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From |
John Litfiba <[email protected]> |

To |
[email protected] |

Subject |
Re: st: too good to be true : lr test in mlogit? |

Date |
Mon, 16 May 2011 12:02:51 +0200 |

Dear Marten, Thank you very much again for your support! Well, when I run a xtlogit (I first type xtset id, where id is the unique id for each of my individual in my database) with on year of data (1million) observations I get the message Yvar is categorical (Yes=1, No=0) and Xvar is also categorical (type1=1, type2=2) ********************************************************************************************************* . xtlogit Yvar Xvar, re Fitting comparison model: Iteration 0: log likelihood = -699882.93 Iteration 1: log likelihood = -669440.74 Iteration 2: log likelihood = -669402.92 Iteration 3: log likelihood = -669402.89 Fitting full model: tau = 0.0 log likelihood = -669402.89 tau = 0.1 log likelihood = -460383.3 tau = 0.2 log likelihood = -425672.08 tau = 0.3 log likelihood = -409117.91 tau = 0.4 log likelihood = -398842.25 tau = 0.5 log likelihood = -391638.85 tau = 0.6 log likelihood = -386752.42 tau = 0.7 log likelihood = -384063.23 tau = 0.8 log likelihood = -383816.98 initial values not feasible r(1400); *********************************************************************************************** and when I run a random effect model I get ************************************************************************************************** . xtlogit Yvar Xvar, fe note: multiple positive outcomes within groups encountered. note: 18475 groups (170046 obs) dropped because of all positive or all negative outcomes. Iteration 0: log likelihood = -1.#INF Iteration 1: log likelihood = -1.#IND Hessian is not negative semidefinite r(430); ************************************************************************************************************************ However, lets say I only keep the last 100 000 observations of my sample and then I get ************************************************************************************************************************ xtlogit Yvar Xvar, fe note: multiple positive outcomes within groups encountered. note: 11791 groups (49177 obs) dropped because of all positive or all negative outcomes. Iteration 0: log likelihood = -22470.418 Iteration 1: log likelihood = -22218.949 Iteration 2: log likelihood = -22218.885 Iteration 3: log likelihood = -22218.885 Conditional fixed-effects logistic regression Number of obs = 69669 Group variable: id2 Number of groups = 3794 Obs per group: min = 2 avg = 18.4 max = 876 LR chi2(1) = 5266.26 Log likelihood = -22218.885 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ Yvar| Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- Xvar| 5.315414 .1412066 37.64 0.000 5.038654 5.592174 ------------------------------------------------------------------------------ and for the random effect I get : *************************************************************************************************** . xtlogit Yvar Xvar, re Fitting comparison model: Iteration 0: log likelihood = -79872.579 Iteration 1: log likelihood = -70108.483 Iteration 2: log likelihood = -69952.535 Iteration 3: log likelihood = -69950.066 Iteration 4: log likelihood = -69950.06 Fitting full model: tau = 0.0 log likelihood = -69950.06 tau = 0.1 log likelihood = -55891.467 tau = 0.2 log likelihood = -51186.623 tau = 0.3 log likelihood = -48260.258 tau = 0.4 log likelihood = -46086.379 tau = 0.5 log likelihood = -44358.837 tau = 0.6 log likelihood = -42957.577 tau = 0.7 log likelihood = -41790.563 tau = 0.8 log likelihood = -40944.535 Iteration 0: log likelihood = -41603.261 Iteration 1: log likelihood = -39231.257 Iteration 2: log likelihood = -38979.35 Iteration 3: log likelihood = -38947.091 Iteration 4: log likelihood = -38947.091 (backed up) Iteration 5: log likelihood = -38947.026 Iteration 6: log likelihood = -38947.026 Random-effects logistic regression Number of obs = 118846 Group variable: id2 Number of groups = 15585 Random effects u_i ~ Gaussian Obs per group: min = 1 avg = 7.6 max = 1255 Wald chi2(1) = 2339.74 Log likelihood = -38947.026 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ Yvar | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- Xvar| 9.349703 .1932923 48.37 0.000 8.970857 9.728549 _cons | -8.300978 .1855708 -44.73 0.000 -8.66469 -7.937266 -------------+---------------------------------------------------------------- /lnsig2u | 2.728813 .0338054 2.662555 2.79507 -------------+---------------------------------------------------------------- sigma_u | 3.913399 .066147 3.785877 4.045216 rho | .8231687 .0049208 .8133168 .8326077 ------------------------------------------------------------------------------ Likelihood-ratio test of rho=0: chibar2(01) = 6.2e+04 Prob >= chibar2 = 0.000 Best Regards On 16 May 2011 09:49, Maarten Buis <[email protected]> wrote: > On Sat, May 14, 2011 at 11:31 AM, John Litfiba wrote: >> 1) The log likelihood doesnt converge when I try to fit a random or >> fixed effect with xtlogit on my entire dataset.. >> I have to chose a very "small" (well, compared to the total size of >> the sample) of about 10000 observations in order to see the results... >> otherwise I get an error message after 3 or 4 iterations > > If you do not tell use what the error message is than we obviously > cannot help you. We need to know exactly what you typed and what Stata > told you in return. > >> 2) The idea of running lets say M regressions over randomly chose >> samples could be a solution, but it is statistically valid ? I mean if >> I obtain the distribution of the parameters across my M simulation can >> I infer something on the parameters of the simulation that should have >> been done on the entire dataset ? > > No, but if you sample correctly a single random sample of higher level > units will be just as valid a sample from your population as your > large sample, just with a smaller N. The added value of additional > observations tends to decrease with sample size, so going from 10 to > 11 observations will have a much bigger effect on your inference than > moving from 100 to 101 observations. There are many estimates for > which the difference between 10000 and 10000000 observations is just > negligible (but there are estimates where it will matter, for example > higher order interaction terms or a categorical variables containing a > rarely occurring category). > > Hope this helps, > Maarten > > -------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > > > http://www.maartenbuis.nl > -------------------------- > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: too good to be true : lr test in mlogit?***From:*Maarten Buis <[email protected]>

**Re: st: too good to be true : lr test in mlogit?***From:*John Litfiba <[email protected]>

**References**:**st: too good to be true : lr test in mlogit?***From:*John Litfiba <[email protected]>

**Re: st: too good to be true : lr test in mlogit?***From:*Maarten Buis <[email protected]>

**Re: st: too good to be true : lr test in mlogit?***From:*John Litfiba <[email protected]>

**Re: st: too good to be true : lr test in mlogit?***From:*Maarten Buis <[email protected]>

**Re: st: too good to be true : lr test in mlogit?***From:*Joerg Luedicke <[email protected]>

**Re: st: too good to be true : lr test in mlogit?***From:*John Litfiba <[email protected]>

**Re: st: too good to be true : lr test in mlogit?***From:*Maarten Buis <[email protected]>

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