Stata 15 help for rocfit

[R] rocfit -- Parametric ROC models


rocfit refvar classvar [if] [in] [weight] [, rocfit_options]

rocfit_options Description ------------------------------------------------------------------------- Model continuous(#) divide classvar into # groups of approximately equal length generate(newvar) create newvar containing classification groups

SE vce(vcetype) vcetype may be oim or opg

Reporting level(#) set confidence level; default is level(95)

Maximization maximize_options control the maximization process; seldom used ------------------------------------------------------------------------- fp is allowed; see prefix. fweights are allowed; see weight. See [R] rocfit postestimation for features available after estimation.


Statistics > Epidemiology and related > ROC analysis > Parametric ROC analysis without covariates


rocfit fits maximum-likelihood ROC models assuming a binormal distribution of the latent variable.

The two variables refvar and classvar must be numeric. The reference variable indicates the true state of the observation, such as diseased and nondiseased or normal and abnormal, and must be coded as 0 and 1. The rating or outcome of the diagnostic test or test modality is recorded in classvar, which must be at least ordinal, with higher values indicating higher risk.

See [R] roc for other commands designed to perform receiver operating characteristic (ROC) analyses with rating and discrete classification data.


+-------+ ----+ Model +------------------------------------------------------------

continuous(#) specifies that the continuous classvar be divided into # groups of approximately equal length. The option is required when classvar takes on more than 20 distinct values.

continuous(.) may be specified to indicate that classvar be used as it is, even though it could have more than 20 distinct values.

generate(newvar) specifies the new variable that is to contain values indicating the groups produced by continuous(#). generate() may be specified only with continuous().

+----+ ----+ SE +---------------------------------------------------------------

vce(vcetype) specifies the type of standard error reported. vcetype may be either oim or opg; see [R] vce_option.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

+--------------+ ----+ Maximization +-----------------------------------------------------

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.

Setting the optimization type to technique(bhhh) resets the default vcetype to vce(opg).


Setup . webuse hanley

Fit a smooth ROC curve assuming a binormal model . rocfit disease rating

Divide rating into 10 groups . rocfit disease rating, cont(10)

group is to contain values indicating groups produced by cont() . rocfit disease rating, cont(10) generate(group)

Use rating as is . rocfit disease rating, cont(.)

Stored results

rocfit stores the following in e():

Scalars e(N) number of observations e(k) number of parameters e(k_eq) number of equations in e(b) e(k_eq_model) number of equations in overall model test e(k_dv) number of dependent variables e(df_m) model degrees of freedom e(ll) log likelihood e(chi2_gf) goodness-of-fit chi-squared e(df_gf) goodness-of-fit degrees of freedom e(p_gf) p-value for goodness-of-fit test e(area) area under the ROC curve e(se_area) standard error for the area under the ROC curve e(deltam) delta(m) e(se_delm) standard area for delta(m) e(de) d(e) index e(se_de) standard error for d(e) index e(da) d(a) index e(se_da) standard error for d(a) index e(rank) rank of e(V) e(ic) number of iterations e(rc) return code e(converged) 1 if converged, 0 otherwise

Macros e(cmd) rocfit e(cmdline) command as typed e(depvar) refvar and classvar e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(chi2type) GOF; type of model chi-squared test e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(opt) type of optimization e(which) max or min; whether optimizer is to perform maximization or minimization e(ml_method) type of ml method e(user) name of likelihood-evaluator program e(technique) maximization technique e(properties) b V

Matrices e(b) coefficient vector e(ilog) iteration log (up to 20 iterations) e(gradient) gradient vector e(V) variance-covariance matrix of the estimators

Functions e(sample) marks estimation sample

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