oprobit、goprobit、regoprob、xtprobit简介理论和应用 - Stata专版

我下载到了。数据也放进去了，可是就是不能估计。是为什么呢。是不是软件本身的问题，数据量太多，内存不足。另外实际上有关该命令的输入格式，我也不是很清楚。看说明书，也没看多明白。 6# sungmoo

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junling_5216 发表于 2009-12-24 21:39 我下载到了。数据也放进去了，可是就是不能估计。是为什么呢。是不是软件本身的问题，数据量太多，内存不足。另外实际上有关该命令的输入格式，我也不是很清楚。看说明书，也没看多明白。

个人认为，最可能的原因是数据问题（对于该数据，软件无法运行）。

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谢谢你的热心帮忙。我再想想办法。我现在有个需要想要一下regoprob这个命令的实例的数据，请问版主能不能找到？非常感谢你。 8# sungmoo

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*你试一下，以下过程应该可能生成可用数据。由于数据是随机生成的，如果一次不成，多运行几次。
clear
set obs 1000
forv i=0/4{
g x`i'=uniform()
}
recode x0 (min/0.2=1)(0.2/0.4=2)(0.4/0.6=3)(0.6/0.8=4)(else=5),g(y)
g id=1+int(_n/250)-(int(_n/250)==_n/250)
bys id: g t=_n
**********************************
regoprob y x1-x4,i(id)

