Hi, I am using a loop to conduct logistic regression for several outcome variables that are binary. I run the code below but I am getting an error that says "outcome does not vary". What am I doing wrong?
frame create results str32 Independentvariable float (OR STD_ERR Z PVALUE LB95 UB95)
foreach v of varlist race married never_married grade collgrad south smsa c_city industry occupation {
logistic `v' i.union
matrix M = r(table)
frame post results ("`v'") (M["b", "`v':union"]) (M["se", "`v':union"]) ///
(M["z", "`v':union"]) (M["pvalue", "`v':union"]) (M["ll", "`v':union"]) ///
(M["ul", "`v':union"])
}
frame change results
format OR STD_ERR LB UB %5.3f
format Z PVALUE %05.3f
quietly compress
list, noobs clean
----------------------- copy starting from the next line -----------------------
------------------ copy up to and including the previous line ------------------
frame create results str32 Independentvariable float (OR STD_ERR Z PVALUE LB95 UB95)
foreach v of varlist race married never_married grade collgrad south smsa c_city industry occupation {
logistic `v' i.union
matrix M = r(table)
frame post results ("`v'") (M["b", "`v':union"]) (M["se", "`v':union"]) ///
(M["z", "`v':union"]) (M["pvalue", "`v':union"]) (M["ll", "`v':union"]) ///
(M["ul", "`v':union"])
}
frame change results
format OR STD_ERR LB UB %5.3f
format Z PVALUE %05.3f
quietly compress
list, noobs clean
----------------------- copy starting from the next line -----------------------
Code:
* Example generated by -dataex-. For more info, type help dataex clear input int idcode byte(age race married never_married grade collgrad south smsa c_city industry occupation union) float wage byte hours float(ttl_exp tenure) 1 37 2 0 0 12 0 0 1 0 5 6 1 11.739125 48 10.333334 5.333333 2 37 2 0 0 12 0 0 1 1 4 5 1 6.400963 40 13.621795 5.25 3 42 2 0 1 12 0 0 1 1 4 3 . 5.016723 40 17.73077 1.25 4 43 1 1 0 17 1 0 1 0 11 13 1 9.0338125 42 13.211537 1.75 6 42 1 1 0 12 0 0 1 0 4 6 0 8.083731 48 17.820513 17.75 7 39 1 1 0 12 0 0 1 0 11 3 0 4.6296296 30 7.326923 2.25 9 37 1 0 0 12 0 0 1 1 5 2 1 10.491142 40 19.04487 19 12 40 1 1 0 18 1 0 1 0 11 2 0 17.206116 45 15.557693 14.166667 13 40 1 1 0 14 0 0 1 0 11 3 0 13.083735 8 14.25 5.5 14 40 1 1 0 15 0 0 1 0 11 1 0 7.745568 50 7.384615 2.25 15 39 1 1 0 16 1 0 1 0 11 1 1 16.795479 16 15.76923 8.416667 16 40 1 1 0 15 0 0 1 0 11 1 0 15.48309 40 16.384615 13.833333 18 40 1 1 0 15 0 0 1 0 6 5 0 5.233495 40 7.75 7.75 19 40 1 0 0 15 0 0 1 0 11 1 1 10.161026 40 14.211538 5.583333 20 39 1 1 0 15 0 0 1 0 11 1 1 9.661837 4 15.858974 4.6666665 22 41 1 1 0 15 0 0 1 0 11 1 0 9.057972 32 15.01282 3 23 42 1 1 0 15 1 0 0 0 11 1 0 8.05153 45 15.346155 8.083333 24 41 1 1 0 14 1 0 1 0 11 1 0 11.095006 24 16.467947 3.416667 25 42 1 1 0 14 1 0 1 1 11 1 0 9.581316 40 17.75 5.416667 36 37 1 0 1 12 0 0 1 0 8 4 . 4.180602 40 15.935898 .9166667 39 44 1 0 0 16 1 0 1 0 11 1 0 9.790657 40 8.839744 7.916667 44 41 1 1 0 18 1 0 1 0 12 1 1 28.45666 35 16.205128 3.5 45 35 1 1 0 12 0 0 1 0 5 3 1 10.18518 38 17.25 17.25 46 44 1 1 0 18 1 0 1 0 11 3 0 3.051529 25 5.544872 2.916667 47 35 1 0 0 12 0 0 1 0 5 5 0 3.526568 40 8.737179 .75 48 35 1 0 0 15 0 0 1 1 7 3 . 