| relimp {relimp} | R Documentation |
Produces a summary of the relative importance of two predictors or two sets of predictors in a fitted model object.
relimp(object, set1=NULL, set2=NULL, label1="set1", label2="set2",
subset=TRUE,
response.cat=NULL, ...)
print.relimp(x, digits=3, ...)
object |
A model object of class
lm, glm, coxph,
survreg,
multinom,
polr, gls or lme |
set1 |
An index or vector of indices for the effects to be included in the numerator of the comparison |
set2 |
An index or vector of indices for the effects to be included in the denominator of the comparison |
label1 |
A character string; mnemonic name for the
variables in set1 |
label2 |
A character string; mnemonic name for the
variables in set2 |
subset |
Either a vector of numeric indices for the cases to be included
in the standardization of effects, or a vector of logicals
(TRUE for inclusion)
whose length is the same as the number of rows in the model frame,
object$model.
The default choice is to include all cases in the model frame. |
response.cat |
If object is of class multinom,
this is a character
string used to specify which regression is of interest (i.e., the
regression
which predicts the log odds on response cat versus the model's
reference category). The response.cat argument should be an
element of
object$lab; or NULL if object is not of class
multinom. |
... |
For models of class glm, one may additionally set
the dispersion parameter for the family (for example,
dispersion=1.69). By default
it is obtained from object. Supplying it here
permits explicit allowance for over-dispersion, for example. |
x |
an object of class relimp |
digits |
The number of decimal places to be used in the printed summary. Default is 3. |
If set1 and set2 both have length 1, relative importance is
measured by the ratio of the two standardized coefficients.
Equivalently this is the ratio of the standard deviations of the two
contributions to the linear predictor, and this provides the
generalization to comparing two sets rather than just a pair of predictors.
The computed ratio is the square root of the variance-ratio quantity denoted as `omega' in Silber, J H, Rosenbaum, P R and Ross, R N (1995). Estimated standard errors are calculated by the delta method, as described in that paper for example.
If set1 and set2 are unspecified, and if the tcltk package has been
loaded, a dialog box is provided (by a call to pickFrom)
for the choice of set1 and set2 from the available model coefficients.
An object of class relimp, with at least the following components:
model |
The call used to construct the model object summarized |
sets |
The two sets of indices specified as arguments |
log.ratio |
The natural logarithm of the ratio of effect standard deviations corresponding to the two sets specified |
se.log.ratio |
An estimated standard error for log.ratio |
If dispersion was supplied as an argument, its value is stored as the
dispersion component of the resultant object.
David Firth d.firth@warwick.ac.uk
Silber, J. H., Rosenbaum, P. R. and Ross, R N (1995) Comparing the Contributions of Groups of Predictors: Which Outcomes Vary with Hospital Rather than Patient Characteristics? JASA 90, 7–18.
x <- rnorm(100) z <- rnorm(100) w <- rnorm(100) y <- 3+ 2*x + z + w + rnorm(100) test <- lm(y ~ x +z +w) print(test) relimp(test, 2, 3) # compares effects of x and z relimp(test, 2, 3:4) # compares effect of x with that of (z,w) combined ## ## Data on housing and satisfaction, from Venables and Ripley ## -- multinomial logit model library(MASS) library(nnet) data(housing) house.mult <- multinom(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) relimp(house.mult, set1 = 2:3, set2 = 7, response.cat = "High")