summary.lmcpp: Summarizing Linear Model Fits
In strongbeamsprout/lmcpp: linear regression model fitting and summary

Summarize the output of "lmCpp" function

1 2	## S3 method for class 'lmcpp' summary(object, correlation = FALSE, prt = TRUE)

`object`	the fitting results of "lmCpp".
`correlation`	logical; if TRUE, the correlation matrix of the estimated parameters is returned and printed.
`prt`	logical; if TRUE, a summarized table will be computed to the screen

A list containing the following elements

call - the fitted linear model fomula.
Residuals - summary of the usual residuals with min, 1st quantile, median, 3rd quantile, max being computed.
Coefficients - a table with the estimated values for each covariates and the intercept as well as their corresponding standard error, t-statistics and (two-sided) p-value.
Residual standard erro - the square root of the estimated variance of the random error and a corresponding degrees of freedom will also be computed.
r.squared - R^2, the 'fraction of variance explained by the model', SSR (variation in fitted values about the overall mean) / SSY (total variation in Y about its overall mean).
adj.r.squared - adjusted version of R^2, penalized based on number of covariates.
cov.unscaled - a table of (unscaled) covariances of the coeficients.
correlation - the correlation table corresponding to the above cov.unscaled table, if correlation = TRUE is specified.