****************
****************
** logistic ** logistic regression
****************
****************
Overview: The "logistic" command estimates a logistic regression for
a binary outcome variable. The default summary is in terms
of exp(beta_j), which is an odds ratio when beta_j is the
coefficient of a main effect in the model.
Usage: "logistic Dvar X1 X2 X3"
Where - Dvar is the disease variable (1=disease, 0=control)
Xj are predictor variables
Summaries: The "logistic" command returns estimates of adjusted odds
ratos, exp( beta_j ).
After fitting the model using logistic, the command "logit" will
display the regression coefficients, beta_j.
Options: (1) "logistic y x1 x2 [freq=count]" -- this is used when the
data are in a "grouped" format with the number of
cases/controls that have a certain covariate combination
given by the variable "freq".
(2) "xi: logistic y i.x1 x2" -- this is used to create
dummy variables for variable x1.
****************
****************
** lrtest ** logistic regression and likelihood ratio tests
****************
****************
Overview: The "logistic" command estimates a logistic regression for
a binary outcome variable. We can save the maximized
log-likelihood for any fitted model using "lrtest". Then
we can compare nested models using "lrtest".
Usage: "logistic Y X1"
"lrtest, saving(1)"
"logistic Y X1 X2"
"lrtest, saving(2)"
"logistic Y X1 X2 X3 X4"
"lrtest, saving(3)"
"lrtest, using(2) model(1)"
"lrtest, using(3) model(2)"
Summaries: The "lrtest" command both stores fitted model log-likelihood
values using the "saving" option and tests for zero coefficients
that correspond to nested models.