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*这一组数据可以得出结果
x1 x2 x3 x4 y id t
.7855145 .4148483 .9672924 .5636365 1 1 1
.4891132 .5820777 .4800692 .1343361 4 1 2
.6336973 .3174483 .7705848 .9074104 4 1 3
.5151367 .4170339 .969061 .835332 1 1 4
.5721931 .6230714 .6681644 .3556956 3 1 5
.3046227 .0383183 .5453745 .9766633 2 1 6
.4318105 .8298915 .1551907 .2055908 2 1 7
.5130663 .1139272 .1167363 .9050711 1 1 8
.8395497 .6312907 .901369 .4074134 4 1 9
.8560454 .8115844 .3548029 .7772152 1 1 10
.1147696 .4184245 .3990681 .4636965 4 1 11
.7432441 .5015272 .1456296 .6004655 3 1 12
.0080549 .7458887 .1503498 .5387357 2 1 13
.5233643 .5261495 .5484185 .0526803 4 1 14
.719352 .5786933 .0788547 .4846123 1 1 15
.1774986 .3588304 .6038918 .0899918 3 1 16
.8127701 .9874201 .0303493 .9148262 4 1 17
.8863347 .1063973 .7053668 .8298107 3 1 18
.1280314 .7416424 .4745232 .8672233 2 1 19
.4280387 .8382017 .5932646 .8111345 2 1 20
.3420053 .3336135 .0543497 .8078092 4 1 21
.4686886 .9585115 .2823193 .375325 4 1 22
.2423129 .1083154 .8570105 .6686282 4 1 23
.5163089 .7613683 .8591404 .1585851 2 1 24
.4493439 .5940258 .5961171 .8792678 1 1 25
.7047736 .3289653 .1172675 .5516298 3 1 26
.9226364 .3502383 .4672639 .3303623 3 1 27
.1667876 .9884339 .4083891 .8535193 1 1 28
.3345577 .526177 .9563726 .3610069 2 1 29
.0334202 .0492676 .2027488 .517252 1 1 30
.3230347 .5101503 .4612937 .8009986 3 1 31
.6149995 .547585 .840754 .1965363 1 1 32
.7707024 .7914839 .622453 .4592512 2 1 33
.5708413 .6342393 .4705188 .7126439 3 1 34
.5249847 .1083541 .3059009 .7841449 3 1 35
.9580671 .5390028 .0804417 .455473 2 1 36
.0867056 .6646335 .6171322 .7538799 2 1 37
.4605925 .9974475 .4883988 .5002643 1 1 38
.3347102 .3627998 .4727273 .3504388 3 1 39
.5278412 .1462441 .9811568 .2283884 1 1 40
.7572482 .9865608 .1242298 .2465122 1 1 41
.3071603 .4854448 .3743305 .3201672 3 1 42
.4797797 .783652 .3901942 .4699183 1 1 43
.8735539 .05794 .7307419 .9928366 1 1 44
.2690743 .9850006 .0355087 .4053905 3 1 45
.915191 .6535023 .5137974 .4732529 2 1 46
.1591169 .1475155 .8377584 .1553158 4 1 47
.8066446 .2084236 .4175089 .9367992 3 1 48
.8375484 .7652485 .4359891 .3182307 3 1 49
.3252038 .4449919 .2090795 .9410909 3 1 50
.7500945 .9660388 .6549914 .7165754 4 1 51
.6254436 .2390605 .1383138 .2442019 2 1 52
.3570203 .8826409 .0560931 .2611444 2 1 53
.4237412 .9111784 .2411674 .1630467 2 1 54
.9389031 .8256416 .0342257 .9812064 2 1 55
.6478928 .660833 .9073706 .3328892 4 1 56
.4113637 .9603657 .6812389 .481461 1 1 57
.6699317 .9148214 .3269282 .2717081 4 1 58
.1133035 .5612336 .732578 .2946592 3 1 59
.4945883 .4921136 .0391642 .4963505 2 1 60
.6590738 .2176286 .4684794 .2165433 1 1 61
.7307997 .4905677 .0244786 .0123056 2 1 62
.0052658 .0801024 .4080208 .4574637 3 1 63
.4503741 .2178805 .6846366 .3314152 2 1 64
.0772914 .2234014 .5452071 .4730301 3 1 65
.4452437 .7875229 .2716686 .1510794 4 1 66
.0617113 .2848507 .8828142 .2427857 4 1 67
.2277197 .8136167 .2877196 .8125443 3 1 68
.6001779 .3547761 .2454932 .4915611 3 1 69
.5015508 .9921632 .2977248 .9993383 2 1 70
.5376317 .872609 .2561656 .4782537 3 1 71
.2292354 .0261509 .5815965 .427813 4 1 72
.9722192 .877807 .9778338 .7997252 2 1 73
.886 .6156721 .0173005 .6583043 1 1 74
.8692363 .4077872 .2441509 .5202078 2 1 75
.8970571 .3225487 .5259095 .1723842 1 1 76
.1566588 .7567852 .0702339 .0635243 2 1 77
.1443027 .4174156 .9778619 .0496896 3 1 78
.6116041 .7083485 .1144601 .159586 3 1 79
.1267317 .4218436 .457881 .1578901 2 1 80
.0319414 .8813681 .8371392 .877988 4 1 81
.4938454 .1945754 .6399719 .5760311 1 1 82
.1316357 .2694365 .3243227 .1012181 4 1 83
.5110014 .4514559 .5814736 .2283208 3 1 84
.6726472 .9144697 .6755065 .9853145 1 1 85
.9829363 .0910812 .3596735 .4429369 2 1 86
.7713345 .3168022 .9812514 .416971 4 1 87
