Maximum-likelihood generalized ordered logit estimation -------------------------------------------------------
^gologit^ depv varlist [weight] [^if^ exp] [^in^ range] [^,^ ^c^luster^(^varname^) l^evel^(^#^) or^ ^r^obust]
This command should share the features of all estimation commands; see help @est@.
This command typed without arguments redisplays previous results. The following options may be given when redisplaying results
^aweight^s, ^fweight^s, and ^pweight^s are allowed. See help @weights@. Using ^pweight^s implies the ^robust^ option.
Coefficients are estimated using Stata's @ml@ interface. Stata's @ologit@ command is used to produce starting values.
This command estimates regression models for ordinal dependent variables. The actual values taken on by the dependent variable are irrelevant except that larger values are assumed to correspond to "higher" outcomes.
The @ologit@ command included with Stata imposes what is called the proportional odds assumption on the data. This model relaxes the proportional odds assumption and allows the effects of the explanatory variables to vary with the point at which the categories of the dependent variable are dichotomized.
^cluster(^varname^)^ specifies that the observations are independent across gro > ups (clusters) but not necessarily within groups. varname specifies to which group each observation belongs; e.g., ^cluster(personid)^ in data with repeated observations on individuals. See ^[U] 26.10 Obtaining robust^ ^variance estimates^. ^cluster()^ can used with @pweight@s to produce esti > mates for unstratified cluster-sampled data. Specifying ^cluster()^ implies ^rob > ust^.
^level(^#^)^ specifies the confidence level, in percent, for calculation of confidence intervals of the odds ratios.
^or^ reports the estimated coefficients transformed to odds ratios, i.e., exp(b > ) rather than b. Standard errors and confidence intervals are similarly transformed. This option affects how results are displayed, not how they are estimated. ^or^ may be specified at estimation or when redisplaying previously estimated results.
^robust^ specifies that the Huber/White/sandwich estimator of variance is to be > used in place of the traditional calculation; see ^[U] 26.10 Obtaining^ ^robust variance estimates^. ^robust^ combined with ^cluster()^ allows observations which are not independent within cluster (although they may be independent between clusters).
More formally, suppose we have an ordinal dependent variable Y which takes on the values 0, 1, 2, ..., m. The generalized ordered logit model estimates a set of coefficients (including one for the constant) for each of the m - 1 points at which the dependent variable can be dichotomized. These sets of coefficients B_k to a set of cumulative distribution functions:
P( Y < k ) = F( -XB_k ) k = 1, ..., m
From this set of cumulative distribution functions, it is straightforward to derive formulas for the probabilities that Y will take on each of the values 0, 1, ..., m:
P( Y = 0 ) = F( -XB_1 ) P( Y = j ) = F( -XB_(j+1) ) - F( -XB_j ) j = 1, ..., m - 1 P( Y = m ) = 1 - F( -XB_m )
The generalized ordered logit model uses the logistic distribution as the cumulative distribution, although other distributions may also be used. The logistic distribution allows researchers to interpret this model in terms of logits:
log[ P( Y >= k ) / P( Y < k ) ] = XB_k k = 1, ..., m
The proportional odds property of Stata's ologit command restricts the B_k coefficients to be the same for every dividing point k = 1, ..., m.
Note that unlike models such as OLS regression and binary logit, the generalized ordered logit model imposes explicit restrictions on the range of the X variables. Since probabilities are by definition constrained to be in the range [0,1], valid combinations the X variables must satisfy the following inequalities:
XB_2 <= XB_1 XB_3 <= XB_2 ... XB_m <= XB_(m-1)
. gologit meduc mwhite fhsd fcoll fba if(mwhite | mmexican) [pw=fpwgt1] . gologit rep78 price mpg, robust . gologit, level(99)
Vincent Kang Fu <vfu@@ucla.edu>, UCLA Department of Sociology
Portions of this help file were taken from help files produced by Stata Corp. Jeremy Freese suggested the changes for making -gologit- compatible with -fitstat-.
Also see --------
Manual: ^[U] 26 Estimation and post-estimation commands^ ^[U] 35 Overview of model estimation^ ^[R] ml^ ^[R] ologit^ ^[R] _robust^ On-line: help for @_robust@, @svyolog@, @ologit@, @ml@, @est@