lm.beta: Add standardized regression coefficients to 'lm'-objects

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/lm.beta.R

Description

Adds standardized regression coefficients to objects created by lm.

Usage

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lm.beta(object)

Arguments

object

object of type lm

Details

Calculates the standardized regression coefficients by common method used for example in SPSS. For translating the formula, functions model.matrix (for the right-hand side) and model.frame (for the left-hand 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.

Value

A list of class lm.beta like a lm-object extended by

Note

Some S3 methods, where standardized coefficients mind, are extended, the others work unchanged.

Author(s)

Stefan Behrendt, r@behrendt-stefan.de

References

Urban, D., Mayerl, J., Sackmann, R. (Hrsg.) Regressionsanalyse : Theorie, Technik und Anwendung, VS-Verlag, 4. Aufl.

Vittinghoff, E. et al (2005) Regression methods in biostatistics: Linear, logistic, survival, and repeated measures models, Springer, p 75

See Also

lm for creating the demanded object and print.lm.beta, summary.lm.beta, coef.lm.beta for extended S3-methods.

Examples

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## 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)

Example output

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 9.55e-15 ***
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 R-squared:  0.07308,	Adjusted R-squared:  0.02158 
F-statistic: 1.419 on 1 and 18 DF,  p-value: 0.249

(Intercept)    groupTrt 
  0.0000000  -0.2703287 

lm.beta documentation built on May 2, 2019, 2:45 a.m.