Description Usage Arguments Details Value Note Author(s) References See Also Examples
Adds standardized regression coefficients to objects created by lm
.
1  lm.beta(object)

object 
object of type 
Calculates the standardized regression coefficients by common method used for example in SPSS. For translating the formula, functions model.matrix
(for the righthand side) and model.frame
(for the lefthand side) are used, so all options saved in the lm
object are supported.
Please regard:
Package lm.beta
standardizes the coefficients after estimating them using the standard deviations or similar measures of the used variables. So there are unstandardized and standardized coefficients available simultaneously.
Standardizing before estimating is not (yet) available in this package, but by using the command scale
you can do this by using basic commands. Hereby please regard that the option center
influences the way of interpretation of the intercept.
Package lm.beta
standardizes all coefficients disregarding the use in interpretation. In this version, all types of scales of the variables (metrical, categorical, ...), all types of contrasts, interaction effects and additional terms on both sides of the formula can be handled if lm
can handle them. The sensitive use in interpretation has to be regarded by the user.
A list of class lm.beta
like a lm
object extended by
standardized.coefficients named vector of the standardized coefficients.
Some S3 methods, where standardized coefficients mind, are extended, the others work unchanged.
Stefan Behrendt, r@behrendtstefan.de
Urban, D., Mayerl, J., Sackmann, R. (Hrsg.) Regressionsanalyse : Theorie, Technik und Anwendung, VSVerlag, 4. Aufl.
Vittinghoff, E. et al (2005) Regression methods in biostatistics: Linear, logistic, survival, and repeated measures models, Springer, p 75
lm
for creating the demanded object and print.lm.beta
, summary.lm.beta
, coef.lm.beta
for extended S3methods.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## Taken from lm help
##
## Annette Dobson (1990) "An Introduction to Generalized Linear Models".
## Page 9: Plant Weight Data.
ctl < c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt < c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group < gl(2, 10, 20, labels = c("Ctl","Trt"))
weight < c(ctl, trt)
lm.D9 < lm(weight ~ group)
# standardize
lm.D9.beta < lm.beta(lm.D9)
print(lm.D9.beta)
summary(lm.D9.beta)
coef(lm.D9.beta)

Call:
lm(formula = weight ~ group)
Standardized Coefficients::
(Intercept) groupTrt
0.0000000 0.2703287
Call:
lm(formula = weight ~ group)
Residuals:
Min 1Q Median 3Q Max
1.0710 0.4938 0.0685 0.2462 1.3690
Coefficients:
Estimate Standardized Std. Error t value Pr(>t)
(Intercept) 5.0320 0.0000 0.2202 22.850 9.55e15 ***
groupTrt 0.3710 0.2703 0.3114 1.191 0.249

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6964 on 18 degrees of freedom
Multiple Rsquared: 0.07308, Adjusted Rsquared: 0.02158
Fstatistic: 1.419 on 1 and 18 DF, pvalue: 0.249
(Intercept) groupTrt
0.0000000 0.2703287
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