lmcpp: Fitting Linear Model
In strongbeamsprout/lmcpp: linear regression model fitting and summary

"lmcpp" is used to fit simple linear model.

1	lmcpp(formula, data, model = FALSE, prt = FALSE)

`formula`	a symbolic description of the model to be fitted with specific pattern (i.e. y ~ x1 + x2 where y is the responding variable; x1 and x2 are the covariates for the model)
`data`	an optional data frame, list or environment containing the variables in the model. If not found in data, the variables are taken from formula.
`model`	logical; if TRUE, the design matrix for model fitting will be computed.
`prt`	logical; if TRUE, a formated output will be printed on the screen.

A list containing the following elements

call - return the fitted linear model fomula and the corresponding data.
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.
residuals - summary of the usual residuals with min, 1st quantile, median, 3rd quantile, max being computed.
fitted.values - the fitted mean values.
design.matrix - the design matrix for model fitting
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.
sd.beta - the corresponding standard error for the estimated coefficient.
t value - the t-statistic for corresponding variable.
Pr(>|t|) - the corresponding (two-sided) p-value for the t-statistic
fstatistic - the test statistic for F-tests. Variation Between Sample Means / Variation Within the Samples.