Stata 15 help for ml

[R] ml -- Maximum likelihood estimation

Syntax

ml model in interactive mode

ml model method progname eq [eq ...] [if] [in] [weight] [, model_options svy diparm_options]

ml model method funcname() eq [eq ...] [if] [in] [weight] [, model_options svy diparm_options]

ml model in noninteractive mode

ml model method progname eq [eq ...] [if] [in] [weight], maximize [model_options svy diparm_options noninteractive_options]

ml model method funcname() eq [eq ...] [if] [in] [weight], maximize [model_options svy diparm_options noninteractive_options]

Noninteractive mode is invoked by specifying the maximize option. Use maximize when ml will be used as a subroutine of another ado-file or program and you want to carry forth the problem, from definition to posting of results, in one command.

ml clear

ml query

ml check

ml search [[/]eqname[:] #lb #ub ] [...] [, search_options]

ml plot [eqname:]name [# [# [#]]] [, saving(filename[, replace])]

ml init { [eqname:]name=# | /eqname=# } [...]

ml init # [# ...], copy

ml init matname [, copy skip ]

ml report

ml trace { on | off }

ml count [ clear | on | off ]

ml maximize [, ml_maximize_options display_options eform_option]

ml graph [#] [, saving(filename[, replace]) ]

ml display [, display_options eform_option]

ml footnote

ml score newvar [if] [in] [, ml_score_options]

ml score newvarlist [if] [in] [, missing]

ml score [type] stub* [if] [in] [, missing]

where method is one of

lf d0 lf0 gf0 d1 lf1 d1debug lf1debug d2 lf2 d2debug lf2debug

or method can be specified using one of the longer, more descriptive names

method Longer name ----------------------------------------------------------------- lf linearform d0 derivative0 d1 derivative1 d1debug derivative1debug d2 derivative2 d2debug derivative2debug lf0 linearform0 lf1 linearform1 lf1debug linearform1debug lf2 linearform2 lf2debug linearform2debug gf0 generalform0 -----------------------------------------------------------------

eq is the equation to be estimated, enclosed in parentheses, and optionally with a name to be given to the equation, preceded by a colon,

( [eqname:] [varlist_y =] [varlist_x] [, eq_options] )

or eq is the name of a parameter, such as sigma, with a slash in front

/eqname which is equivalent to (eqname:, freeparm)

and diparm_options is one or more diparm(diparm_args) options where diparm_args is either __sep__ or anything accepted by the "undocumented" _diparm command; see [P] _diparm.

eq_options Description ------------------------------------------------------------------------- noconstant do not include an intercept in the equation offset(varname_o) include varname_o in model with coefficient constrained to 1 exposure(varname_e) include ln(varname_e) in model with coefficient constrained to 1 freeparm eqname is a free parameter -------------------------------------------------------------------------

model_options Description ------------------------------------------------------------------------- group(varname) use varname to identify groups vce(vcetype) vcetype may be robust, cluster clustvar, oim, or opg constraints(numlist) constraints by number to be applied constraints(matname) matrix that contains the constraints to be applied nocnsnotes do not display notes when constraints are dropped title(string) place a title on the estimation output nopreserve do not preserve the estimation subsample in memory collinear keep collinear variables within equations missing keep observations containing variables with missing values lf0(#k #ll) number of parameters and log-likelihood value of the constant-only model continue specifies that a model has been fit and sets the initial values b_0 for the model to be fit based on those results waldtest(#) perform a Wald test; see Options for use with ml model in interactive or noninteractive mode below obs(#) number of observations crittype(string) describe the criterion optimized by ml subpop(varname) compute estimates for the single subpopulation nosvyadjust carry out Wald test as W/k distributed F(k,d) technique(nr) Stata's modified Newton-Raphson (NR) algorithm technique(bhhh) Berndt-Hall-Hall-Hausman (BHHH) algorithm technique(dfp) Davidon-Fletcher-Powell (DFP) algorithm technique(bfgs) Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm -------------------------------------------------------------------------

noninteractive_options Description ------------------------------------------------------------------------- init(ml_init_args) set the initial values b_0 search(on) equivalent to ml search, repeat(0); the default search(norescale) equivalent to ml search, repeat(0) norescale search(quietly) same as search(on), except that output is suppressed search(off) prevents calling ml search repeat(#) ml search's repeat() option; see below bounds(ml_search_bounds) specify bounds for ml search nowarning suppress "convergence not achieved" message of iterate(0) novce substitute the zero matrix for the variance matrix negh indicates that the evaluator returns the negative Hessian matrix score(newvars) new variables containing the contribution to the score maximize_options control the maximization process; seldom used -------------------------------------------------------------------------

