linear_model: Fit a linear regression model

In JasperHG90/meerkat: Assignment 2

Description

Use linear_model() to fit a linear regression model. The function also calculates standard errors, p-values, the F-statistic & R-squared

Usage

linear_model(formula, data)

Arguments

formula	an object of class 'formula' or a string that can be coerced to this class. The formula is a symbolic description of the data and should be passed as 'DV ~ IV1 + IV2 + ...' (where DV stands for 'dependent variable' and the IVs are the independent variables). You are also allowed to pass 'DV ~ .' indicating that you will use all all variables in the data as predictors.
data	data frame containing the variables of interest

Value

list of class 'linear_model' containing:

• inputs: user inputs: formula, DV and IV(s), data entered, number of observations (n), number of predictors (m)

• summary_statistics: mean, variance, minimum, maximum for each variable. Also contains the number of observations (n) and the degrees of freedom (df)

• coefficients: estimators for each predictor in the model obtained from the linear regression model

• tests: list containing the results of analysis of the coefficients. Specifically:

  • coef: a list containing standard errors, t-values and p-values for the estimators.

  • predicted: predicted values.

  • residuals: residuals

  • sums_of_squares: a list containg the sums of squares (total, model, residual sums of squares)

  • means_of_squares: a list containing the mean sums of squares (total, model and residual mean of squares

  • f_test: a list containing F-statistic and associated p-value.

  • R_squared: unajusted R-squared value

Author(s)

Jasper Ginn

JasperHG90/meerkat documentation built on May 31, 2019, 5:39 a.m.