Description Usage Arguments Details Value Author(s) References See Also Examples
stdize
standardizes variables by centring and scaling.
stdizeFit
modifies a model call or existing model to use standardized
variables.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  ## Default S3 method:
stdize(x, center = TRUE, scale = TRUE, ...)
## S3 method for class 'logical'
stdize(x, binary = c("center", "scale", "binary", "half", "omit"),
center = TRUE, scale = FALSE, ...)
## also for twolevel factors
## S3 method for class 'data.frame'
stdize(x, binary = c("center", "scale", "binary", "half", "omit"),
center = TRUE, scale = TRUE, omit.cols = NULL, source = NULL,
prefix = TRUE, append = FALSE, ...)
## S3 method for class 'formula'
stdize(x, data = NULL, response = FALSE,
binary = c("center", "scale", "binary", "half", "omit"),
center = TRUE, scale = TRUE, omit.cols = NULL, prefix = TRUE,
append = FALSE, ...)
stdizeFit(object, data, which = c("formula", "subset", "offset", "weights"),
evaluate = TRUE, quote = NA)

x 
a numeric or logical vector, factor, numeric matrix,

center, scale 
either a logical value or a logical or numeric vector
of length equal to the number of columns of 
binary 
specifies how binary variables (logical or twolevel factors)
are scaled. Default is to 
source 
a reference 
omit.cols 
column names or numeric indices of columns that should be left unaltered. 
prefix 
either a logical value specifying whether the names of transformed columns should be prefixed, or a twoelement character vector giving the prefixes. The prefixes default to “z.” for scaled and “c.” for centred variables. 
append 
logical, if 
response 
logical, stating whether the response should be standardized. By default, only variables on the righthand side of the formula are standardized. 
data 
an object coercible to For 
... 
for the 
object 
a fitted model object or an expression being a 
which 
a character string naming arguments which should be modified.
This should be all arguments which are evaluated in the 
evaluate 
if 
quote 
if 
stdize
resembles scale
, but uses special rules
for factors, similarly to standardize
in package arm.
stdize
differs from standardize
in that it is used on
data rather than on the fitted model object. The scaled data should afterwards
be passed to the modelling function, instead of the original data.
Unlike standardize
, it applies special ‘binary’ scaling only to
twolevel factor
s and logical variables, rather than to any variable with
two unique values.
Variables of only one unique value are unchanged.
By default, stdize
scales by dividing by standard deviation rather than twice
the SD as standardize
does. Scaling by SD is used
also on uncentred values, which is different from scale
where
rootmeansquare is used.
If center
or scale
are logical scalars or vectors of length equal
to the number of columns of x
, the centring is done by subtracting the
mean (if center
corresponding to the column is TRUE
), and scaling
is done by dividing the (centred) value by standard deviation (if corresponding
scale
is TRUE
).
If center
or scale
are numeric vectors with length equal
to the number of columns of x
(or numeric scalars for vector methods),
then these are used instead. Any NA
s in the numeric vector result in no
centring or scaling on the corresponding column.
Note that scale = 0
is equivalent to no scaling (i.e. scale = 1
).
Binary variables, logical or factors with two levels, are converted to
numeric variables and transformed according to the argument binary
,
unless center
or scale
are explicitly given.
stdize
returns a vector or object of the same dimensions as x
,
where the values are centred and/or scaled. Transformation is carried out
columnwise in data.frame
s and matrices.
The returned value is compatible with that of scale
in that the
numeric centring and scalings used are stored in attributes
"scaled:center"
and "scaled:scale"
(these can be NA
if no
centring or scaling has been done).
stdizeFit
returns a modified, unevaluated call where the variable names
are replaced to point the transformed variables, or if evaluate
is
TRUE
, a fitted model object.
Kamil Bartoń
Gelman, A. (2008) Scaling regression inputs by dividing by two standard deviations. Statistics in medicine 27, 28652873.
Compare with scale
and standardize
or
rescale
(the latter two in package arm).
For typical standardizing, model coefficients transformation may be
easier, see std.coef
.
apply
and sweep
for arbitrary transformations of
columns in a data.frame
.
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 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57  # compare "stdize" and "scale"
nmat < matrix(runif(15, 0, 10), ncol = 3)
stdize(nmat)
scale(nmat)
rootmeansq < function(v) {
v < v[!is.na(v)]
sqrt(sum(v^2) / max(1, length(v)  1L))
}
scale(nmat, center = FALSE)
stdize(nmat, center = FALSE, scale = rootmeansq)
if(require(lme4)) {
# define scale function as twice the SD to reproduce "arm::standardize"
twosd < function(v) 2 * sd(v, na.rm = TRUE)
# standardize data (scaled variables are prefixed with "z.")
z.CO2 < stdize(uptake ~ conc + Plant, data = CO2, omit = "Plant", scale = twosd)
summary(z.CO2)
fmz < stdizeFit(lmer(uptake ~ conc + I(conc^2) + (1  Plant)), data = z.CO2)
# produces:
# lmer(uptake ~ z.conc + I(z.conc^2) + (1  Plant), data = z.CO2)
## standardize using scale and center from "z.CO2", keeping the original data:
z.CO2a < stdize(CO2, source = z.CO2, append = TRUE)
# Here, the "subset" expression uses untransformed variable, so we modify only
# "formula" argument, keeping "subset" asis. For that reason we needed the
# untransformed variables in "data".
stdizeFit(lmer(uptake ~ conc + I(conc^2) + (1  Plant),
subset = conc > 100,
), data = z.CO2a, which = "formula", evaluate = FALSE)
# create new data as a sequence along "conc"
newdata < data.frame(conc = seq(min(CO2$conc), max(CO2$conc), length = 10))
# scale new data using scale and center of the original scaled data:
z.newdata < stdize(newdata, source = z.CO2)
# plot predictions against "conc" on real scale:
plot(newdata$conc, predict(fmz, z.newdata, re.form = NA))
# compare with "arm::standardize"
## Not run:
library(arm)
fms < standardize(lmer(uptake ~ conc + I(conc^2) + (1  Plant), data = CO2))
plot(newdata$conc, predict(fms, z.newdata, re.form = NA))
## End(Not run)
}

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