logistic regression by glm

HI

I use glm in R to do logistic regression. and treat both response and
predictor as factor
In my first try:

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Call:
glm(formula = as.factor(diagnostic) ~ as.factor(7161521) +
as.factor(2281517), family = binomial())

Deviance Residuals:
Min 1Q Median 3Q Max
-1.5370 -1.0431 -0.9416 1.3065 1.4331

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.58363 0.27948 -2.088 0.0368 *
as.factor(7161521)2 1.39811 0.66618 2.099 0.0358 *
as.factor(7161521)3 0.28192 0.83255 0.339 0.7349
as.factor(2281517)2 -1.11284 0.63692 -1.747 0.0806 .
as.factor(2281517)3 -0.02286 0.80708 -0.028 0.9774
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 678.55 on 498 degrees of freedom
Residual deviance: 671.20 on 494 degrees of freedom
AIC: 681.2

Number of Fisher Scoring iterations: 4
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And I remodel it and *want no intercept*:
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Call:
glm(formula = as.factor(diagnostic) ~ as.factor(2281517) +
as.factor(7161521) - 1, family = binomial())

Deviance Residuals:
Min 1Q Median 3Q Max
-1.5370 -1.0431 -0.9416 1.3065 1.4331

Coefficients:
Estimate Std. Error z value Pr(>|z|)
as.factor(2281517)1 -0.5836 0.2795 -2.088 0.0368 *
as.factor(2281517)2 -1.6965 0.6751 -2.513 0.0120 *
as.factor(2281517)3 -0.6065 0.8325 -0.728 0.4663
as.factor(7161521)2 1.3981 0.6662 2.099 0.0358 *
as.factor(7161521)3 0.2819 0.8325 0.339 0.7349
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 691.76 on 499 degrees of freedom
Residual deviance: 671.20 on 494 degrees of freedom
AIC: 681.2

Number of Fisher Scoring iterations: 4
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*As show above in my second model it return no intercept but look this:
Model1:
(Intercept) -0.58363 0.27948 -2.088 0.0368 *
Model2:
as.factor(2281517)1 -0.5836 0.2795 -2.088 0.0368 **

They are exactly the same. Could you please tell me what happen?

logistic regression by glm

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