lm.beta {REAT} | R Documentation |
Calculating the standardized (beta) regression coefficients of linear models
lm.beta(linmod, dummy.na = TRUE)
linmod |
A |
dummy.na |
logical argument that indicates if dummy variables should be ignored when calculating the beta weights (default: |
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 \beta
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 \beta
values do not make any sense for dummy variables since they cannot change by a standard deviation.
A list
containing all independent variables and the corresponding standardized coefficients.
Thomas Wieland
Backhaus, K./Erichson, B./Plinke, W./Weiber, R. (2016): “Multivariate Analysemethoden: Eine anwendungsorientierte Einfuehrung”. Berlin: Springer.
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