--------------------------------------------------------------------------------------------------

name:  <unnamed>

log:  C:\Users\mexmi\Documents\newer web pages\Soc_382\logs\3rd loglin intermar class.log

log type:  text

opened on:   5 Feb 2019, 10:16:22

. use "C:\Users\mexmi\Documents\newer web pages\Soc_382\Treimans ch 12 occ mobility data 6x6 versi

> on with extra vars.dta", clear

. clear all

> clear

* If you go back to the beginning of intermarriage class 2 log, you will see a description of how the crossings terms were created. We have 4 crossings terms, each of which can be thought of as indicating the difficulty from crossing from group x to group x+1. So cross1 is an estimate of the likelihood of marriage between category 1 (black people) and category 2 (Mexican American people).

. poisson count i.meth_num i.feth_num i.cross*

Iteration 0:   log likelihood = -1628492.6

Iteration 1:   log likelihood = -514542.18  (backed up)

Iteration 2:   log likelihood = -405521.33  (backed up)

Iteration 3:   log likelihood = -241959.01  (backed up)

Iteration 4:   log likelihood = -147645.83

Iteration 5:   log likelihood = -59259.102

Iteration 6:   log likelihood = -12308.206

Iteration 7:   log likelihood = -10257.604

Iteration 8:   log likelihood =  -10246.31

Iteration 9:   log likelihood = -10246.309

Poisson regression                                Number of obs   =         25

LR chi2(12)     = 3132470.69

Prob > chi2     =     0.0000

Log likelihood = -10246.309                       Pseudo R2       =     0.9935

-------------------------------------------------------------------------------------

count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

--------------------+----------------------------------------------------------------

meth_num |

Mexican American  |   -.977803   .0181359   -53.92   0.000    -1.013349   -.9422572

Hispanic Other  |  -1.938943    .020284   -95.59   0.000    -1.978698   -1.899187

Non Hispanic Other  |  -2.506451   .0216409  -115.82   0.000    -2.548866   -2.464035

White non Hispanic  |   .6765227   .0185343    36.50   0.000     .6401961    .7128493

|

feth_num |

Mexican American  |  -.0199891    .018195    -1.10   0.272    -.0556507    .0156725

Hispanic Other  |  -.8181779   .0202753   -40.35   0.000    -.8579168    -.778439

Non Hispanic Other  |  -1.107257   .0209967   -52.73   0.000     -1.14841   -1.066104

White non Hispanic  |   1.871097   .0185343   100.95   0.000      1.83477    1.907423

|

1.cross1 |  -3.026666    .017803  -170.01   0.000    -3.061559   -2.991773

1.cross2 |  -1.014698   .0101682   -99.79   0.000    -1.034627   -.9947684

1.cross3 |  -.5411649   .0113079   -47.86   0.000     -.563328   -.5190018

1.cross4 |  -1.444942   .0092927  -155.49   0.000    -1.463155   -1.426728

_cons |   10.65775   .0048495  2197.69   0.000     10.64825    10.66726

-------------------------------------------------------------------------------------

. poisgof

Deviance goodness-of-fit =  20262.62

Prob > chi2(12)          =    0.0000

Pearson goodness-of-fit  =  21598.27

Prob > chi2(12)          =    0.0000

. * that is the crossings model. And it fits poorly, as the crossings model and the uniform association model below assume that the categories are ordinal, and these categories are not ordinal.

. gen score=meth_num*feth_num

. poisson count i.meth_num i.feth_num score

Iteration 0:   log likelihood = -448819.89

Iteration 1:   log likelihood = -199150.22  (backed up)

Iteration 2:   log likelihood = -72023.171

Iteration 3:   log likelihood = -48477.638

Iteration 4:   log likelihood = -46237.124

Iteration 5:   log likelihood = -46234.659

Iteration 6:   log likelihood = -46234.659

Poisson regression                                Number of obs   =         25

LR chi2(9)      = 3060493.99

Prob > chi2     =     0.0000

Log likelihood = -46234.659                       Pseudo R2       =     0.9707

-------------------------------------------------------------------------------------

count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

--------------------+----------------------------------------------------------------

meth_num |

Mexican American  |  -1.712384   .0084087  -203.64   0.000    -1.728865   -1.695903