.0409962 .3161408 .8804903 .5329913 1 1 88
.9871545 .5842952 .993685 .6375548 3 1 89
.0793685 .8410327 .1744299 .8595076 4 1 90
.482103 .1260545 .5884111 .6314836 2 1 91
.8982404 .3924674 .0862782 .7121373 3 1 92
.9382811 .0659715 .6155257 .6223851 2 1 93
.9562103 .8125947 .8017021 .6986768 1 1 94
.3134825 .434164 .2661172 .7651535 1 1 95
.7008294 .7555426 .0380007 .2641591 2 1 96
.5027795 .2884779 .4451945 .2082761 2 1 97
.7511916 .9822671 .9034489 .8624913 4 1 98
.0013767 .4182468 .9754953 .487664 2 1 99
.6290751 .5416162 .2426502 .2118131 2 1 100
.4759274 .4462313 .6821705 .3151738 4 1 101
.4973904 .6199433 .9357533 .6210889 3 1 102
.1348747 .3429332 .9665413 .6458717 3 1 103
.0056453 .604077 .0496716 .6708856 1 1 104
.073661 .4498866 .190069 .6541304 3 1 105
.8232573 .8811182 .1525676 .8607178 2 1 106
.9233582 .2389475 .4930585 .4302932 1 1 107
.9865968 .2137033 .2056253 .0501201 1 1 108
.5320074 .5543296 .5515926 .2048885 3 1 109
.1052318 .9464126 .3260377 .3450623 2 1 110
.028998 .4010553 .6310734 .0699077 4 1 111
.2037546 .9057577 .4190668 .3003396 1 1 112
.9007521 .9017127 .040817 .9986017 4 1 113
.2416597 .8655288 .7278075 .4276255 1 1 114
.5744746 .2103215 .0851722 .8629059 4 1 115
.7599005 .8513778 .4613211 .2391407 3 1 116
.9808491 .2433287 .3585564 .3882035 3 1 117
.0135436 .3735442 .1446253 .4866022 4 1 118
.946079 .9275426 .8489553 .2601428 2 1 119
.6101761 .1673443 .24864 .1161869 2 1 120
.1375939 .6814021 .5043892 .3979934 4 1 121
.7487686 .0360403 .1143343 .5937338 3 1 122
.7749617 .5646878 .208024 .1492911 3 1 123
.7266984 .1626352 .4786733 .6584919 4 1 124
.0406623 .1522801 .1610128 .8317133 1 1 125
.0506453 .1973153 .6830398 .6580656 2 1 126
.7163598 .2358761 .6124562 .4271416 2 1 127
.5785286 .6482517 .6377956 .5049917 3 1 128
.0192207 .0764925 .442948 .4032288 4 1 129
.8833736 .2546078 .3793205 .6806586 1 1 130
.3592209 .0685272 .6499889 .6348337 2 1 131
.2404828 .6069414 .2761879 .6104302 1 1 132
.1615352 .1537957 .2860656 .7041669 3 1 133
.1567783 .8401517 .6879741 .1681181 4 1 134
.6056381 .3313701 .8807985 .1019655 4 1 135
.6159174 .4622809 .1909091 .313917 3 1 136
.99767 .1278157 .8974137 .7167974 2 1 137
.2878791 .0824089 .8235567 .9071836 2 1 138
.1389311 .3253918 .8536341 .912024 1 1 139
.4636583 .1725451 .4237637 .8956759 4 1 140
.5733725 .906852 .6857024 .5222944 1 1 141
.1549788 .9259624 .7401133 .8182269 4 1 142
.2566348 .4304817 .1536062 .3373489 4 1 143
.3374544 .8562615 .0192113 .1246695 4 1 144
.6633656 .8809423 .0740551 .3329611 1 1 145
.6350606 .1330018 .9516941 .9573291 1 1 146
.5140995 .230367 .057764 .7547787 3 1 147
.5166733 .442343 .4563105 .7408317 3 1 148
.9364955 .5102558 .6980116 .9523657 4 1 149
.8427545 .1246985 .0566201 .5007877 2 1 150
.1582852 .9802341 .7774711 .1784154 2 1 151
.3679178 .5261233 .9387686 .3598071 2 1 152
.3982722 .7831116 .5826824 .2430695 3 1 153
.8336058 .8127115 .1095343 .691828 1 1 154
.3580716 .9217985 .4349246 .8381608 4 1 155
.1115983 .7008996 .2979989 .842479 2 1 156
.0393762 .6244469 .569896 .1683919 3 1 157
.2852333 .001509 .4686183 .0431814 4 1 158
.3808053 .0323418 .9321321 .8880069 1 1 159
.291751 .2445307 .5064799 .5204458 2 1 160
.422378 .9798661 .5992907 .4318916 1 1 161
.6773286 .7838823 .2886779 .2296784 1 1 162
.2759593 .6659176 .3332781 .2210753 1 1 163
.5799008 .949637 .5015507 .4812491 4 1 164
.2787427 .0530284 .8294027 .9150185 1 1 165
.2292034 .6636735 .1749772 .5670961 3 1 166
.465944 .0406921 .2355569 .0574344 4 1 167
.6825054 .1333841 .1739394 .8201067 1 1 168
.5531755 .3705148 .8613914 .0191226 2 1 169
.0229041 .9231344 .1872425 .6502181 3 1 170
.4991944 .5885478 .3791307 .7879745 3 1 171
.8579348 .9736162 .302606 .1212826 4 1 172
.9406667 .3861107 .6771514 .624834 2 1 173
.5269104 .9612225 .1324736 .6187131 4 1 174
.0184338 .5226443 .8542232 .463754 2 1 175
