lm_function: Fitting Linear Models

In CatherineAmmann/regression: Linear Regression and Logistic Regression

Description

This function can be used to create a simple linear regression model.

Usage

  lm_function(Y, X, data)

Arguments

Y	the dependent variable is requested as a matrix (Y = as.matrix(Y)).
X	the independent variable must include the intercept and the X-values (X = cbind(1, X1, X2, ..., Xk)).
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.

Details

The "linear regression" attempts to model the relationship between two or more variables by fitting a linear equation to observed data. The most common method for fitting a regression line is the method of least-squares. This method calculates the best-fitting line for the observed data by minimizing the sum of the squares of the vertical deviations from each data point to the line.

Author(s)

Catherine Ammann: ([e-mail]) and Sergio Roethlisberger: ([e-mail])

Examples

## model = lm_function(Y = as.matrix(my.data$Y), X=cbind(1, my.data$X1, my.data$X2), data = my.data)
## model

CatherineAmmann/regression documentation built on May 28, 2019, 12:17 p.m.