search_options Description ------------------------------------------------------------------------- repeat(#) number of random attempts to find better initial-value vector; default is repeat(10) in interactive mode and repeat(0) in noninteractive mode restart use random actions to find starting values; not recommended norescale do not rescale to improve parameter vector; not recommended maximize_options control the maximization process; seldom used -------------------------------------------------------------------------

ml_maximize_options Description ------------------------------------------------------------------------- nowarning suppress "convergence not achieved" message of iterate(0) novce substitute the zero matrix for the variance matrix negh indicates that the evaluator returns the negative Hessian matrix score(newvars | stub*) new variables containing the contribution to the score nooutput suppress display of final results noclear do not clear ml problem definition after model has converged maximize_options control the maximization process; seldom used -------------------------------------------------------------------------

display_options Description ------------------------------------------------------------------------- noheader suppress header display above the coefficient table nofootnote suppress footnote display below the coefficient table level(#) set confidence level; default is level(95) first display coefficient table reporting results for first equation only neq(#) display coefficient table reporting first # equations showeqns display equation names in the coefficient table plus display coefficient table ending in dashes-plus-sign-dashes nocnsreport suppress constraints display above the coefficient table noomitted suppress display of omitted variables vsquish suppress blank space separating factor-variable terms or time-series-operated variables from other variables noemptycells suppress empty cells for interactions of factor variables baselevels report base levels of factor variables and interactions allbaselevels display all base levels of factor variables and interactions cformat(%fmt) format the coefficients, standard errors, and confidence limits in the coefficient table pformat(%fmt) format the p-values in the coefficient table sformat(%fmt) format the test statistics in the coefficient table nolstretch do not automatically widen the coefficient table to accommodate longer variable names coeflegend display legend instead of statistics -------------------------------------------------------------------------

eform_option Description ------------------------------------------------------------------------- eform(string) display exponentiated coefficients; column title is "string" eform display exponentiated coefficients; column title is "exp(b)" hr report hazard ratios shr report subhazard ratios irr report incidence-rate ratios or report odds ratios rrr report relative-risk ratios ------------------------------------------------------------------------- fweights, aweights, iweights, and pweights are allowed; see weight. With all but methods lf, you must write your likelihood-evaluation program carefully if pweights are to be specified, and pweights may not be specified with method d0, d1, d1debug, d2, or d2debug. See Gould, Pitblado, and Poi (2010, chap. 6) for details. See estcom for more capabilities of estimation commands. To redisplay results, type ml display.

Description

ml model defines the current problem.

ml clear clears the current problem definition. This command is rarely used because when you type ml model, any previous problem is automatically cleared.

ml query displays a description of the current problem.

ml check verifies that the log-likelihood evaluator you have written works. We strongly recommend using this command.

ml search searches for (better) initial values. We recommend using this command.

ml plot provides a graphical way of searching for (better) initial values.

ml init provides a way to specify initial values.

ml report reports ln L's values, gradient, and Hessian at the initial values or current parameter estimates, b_0.

ml trace traces the execution of the user-defined log-likelihood evaluation program.

ml count counts the number of times the user-defined log-likelihood evaluation program is called; this command is seldom used. ml count clear clears the counter. ml count on turns on the counter. ml count without arguments reports the current values of the counter. ml count off stops counting calls.

ml maximize maximizes the likelihood function and reports results. Once ml maximize has successfully completed, the previously mentioned ml commands may no longer be used unless noclear is specified. ml graph and ml display may be used whether or not noclear is specified.

ml graph graphs the log-likelihood values against the iteration number.

ml display redisplays results.

ml footnote displays a warning message when the model did not converge within the specified number of iterations.

ml score creates new variables containing the equation-level scores. The variables generated by ml score are equivalent to those generated by specifying the score() option of ml maximize (and ml model ..., ... maximize).

progname is the name of a Stata program you write to evaluate the log-likelihood function.

funcname() is the name of a Mata function you write to evaluate the log-likelihood function.

In this documentation, progname and funcname() are referred to as the user-written evaluator, the likelihood evaluator, or sometimes simply as the evaluator. The program you write is written in the style required by the method you choose. The methods are lf, d0, d1, d2, lf0, lf1, lf2, and, gf0. Thus, if you choose to use method lf, your program is called a method-lf evaluator. mlmethod shows outlines of evaluator programs for each of these methods.

