Logit message meaning requested

[Sunita Rao]

I am unable to post a data sample, as I am getting the following message:
dataex in 1/101
input statement exceeds linesize limit. Try specifying fewer variables
r(1000);




I undertook the following logistic regression:

logit AssuredOrNot Words BoilerWords Fog HardInfoMix1000 RedundantWords Specificity1000 Polarity1000 Subjectivity1000 LnTotalAssets roa lev GovSysAtt Indpres FX_INC SP_ESG_SCORE INSTOWN_PERCENT ESI ib0.NumIndsector Fiscalyear, vce(cluster CompName) baselevels

where NumIndsector is an indicator variable with levels 0-9:
Indsectorlabel:
0 Communication Services
1 Consumer Discretionary
2 Energy
3 Industrials
4 Health Care
5 Information Technology
6 Financials
7 Materials
8 Consumer Staples
9 Utilities
10 (Invalid Identifier)

The tab command provides the following:

tab NumIndsector

tab NumIndsector
Ind sector	Freq.	Percent	Cum.
Communication Services	38	3.08	3.08
Consumer Discretionary	102	8.27	11.35
Energy	89	7.22	18.57
Industrials	187	15.17	33.74
Health Care	161	13.06	46.80
Information Technology	152	12.33	59.12
Financials	189	15.33	74.45
Materials	75	6.08	80.54
Consumer Staples	126	10.22	90.75
Utilities	114	9.25	100.00
Total	1,233	100.00

I am receiving the following message in the output, and, I would be very grateful if someone would please let me know what '0.NumIndsector != 0 predicts failure perfectly' means and how I should interpret it:

note: 0.NumIndsector != 0 predicts failure perfectly;

0.NumIndsector omitted and 37 obs not used.

note: 9.NumIndsector omitted because of collinearity.

Iteration 0: log pseudolikelihood = -589.832

Iteration 1: log pseudolikelihood = -456.34971

Iteration 2: log pseudolikelihood = -439.17782

Iteration 3: log pseudolikelihood = -438.79576

Iteration 4: log pseudolikelihood = -438.79559

Iteration 5: log pseudolikelihood = -438.79559

Logistic regression	Number of obs = 1,171
	Wald chi2(26) = 107.06
	Prob > chi2 = 0.0000
Log pseudolikelihood = -438.79559	Pseudo R2 = 0.2561
	(Std. err.	adjusted for 186 clusters in	CompName)
Robust
AssuredOrNot Coefficient	std. err.	z P>z [95% conf.	interval]
Words .0000148	.0000111	1.34 0.182 -6.93e-06	.0000366
BoilerWords .1125526	2.702291	0.04 0.967 -5.183841	5.408946
Fog -.0051466	.0569717	-0.09 0.928 -.1168091	.106516
HardInfoMix1000 .0228301	.0080042	2.85 0.004 .0071422	.0385181
RedundantWords 3.532224	8.404199	0.42 0.674 -12.9397	20.00415
Specificity1000 -.0364986	.0115707	-3.15 0.002 -.0591767	-.0138204
Polarity1000 -.0250452	.0077966	-3.21 0.001 -.0403262	-.0097642
Subjectivity1000 -.0073231	.0046093	-1.59 0.112 -.0163571	.001711
LnTotalAssets .223048	.1701327	1.31 0.190 -.1104059	.5565019
roa 3.545522	2.142119	1.66 0.098 -.6529535	7.743997
leverage -.9055115	1.217426	-0.74 0.457 -3.291623	1.4806
GovSysAtt .5243119	.2818438	1.86 0.063 -.0280918	1.076716
Indpres -.3471263	.0902281	-3.85 0.000 -.5239701	-.1702825
FX_INC 2.68e-06	1.72e-06	1.56 0.119 -6.85e-07	6.04e-06
SP_ESG_SCORE .0123406	.0099661	1.24 0.216 -.0071925	.0318738
INSTOWN_PERCENT -.0119581	.0270028	-0.44 0.658 -.0648827	.0409664
ESI -.1851819	.624183	-0.30 0.767 -1.408558	1.038194
NumIndsector
Communication Services 0	(empty)
Consumer Discretionary 3.439047	1.510276	2.28 0.023 .4789607	6.399133
Energy 2.107597	1.255424	1.68 0.093 -.352989	4.568183
Industrials 2.482482	1.472968	1.69 0.092 -.4044817	5.369447
Health Care .982115	1.384626	0.71 0.478 -1.731702	3.695933
Information Technology .4398975	1.439663	0.31 0.760 -2.381791	3.261586
Financials .8451889	1.567424	0.54 0.590 -2.226905	3.917283
Materials 1.804325	1.3748	1.31 0.189 -.8902331	4.498882
Consumer Staples 2.030461	1.370176	1.48 0.138 -.6550339	4.715956
Utilities 0	(omitted)
Fiscalyear .1213966	.0554497	2.19 0.029 .0127171	.230076
_cons -242.4183	111.6316	-2.17 0.030 -461.2121	-23.6244
.
end of do-file

[Carlo Lazzaro]

Sunita:
logistic regression works well of the regressand is composed of 0 and 1. If, for any reason, a given predictor causes the regressand to be always 0 or 1, the MLE amchinery cannot work.
Hence, the cuplrit level of the categorical predictor is omitted along with the observations related to it.

[William Lisowski]

Every observation in Communication Services has AssuredOrNot==0 so it is not possible to estimate a coefficient for Communication Services in a logistic model (all failures implies the coefficient is negative infinity).

Because of that, the Communication Services observations are omitted from the model.

[Sunita Rao]

Thank you so much! This is so helpful.