year)13 as.factor(year)14 as.factor(year)15
## 0.108083238 0.099734481 0.090570954
## as.factor(year)16 as.factor(year)17 as.factor(year)18
## 0.079045733 0.072640310 0.040168476
## as.factor(year)19 as.factor(year)20 as.factor(year)21
## 0.020389925 0.015868469 0.012606040
## rural as.factor(partneredu)1 as.factor(partneredu)2
## 0.549065973 0.544711031 0.515469028
## as.factor(partneredu)3 as.factor(partneredu)8 partnerage
## 0.533550158 0.533627326 0.498896841
1/(1+exp(-coef(fit.2)))
## as.factor(year)1 as.factor(year)2
## 0.005424999 0.045866744
## as.factor(year)3 as.factor(year)4
## 0.087882880 0.095372533
## as.factor(year)5 as.factor(year)6
## 0.102518919 0.115173935
## as.factor(year)7 as.factor(year)8
## 0.127683780 0.095604263
## as.factor(year)9 as.factor(year)10
## 0.103646047 0.092456750
## as.factor(year)11 as.factor(year)12
## 0.103409597 0.073975768
## as.factor(year)13 as.factor(year)14
## 0.082335382 0.075735405
## as.factor(year)15 as.factor(year)16
## 0.068649251 0.059763596
## as.factor(year)17 as.factor(year)18
## 0.054840909 0.030068743
## as.factor(year)19 as.factor(year)20
## 0.015191245 0.011839049
## as.factor(year)21 rural
## 0.009403671 0.547971991
## as.factor(partneredu)1 as.factor(partneredu)2
## 0.682551285 0.598043247
## as.factor(partneredu)3 as.factor(partneredu)8
## 0.556548736 0.364065783
## partnerage as.factor(partneredu)1:partnerage
## 0.500488417 0.496923284
## as.factor(partneredu)2:partnerage as.factor(partneredu)3:partnerage
## 0.498233727 0.499693538
## as.factor(partneredu)8:partnerage
## 0.504241965
#Linear term for time
fit.l<-svyglm(b2event~year,
design=des ,
family=binomial(link="cloglog"))
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
summary(fit.l)
##
## Call:
## svyglm(formula = b2event ~ year, design = des, family = binomial(link = "cloglog"))
##
## Survey design:
## survey::svydesign(ids = ~psu, strata = ~strata, data = pp, weight = ~weight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.416650 0.028047 -86.164 < 2e-16 ***
## year 0.033726 0.004761 7.084 3.47e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.9952062)
##
## Number of Fisher Scoring iterations: 5
fit.s<-svyglm(b2event~year+I(year^2),
design=des ,
family=binomial(link="cloglog"))
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
summary(fit.s)
##
## Call:
## svyglm(formula = b2event ~ year + I(year^2), design = des, family = binomial(link = "cloglog"))
##
## Survey design:
## survey::svydesign(ids = ~psu, strata = ~strata, data = pp, weight = ~weight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.35596 0.06434 -52.16 <2e-16 ***
## year 0.38369 0.02195 17.48 <2e-16 ***
## I(year^2) -0.02209 0.00149 -14.82 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.009634)
##
## Number of Fisher Scoring iterations: 6
fit.c<-svyglm(b2event~year+I(year^2)+I(year^3 ),
design=des ,
family=binomial(link="cloglog"))
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
summary(fit.c)
##
## Call:
## svyglm(formula = b2event ~ year + I(year^2) + I(year^3), design = des,
## family = binomial(link = "cloglog"))
##
## Survey design:
## survey::svydesign(ids = ~psu, strata = ~strata, data = pp, weight = ~weight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.1368559 0.0912631 -45.33 <2e-16 ***
## year 0.8285081 0.0401488 20.64 <2e-16 ***
## I(year^2) -0.0843188 0.0048356 -17.44 <2e-16 ***
## I(year^3) 0.0023341 0.0001608 14.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.964793)
##
## Number of Fisher Scoring iterations: 6
fit.q<-svyglm(b2event~year+I(year^2)+I(year^3 )+I(year^4),
design=des ,
family=binomial(link="cloglog"))
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
summary(fit.q)
##
## Call:
## svyglm(formula = b2event ~ year + I(year^2) + I(year^3) + I(year^4),
## design = des, family = binomial(link = "cloglog"))
##
## Survey design:
## survey::svydesign(ids = ~psu, strata = ~strata, data = pp, weight = ~weight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.216e+00 1.431e-01 -36.444 < 2e-16 ***
## year 1.659e+00 1.006e-01 16.486 < 2e-16 ***
## I(year^2) -2.680e-01 2.281e-02 -11.750 < 2e-16 ***
## I(year^3) 1.713e-02 1.943e-03 8.816 < 2e-16 ***
## I(year^4) -3.835e-04 5.415e-05 -7.082 3.53e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.9679146)
##
## Number of Fisher Scoring iterations: 6
library(splines)
fit.sp<-svyglm(b2event~ns(year, df = 3),
design=des ,
family=binomial(link="cloglog"))
