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Description
Obtain covariance and correlation matrices for the coefficient estimates within rxLinMod
,
rxLogit
, and rxGlm
objects and
explanatory variables within rxLinMod
and rxLogit
objects.
Usage
rxCovCoef(x)
rxCorCoef(x)
rxCovData(x)
rxCorData(x)
Arguments
x
object of class rxLinMod
, rxLogit
, or rxGlm
that satisfies conditions in the Details section.
Details
For rxCovCoef
and rxCorCoef
, the rxLinMod, rxLogit, or rxGlm object must
have been fit with covCoef = TRUE
and cube = FALSE
. The degrees
of freedom must be greater than 0.
For rxCovData
and rxCorData
, the rxLinMod or rxLogit object must
have been fit with an intercept term as well as with covData = TRUE
and
cube = FALSE
.
Value
If p
is the number of columns in the model matrix, then
For rxCovCoef
a p x p
numeric matrix containing the
covariances of the model coefficients.
For rxCorCoef
a p x p
numeric matrix containing the
correlations amongst the model coefficients.
For rxCovData
a (p - 1) x (p - 1)
numeric matrix containing the covariances of the non-intercept terms in the
model matrix.
For rxCorData
a (p - 1) x (p - 1)
numeric matrix containing the correlations amongst the non-intercept terms in
the model matrix.
Author(s)
Microsoft Corporation Microsoft Technical Support
See Also
Examples
## Example 1
# Get the covariance matrix of the estimated model coefficients
kyphXdfFileName <- file.path(rxGetOption("sampleDataDir"), "kyphosis.xdf")
kyphLogitWithCovCoef <-
rxLogit(Kyphosis ~ Age + Number + Start, data = kyphXdfFileName,
covCoef = TRUE, reportProgress = 0)
rxCovCoef(kyphLogitWithCovCoef)
# Compare results with results from stats::glm function
data(kyphosis, package = "rpart")
kyphGlmSummary <-
summary(glm(Kyphosis ~ Age + Number + Start, data = kyphosis,
family = binomial()))
kyphGlmSummary[["cov.scaled"]]
## Example 2
# Get the covariance matrix of the data
kyphXdfFileName <- file.path(rxGetOption("sampleDataDir"), "kyphosis.xdf")
kyphLogitWithCovData <-
rxLogit(Kyphosis ~ Age + Number + Start, data = kyphXdfFileName,
covData = TRUE, reportProgress = 0)
rxCovData(kyphLogitWithCovData)
# Compare results with stats::cov function
cov(kyphosis[2:4])
## Example 3
# Find the correlation matrices for both the coefficient estimates and the
# explanatory variables
rxCorCoef(kyphLogitWithCovCoef)
rxCorData(kyphLogitWithCovData)