Several commands are helpful in writing a user-written evaluator for use with ml. See mleval for details of the mleval, mlsum, mlvecsum, mlmatsum, and mlmatbysum commands if your evaluator is a Stata program. See moptimize() for details on moptimize_util_sum(), moptimize_util_vecsum(), moptimize_util_matsum(), and moptimize_util_matbysum() functions if your evaluator is a Mata function.

Options for use with ml model in interactive or noninteractive mode

group(varname) specifies the numeric variable that identifies groups. This option is typically used to identify panels for panel-data models.

vce(vcetype) specifies the type of standard error reported, which includes types that are robust to some kinds of misspecification (robust), that allow for intragroup correlation (cluster clustvar), and that are derived from asymptotic theory (oim, opg); see [R] vce_option.

vce(robust), vce(cluster clustvar), pweight, and svy will work with evaluators of methods lf, lf0, lf1, lf2, and gf0 evaluators; all you need to do is specify them.

These options will not work with evaluators of methods d0, d1, or d2, and specifying these options will produce an error message.

constraints(numlist | matname) specifies the linear constraints to be applied during estimation. constraints(numlist) specifies the constraints by number. Constraints are defined by using the constraint command; see [R] constraint. constraint(matname) specifies a matrix that contains the constraints.

nocnsnotes prevents notes from being displayed when constraints are dropped. A constraint will be dropped if it is inconsistent, contradicts other constraints, or causes some other error when the constraint matrix is being built. Constraints are checked in the order in which they are specified.

title(string) specifies the title for the estimation output when results are complete.

nopreserve specifies that ml need not ensure that only the estimation subsample is in memory when the user-written likelihood evaluator is called. nopreserve is irrelevant when you use method lf. See the nopreserve option in [R] ml for more details.

collinear specifies that ml not remove the collinear variables within equations. There is no reason to leave collinear variables in place, but this option is of interest to programmers who, in their code, have already removed collinear variables and do not want ml to waste computer time checking again.

missing specifies that observations containing variables with missing values not be eliminated from the estimation sample. There are two reasons you might want to specify missing:

Programmers may wish to specify missing because, in other parts of their code, they have already eliminated observations with missing values and do not want ml to waste computer time looking again.

You may wish to specify missing if your model explicitly deals with missing values. Stata's heckman command is a good example of this. In such cases, there will be observations where missing values are allowed and other observations where they are not -- where their presence should cause the observation to be eliminated. If you specify missing, it is your responsibility to specify an if exp that eliminates the irrelevant observations.

lf0(#k #ll) is typically used by programmers. It specifies the number of parameters and log-likelihood value of the constant-only model so that ml can report a likelihood-ratio test rather than a Wald test. These values may have been analytically determined, or they may have been determined by a previous fitting of the constant-only model on the estimation sample.

Also see the continue option directly below.

If you specify lf0(), it must be safe for you to specify the missing option, too, else how did you calculate the log likelihood for the constant-only model on the same sample? You must have identified the estimation sample, and done so correctly, so there is no reason for ml to waste time rechecking your results. All of which is to say, do not specify lf0() unless you are certain your code identifies the estimation sample correctly.

lf0(), even if specified, is ignored if vce(robust), vce(cluster clustvar), pweight, or svy is specified because, in that case, a likelihood-ratio test would be inappropriate.

continue is typically specified by programmers and does two things:

First, it specifies that a model has just been fit by either ml or some other estimation command, such as logit, and that the likelihood value stored in e(ll) and the number of parameters stored in e(b) as of that instant are the relevant values of the constant-only model. The current value of the log likelihood is used to present a likelihood-ratio test unless vce(robust), vce(cluster clustvar), pweight, svy, or constraints() is specified. A likelihood-ratio test is inappropriate when vce(robust), vce(cluster clustvar), pweight, or svy is specified. We suggest using lrtest when constraints() is specified; see [R] lrtest.

Second, continue sets the initial values, b_0, for the model about to be fit according to the e(b) currently stored.