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
summary(fit.sp)
##
## Call:
## svyglm(formula = b2event ~ ns(year, df = 3), design = des, family = binomial(link = "cloglog"))
##
## Survey design:
## survey::svydesign(ids = ~psu, strata = ~strata, data = pp, weight = ~weight)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.82491 0.07575 -50.492 < 2e-16 ***
## ns(year, df = 3)1 0.95545 0.11930 8.009 4.97e-15 ***
## ns(year, df = 3)2 3.18995 0.16248 19.633 < 2e-16 ***
## ns(year, df = 3)3 -0.16044 0.15785 -1.016 0.31
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.9607684)
##
## Number of Fisher Scoring iterations: 6
## Hazards
dat<-expand.grid(year=seq(1,20,1))
dat$genmod<-predict(fit.0, newdata=data.frame(year=as.factor(1:20 )), type="response")
dat$cons<-predict(fit.c,newdata=dat, type="response")
dat$lin<-predict(fit.l, newdata=dat, type="response")
dat$sq<-predict(fit.s, newdata=dat, type="response")
dat$cub<-predict(fit.c, newdata=dat, type="response")
dat$quart<-predict(fit.q, newdata=dat, type="response")
dat$spline<-predict(fit.sp, newdata=expand.grid(year=seq(1,20,1)), type="response")
dat
## year genmod cons lin sq cub quart
## 1 1 0.1022329 0.03313604 0.08815054 0.04883565 0.03313604 0.02193840
## 2 2 0.1022329 0.05908727 0.09103227 0.06646075 0.05908727 0.05678264
## 3 3 0.1022329 0.09120755 0.09400326 0.08641637 0.09120755 0.10291220
## 4 4 0.1022329 0.12393847 0.09706594 0.10742423 0.12393847 0.14275615
## 5 5 0.1022329 0.15089228 0.10022279 0.12778480 0.15089228 0.16388223
## 6 6 0.1022329 0.16740835 0.10347630 0.14561261 0.16740835 0.16570870
## 7 7 0.1022329 0.17185330 0.10682904 0.15912493 0.17185330 0.15489811
## 8 8 0.1022329 0.16543487 0.11028360 0.16691445 0.16543487 0.13909384
## 9 9 0.1022329 0.15116988 0.11384259 0.16815444 0.15116988 0.12371293
## 10 10 0.1022329 0.13267571 0.11750868 0.16271246 0.13267571 0.11152262
## 11 11 0.1022329 0.11320260 0.12128457 0.15116809 0.11320260 0.10342332
## 12 12 0.1022329 0.09509968 0.12517297 0.13473441 0.09509968 0.09928661
## 13 13 0.1022329 0.07971626 0.12917666 0.11508504 0.07971626 0.09840487
## 14 14 0.1022329 0.06759968 0.13329839 0.09410183 0.06759968 0.09953025
## 15 15 0.1022329 0.05881197 0.13754099 0.07358435 0.05881197 0.10065714
## 16 16 0.1022329 0.05323979 0.14190726 0.05498699 0.05323979 0.09886525
## 17 17 0.1022329 0.05085754 0.14640005 0.03924959 0.05085754 0.09078436
## 18 18 0.1022329 0.05198023 0.15102220 0.02675798 0.05198023 0.07428135
## 19 19 0.1022329 0.05761491 0.15577656 0.01742441 0.05761491 0.05103379
## 20 20 0.1022329 0.07013976 0.16066599 0.01084067 0.07013976 0.02742312
## spline
## 1 0.02158409
## 2 0.03066431
## 3 0.04298980
## 4 0.05878068
## 5 0.07750134
## 6 0.09748243
## 7 0.11581018
## 8 0.12887238
## 9 0.13492314
## 10 0.13449755
## 11 0.12912848
## 12 0.12074750
## 13 0.11121754
## 14 0.10206049
## 15 0.09396874
## 16 0.08680359
## 17 0.08039563
## 18 0.07460558
## 19 0.06931880
## 20 0.06444111
plot(genmod~year, dat, type="l", ylab="h(t)", xlab="Time", ylim=c(0, .25), xlim=c(0, 20))
title(main="Hazard function from different time parameterizations")
lines(cons~year, dat, col=1, lwd=2, lty=3)
lines(lin~year, dat, col=2, lwd=2)
lines(sq~year, dat, col=3, lwd=2)
lines(cub~year, dat, col=4, lwd=2)
lines(quart~year, dat, col=5, lwd=2)
lines(spline~year, dat, col=6, lwd=2)
legend("topleft",
legend=c("General Model","constant", "Linear","Square", "Cubic", "Quartic", "Natural spline"),
col=c(1,1:6), lty=c(1,3,1,1,1,1,1), lwd=1.5)
#AIC table
aic<-round(c(
fit.l$deviance+2*length(fit.l$coefficients),
fit.s$deviance+2*length(fit.s$coefficients),
fit.c$deviance+2*length(fit.c$coefficients),
fit.q$deviance+2*length(fit.q$coefficients),
fit.sp$deviance+2*length(fit.sp$coefficients),
fit.0$deviance+2*length(fit.0$coefficients)),2)
#compare all aics to the one from the general model
dif.aic<-round(aic-aic[6],2)
data.frame(model =c( "linear","square", "cubic", "quartic","spline", "general"),
aic=aic,
aic_dif=dif.aic)
## model aic aic_dif
## 1 linear 25090.01 -90.04
## 2 square 24386.34 -793.71
## 3 cubic 24158.01 -1022.04
## 4 quartic 24010.14 -1169.91
## 5 spline 24002.84 -1177.21
## 6 general 25180.05 0.00
## According to the model aics, the best fit for the model would be the spline. However, it should be noted that the quartic comes pretty close to that of the spline model, and is almost as much a fit for the data.