I have estimated 3 level random intercept logit model. Also, I have predicted the random effects and its std.error.
The codes used are as follows.
Code: Select all
#delimit ;
runmlwin d2 cons t2-t10 i.b4_1 i.b4_2 mort1 mort2 i.ageR2 i.yrsch i.wealthq i.work i.v025 i.caste i.religion diff
female_lit density urban sexratio poverty imr female_work nsdp med_age_marr,
level3(state:cons,residuals(v))level2(stateyear1:cons, residuals(u))level1(episodeid:)
discrete(distribution(binomial) link(logit) denominator(cons) pql2)
mlwinsettings(size(536000) levels(5) columns(1500) variables(300) optimat) nopaus;
#delimit cr
The results are given below.
Thank you.
-----------------------------------------------------------
| No. of Observations per Group
Level Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
state | 25 5191 19054.8 59357
stateyear1 | 900 17 529.3 2992
-----------------------------------------------------------
Run time (seconds) = 796.44
Number of iterations = 12
------------------------------------------------------------------------------
d2 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cons | .1090683 .9636476 0.11 0.910 -1.779646 1.997783
t2 | 1.931034 .0107587 179.49 0.000 1.909947 1.952121
t3 | 2.081403 .0121121 171.85 0.000 2.057663 2.105142
t4 | 1.751582 .014734 118.88 0.000 1.722704 1.78046
t5 | 1.456051 .0182646 79.72 0.000 1.420253 1.491849
t6 | 1.097937 .0233003 47.12 0.000 1.052269 1.143605
t7 | .7370105 .0299055 24.64 0.000 .6783968 .7956243
t8 | .4126172 .0377839 10.92 0.000 .338562 .4866724
t9 | .010698 .0494809 0.22 0.829 -.0862828 .1076787
t10 | .7283634 .0414813 17.56 0.000 .6470615 .8096653
_1b_b4_1 | 0 0 . . 0 0
_2_b4_1 | .1017074 .0073527 13.83 0.000 .0872964 .1161183
_1b_b4_2 | 0 0 . . 0 0
_2_b4_2 | .1573613 .0073399 21.44 0.000 .1429753 .1717472
mort1 | .0175835 .0112936 1.56 0.119 -.0045514 .0397185
mort2 | .2186041 .0141917 15.40 0.000 .1907888 .2464194
_1b_ageR2 | 0 0 . . 0 0
_2_ageR2 | -.1019088 .0087927 -11.59 0.000 -.1191423 -.0846754
_3_ageR2 | -.3416576 .0129397 -26.40 0.000 -.3670189 -.3162963
_4_ageR2 | -.6870595 .0272384 -25.22 0.000 -.7404458 -.6336732
_5_ageR2 | -1.03705 .0786868 -13.18 0.000 -1.191273 -.8828265
_0b_yrsch | 0 0 . . 0 0
_1_yrsch | -.0501475 .0110335 -4.55 0.000 -.0717727 -.0285223
_2_yrsch | -.2356844 .0123127 -19.14 0.000 -.2598167 -.211552
_3_yrsch | -.8280034 .0152081 -54.45 0.000 -.8578107 -.7981961
_1b_wealthq | 0 0 . . 0 0
_2_wealthq | .0584479 .0127415 4.59 0.000 .0334749 .0834209
_3_wealthq | .1113001 .0128843 8.64 0.000 .0860474 .1365527
_4_wealthq | .082883 .0138856 5.97 0.000 .0556676 .1100983
_5_wealthq | -.1020844 .0168653 -6.05 0.000 -.1351397 -.069029
_0b_work | 0 0 . . 0 0
_1_work | -.0093469 .0092149 -1.01 0.310 -.0274077 .0087139
_1b_v025 | 0 0 . . 0 0
_2_v025 | .0420282 .0102944 4.08 0.000 .0218516 .0622048
_1b_caste | 0 0 . . 0 0
_2_caste | -.0424186 .0159984 -2.65 0.008 -.0737749 -.0110623
_3_caste | -.140725 .0106329 -13.23 0.000 -.1615652 -.1198849
_1b_religion | 0 0 . . 0 0
_2_religion | .2270972 .0120155 18.90 0.000 .2035473 .2506471
_3_religion | -.0016808 .0157864 -0.11 0.915 -.0326217 .02926
diff | -.0130624 .0002427 -53.82 0.000 -.0135381 -.0125867
female_lit | -.0139716 .002543 -5.49 0.000 -.0189559 -.0089874
density | .0000225 .0000216 1.04 0.299 -.0000199 .0000649
urban | .0137419 .0029296 4.69 0.000 .0080001 .0194838
sexratio | -.0009488 .000866 -1.10 0.273 -.0026462 .0007486
poverty | .0015769 .0019049 0.83 0.408 -.0021566 .0053104
imr | -.0008813 .0008145 -1.08 0.279 -.0024777 .0007152
female_work | -.0024098 .0021077 -1.14 0.253 -.0065409 .0017213
nsdp | -.0000258 2.47e-06 -10.43 0.000 -.0000306 -.0000209
med_age_marr | -.0231153 .0261615 -0.88 0.377 -.0743909 .0281604
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 3: state |
var(cons) | .0809512 .023465 .0349607 .1269417
-----------------------------+------------------------------------------------
Level 2: stateyear1 |
var(cons) | .0520777 .003336 .0455392 .0586163
------------------------------------------------------------------------------
Summary statistics: mean
by categories of: state (RECODE of v024 (state))
state | v0 v0se
-----------------+--------------------
Jammu and Kashmi | -.3359869 .1059516
Himachal Pradesh | .3888001 .0989626
Punjab | .0884756 .1046537
Haryana | .0597908 .1045056
Delhi | -.4607291 .2160253
Rajasthan | -.2377929 .0832677
Uttar pradesh | -.2503015 .0909252
Bihar | -.3126784 .0787578
Arunanchal Prade | -.0454318 .114413
Nagaland | .4550074 .1044245
Manipur | .1553641 .0963122
Mizoram | .7130891 .1104397
Tripura | -.2760562 .0867473
Meghalaya | .2167409 .0934108
Assam | -.1248221 .0863468
West bengal | -.4308518 .0826869
Orrisa | -.1824791 .0916045
Madhya Pradesh | -.0825534 .0819738
Gujrat | .124661 .0787473
Maharashtra | .1662242 .0862267
Andhra Pradesh | -.0408329 .1008229
Karnataka | .0449862 .0796471
Goa | .1684438 .094427
Kerala | .2842513 .1262546
Tamil Nadu | -.0858196 .0957084
-----------------+--------------------
Total | -.0774662 .0966015
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