The comments made about specifying missing with lf0() apply equally well here.

waldtest(#) is typically specified by programmers. By default, ml presents a Wald test, but that is overridden if the lf0() or continue option is specified. A Wald test is performed if vce(robust), vce(cluster clustvar), or pweight is specified.

waldtest(0) prevents even the Wald test from being reported.

waldtest(-1) is the default. It specifies that a Wald test be performed by constraining all coefficients except the intercept to 0 in the first equation. Remaining equations are to be unconstrained. A Wald test is performed if neither lf0() nor continue was specified, and a Wald test is forced if vce(robust), vce(cluster clustvar), or pweight was specified.

waldtest(k) for k < -1 specifies that a Wald test be performed by constraining all coefficients except intercepts to 0 in the first |k| equations; remaining equations are to be unconstrained. A Wald test is performed if neither lf0() nor continue was specified, and a Wald test is forced if vce(robust), vce(cluster clustvar), or pweight was specified.

waldtest(k) for k > 1 works like the options above, except that it forces a Wald test to be reported even if the information to perform the likelihood-ratio test is available and even if none of vce(robust), vce(cluster clustvar), or pweight was specified. waldtest(k), k > 1, may not be specified with lf0().

obs(#) is used mostly by programmers. It specifies that the number of observations reported and ultimately stored in e(N) be #. Ordinarily, ml works that out for itself. Programmers may want to specify this option when, for the likelihood evaluator to work for N observations, they first had to modify the dataset so that it contained a different number of observations.

crittype(string) is used mostly by programmers. It allows programmers to supply a string (up to 32 characters long) that describes the criterion that is being optimized by ml. The default is "log likelihood" for nonrobust and "log pseudolikelihood" for robust estimation.

svy indicates that ml is to pick up the svy settings set by svyset and use the robust variance estimator. This option requires the data to be svyset. svy may not be specified with vce() or weights.

subpop(varname) specifies that estimates be computed for the single subpopulation defined by the observations for which varname != 0. Typically, varname = 1 defines the subpopulation, and varname = 0 indicates observations not belonging to the subpopulation. For observations whose subpopulation status is uncertain, varname should be set to missing ('.'). This option requires the svy option.

nosvyadjust specifies that the model Wald test be carried out as W/k distributed F(k,d), where W is the Wald test statistic, k is the number of terms in the model excluding the constant term, d is the total number of sampled PSUs minus the total number of strata, and F(k,d) is an F distribution with k numerator degrees of freedom and d denominator degrees of freedom. By default, an adjusted Wald test is conducted: (d-k+1)W/(kd) distributed F(k,d-k+1). See Korn and Graubard (1990) for a discussion of the Wald test and the adjustments thereof. This option requires the svy option.

technique(algorithm_spec) specifies how the likelihood function is to be maximized. The following algorithms are currently implemented in ml. For details, see Gould, Pitblado, and Poi (2010).

technique(nr) specifies Stata's modified Newton-Raphson (NR) algorithm.

technique(bhhh) specifies the Berndt-Hall-Hall-Hausman (BHHH) algorithm.

technique(dfp) specifies Davidon-Fletcher-Powell (DFP) algorithm.

technique(bfgs) specifies the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm.

The default is technique(nr).

You can switch between algorithms by specifying more than one in the technique() option. By default, ml will use an algorithm for five iterations before switching to the next algorithm. To specify a different number of iterations, include the number after the technique in the option. For example, specifying technique(bhhh 10 nr 1000) requests that ml perform 10 iterations using the BHHH algorithm, followed by 1,000 iterations using the NR algorithm, and then switch back to BHHH for 10 iterations, and so on. The process continues until convergence or until reaching the maximum number of iterations.

Options for use with ml model in noninteractive mode

The following extra options are for use with ml model in noninteractive mode. Noninteractive mode is for programmers who use ml as a subroutine and want to issue one command that will carry forth the estimation from start to finish.

maximize is required. It specifies noninteractive mode.

init(ml_init_args) sets the initial values, b_0. ml_init_args are whatever you would type after the ml init command.

search(on|norescale|quietly|off) specifies whether ml search is to be used to improve the initial values. search(on) is the default and is equivalent to separately running ml search, repeat(0). search(norescale) is equivalent to separately running ml search, repeat(0) norescale. search(quietly) is equivalent to search(on), except that it suppresses ml search's output. search(off) prevents calling ml search.

repeat(#) is ml search's repeat() option. repeat(0) is the default.

bounds(ml_search_bounds) specifies the search bounds. ml_search_bounds is specified as

[eqn_name] lower_bound upper_bound ... [eqn_name] lower_bound upper_bound

for instance, bounds(100 100 lnsigma 0 10). The ml model command issues ml search ml_search_bounds, repeat(#). Specifying search bounds is optional.

nowarning, novce, negh, and score() are ml maximize's equivalent options.

maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance, and from(init_specs); see [R] maximize. These options are seldom used.