Hispanic Other  |   -4.42779     .01369  -323.43   0.000    -4.454622   -4.400958

Non Hispanic Other  |  -7.618328   .0212675  -358.21   0.000    -7.660012   -7.576645

White non Hispanic  |   -6.52374   .0275075  -237.16   0.000    -6.577654   -6.469826

|

feth_num |

Mexican American  |  -1.609809   .0084125  -191.36   0.000    -1.626297   -1.593321

Hispanic Other  |  -4.125007    .012931  -319.00   0.000    -4.150352   -4.099663

Non Hispanic Other  |  -6.986868   .0190834  -366.12   0.000    -7.024271   -6.949465

White non Hispanic  |  -6.092624   .0254213  -239.67   0.000    -6.142449   -6.042799

|

score |   .6448603   .0020341   317.02   0.000     .6408736    .6488471

_cons |   9.695309   .0055428  1749.17   0.000     9.684445    9.706172

-------------------------------------------------------------------------------------

. poisgof

Deviance goodness-of-fit =  92239.32

Prob > chi2(15)          =    0.0000

Pearson goodness-of-fit  =    123297

Prob > chi2(15)          =    0.0000

>  replace

> d

. *that was the uniform model

. table meth_num feth_num, contents(mean cross1) cellwidth(10)

note: cellwidth too small, variable name truncated;

to increase cellwidth, specify cellwidth(#)

--------------------------------------------------------------------------------

husband's           |                   wife's race/ethnicity

race/ethnicity      | Black, non  Mexican Am  Hispanic O  Non Hispan  White non

--------------------+-----------------------------------------------------------

Black, non Hispanic |          0           1           1           1           1

Mexican American |          1           0           0           0           0

Hispanic Other |          1           0           0           0           0

Non Hispanic Other |          1           0           0           0           0

White non Hispanic |          1           0           0           0           0

--------------------------------------------------------------------------------

. table meth_num feth_num, contents(mean cross2) cellwidth(10)

note: cellwidth too small, variable name truncated;

to increase cellwidth, specify cellwidth(#)

--------------------------------------------------------------------------------

husband's           |                   wife's race/ethnicity

race/ethnicity      | Black, non  Mexican Am  Hispanic O  Non Hispan  White non

--------------------+-----------------------------------------------------------

Black, non Hispanic |          0           0           1           1           1

Mexican American |          0           0           1           1           1

Hispanic Other |          1           1           0           0           0

Non Hispanic Other |          1           1           0           0           0

White non Hispanic |          1           1           0           0           0

--------------------------------------------------------------------------------

. table meth_num feth_num, contents(mean cross3) cellwidth(10)

note: cellwidth too small, variable name truncated;

to increase cellwidth, specify cellwidth(#)

--------------------------------------------------------------------------------

husband's           |                   wife's race/ethnicity

race/ethnicity      | Black, non  Mexican Am  Hispanic O  Non Hispan  White non

--------------------+-----------------------------------------------------------

Black, non Hispanic |          0           0           0           1           1

Mexican American |          0           0           0           1           1

Hispanic Other |          0           0           0           1           1

Non Hispanic Other |          1           1           1           0           0

White non Hispanic |          1           1           1           0           0

--------------------------------------------------------------------------------

. table meth_num feth_num, contents(mean cross4) cellwidth(10)

note: cellwidth too small, variable name truncated;

to increase cellwidth, specify cellwidth(#)

--------------------------------------------------------------------------------

husband's           |                   wife's race/ethnicity

race/ethnicity      | Black, non  Mexican Am  Hispanic O  Non Hispan  White non

--------------------+-----------------------------------------------------------

Black, non Hispanic |          0           0           0           0           1

Mexican American |          0           0           0           0           1

Hispanic Other |          0           0           0           0           1

Non Hispanic Other |          0           0           0           0           1

White non Hispanic |          1           1           1           1           0

--------------------------------------------------------------------------------

. poisson count i.meth_num i.feth_num i.cross*

Iteration 0:   log likelihood = -1628492.6

Iteration 1:   log likelihood = -514542.18  (backed up)

