anova.mvl1lm {MNM} | R Documentation |
Comparisons between nested multivariate linear models fitted by mv.l1lm
. The comparison can be based on score type of tests and Wald type of tests.
## S3 method for class 'mvl1lm': anova(object, object2 = NULL, test = "Score", ...)
object |
an object of class mvl1lm . This gives the full model.
|
object2 |
an object of class mvl1lm or NULL . This gives the restricted (nested) model.
|
test |
The test to be used. Options are Score and Wald . The score version is the default.
|
... |
needed for other methods. |
If only object
is provided the function tests if all parameters equal zero. If object
and object2
are provided the function tests the
null hypothesis that the the restricted model (object2
) is true.
For details see chapter 13 of the MNM book. Note that it is the users responsibility to make sure that the two models are nested and fitted on the same data.
For the regular L2 regression anova.mlm
provides more options.
A list with class 'anovamvl1lm' containing the following components:
models |
the model call(s) of object and object2 . |
method |
type of the test used. |
statistic |
value of the test statistic. |
parameter |
degrees of freedom. |
p.value |
p-value of the test. |
Klaus Nordhausen, klaus.nordhausen@uta.fi
Oja, H. (2010), Multivariate Nonparametric Methods with R, Springer.
# creating simple data X <- cbind(rep(1,100),rmvnorm(100,c(0,0,0)) ) B <- matrix(c(4,1,1,0.5,-3,2,2,2),ncol=4, byrow=TRUE) Y <- X %*% t(B)+ rmvnorm(100,c(0,0), diag(0.2,2)) DAT <- data.frame(x1=X[,2],x2=X[,3], x3=X[,4]) FullModel <- mv.l1lm(Y ~ x1 + x2 + x3, scores= "s", stand="i", data=DAT) RestModel <- mv.l1lm(Y ~ x1, scores= "s", stand="i", data=DAT) anova(FullModel) anova(FullModel, RestModel) anova(FullModel, RestModel, test="W")