Options for use when specifying equations

noconstant specifies that the equation not include an intercept.

offset(varname_o) specifies that the equation be xb + varname_o -- that it include varname_o with coefficient constrained to be 1.

exposure(varname_e) is an alternative to offset(varname_o); it specifies that the equation be xb + ln(varname_e). The equation is to include ln(varname_e) with coefficient constrained to be 1.

freeparm specifies that the associated eqname is a free parameter. The corresponding full column name on e(b) will be /eqname instead of eqname:_cons. This option is not allowed with varlist_x.

Options for use with ml search

repeat(#) specifies the number of random attempts that are to be made to find a better initial-value vector. The default is repeat(10).

repeat(0) specifies that no random attempts be made. More precisely, repeat(0) specifies that no random attempts be made if the first initial-value vector is a feasible starting point. If it is not, ml search will make random attempts, even if you specify repeat(0), because it has no alternative. The repeat() option refers to the number of random attempts to be made to improve the initial values. When the initial starting value vector is not feasible, ml search will make up to 1,000 random attempts to find starting values. It stops when it finds one set of values that works and then moves into its improve-initial-values logic.

repeat(k), k > 0, specifies the number of random attempts to be made to improve the initial values.

restart specifies that random actions be taken to obtain starting values and that the resulting starting values not be a deterministic function of the current values. Generally, you should not specify this option because, with restart, ml search intentionally does not produce as good a set of starting values as it could. restart is included for use by the optimizer when it gets into serious trouble. The random actions ensure that the optimizer and ml search, working together, do not cause an endless loop.

restart implies norescale, which is why we recommend that you do not specify restart. In testing, sometimes rescale worked so well that, even after randomization, the rescaler would bring the starting values right back to where they had been the first time and thus defeat the intended randomization.

norescale specifies that ml search not engage in its rescaling actions to improve the parameter vector. We do not recommend specifying this option because rescaling tends to work so well.

maximize_options: [no]log and trace; see [R] maximize. These options are seldom used.

Option for use with ml plot

saving(filename[, replace]) specifies that the graph be saved in filename.gph.

Options for use with ml init

copy specifies that the list of numbers or the initialization vector be copied into the initial-value vector by position rather than by name.

skip specifies that any parameters found in the specified initialization vector that are not also found in the model be ignored. The default action is to issue an error message.

Options for use with ml maximize

nowarning is allowed only with iterate(0). nowarning suppresses the "convergence not achieved" message. Programmers might specify iterate(0) nowarning when they have a vector b already containing the final estimates and want ml to calculate the variance matrix and postestimation results. Then specify init(b) search(off) iterate(0) nowarning nolog.

novce is allowed only with iterate(0). novce substitutes the zero matrix for the variance matrix, which in effect posts estimation results as fixed constants.

negh indicates that the evaluator returns the negative Hessian matrix. By default, ml assumes d2 and lf2 evaluators return the Hessian matrix.

score(newvars | stub*) creates new variables containing the contributions to the score for each equation and ancillary parameter in the model; see [U] 20.23 Obtaining scores.

If score(newvars) is specified, the newvars must contain k new variables. For evaluators of methods lf, lf0, lf1, and lf2, k is the number of equations. For evaluators of method gf0, k is the number of parameters. If score(stub*) is specified, variables named stub1, stub2, ..., stubk are created.

For evaluators of methods lf, lf0, lf1, and lf2, the first variable contains d(ln l_j)/d(x_1j b_1), the second variable contains d(ln l_j)/d(x_2j b_2), and so on.

For evaluators of method gf0, the first variable contains d(ln l_j)/d(b_1), the second variable contains d(ln l_j)/d(b_2), and so on.

nooutput suppresses display of results. This option is different from prefixing ml maximize with quietly in that the iteration log is still displayed (assuming that nolog is not specified).

noclear specifies that the ml problem definition not be cleared after the model has converged. Perhaps you are having convergence problems and intend to run the model to convergence. If so, use ml search to see if those values can be improved, and then restart the estimation.

maximize_options: difficult, iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), and nonrtolerance; see [R] maximize. These options are seldom used.

display_options; see Options for use with ml display.

eform_option; see Options for use with ml display.

Option for use with ml graph

saving(filename[, replace]) specifies that the graph be saved in filename.gph.