5.852843 40 5.557693 .3333333 50 36 1 0 0 16 1 0 1 1 11 1 . 35.731617 45 9.423077 6.416667 51 38 1 1 0 12 0 0 1 0 11 1 0 4.4283414 18 10.26923 3.25 54 40 1 0 1 12 0 0 1 1 11 8 0 5.032206 55 10.397436 .75 57 42 1 1 0 12 0 0 1 1 6 3 0 5.780996 40 12.846154 7.25 62 38 1 1 0 10 0 0 0 0 6 3 0 5.11272 40 7.365385 5.916667 63 44 1 0 0 15 1 0 1 1 11 1 1 25.1606 18 10.384615 2.0833333 64 38 1 1 0 12 0 0 1 0 11 8 1 4.404185 30 7.384615 3.333333 66 39 1 1 0 13 0 0 1 1 6 4 0 4.7101436 9 11.48077 9.75 67 40 1 1 0 15 0 0 0 0 11 1 0 8.05153 24 11.038462 3.166667 70 36 1 1 0 13 0 0 0 0 5 3 0 16.739124 38 16.083334 16.083334 71 34 1 1 0 12 0 0 0 0 4 3 0 5.724636 38 10.5 2.5 72 36 1 1 0 12 0 0 1 0 11 3 1 7.458193 40 9.442307 1.0833334 73 36 1 0 1 14 0 0 0 0 4 3 . 6.793478 40 14.442307 .5 75 39 2 0 0 11 0 0 0 0 4 6 1 12.5 40 14.5 9.333333 78 40 1 1 0 12 0 0 1 1 7 3 0 17.697258 35 16.326923 9.083333 80 45 1 1 0 14 0 0 1 0 11 3 0 12.077295 8 5.717949 0 85 38 1 0 0 11 0 0 1 1 4 3 . 9.406354 40 6.192307 1.5 86 44 1 0 1 17 1 0 1 0 10 8 1 13.17229 44 16.26923 1.25 103 36 1 1 0 18 1 0 1 1 11 13 1 11.0628 35 9.833333 0 104 41 1 1 0 17 1 0 1 0 11 13 0 3.7439594 18 8.948718 2.75 105 41 1 1 0 16 1 0 1 0 6 3 0 4.025765 8 8.788462 5.583333 106 38 2 0 0 12 0 1 1 1 12 3 0 10.837358 40 17.416666 17.416666 107 43 2 0 0 8 0 0 1 1 4 6 1 4.347824 40 7.544871 1.0833334 110 45 1 1 0 12 0 0 1 0 11 3 1 4.7101436 20 6.660256 2.583333 121 39 2 1 0 12 0 0 1 1 5 3 1 9.726243 38 20.69872 20.666666 123 41 3 0 0 6 0 0 1 1 8 6 1 2.898549 37 7.320513 .75 126 39 2 0 0 12 0 0 1 1 11 1 0 10.064413 40 19.75 19.75 128 36 1 1 0 12 0 0 1 0 7 1 0 8.518515 40 19.032053 19 129 43 1 0 0 12 0 0 1 0 11 3 0 8.49436 37 14.23077 10.166667 130 43 1 1 0 12 0 0 1 0 4 3 0 5.600358 40 6.153846 .6666667 131 41 1 0 0 12 0 0 1 1 11 3 1 4.227053 38 12.26282 .25 132 36 1 0 0 10 0 0 1 1 4 6 0 5.619967 43 10.24359 2.666667 134 42 1 1 0 12 0 0 1 0 12 2 0 4.64573 30 6.128205 1.5 137 36 2 1 0 17 1 0 1 0 11 13 1 15.48309 30 9.833333 9.833333 139 44 2 0 0 9 0 0 1 1 11 3 1 4.830918 35 8.5 8.5 141 43 1 0 0 14 0 0 1 0 11 3 . 40.19808 40 12.910256 1.1666666 142 43 1 1 0 18 1 0 1 0 11 1 1 15.48309 40 15.115384 5.75 143 37 1 1 0 15 0 0 1 0 11 1 0 10.033444 40 7.076923 .5833333 144 43 1 1 0 10 0 1 1 0 1 3 0 3.6231885 40 14.326923 .3333333 147 40 1 1 0 12 0 0 1 0 7 4 0 9.299514 20 7.666667 3.083333 152 37 1 1 0 18 1 0 1 0 11 13 1 10.217388 50 13.673077 4.833333 159 36 1 1 0 11 0 0 1 0 7 4 0 10.008045 37 17.397436 17.333334 166 38 1 1 0 14 0 0 1 0 11 13 0 4.227053 4 10.602565 1.0833334 167 37 1 1 0 13 0 1 0 0 3 3 0 4.830918 25 16.980768 7.5 168 41 1 1 0 17 1 1 1 0 11 13 0 7.745568 40 11.301282 3.833333 169 36 1 0 0 16 1 0 1 0 11 13 1 9.847019 37 14.833333 14.833333 172 44 1 1 0 15 1 0 1 1 11 1 0 10.869566 30 17.833334 3.416667 173 43 2 0 0 12 0 0 1 1 9 7 . 3.220612 35 11.50641 2.916667 176 38 2 0 1 12 0 0 1 1 12 1 1 8.164251 40 12.846153 3.5 183 43 1 0 0 12 0 0 1 0 4 2 1 8.454107 40 17.121796 13.25 184 41 1 1 0 12 0 0 1 0 11 3 0 8.293073 40 13.788462 12.083333 188 41 1 1 0 12 0 0 1 0 4 3 1 9.677936 40 13.26923 8.416667 195 36 1 1 0 12 0 0 1 0 1 3 0 4.5330095 40 12.801282 2.1666667 202 36 1 0 0 14 0 1 0 0 6 8 . 