print.ols

number of significant digits to print

digits

set to <code>TRUE</code> to print the correlation matrix of
 parameter estimates

long

specify <code>coefs=FALSE</code> to suppress printing the table
 of model coefficients, standard errors, etc. Specify <code>coefs=n</code>
 to print only the first <code>n</code> regression coefficients in the
 model.

coefs

a character string title to be passed to <code>prModFit</code>

title

other parameters to pass to <code>print</code> or <code>format</code>

&#8230;

Formatted printing of an object of class <code>ols</code> using methods taken
from <code>print.lm</code> and <code>summary.lm</code>. Prints R-squared, adjusted
R-squared, parameter estimates, standard errors, and t-statistics (Z
statistics if penalized estimation was used). For penalized estimation,
prints the maximum penalized likelihood estimate of the residual
standard deviation (<code>Sigma</code>) instead of the usual root mean squared
error.
Format of output is controlled by the user previously running
<code>options(prType="lang")</code> where <code>lang</code> is <code>"plain"</code> (the default),
<code>"latex"</code>, or <code>"html"</code>.

print

Regression modeling, testing, estimation, validation,
graphics, prediction, and typesetting by storing enhanced model design
attributes in the fit.  'rms' is a collection of functions that
assist with and streamline modeling.  It also contains functions for
binary and ordinal logistic regression models, ordinal models for
continuous Y with a variety of distribution families, and the Buckley-James
multiple regression model for right-censored responses, and implements
penalized maximum likelihood estimation for logistic and ordinary
linear models.  'rms' works with almost any regression model, but it
was especially written to work with binary or ordinal regression
models, Cox regression, accelerated failure time models,
ordinary linear models,	the Buckley-James model, generalized least
squares for serially or spatially correlated observations, generalized
linear models, and quantile regression.  Bayesian methods are implemented for binary and ordinal logistic regression with or without random effects.

print.ols: Print ols

Description

Usage

Arguments

See Also