.3134878 .6063925 .8703751 .1084344 1 1 176
.5451115 .7706179 .9904466 .2139046 1 1 177
.1919398 .1657647 .9138053 .6192681 2 1 178
.6836305 .1435196 .6775239 .1730382 1 1 179
.0678752 .9393514 .8948436 .8317994 2 1 180
.1102304 .5405291 .9976049 .7189314 4 1 181
.2619431 .1829199 .6865157 .2271836 2 1 182
.10829 .8917106 .6261765 .4209354 4 1 183
.3583778 .7020181 .9791332 .382041 2 1 184
.874966 .6265592 .0476132 .9158894 1 1 185
.1837441 .6639122 .7199154 .2385623 4 1 186
.6348295 .0874636 .1139756 .9075285 2 1 187
.1393068 .8646094 .9334516 .4427864 1 1 188
.9771147 .5292804 .1459899 .1265606 1 1 189
.3675761 .2851412 .4103788 .8286859 3 1 190
.365127 .935394 .3551008 .7724303 3 1 191
.9585325 .0542944 .3278426 .5684303 2 1 192
.6373361 .6045994 .4085642 .008804 2 1 193
.2173309 .5303987 .8303661 .546659 1 1 194
.9888514 .8401497 .8891466 .4189134 2 1 195
.4544235 .5847683 .2552857 .4535307 3 1 196
.721113 .8149508 .244559 .9485071 3 1 197
.960415 .2548587 .5145041 .9394501 3 1 198
.8568342 .6406687 .3165032 .0861949 3 1 199
.6995465 .8494591 .3178859 .9080902 2 1 200
.7320138 .8101223 .1672727 .1473017 1 1 201
.0511067 .7510507 .3513776 .0789705 2 1 202
.8486063 .4345607 .6481459 .1670618 1 1 203
.3125514 .8592692 .9382461 .166756 3 1 204
.464539 .6203176 .1854832 .784317 1 1 205
.4248187 .4010694 .514384 .6389041 1 1 206
.7165724 .3803359 .507932 .4234621 3 1 207
.8756166 .3830702 .979656 .9355761 4 1 208
.1063849 .9182948 .620867 .145053 2 1 209
.4190967 .6093141 .8031569 .5093725 4 1 210
.2336246 .0001774 .4753983 .7374537 4 1 211
.8818326 .1523537 .2612972 .459236 2 1 212
.3083365 .5680614 .7434359 .288261 2 1 213
.4969273 .7434076 .2225181 .4933921 3 1 214
.4506229 .2995077 .7703393 .7679728 1 1 215
.5828446 .3988291 .1157149 .2330008 3 1 216
.3812631 .4778081 .0290553 .9648561 1 1 217
.8521291 .3064208 .4126205 .723829 1 1 218
.7944592 .3032301 .4338583 .4919899 1 1 219
.9206061 .0690565 .5752527 .9750842 1 1 220
.1712122 .1677086 .6236976 .2817079 1 1 221
.7628124 .6672755 .2497626 .7999972 3 1 222
.2571519 .8279254 .0885929 .9193971 3 1 223
.8422519 .5482205 .8265988 .2292288 4 1 224
.2628773 .3668588 .5985714 .80173 1 1 225
.8141919 .9678146 .1268378 .4331794 2 1 226
.8331289 .8568926 .6272883 .1956645 1 1 227
.3144807 .5702216 .2265212 .0250082 3 1 228
.9393002 .9307356 .2668879 .5958664 2 1 229
.9872828 .0240003 .0950466 .495687 1 1 230
.3667374 .9158973 .416705 .7022671 2 1 231
.1557477 .3269362 .5324672 .1625937 2 1 232
.8710622 .1163609 .7067542 .0169607 2 1 233
.8958352 .9799211 .7348125 .9332435 1 1 234
.6277738 .4602692 .2007036 .5600886 2 1 235
.3542745 .8428854 .10961 .3361284 2 1 236
.6334331 .5637742 .3314492 .2613588 1 1 237
.3574556 .135533 .0570239 .5853697 1 1 238
.2380058 .7152774 .1739593 .1262367 2 1 239
.06338 .0433249 .1376795 .9096695 3 1 240
.5075237 .2419713 .8531032 .2008651 4 1 241
.4007396 .9057964 .7239824 .8243871 4 1 242
.4557962 .7957904 .9261854 .8977522 1 1 243
.6027666 .7100443 .189772 .3902941 1 1 244
.951116 .8820599 .4282424 .0558299 1 1 245
.9877371 .0400134 .6142458 .3121476 4 1 246
.7993377 .8105879 .2571312 .5767158 1 1 247
.2908651 .9374716 .1523911 .0697632 2 1 248
.0266389 .7801833 .7340469 .8267254 2 1 249
.2060853 .1680128 .0043629 .2396114 3 1 250
.9924168 .8634285 .7261488 .8762687 4 2 1
.6585999 .8124784 .9230229 .0309242 2 2 2
.88672 .8469374 .6600358 .1552496 2 2 3
.3765016 .625726 .795419 .4231039 1 2 4
.5768698 .6007692 .3476692 .5210249 4 2 5
.3200255 .7298059 .4086711 .7108572 3 2 6
.8456999 .1749045 .0697416 .8696835 2 2 7
.7488065 .9519762 .5336132 .0297403 1 2 8
.0255115 .396525 .7125065 .6978047 4 2 9
.0734303 .1238396 .8272507 .2350564 1 2 10
.1899861 .4833674 .4285873 .6071819 2 2 11
.1761802 .7899034 .6515132 .8204691 2 2 12
.8953803 .146367 .4837807 .0542357 4 2 13
