lm.beta: Beta regression coefficients

In MCI: Multiplicative Competitive Interaction (MCI) Model

Description

Calculating the standardized (beta) regression coefficients of linear models

Usage

lm.beta(linmod, dummy.na = TRUE)

Arguments

linmod	A lm object (linear regression model) with more than one independent variable
dummy.na	logical argument that indicates if dummy variables should be ignored when calculating the beta weights (default: TRUE). Note that beta weights of dummy variables do not make any sense

Details

Standardized coefficients (beta coefficients) show how many standard deviations a dependent variable will change when the regarded independent variable is increased by a standard deviation. The β values are used in multiple linear regression models to compare the real effect (power) of the independent variables when they are measured in different units. Note that β values do not make any sense for dummy variables since they cannot change by a standard deviation.

Value

A list containing all independent variables and the corresponding standardized coefficients.

Author(s)

Thomas Wieland

References

Backhaus, K./Erichson, B./Plinke, W./Weiber, R. (2016): “Multivariate Analysemethoden: Eine anwendungsorientierte Einfuehrung”. Berlin: Springer.

Examples

x1 <- runif(100)
x2 <- runif(100)
# random values for two independent variables (x1, x2)
y <- runif(100)
# random values for the dependent variable (y)
testmodel <- lm(y~x1+x2)
# OLS regression
summary(testmodel)
# summary
lm.beta(testmodel)
# beta coefficients

MCI documentation built on May 2, 2019, 6:02 a.m.