Iteration 2:   log likelihood = -405521.33  (backed up)

Iteration 3:   log likelihood = -241959.01  (backed up)

Iteration 4:   log likelihood = -147645.83

Iteration 5:   log likelihood = -59259.102

Iteration 6:   log likelihood = -12308.206

Iteration 7:   log likelihood = -10257.604

Iteration 8:   log likelihood =  -10246.31

Iteration 9:   log likelihood = -10246.309

Poisson regression                                Number of obs   =         25

LR chi2(12)     = 3132470.69

Prob > chi2     =     0.0000

Log likelihood = -10246.309                       Pseudo R2       =     0.9935

-------------------------------------------------------------------------------------

count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

--------------------+----------------------------------------------------------------

meth_num |

Mexican American  |   -.977803   .0181359   -53.92   0.000    -1.013349   -.9422572

Hispanic Other  |  -1.938943    .020284   -95.59   0.000    -1.978698   -1.899187

Non Hispanic Other  |  -2.506451   .0216409  -115.82   0.000    -2.548866   -2.464035

White non Hispanic  |   .6765227   .0185343    36.50   0.000     .6401961    .7128493

|

feth_num |

Mexican American  |  -.0199891    .018195    -1.10   0.272    -.0556507    .0156725

Hispanic Other  |  -.8181779   .0202753   -40.35   0.000    -.8579168    -.778439

Non Hispanic Other  |  -1.107257   .0209967   -52.73   0.000     -1.14841   -1.066104

White non Hispanic  |   1.871097   .0185343   100.95   0.000      1.83477    1.907423

|

1.cross1 |  -3.026666    .017803  -170.01   0.000    -3.061559   -2.991773

1.cross2 |  -1.014698   .0101682   -99.79   0.000    -1.034627   -.9947684

1.cross3 |  -.5411649   .0113079   -47.86   0.000     -.563328   -.5190018

1.cross4 |  -1.444942   .0092927  -155.49   0.000    -1.463155   -1.426728

_cons |   10.65775   .0048495  2197.69   0.000     10.64825    10.66726

-------------------------------------------------------------------------------------

. poisgof

Deviance goodness-of-fit =  20262.62

Prob > chi2(12)          =    0.0000

Pearson goodness-of-fit  =  21598.27

Prob > chi2(12)          =    0.0000

. drop score

. gen score=meth_num*feth_num

. table meth_num feth_num, contents(mean score) cellwidth(10)

note: cellwidth too small, variable name truncated;

to increase cellwidth, specify cellwidth(#)

--------------------------------------------------------------------------------

husband's           |                   wife's race/ethnicity

race/ethnicity      | Black, non  Mexican Am  Hispanic O  Non Hispan  White non

--------------------+-----------------------------------------------------------

Black, non Hispanic |          1           2           3           4           5

Mexican American |          2           4           6           8          10

Hispanic Other |          3           6           9          12          15

Non Hispanic Other |          4           8          12          16          20

White non Hispanic |          5          10          15          20          25

--------------------------------------------------------------------------------

. poisson count i.meth_num i.feth_num score

Iteration 0:   log likelihood = -448819.89

Iteration 1:   log likelihood = -199150.22  (backed up)

Iteration 2:   log likelihood = -72023.171

Iteration 3:   log likelihood = -48477.638

Iteration 4:   log likelihood = -46237.124

Iteration 5:   log likelihood = -46234.659

Iteration 6:   log likelihood = -46234.659

Poisson regression                                Number of obs   =         25

LR chi2(9)      = 3060493.99

Prob > chi2     =     0.0000

Log likelihood = -46234.659                       Pseudo R2       =     0.9707

-------------------------------------------------------------------------------------

count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

--------------------+----------------------------------------------------------------

meth_num |

Mexican American  |  -1.712384   .0084087  -203.64   0.000    -1.728865   -1.695903

Hispanic Other  |   -4.42779     .01369  -323.43   0.000    -4.454622   -4.400958

Non Hispanic Other  |  -7.618328   .0212675  -358.21   0.000    -7.660012   -7.576645

