summary: summary

In balr411/linearRegression: Linear Regression Analysis

Description

Determines various characteristics of a linearRegression model

Usage

summary(model)

Arguments

model

a model fit through linearRegression

Value

a list containing the model formula, residuals, a data frame (entitled 'coefficients') with estimated regression coefficients, standard errors, t-statistics, and p-values for each coefficient, as well as R-squared, adjusted R-squared, degrees of freedom, model F-statistic, p-value, and residual standard error

Examples

fit<-linearRegression(dist~speed, cars); summary(fit)
fit2<-linearRegression(mpg~cyl+disp+hp+wt+cyl:disp+disp:wt, dat=mtcars); summary(fit2)$coefficients
y<-rnorm(100); x<-rnorm(100); fit3<-linearRegression(y~x, subs=1:50); summary(fit3)

balr411/linearRegression documentation built on Dec. 31, 2020, 7:55 p.m.