.0951767 .8558667 .2225934 .2675848 2 2 14
.1329907 .1553986 .1494194 .0035902 3 2 15
.7885634 .8792081 .4173485 .6815264 1 2 16
.6180655 .1144333 .5068871 .4672084 4 2 17
.183534 .015729 .6894175 .0197788 1 2 18
.9143444 .5034209 .7300938 .1152814 1 2 19
.377725 .5221047 .5406409 .6455683 4 2 20
.1644077 .0874454 .2862312 .6092709 3 2 21
.2091642 .3103982 .4545103 .4182734 4 2 22
.6922542 .1540202 .4432642 .9873229 3 2 23
.3767254 .744581 .4768258 .0908039 2 2 24
.8401285 .4830278 .997409 .0847936 4 2 25
.8145832 .3639091 .7760997 .0756442 1 2 26
.2880347 .3897126 .5764259 .0986987 2 2 27
.1417603 .5253173 .7453513 .5047066 4 2 28
.1322287 .9687446 .3926734 .5341589 3 2 29
.8912565 .5440818 .2675519 .7309288 1 2 30
.266235 .9022146 .616594 .5737275 1 2 31
.2081157 .3590804 .8601603 .7358977 4 2 32
.0949207 .5987629 .2236754 .5046503 4 2 33
.4976119 .5768124 .4707342 .028175 1 2 34
.4519371 .5939229 .5992538 .5751411 3 2 35
.6657434 .4734882 .1957312 .7659838 4 2 36
.4294027 .4704399 .4783466 .16585 2 2 37
.8475239 .7216448 .1891647 .9966583 2 2 38
.5389329 .7945686 .9301797 .7019783 1 2 39
.2743161 .0623022 .6149842 .8803955 2 2 40
.5147119 .5305322 .9600978 .6733439 4 2 41
.8010023 .4216239 .4491889 .9064494 1 2 42
.2628711 .1288822 .7944146 .9943272 1 2 43
.6065179 .5777773 .9875773 .8358241 4 2 44
.7869316 .2826775 .2687622 .8866268 1 2 45
.7529141 .4362356 .9919817 .688464 3 2 46
.2680383 .6451758 .6518237 .4368776 1 2 47
.4260297 .3898382 .3412597 .9993565 1 2 48
.5814086 .8129841 .0869245 .3779924 1 2 49
.8200145 .6338928 .9452679 .2473958 1 2 50
.5871438 .4561145 .331688 .0721824 1 2 51
.2069717 .5024374 .8973928 .6900719 1 2 52
.7125723 .2391891 .8099619 .6451684 1 2 53
.5266603 .6069133 .5075247 .4187244 2 2 54
.7034705 .5442907 .3262041 .4989046 1 2 55
.4327555 .0614415 .753901 .8701519 3 2 56
.9778572 .6257654 .96697 .5387402 1 2 57
.9877325 .0129739 .9248663 .6996905 2 2 58
.1979383 .4183207 .1932374 .015715 1 2 59
.5473087 .3605982 .2913883 .6451868 3 2 60
.6344218 .2175776 .8648042 .5725231 2 2 61
.199402 .5315834 .2104516 .0336745 2 2 62
.7819489 .0588471 .9257284 .98653 3 2 63
.4835737 .5025578 .1206605 .3491685 2 2 64
.0352822 .7422031 .0531101 .4201309 4 2 65
.836066 .5475674 .6553397 .3571266 3 2 66
.0587876 .6413061 .7154374 .2592532 2 2 67
.0749215 .8270121 .829073 .2748566 2 2 68
.8945436 .7501832 .1971815 .5311838 4 2 69
.5755935 .3470289 .282057 .4026194 2 2 70
.2155076 .2675704 .7296976 .4073878 3 2 71
.3573165 .9090436 .7192955 .2037906 4 2 72
.7667295 .8730378 .3581514 .0492005 1 2 73
.1533789 .6758503 .2119037 .6112717 4 2 74
.4970248 .6772368 .6917379 .9230335 2 2 75
.3874604 .9213333 .5684109 .6059403 4 2 76
.3831075 .5092116 .9703602 .3120208 1 2 77
.9650152 .1255235 .5286785 .8635272 2 2 78
.0691698 .2341321 .3883706 .8161131 2 2 79
.918054 .5719717 .0013407 .7571348 3 2 80
.7548114 .27548 .1721288 .6209606 4 2 81
.0794449 .5107328 .1451097 .7787383 1 2 82
.4229872 .2846925 .3981003 .5911999 4 2 83
.9959158 .2435143 .1145452 .4269932 4 2 84
.7448239 .6279216 .9895005 .1377638 2 2 85
.4499256 .9472181 .2022612 .9859949 1 2 86
.3934699 .8358155 .6311363 .4706861 2 2 87
.1878048 .0385075 .7546543 .735594 2 2 88
.1445477 .0184226 .4812377 .6391422 2 2 89
.143824 .321708 .7450025 .2888132 1 2 90
.7478611 .8157353 .6869035 .5875332 4 2 91
.4541676 .1521765 .9763687 .4644111 3 2 92
.162429 .5553541 .6945962 .4282224 2 2 93
.989635 .9329522 .2672253 .4201737 1 2 94
.5355673 .1201291 .7570286 .2623193 3 2 95
.4539221 .3207953 .1233122 .473433 3 2 96
.3088879 .8088563 .1031434 .5112345 1 2 97
.8329884 .2596198 .2287303 .566895 4 2 98
.0929139 .2511215 .9500923 .9082905 4 2 99
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. g id=1+int(_n/250)-(int(_n/250)=_n/250)
Unknown function ()
r(133);
这是为什么