White non Hispanic  |   -6.52374   .0275075  -237.16   0.000    -6.577654   -6.469826

|

feth_num |

Mexican American  |  -1.609809   .0084125  -191.36   0.000    -1.626297   -1.593321

Hispanic Other  |  -4.125007    .012931  -319.00   0.000    -4.150352   -4.099663

Non Hispanic Other  |  -6.986868   .0190834  -366.12   0.000    -7.024271   -6.949465

White non Hispanic  |  -6.092624   .0254213  -239.67   0.000    -6.142449   -6.042799

|

score |   .6448603   .0020341   317.02   0.000     .6408736    .6488471

_cons |   9.695309   .0055428  1749.17   0.000     9.684445    9.706172

-------------------------------------------------------------------------------------

. poisgof

Deviance goodness-of-fit =  92239.32

Prob > chi2(15)          =    0.0000

Pearson goodness-of-fit  =    123297

Prob > chi2(15)          =    0.0000

. *That was the uniform association model.

. nbreg count i.meth_num i.feth_num score

Fitting Poisson model:

Iteration 0:   log likelihood = -448819.89

Iteration 1:   log likelihood = -199150.22  (backed up)

Iteration 2:   log likelihood = -72023.171

Iteration 3:   log likelihood = -48477.638

Iteration 4:   log likelihood = -46237.124

Iteration 5:   log likelihood = -46234.659

Iteration 6:   log likelihood = -46234.659

Fitting constant-only model:

Iteration 0:   log likelihood = -279.13989

Iteration 1:   log likelihood = -248.51927

Iteration 2:   log likelihood = -248.45842

Iteration 3:   log likelihood = -248.45835

Iteration 4:   log likelihood = -248.45835

Fitting full model:

Iteration 0:   log likelihood = -243.05506  (not concave)

Iteration 1:   log likelihood = -233.78896  (not concave)

Iteration 2:   log likelihood = -229.91714

Iteration 3:   log likelihood = -226.12104

Iteration 4:   log likelihood =  -224.7236

Iteration 5:   log likelihood = -224.64999

Iteration 6:   log likelihood = -224.64972

Iteration 7:   log likelihood = -224.64972

Negative binomial regression                      Number of obs   =         25

LR chi2(9)      =      47.62

Dispersion     = mean                             Prob > chi2     =     0.0000

Log likelihood = -224.64972                       Pseudo R2       =     0.0958

-------------------------------------------------------------------------------------

count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

--------------------+----------------------------------------------------------------

meth_num |

Mexican American  |  -1.868956   .8171959    -2.29   0.022    -3.470631   -.2672817

Hispanic Other  |  -4.044556   1.002073    -4.04   0.000    -6.008583   -2.080529

Non Hispanic Other  |  -6.540186   1.122988    -5.82   0.000    -8.741201   -4.339171

White non Hispanic  |  -6.085678   1.233325    -4.93   0.000     -8.50295   -3.668406

|

feth_num |

Mexican American  |  -.8690387   .8117657    -1.07   0.284     -2.46007    .7219929

Hispanic Other  |  -3.111358   .9921977    -3.14   0.002    -5.056029   -1.166686

Non Hispanic Other  |  -5.213732   1.116058    -4.67   0.000    -7.401166   -3.026298

White non Hispanic  |  -4.958452   1.210836    -4.10   0.000    -7.331646   -2.585258

|

score |   .5990573   .0937284     6.39   0.000      .415353    .7827615

_cons |   9.135195   .5880045    15.54   0.000     7.982727    10.28766

--------------------+----------------------------------------------------------------

/lnalpha |   .0903066   .2472213                     -.3942383    .5748515

--------------------+----------------------------------------------------------------

alpha |    1.09451   .2705861                      .6741934    1.776867

-------------------------------------------------------------------------------------

Likelihood-ratio test of alpha=0:  chibar2(01) = 9.2e+04 Prob>=chibar2 = 0.000

* The only test we have here is the chibar test for whether alpha, the overdispersion parameter, is significantly different from zero. Here we reject the null hypothesis that the alpha parameter is zero, which means in this case the nbreg model fits better than its poisson model twin.