3.344482 30 11.551282 .25 203 41 1 1 0 11 0 1 1 1 7 4 0 8.454107 24 19.00641 3.166667 204 38 2 1 0 10 0 0 1 1 6 2 0 6.038648 40 5.596154 4 206 37 1 1 0 14 0 0 1 1 11 1 0 10.32206 18 10.333333 1.3333334 207 38 2 0 1 18 1 0 1 1 11 1 1 11.610305 50 13.26923 2.0833333 210 37 2 0 0 11 0 0 1 0 4 6 . 5.016723 40 15.641026 10.916667 213 43 1 0 0 10 0 0 1 1 4 6 0 6.441224 40 16.903847 14.416667 215 36 1 0 1 16 1 0 1 0 6 3 0 5.032206 40 15.852564 3.75 218 35 1 1 0 12 0 0 0 0 11 2 0 4.830918 38 10.115384 2.0833333 219 35 1 0 1 13 0 0 1 1 11 8 0 11.070854 40 16.858974 8.916667 221 40 1 1 0 16 1 0 1 0 4 1 0 8.317226 35 9.256411 1.8333334 223 37 1 0 0 16 1 0 1 0 10 1 0 13.083735 40 15.916667 8.75 224 38 1 1 0 13 0 0 1 0 11 3 1 5.628018 40 10.788462 4.6666665 225 35 1 0 0 16 1 0 1 0 4 2 0 12.560386 45 16.692308 15.75 226 34 1 1 0 16 1 1 1 0 12 1 0 6.682766 40 7.692307 6.666667 227 42 1 0 0 15 0 1 1 1 4 3 0 5.636071 40 18.788462 1.3333334 228 42 1 1 0 16 1 1 1 0 7 3 0 4.025765 15 4.980769 .3333333 231 42 1 1 0 16 1 0 1 0 8 3 0 18.067625 30 10.589743 9.833333 233 36 1 1 0 16 1 0 0 0 11 1 0 5.805152 40 13.923077 13.916667 236 36 1 1 0 18 1 0 1 1 11 1 0 24.66183 20 9.480769 3.75 237 36 1 0 1 17 1 0 1 1 11 3 1 7.21417 40 12.096154 1.75 end label values race racelbl label def racelbl 1 "White", modify label def racelbl 2 "Black", modify label def racelbl 3 "Other", modify label values married marlbl label def marlbl 0 "Single", modify label def marlbl 1 "Married", modify label values never_married nev_mar label def nev_mar 0 "Has been married", modify label def nev_mar 1 "Never married", modify label values collgrad gradlbl label def gradlbl 0 "Not college grad", modify label def gradlbl 1 "College grad", modify label values south southlbl label def southlbl 0 "Not south", modify label def southlbl 1 "South", modify label values smsa smsalbl label def smsalbl 0 "Not SMSA", modify label def smsalbl 1 "SMSA", modify label values c_city ccitylbl label def ccitylbl 0 "Not central city", modify label def ccitylbl 1 "Central city", modify label values industry indlbl label def indlbl 1 "Ag/Forestry/Fisheries", modify label def indlbl 3 "Construction", modify label def indlbl 4 "Manufacturing", modify label def indlbl 5 "Transport/Comm/Utility", modify label def indlbl 6 "Wholesale/Retail trade", modify label def indlbl 7 "Finance/Ins/Real estate", modify label def indlbl 8 "Business/Repair svc", modify label def indlbl 9 "Personal services", modify label def indlbl 10 "Entertainment/Rec svc", modify label def indlbl 11 "Professional services", modify label def indlbl 12 "Public administration", modify label values occupation occlbl label def occlbl 1 "Professional/Technical", modify label def occlbl 2 "Managers/Admin", modify label def occlbl 3 "Sales", modify label def occlbl 4 "Clerical/Unskilled", modify label def occlbl 5 "Craftsmen", modify label def occlbl 6 "Operatives", modify label def occlbl 7 "Transport", modify label def occlbl 8 "Laborers", modify label def occlbl 13 "Other", modify label values union unionlbl label def unionlbl 0 "Nonunion", modify label def unionlbl 1 "Union", modify
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