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. regoprob y x1,i(id)
Fitting constant-only model:
Iteration 0: log likelihood = -1686.7245  (not concave)
Iteration 1: log likelihood = -1664.6027
Iteration 2: log likelihood = -1613.7572  (not concave)
Iteration 3: log likelihood = -1613.6713
Iteration 4: log likelihood = -1612.4813
Iteration 5: log likelihood = -1612.4787
Iteration 6: log likelihood = -1612.4787
Fitting full model:
Iteration 0: log likelihood = -1683.4915  (not concave)
Iteration 1: log likelihood = -1678.7321
no observations
r(2000);
.
在这一步还是出错了。 10# sungmoo

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goprobit、regoprob
怎么在STATA中找不到

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satat-help-search-on net - regoprob-click(install) 14# fentone

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junling_5216 发表于 2009-12-25 10:10 在这一步还是出错了

*对11楼数据使用
regoprob y x1-x4,i(id)

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fentone 发表于 2009-12-25 10:09 . g id=1+int(_n/250)-(int(_n/250)=_n/250)
Unknown function ()
r(133);
这是为什么

应该用“==”。

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由于新版本都是免安装的，我怕是软件本身的问题。所以我刚安装了个statat8版，可以出来结果。但是又一个问题为什么变量放的少些时可以估计出来结果，多了就不行了。是数据本身的问题，还是软件处理的问题。我就是放的你给我随机生成的数据。regoprob y x1 x2,i(id) 可以；但是regoprob y x1 x2 x3 x4,i(id)就报道出来没有观察值。你做了吗，出现什么情况，我想是不是我的软件本身的问题？？？谢谢你！ 17# sungmoo

