Grocer Function
NAME
logit - logit regression
CALLING SEQUENCE
[rlogit]=logit(grocer_namey,arg1,...,argn)
PARAMETERS
Input
-
grocer_namey = a time series, a real (nx1) vector or a string equal to the name of a time series or a (nx1) real vector between quotes
-
argi = an argument which can be:
-
argi = an argument that can be:
- a time series
- a real (nx1) vector
- a real (nxk) matrix
- a string equal to the name of a time series or a (nxk) real vector or matrix between quotes
- a list of such elements
- the string 'noprint' if the user doesn't want to display the results of the regression
- the string 'maxit=xx' if the user wants to set the maximum # of iterations to xx (default=100)
- the string 'tol=xx' if the user wants to set the convergence criterion to xx (default=1e-6)
Output
-
rlogit = a results tlist with
- rlogit('meth') = 'logit'
- rlogit('y') = y data vector
- rlogit('x') = x data matrix
- rlogit('nobs') = # observations
- rlogit('nvar') = # variables
- rlogit('beta') = bhat
- rlogit('yhat') = yhat
- rlogit('resid') = residuals
- rlogit('vcovar') = estimated variance-covariance matrix of beta
- rlogit('tstat') = t-stats
- rlogit('pvalue') = pvalue of the betas
- rlogit('r2mf') = = McFadden pseudo-Rē
- rlogit('rsqr') = = Estrella Rē
- rlogit('lratio') = LR-ratio test against intercept model
- rlogit('lik') = unrestricted Likelihood
- rlogit('zip') = # of 0's
- rlogit('one) = # of 1's
- rlogit('iter') = # of iterations
- rlogit('crit') = convergence criterion
- rlogit('namey') = name of the y variable
- rlogit('namex') = name of the x variables
- rlogit('prests') = boolean indicating the presence or absence of a time series in the regression
- rlogit('prescte') = %f (for printings)
- rlogit('bounds') = if there is a timeseries in the regression, the bounds of the regression
DESCRIPTION
Computes Logit Regression. If the user has not given the argument 'noprint', displays on screen the results of the regression and various diagnostics. References: Arturo Estrella (1998) 'A new measure of fit for equations with dichotomous dependent variable', JBES, Vol. 16, #2, April, 1998.
EXAMPLE
1) logit('grade','cte','psi','tuce','gpa');
2) logit('grade','cte','psi','tuce','gpa','maxit=200','crit=sqrt(%eps)','noprint');
Example 1, taken from logit_d, provides the logit regression of vector grade on a constant and the vectors psi, tuce, gpa.
Example 2 does the same, except that the maximum number of iterations is set to 200 (instead of 100), the convergence criterion to sqrt(%eps) (instead of 0.000001) and the results are not displayed on screen.
AUTHOR
Eric Dubois 2002