. poisson count i.meth_num i.feth_num i.cross*

Iteration 0:   log likelihood = -1628492.6

Iteration 1:   log likelihood = -514542.18  (backed up)

Iteration 2:   log likelihood = -405521.33  (backed up)

Iteration 3:   log likelihood = -241959.01  (backed up)

Iteration 4:   log likelihood = -147645.83

Iteration 5:   log likelihood = -59259.102

Iteration 6:   log likelihood = -12308.206

Iteration 7:   log likelihood = -10257.604

Iteration 8:   log likelihood =  -10246.31

Iteration 9:   log likelihood = -10246.309

Poisson regression                                Number of obs   =         25

LR chi2(12)     = 3132470.69

Prob > chi2     =     0.0000

Log likelihood = -10246.309                       Pseudo R2       =     0.9935

-------------------------------------------------------------------------------------

count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

--------------------+----------------------------------------------------------------

meth_num |

Mexican American  |   -.977803   .0181359   -53.92   0.000    -1.013349   -.9422572

Hispanic Other  |  -1.938943    .020284   -95.59   0.000    -1.978698   -1.899187

Non Hispanic Other  |  -2.506451   .0216409  -115.82   0.000    -2.548866   -2.464035

White non Hispanic  |   .6765227   .0185343    36.50   0.000     .6401961    .7128493

|

feth_num |

Mexican American  |  -.0199891    .018195    -1.10   0.272    -.0556507    .0156725

Hispanic Other  |  -.8181779   .0202753   -40.35   0.000    -.8579168    -.778439

Non Hispanic Other  |  -1.107257   .0209967   -52.73   0.000     -1.14841   -1.066104

White non Hispanic  |   1.871097   .0185343   100.95   0.000      1.83477    1.907423

|

1.cross1 |  -3.026666    .017803  -170.01   0.000    -3.061559   -2.991773

1.cross2 |  -1.014698   .0101682   -99.79   0.000    -1.034627   -.9947684

1.cross3 |  -.5411649   .0113079   -47.86   0.000     -.563328   -.5190018

1.cross4 |  -1.444942   .0092927  -155.49   0.000    -1.463155   -1.426728

_cons |   10.65775   .0048495  2197.69   0.000     10.64825    10.66726

-------------------------------------------------------------------------------------

. nbreg count i.meth_num i.feth_num i.cross*

Fitting Poisson model:

Iteration 0:   log likelihood = -1628492.6

Iteration 1:   log likelihood = -514542.18  (backed up)

Iteration 2:   log likelihood = -405521.33  (backed up)

Iteration 3:   log likelihood = -241959.01  (backed up)

Iteration 4:   log likelihood = -147645.83

Iteration 5:   log likelihood = -59259.102

Iteration 6:   log likelihood = -12308.206

Iteration 7:   log likelihood = -10257.604

Iteration 8:   log likelihood =  -10246.31

Iteration 9:   log likelihood = -10246.309

Fitting constant-only model:

Iteration 0:   log likelihood = -279.13989

Iteration 1:   log likelihood = -248.51927

Iteration 2:   log likelihood = -248.45842

Iteration 3:   log likelihood = -248.45835

Iteration 4:   log likelihood = -248.45835

Fitting full model:

Iteration 0:   log likelihood = -242.73332  (not concave)

Iteration 1:   log likelihood = -233.56685  (not concave)

Iteration 2:   log likelihood = -225.12744

Iteration 3:   log likelihood = -223.79639

Iteration 4:   log likelihood = -218.70413

Iteration 5:   log likelihood = -213.73583

Iteration 6:   log likelihood = -213.71902

Iteration 7:   log likelihood = -213.71901

Negative binomial regression                      Number of obs   =         25

LR chi2(12)     =      69.48

Dispersion     = mean                             Prob > chi2     =     0.0000

Log likelihood = -213.71901                       Pseudo R2       =     0.1398

-------------------------------------------------------------------------------------

count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

--------------------+----------------------------------------------------------------

meth_num |

Mexican American  |  -1.384657   .6048302    -2.29   0.022    -2.570103   -.1992119

Hispanic Other  |  -1.817025   .5603073    -3.24   0.001    -2.915207   -.7188427

