vcov.rsm: Calculate Variance-Covariance Matrix for a Fitted RSM Model

In marg: Approximate Marginal Inference for Regression-Scale Models

Description

Returns the variance-covariance matrix of the parameters of a fitted rsm model object.

Usage

## S3 method for class 'rsm'
vcov(object, correlation = FALSE, ...)

Arguments

object	a fitted model object of class rsm.
correlation	if TRUE the correlation matrix is returned instead of the variance-covariance matrix.
...	absobs any additional argument.

Details

This is a method for function vcov for objects of class rsm.

Value

A matrix of the estimated covariances between the parameter estimates of a fitted regression-scale model, or, if dispersion = TRUE the correlation matrix.

See Also

vcov, rsm.object, rsm, summary.rsm

Examples

## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(2*pi*1:51/18.62)
venice.rsm <- rsm(sea ~ Year + I(Year^2) + c11 + s11 + c19 + s19, 
                  family = extreme)
##
vcov(venice.rsm)
vcov(venice.rsm, corr = TRUE)
##
detach()

Example output

Loading required package: statmod
Loading required package: survival

   Package "marg" 1.2-2 (2014-03-31) 
    Copyright (C) 2000-2014 A. R. Brazzale

 This is free software, and you are welcome to redistribute
 it and/or modify it under the terms of the GNU General
 Public License published by the Free Software Foundation.
 Package "marg" comes with ABSOLUTELY NO WARRANTY.

 type `help(package="marg")' for summary information

            (Intercept)         Year    I(Year^2)         c11        s11
(Intercept)  26.4236263  -99.6908211   79.2874795 -1.87008823 -2.2077626
Year        -99.6908211  492.4798242 -441.2333734  8.22710818  5.9492256
I(Year^2)    79.2874795 -441.2333734  424.4034807 -5.99596747 -2.9109546
c11          -1.8700882    8.2271082   -5.9959675  6.02593102 -0.1431278
s11          -2.2077626    5.9492256   -2.9109546 -0.14312780  4.7131884
c19          -1.6423903    5.9368779   -2.8778449  0.34054756  0.5295069
s19          -0.7599612    2.0630414   -1.3525486 -0.35879919  0.4244374
scale         0.9296567   -0.9963357    0.5760934 -0.07842388  0.1065999
                    c19         s19       scale
(Intercept) -1.64239034 -0.75996124  0.92965668
Year         5.93687794  2.06304137 -0.99633566
I(Year^2)   -2.87784486 -1.35254864  0.57609339
c11          0.34054756 -0.35879919 -0.07842388
s11          0.52950692  0.42443739  0.10659989
c19          5.28490130  0.09399944  0.13163355
s19          0.09399944  5.13385833  0.29860320
scale        0.13163355  0.29860320  1.55150305
            (Intercept)        Year   I(Year^2)         c11         s11
(Intercept)  1.00000000 -0.87390604  0.74871914 -0.14820191 -0.19783301
Year        -0.87390604  1.00000000 -0.96512744  0.15102224  0.12348347
I(Year^2)    0.74871914 -0.96512744  1.00000000 -0.11856533 -0.06508615
c11         -0.14820191  0.15102224 -0.11856533  1.00000000 -0.02685682
s11         -0.19783301  0.12348347 -0.06508615 -0.02685682  1.00000000
c19         -0.13898302  0.11637119 -0.06076583  0.06034583  0.10609521
s19         -0.06524892  0.04102908 -0.02897620 -0.06450857  0.08628480
scale        0.14519447 -0.03604418  0.02245056 -0.02564838  0.03942058
                    c19         s19       scale
(Intercept) -0.13898302 -0.06524892  0.14519447
Year         0.11637119  0.04102908 -0.03604418
I(Year^2)   -0.06076583 -0.02897620  0.02245056
c11          0.06034583 -0.06450857 -0.02564838
s11          0.10609521  0.08628480  0.03942058
c19          1.00000000  0.01804616  0.04596972
s19          0.01804616  1.00000000  0.10580257
scale        0.04596972  0.10580257  1.00000000

marg documentation built on May 2, 2019, 7:55 a.m.