View source: R/rrvglm.control.q
rrvglm.control | R Documentation |
Algorithmic constants and parameters for running rrvglm
are set using this function.
rrvglm.control(Rank = 1, Algorithm = c("alternating", "derivative"),
Corner = TRUE, Uncorrelated.latvar = FALSE,
Wmat = NULL, Svd.arg = FALSE,
Index.corner = if (length(str0))
head((1:1000)[-str0], Rank) else 1:Rank,
Ainit = NULL, Alpha = 0.5, Bestof = 1, Cinit = NULL,
Etamat.colmax = 10,
sd.Ainit = 0.02, sd.Cinit = 0.02, str0 = NULL,
noRRR = ~1, Norrr = NA,
noWarning = FALSE,
trace = FALSE, Use.Init.Poisson.QO = FALSE,
checkwz = TRUE, Check.rank = TRUE, Check.cm.rank = TRUE,
wzepsilon = .Machine$double.eps^0.75, ...)
Rank |
The numerical rank |
Algorithm |
Character string indicating what algorithm is to be used. The default is the first one. |
Corner |
Logical indicating whether corner constraints are
to be used. This is one method for ensuring a unique solution.
If |
Uncorrelated.latvar |
Logical indicating whether uncorrelated latent variables are to
be used. This is normalization forces the variance-covariance
matrix of the latent variables to be |
Wmat |
Yet to be done. |
Svd.arg |
Logical indicating whether a singular value decomposition
of the outer product is to computed. This is another
normalization which ensures uniqueness. See the argument
|
Index.corner |
Specifies the |
Alpha |
The exponent in the singular value decomposition that is used in
the first part: if the SVD is
|
Bestof |
Integer. The best of |
Ainit, Cinit |
Initial A and C matrices which may speed up convergence. They must be of the correct dimension. |
Etamat.colmax |
Positive integer, no smaller than |
str0 |
Integer vector specifying which rows
of the estimated constraint matrices (A) are
to be all zeros.
These are called structural zeros.
Must not have any common value with |
sd.Ainit, sd.Cinit |
Standard deviation of the initial values for the elements
of A and C.
These are normally distributed with mean zero.
This argument is used only if |
noRRR |
Formula giving terms that are not to be included
in the reduced-rank regression.
That is, |
Norrr |
Defunct. Please use |
trace |
Logical indicating if output should be produced for each iteration. |
Use.Init.Poisson.QO |
Logical indicating whether the |
checkwz |
logical indicating whether the diagonal elements of the working
weight matrices should be checked whether they are sufficiently
positive, i.e., greater than |
noWarning, Check.rank, Check.cm.rank |
Same as |
wzepsilon |
Small positive number used to test whether the diagonals of the working weight matrices are sufficiently positive. |
... |
Variables in ... are passed into
|
In the above, R
is the Rank
and
M
is the number of linear predictors.
VGAM supports three normalizations to ensure a unique
solution. Of these, only corner constraints will work with
summary
of RR-VGLM objects.
A list with components matching the input names. Some error checking is done, but not much.
The arguments in this function begin with an upper case letter to help
avoid interference with those of vglm.control
.
In the example below a rank-1 stereotype model (Anderson, 1984) is fitted.
Thomas W. Yee
Yee, T. W. and Hastie, T. J. (2003). Reduced-rank vector generalized linear models. Statistical Modelling, 3, 15–41.
rrvglm
,
rrvglm.optim.control
,
rrvglm-class
,
vglm
,
vglm.control
,
cqo
.
## Not run:
set.seed(111)
pneumo <- transform(pneumo, let = log(exposure.time),
x3 = runif(nrow(pneumo))) # Unrelated
fit <- rrvglm(cbind(normal, mild, severe) ~ let + x3,
multinomial, pneumo, Rank = 1, Index.corner = 2)
constraints(fit)
vcov(fit)
summary(fit)
## End(Not run)
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