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完全不懂啊

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计量经济学现在前沿太多呢，发展速度几天就是一个台阶。

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junling_5216 发表于 2009-12-25 11:26 由于新版本都是免安装的，我怕是软件本身的问题。所以我刚安装了个statat8版，可以出来结果。但是又一个问题为什么变量放的少些时可以估计出来结果，多了就不行了。是数据本身的问题，还是软件处理的问题。我就是放的你给我随机生成的数据。regoprob y x1 x2,i(id) 可以；但是regoprob y x1 x2 x3 x4,i(id)就报道出来没有观察值。你做了吗，出现什么情况，我想是不是我的软件本身的问题？？？谢谢你！

个人感觉应该不主要是软件的问题。换了变量（或其观测值），涉及的估计的计算内容就不一样了，当然可能有的出结果，有的无结果。

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junling_5216 发表于 2009-12-25 11:26
由于新版本都是免安装的，我怕是软件本身的问题。所以我刚安装了个statat8版，可以出来结果。但是又一个问题为什么变量放的少些时可以估计出来结果，多了就不行了。是数据本身的问题，还是软件处理的问题。我就是放的你给我随机生成的数据。regoprob y x1 x2,i(id) 可以；但是regoprob y x1 x2 x3 x4,i(id)就报道出来没有观察值。你做了吗，出现什么情况，我想是不是我的软件本身的问题？？？谢谢你！ 17# sungmoo

i(id)表示什么意思

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fentone 发表于 2009-12-25 13:23 i(id)表示什么意思

以id为panel data的分组（或个体）变量。

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谢谢你。我再试试。反正是既然能有的算出来结果，说明可估。目前就是数据的问题。呵呵，还有个问题想请教，就是边际概率效应值是怎么估计出来的，命令MFX如何具体写啊，我也找不到Long(2001）的资料，据说是有关这方面介绍的。我对使用stata还是不熟悉，请见谅，希望您能再给指导。给发些资料。[email=fanqingquan@yahoo.cn;另外有关regoprob的实例数据我向老外作者要了，不清楚人家有没有时间或者方不方便给数据。祝版主圣诞节快日，辛苦了。fanqingquan@yahoo.cn;另外有关regoprob的实例数据我向老外作者要了，不清楚人家有没有时间或者方不方便给数据。祝版主圣诞节快日，辛苦了。[b[/email]] 21# sungmoo

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regoprob y x1-x4,i(id)

Fitting constant-only model:

Iteration 0: log likelihood = -1435.1562  (not concave)
Iteration 1: log likelihood =  -1416.286
Iteration 2: log likelihood = -1369.8608  (not concave)
Iteration 3: log likelihood = -1368.9282
Iteration 4: log likelihood = -1368.8875
Iteration 5: log likelihood = -1368.8875

Fitting full model:

Iteration 0: log likelihood = -1427.5407  (not concave)
Iteration 1: log likelihood = -1419.9814  (not concave)
Iteration 2: log likelihood =  -1363.951
Iteration 3: log likelihood =  -1361.945
Iteration 4: log likelihood =  -1361.943  (not concave)
Iteration 5: log likelihood =  -1361.943

Random Effects Generalized Ordered Probit       Number of obs =    1000
                                                LR chi2(12)    =    -9.07
Log likelihood =  -1361.943                      Prob > chi2    =    1.0000