Non Hispanic Other  |  -2.269184   .5464699    -4.15   0.000    -3.340245   -1.198122

White non Hispanic  |   .7621326   .6156133     1.24   0.216    -.4444473    1.968713

|

feth_num |

Mexican American  |  -.4742313   .6024889    -0.79   0.431    -1.655088    .7066252

Hispanic Other  |  -1.087938   .5654124    -1.92   0.054    -2.196126    .0202498

Non Hispanic Other  |  -.9830277   .5456848    -1.80   0.072     -2.05255    .0864949

White non Hispanic  |   1.785483   .6156133     2.90   0.004     .5789029    2.992063

|

1.cross1 |  -2.604259   .5915104    -4.40   0.000    -3.763598    -1.44492

1.cross2 |  -.6298125   .4950926    -1.27   0.203    -1.600176     .340551

1.cross3 |   -.956327    .459685    -2.08   0.037    -1.857293   -.0553608

1.cross4 |  -1.021661   .5446708    -1.88   0.061    -2.089197    .0458737

_cons |   10.65775   .7338531    14.52   0.000     9.219427    12.09608

--------------------+----------------------------------------------------------------

/lnalpha |  -.6189361   .2620997                     -1.132642   -.1052301

--------------------+----------------------------------------------------------------

alpha |   .5385171   .1411452                      .3221809    .9001174

-------------------------------------------------------------------------------------

Likelihood-ratio test of alpha=0:  chibar2(01) = 2.0e+04 Prob>=chibar2 = 0.000

. poisson count i.meth_num i.feth_num cross1 cross2 cross3 cross4

Iteration 0:   log likelihood = -1628492.6

Iteration 1:   log likelihood = -514542.18  (backed up)

Iteration 2:   log likelihood = -405521.33  (backed up)

Iteration 3:   log likelihood = -241959.01  (backed up)

Iteration 4:   log likelihood = -147645.83

Iteration 5:   log likelihood = -59259.102

Iteration 6:   log likelihood = -12308.206

Iteration 7:   log likelihood = -10257.604

Iteration 8:   log likelihood =  -10246.31

Iteration 9:   log likelihood = -10246.309

Poisson regression                                Number of obs   =         25

LR chi2(12)     = 3132470.69

Prob > chi2     =     0.0000

Log likelihood = -10246.309                       Pseudo R2       =     0.9935

-------------------------------------------------------------------------------------

count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

--------------------+----------------------------------------------------------------

meth_num |

Mexican American  |   -.977803   .0181359   -53.92   0.000    -1.013349   -.9422572

Hispanic Other  |  -1.938943    .020284   -95.59   0.000    -1.978698   -1.899187

Non Hispanic Other  |  -2.506451   .0216409  -115.82   0.000    -2.548866   -2.464035

White non Hispanic  |   .6765227   .0185343    36.50   0.000     .6401961    .7128493

|

feth_num |

Mexican American  |  -.0199891    .018195    -1.10   0.272    -.0556507    .0156725

Hispanic Other  |  -.8181779   .0202753   -40.35   0.000    -.8579168    -.778439

Non Hispanic Other  |  -1.107257   .0209967   -52.73   0.000     -1.14841   -1.066104

White non Hispanic  |   1.871097   .0185343   100.95   0.000      1.83477    1.907423

|

cross1 |  -3.026666    .017803  -170.01   0.000    -3.061559   -2.991773

cross2 |  -1.014698   .0101682   -99.79   0.000    -1.034627   -.9947684

cross3 |  -.5411649   .0113079   -47.86   0.000     -.563328   -.5190018

cross4 |  -1.444942   .0092927  -155.49   0.000    -1.463155   -1.426728

_cons |   10.65775   .0048495  2197.69   0.000     10.64825    10.66726

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. poisgof

Deviance goodness-of-fit =  20262.62

Prob > chi2(12)          =    0.0000

Pearson goodness-of-fit  =  21598.27

Prob > chi2(12)          =    0.0000

. log close

name:  <unnamed>

log:  C:\Users\mexmi\Documents\newer web pages\Soc_382\logs\3rd loglin intermar class.log

log type:  text

closed on:   5 Feb 2019, 11:41:48

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