Stata 如何在mlogit之后创建混淆矩阵？
我有一个分类变量Stata 如何在mlogit之后创建混淆矩阵？,stata,Stata,我有一个分类变量n_produttore：a-B-C-D-E-F 以及多项式logit（mlogit）的输出 如何创建混淆矩阵 mlogit n_produttore UVAtevola NUTILIZZODIVOLTE EXPOSTFIORITURA DIMENSIONE DOSI Iteration 0: log likelihood = -898.93386 Iteration 1: log likelihood = -868.27679 Iteration 2:
n_produttoremlogit n_produttore UVAtevola NUTILIZZODIVOLTE EXPOSTFIORITURA DIMENSIONE DOSI
Iteration 0: log likelihood = -898.93386
Iteration 1: log likelihood = -868.27679
Iteration 2: log likelihood = -864.38774
Iteration 3: log likelihood = -864.28614
Iteration 4: log likelihood = -864.26279
Iteration 5: log likelihood = -864.25805
Iteration 6: log likelihood = -864.25724
Iteration 7: log likelihood = -864.25705
Iteration 8: log likelihood = -864.25701
Iteration 9: log likelihood = -864.257
Multinomial logistic regression Number of obs = 929
LR chi2(25) = 69.35
Prob > chi2 = 0.0000
Log likelihood = -864.257 Pseudo R2 = 0.0386
----------------------------------------------------------------------------------
n_produttore | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
ALTRO | (base outcome)
-----------------+----------------------------------------------------------------
A |
UVAtevola | -.1579633 .3240817 -0.49 0.626 -.7931517 .4772251
NUTILIZZODIVOLTE | -.2306291 .0957196 -2.41 0.016 -.418236 -.0430221
EXPOSTFIORITURA | -.1879822 .2277447 -0.83 0.409 -.6343536 .2583893
DIMENSIONE | -.0621528 .022512 -2.76 0.006 -.1062755 -.01803
DOSI | .0749472 .0469926 1.59 0.111 -.0171565 .167051
_cons | -.9914935 .2967274 -3.34 0.001 -1.573068 -.4099185
-----------------+----------------------------------------------------------------
B |
UVAtevola | -1.125263 .5444485 -2.07 0.039 -2.192362 -.0581633
NUTILIZZODIVOLTE | -.0667538 .0966905 -0.69 0.490 -.2562637 .1227562
EXPOSTFIORITURA | -.769514 .2891922 -2.66 0.008 -1.33632 -.2027077
DIMENSIONE | -.0293445 .022586 -1.30 0.194 -.0736122 .0149232
DOSI | -.0451004 .1109894 -0.41 0.684 -.2626356 .1724349
_cons | -1.361353 .3900545 -3.49 0.000 -2.125846 -.5968602
-----------------+----------------------------------------------------------------
C |
UVAtevola | -1.232848 1.075072 -1.15 0.251 -3.33995 .8742545
NUTILIZZODIVOLTE | -.1639186 .2256885 -0.73 0.468 -.6062599 .2784227
EXPOSTFIORITURA | -.154228 .5543342 -0.28 0.781 -1.240703 .9322469
DIMENSIONE | -.0993675 .0590232 -1.68 0.092 -.2150508 .0163159
DOSI | .0816812 .1273864 0.64 0.521 -.1679916 .3313541
_cons | -2.727106 .7170044 -3.80 0.000 -4.132409 -1.321803
-----------------+----------------------------------------------------------------
D |
UVAtevola | -14.83818 1290.627 -0.01 0.991 -2544.421 2514.745
NUTILIZZODIVOLTE | -.3792106 .4314916 -0.88 0.379 -1.224919 .4664973
EXPOSTFIORITURA | -.4976473 .8798813 -0.57 0.572 -2.222183 1.226888
DIMENSIONE | -.0976071 .0905061 -1.08 0.281 -.2749958 .0797817
DOSI | -.2036094 .4729157 -0.43 0.667 -1.130507 .7232883
_cons | -2.242187 1.425189 -1.57 0.116 -5.035506 .5511316
-----------------+----------------------------------------------------------------
E |
UVAtevola | .7193533 .2948825 2.44 0.015 .1413942 1.297312
NUTILIZZODIVOLTE | -.1058946 .0921645 -1.15 0.251 -.2865337 .0747446
EXPOSTFIORITURA | -.4057074 .2529228 -1.60 0.109 -.901427 .0900122
DIMENSIONE | -.0641196 .025192 -2.55 0.011 -.113495 -.0147442
DOSI | .0965401 .0441483 2.19 0.029 .010011 .1830692
_cons | -1.615742 .3101875 -5.21 0.000 -2.223698 -1.007786
----------------------------------------------------------------------------------
predict prob*
egen pred_max = rowmax(prob*)
(23 missing values generated)
.
.
.
. g pred_choice = .
(952 missing values generated)
.
. forvalues i = 1/6 {
2.
. replace pred_choice = `i' if (pred_max == prob`i')
3.
. }
(951 real changes made)
(23 real changes made)
(23 real changes made)
(23 real changes made)
(23 real changes made)
(24 real changes made)
.
.
.
. local produttore_lab: value label n_produttore
.
. label values pred_choice `produttore_lab'
.
. tab pred_choice n_produttore
pred_choic | n_produttore
e | ALTRO A B C D E | Total
-----------+------------------------------------------------------------------+----------
ALTRO | 666 95 67 14 6 80 | 928
E | 21 1 0 0 0 2 | 24
-----------+------------------------------------------------------------------+----------
Total | 687 96 67 14 6 82 | 952
多西：1…20你的密码似乎是从年的Tzymund McFarlane的回答中提取出来的。我复制了以下完整的工作示例：
webuse sysdsn1, clear
mlogit insure age male nonwhite i.site
predict prob*
egen pred_max = rowmax(prob*)
g pred_choice = .
forv i=1/3 {
replace pred_choice = `i' if (pred_max == prob`i')
}
local insure_lab: value label insure
label values pred_choice `insure_lab'
tab pred_choice insure
没有任何东西可以让人们一起工作。良好做法是发布准确的输入/输出，包括错误。请阅读中完整的提问部分。对不起，型号：mlogit n_produttore UVAtevola nutilizzodivole expositfouritora DIMENSIONE DOSI其中：n_produttore=ALTRO A B C D E UVAtevola=虚拟0或1 nutilizzodivole=1…15 expositfouritora=虚拟0或1 DIMENSIONE=1…50 kg剂量：1…20Elisa，仍不完整的输入/输出；事实上，你从原来的帖子中删除了看起来很重要的代码。您仍然没有描述代码为什么不起作用，并且似乎不关心花时间来阐述您的问题。这里的人们试图帮助别人，只是为了爱。但是如果你不帮助别人，帮助你，那么合理的结果就是你不会得到帮助