# 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

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## 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 |

`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

### See Also

`rescale`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
# 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(M1.1)
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
``` |