Options for use with ml display

noheader suppresses the header display above the coefficient table that displays the final log-likelihood value, the number of observations, and the model significance test.

nofootnote suppresses the footnote display below the coefficient table, which displays a warning if the model fit did not converge within the specified number of iterations. Use ml footnote to display the warning if 1) you add to the coefficient table using the plus option or 2) you have your own footnotes and want the warning to be last.

level(#) is the standard confidence-level option. It specifies the confidence level, as a percentage, for confidence intervals of the coefficients. The default is level(95) or as set by set level; see [R] level.

first displays a coefficient table reporting results for the first equation only, and the report makes it appear that the first equation is the only equation. This option is used by programmers who estimate ancillary parameters in the second and subsequent equations and who wish to report the values of such parameters themselves.

neq(#) is an alternative to first. neq(#) displays a coefficient table reporting results for the first # equations. This option is used by programmers who estimate ancillary parameters in the #+1 and subsequent equations and who wish to report the values of such parameters themselves.

showeqns is a seldom-used option that displays the equation names in the coefficient table. ml display uses the numbers stored in e(k_eq) and e(k_aux) to determine how to display the coefficient table. e(k_eq) identifies the number of equations, and e(k_aux) identifies how many of these are for ancillary parameters. The first option is implied when showeqns is not specified and all but the first equation are for ancillary parameters.

plus displays the coefficient table, but rather than ending the table in a line of dashes, ends it in dashes-plus-sign-dashes. This is so that programmers can write additional display code to add more results to the table and make it appear as if the combined result is one table. Programmers typically specify plus with the first or neq() options. This option implies nofootnote.

nocnsreport suppresses the display of constraints above the coefficient table. This option is ignored if constraints were not used to fit the model.

noomitted specifies that variables that were omitted because of collinearity not be displayed. The default is to include in the table any variables omitted because of collinearity and to label them as "(omitted)".

vsquish specifies that the blank space separating factor-variable terms or time-series-operated variables from other variables in the model be suppressed.

noemptycells specifies that empty cells for interactions of factor variables not be displayed. The default is to include in the table interaction cells that do not occur in the estimation sample and to label them as "(empty)".

baselevels and allbaselevels control whether the base levels of factor variables and interactions are displayed. The default is to exclude from the table all base categories.

baselevels specifies that base levels be reported for factor variables and for interactions whose bases cannot be inferred from their component factor variables.

allbaselevels specifies that all base levels of factor variables and interactions be reported.

cformat(%fmt) specifies how to format coefficients, standard errors, and confidence limits in the coefficient table.

pformat(%fmt) specifies how to format p-values in the coefficient table.

sformat(%fmt) specifies how to format test statistics in the coefficient table.

nolstretch specifies that the width of the coefficient table not be automatically widened to accommodate longer variable names. The default, lstretch, is to automatically widen the coefficient table up to the width of the Results window. To change the default, use set lstretch off. nolstretch is not shown in the dialog box.

coeflegend specifies that the legend of the coefficients and how to specify them in an expression be displayed rather than displaying the statistics for the coefficients.

eform_option: eform(string), eform, hr, shr, irr, or, and rrr display the coefficient table in exponentiated form: for each coefficient, exp(b) rather than b is displayed, and standard errors and confidence intervals are transformed. string is the table header that will be displayed above the transformed coefficients and must be 11 characters or shorter in length -- for example, eform("Odds ratio"). The options eform, hr, shr, irr, or, and rrr provide a default string equivalent to "exp(b)", "Haz. Ratio", "SHR", "IRR", "Odds Ratio", and "RRR", respectively. These options may not be combined.

ml display looks at e(k_eform) to determine how many equations are affected by an eform_option; by default, only the first equation is affected. Type ereturn list, all to view e(k_eform); see [P] ereturn.

Examples

See [R] ml for examples. More examples are available in Gould, Pitblado, and Poi (2010) -- available from StataCorp.

Stored results

For results stored by ml without the svy option, see [R] maximize.

For results stored by ml with the svy option, see [SVY] svy.

References

Gould, W. W., J. Pitblado, and B. P. Poi. 2010. Maximum Likelihood Estimation with Stata. 4th ed. College Station, TX: Stata Press.

Korn, E. L., and B. I. Graubard. 1990. Simultaneous testing of regression coefficients with complex survey data: Use of Bonferroni t statistics. American Statistician 44: 270-276.


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