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From |
Richard Williams <Richard.A.Williams.5@ND.edu> |

To |
statalist@hsphsun2.harvard.edu, statalist@hsphsun2.harvard.edu |

Subject |
Re: st: What form of data needed for ipf function? |

Date |
Fri, 15 Aug 2008 14:38:32 -0500 |

At 01:01 PM 8/15/2008, lcrawley@stanford.edu wrote:

I don't use this stuff much but I think the glm command can give you the same info as ipf:Thanks for the correction (command, not function). I had written Adrian Mander and have not yet received a reply. I have also read all the suggested support info as well as other that I have found. I tried to put my data into the exact form of the example data that I found on the suggested resources. I still get the error message. Just can't quite figure out what step I am missing. Hopefully Dr. Mander will come to my rescue! Thanks.

. use "http://www.nd.edu/~rwilliam/stats1/statafiles/categ-III.dta";, clear

(Categorical Case III - Tests of Association for N-Dimensional Tables)

. ipf [fw = freq], fit(gender + race + party)

Deleting all matrices......

Expansion of the various marginal models

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

marginal model 1 varlist : gender

marginal model 2 varlist : race

marginal model 3 varlist : party

unique varlist gender race party

N.B. structural/sampling zeroes may lead to an incorrect df

Residual degrees of freedom = 4

Number of parameters = 4

Number of cells = 8

Loglikelihood = 166.0760865136649

Loglikelihood = 166.0760865136649

Goodness of Fit Tests

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

df = 4

Likelihood Ratio Statistic G² = 9.0042 p-value = 0.061

Pearson Statistic X² = 9.2798 p-value = 0.054

(Categorical Case III - Tests of Association for N-Dimensional Tables)

. glm freq gender party race, family(poisson) link(log)

Iteration 0: log likelihood = -21.182715

Iteration 1: log likelihood = -21.057385

Iteration 2: log likelihood = -21.057265

Iteration 3: log likelihood = -21.057265

Generalized linear models No. of obs = 8

Optimization : ML Residual df = 4

Scale parameter = 1

Deviance = 9.004151178 (1/df) Deviance = 2.251038

Pearson = 9.279843932 (1/df) Pearson = 2.319961

Variance function: V(u) = u [Poisson]

Link function : g(u) = ln(u) [Log]

AIC = 6.264316

Log likelihood = -21.05726505 BIC = .686385

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

| OIM

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

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

gender | -.4054651 .2041241 -1.99 0.047 -.8055411 -.0053891

party | .2006707 .2010076 1.00 0.318 -.1932969 .5946383

race | -.9946226 .2252458 -4.42 0.000 -1.436096 -.5531489

_cons | 4.180543 .5201624 8.04 0.000 3.161044 5.200043

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

Note that ipf's G^2 and X^2 are the Deviance and Pearson stats in glm. If you want interaction terms, you have to compute them yourself.

Also, note the ipf warning that "structural/sampling zeroes may lead to an incorrect df." I wonder if that is causing you problems.

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

Richard Williams, Notre Dame Dept of Sociology

OFFICE: (574)631-6668, (574)631-6463

HOME: (574)289-5227

EMAIL: Richard.A.Williams.5@ND.Edu

WWW: http://www.nd.edu/~rwilliam

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**Follow-Ups**:**Re: st: What form of data needed for ipf function?***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**Re: st: What form of data needed for ipf function?***From:*lcrawley@stanford.edu

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