dvar | R Documentation |
cvar and dvar are designed to be used in regression formulas to
create a contextual mean of a cluster-varying variable and
a 'centered-within-groups' version. See also cvar
.
dvar(x, id, all, na.rm, ...)
## S3 method for class 'factor'
dvar(x, id, all = FALSE, na.rm = TRUE, ...)
## Default S3 method:
dvar(x, id, all, na.rm = TRUE, ...)
x |
variable to be centered or residualized within groups. If x is a factor, cvar and dvar return matrices whose columns are named consistently with the names of coding variables for factors. |
id |
identifies clusters |
all |
(default FALSE) if TRUE cvar.factor returns the columns means of an incidence matrix including the first level. Otherwise, the first level is dropped for use in a linear model. |
na.rm |
(default TRUE) whether to drop missing values |
dvar(factor)
: method for class 'factor'
dvar(default)
: default method
## Not run:
dd <- data.frame(x= 1:100, id = rep( LETTERS[1:10], each = 10))
dd$a <- factor(sample( c('a','b','c'), 100, replace = T))
dd$y <- dd$x + rep(rnorm(10), each = 10) + rnorm(100) + as.numeric(dd$a)
library(nlme)
fit <- lme( y ~ x + cvar(x,id), dd, random = ~ 1 + dvar(x,id) | id)
anova( fit , type = 'm')
# The output of 'anova' can be used to test whether a contextual variable
# should be included in the model
## End(Not run)
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