------------------------------------------------------------------------------
         y |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
mleq1       |
      x1 |  -.0857007 .1432273 -0.60 0.550 -.3664211 .1950197
      x2 |  -.2557927 .1458197 -1.75 0.079    -.541594 .0300086
      x3 |  -.1914822 .142547 -1.34 0.179 -.4708691 .0879047
      x4 |  -.0887955 .1471338 -0.60 0.546 -.3771724 .1995814
   _cons | .4996052 46.33875    0.01 0.991 -90.32268 91.32189
-------------+----------------------------------------------------------------
mleq2       |
      x1 | .0150964 .1386386    0.11 0.913 -.2566303    .286823
      x2 | .0043671 .1405979    0.03 0.975 -.2711998 .2799339
      x3 |  -.1451427 .1382748 -1.05 0.294 -.4161563 .1258709
      x4 | .1023076 .1427602    0.72 0.474 -.1774972 .3821125
   _cons | -.587587 46.33874 -0.01 0.990 -91.40984 90.23467
-------------+----------------------------------------------------------------
mleq3       |
      x1 |  -.0960871 .1560995 -0.62 0.538 -.4020365 .2098622
      x2 | .0951107 .1578431    0.60 0.547 -.2142561 .4044775
      x3 | .1087849 .1540348    0.71 0.480 -.1931178 .4106876
      x4 | .0034037 .1590347    0.02 0.983 -.3082986 .3151059
   _cons |  -1.191301 46.3388 -0.03 0.979 -92.01368 89.63108
-------------+----------------------------------------------------------------
rho       |
   _cons | .3681392 63.55257    0.01 0.995 -124.1926 124.9289
------------------------------------------------------------------------------
谁能解释一下这个结果吗?

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. goprobit y x1-x4

Iteration 0: log likelihood = -1364.3505
Iteration 1: log likelihood = -1357.4988
Iteration 2: log likelihood = -1357.4061
Iteration 3: log likelihood = -1357.4061

Generalized Ordered Probit Estimates             Number of obs =    1000
                                                LR chi2(12)    =    13.89
Log likelihood = -1357.4061                      Prob > chi2    =    0.3079

------------------------------------------------------------------------------
         y |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
mleq1       |
      x1 | -.085705 .1432272 -0.60 0.550    -.366425 .1950151
      x2 |  -.2557969 .1458195 -1.75 0.079 -.5415979 .0300041
      x3 | -.191487 .1425468 -1.34 0.179 -.4708737 .0878997
      x4 |  -.0888005 .1471336 -0.60 0.546 -.3771771 .1995762
   _cons | .8388252 .1529905    5.48 0.000    .5389693 1.138681
-------------+----------------------------------------------------------------
mleq2       |
      x1 | .0151004 .1386384    0.11 0.913    -.256626 .2868267
      x2 | .0043704 .1405978    0.03 0.975 -.2711962    .279937
      x3 |  -.1451391 .1382746 -1.05 0.294 -.4161525 .1258742
      x4 | .1023124 .1427601    0.72 0.474 -.1774922    .382117
   _cons |  -.2483814 .1482034 -1.68 0.094 -.5388548    .042092
-------------+----------------------------------------------------------------
mleq3       |
      x1 |  -.0960845 .1560994 -0.62 0.538 -.4020337 .2098646
      x2 | .0951118 .157843    0.60 0.547 -.2142548 .4044785
      x3 | .1087871 .1540347    0.71 0.480 -.1931154 .4106896
      x4 | .0034064 .1590346    0.02 0.983 -.3082956 .3151085
   _cons |  -.8520915 .166328 -5.12 0.000 -1.178088 -.5260947
------------------------------------------------------------------------------
做出来结果,但不知道是什么意思?请高手相助,解释一下

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oprobit y x1-x4

Iteration 0: log likelihood = -1364.3505
Iteration 1: log likelihood = -1363.6934
Iteration 2: log likelihood = -1363.6934

Ordered probit regression                      Number of obs =    1000
                                                LR chi2(4)    =    1.31
                                                Prob > chi2    =    0.8590
Log likelihood = -1363.6934                      Pseudo R2    =    0.0005

------------------------------------------------------------------------------
         y |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
      x1 |  -.0624338 .1188711 -0.53 0.599 -.2954169 .1705494
      x2 |  -.0803227 .120152 -0.67 0.504 -.3158163 .1551708
      x3 |  -.0908169 .117516 -0.77 0.440 -.3211441 .1395103
      x4 |  -.0063075 .1215458 -0.05 0.959    -.244533    .231918
-------------+----------------------------------------------------------------
   /cut1 |  -.6448426 .1280714                   -.8958579 -.3938274
   /cut2 | .1391021 .1269439                   -.1097033 .3879075
   /cut3 | .6764361 .1286613                   .4242646 .9286076
请高手解释一下结果

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请问用的是stata的哪个版本的？？能给我发过来吗？[email=fanqingquan@yahoo.cnfanqingquan@yahoo.cn[b[/email]] 25# fentone

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支持一下，qq 1034843713

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东西是好，可是太贵

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