arm: standardize – R documentation

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standardize

Function for Standardizing Regression Predictors by Centering and Dividing by 2 sd's

Description

Numeric variables that take on more than two values are each rescaled to have a mean of 0 and a sd of 0.5; Binary variables are rescaled to have a mean of 0 and a difference of 1 between their two categories; Non-numeric variables that take on more than two values are unchanged; Variables that take on only one value are unchanged

Usage

## S4 method for signature 'lm'
standardize(object, unchanged = NULL, 
    standardize.y = FALSE, binary.inputs = "center")
## S4 method for signature 'glm'
standardize(object, unchanged = NULL, 
    standardize.y = FALSE, binary.inputs = "center")
## S4 method for signature 'merMod'
standardize(object, unchanged = NULL, 
    standardize.y = FALSE, binary.inputs = "center")
## S4 method for signature 'polr'
standardize(object, unchanged = NULL, 
    standardize.y = FALSE, binary.inputs = "center")

Arguments

`object`	an object of class `lm` or `glm`
`unchanged`	vector of names of parameters to leave unstandardized
`standardize.y`	if TRUE, the outcome variable is standardized also
`binary.inputs`	options for standardizing binary variables

Details

"0/1" (rescale so that the lower value is 0 and the upper is 1) "-0.5/0.5" (rescale so that the lower value is -0.5 and upper is 0.5) "center" (rescale so that the mean of the data is 0 and the difference between the two categories is 1) "full" (rescale by subtracting the mean and dividing by 2 sd's) "leave.alone" (do nothing)

Author(s)

Andrew Gelman gelman@stat.columbia.edu Yu-Sung Su suyusung@tsinghua.edu.cn

References

Andrew Gelman. (2008). “Scaling regression inputs by dividing by two standard deviations.” Statistics in Medicine 27: 2865–2873. http://www.stat.columbia.edu/~gelman/research/published/standardizing7.pdf

Examples

# Set up the fake data
  n <- 100
  x <- rnorm (n, 2, 1)
  x1 <- rnorm (n)
  x1 <- (x1-mean(x1))/(2*sd(x1))   # standardization
  x2 <- rbinom (n, 1, .5)
  b0 <- 1
  b1 <- 1.5
  b2 <- 2
  y <- rbinom (n, 1, invlogit(b0+b1*x1+b2*x2))
  y2 <- sample(1:5, n, replace=TRUE)
  M1 <- glm (y ~ x, family=binomial(link="logit"))
  display(M1)
  M1.1 <- glm (y ~ rescale(x), family=binomial(link="logit"))
  display(M1.1)
  M1.2 <- standardize(M1)
  display(M1.2)
  # M1.1 & M1.2 should be the same
  M2 <- polr(ordered(y2) ~ x)
  display(M2)
  M2.1 <- polr(ordered(y2) ~ rescale(x))
  display(M2.1)
  M2.2 <- standardize(M2.1)
  display(M2.2)
  # M2.1 & M2.2 should be the same

arm

Data Analysis Using Regression and Multilevel/Hierarchical Models

v1.11-2

GPL (> 2)

Authors

Andrew Gelman [aut], Yu-Sung Su [aut, cre], Masanao Yajima [ctb], Jennifer Hill [ctb], Maria Grazia Pittau [ctb], Jouni Kerman [ctb], Tian Zheng [ctb], Vincent Dorie [ctb]

Initial release

2020-7-27