lvplot.rrvglm {VGAM} | R Documentation |
Produces an ordination diagram (also known as a biplot or latent variable plot) for reduced-rank vector generalized linear models (RR-VGLMs). For rank-2 models only, the x- and y-axis are the first and second canonical axes respectively.
lvplot.rrvglm(object, A = TRUE, C = TRUE, scores = FALSE, plot.it = TRUE, groups = rep(1, n), gapC = sqrt(sum(par()$cxy^2)), scaleA = 1, xlab = "Latent Variable 1", ylab = "Latent Variable 2", Alabels = if (length(object@misc$predictors.names)) object@misc$predictors.names else paste("LP", 1:M, sep = ""), Aadj = par()$adj, Acex = par()$cex, Acol = par()$col, Apch = NULL, Clabels = dimnames(Cmat)[[1]], Cadj = par()$adj, Ccex = par()$cex, Ccol = par()$col, Clty = par()$lty, Clwd = par()$lwd, chull.arg = FALSE, ccex = par()$cex, ccol = par()$col, clty = par()$lty, clwd = par()$lwd, spch = NULL, scex = par()$cex, scol = par()$col, slabels = dimnames(x2mat)[[1]], ...)
object |
Object of class "rrvglm" . |
A |
Logical. Allow the plotting of A? |
C |
Logical. Allow the plotting of C? If TRUE then
C is represented by arrows emenating from the origin. |
scores |
Logical. Allow the plotting of the n scores? The scores are the values of the latent variables for each observation. |
plot.it |
Logical. Plot it? If FALSE , no plot is produced
and the matrix of scores (n latent variable values) is returned.
If TRUE , the rank of object need not be 2.
|
groups |
A vector whose distinct values indicate which group the
observation belongs to. By default, all the observations belong to a
single group. Useful for the multinomial logit model (see
multinomial . |
gapC |
The gap between the end of the arrow and the text labelling of C, in latent variable units. |
scaleA |
Numerical value that is multiplied by A, so that C is divided by this value. |
xlab |
Caption for the x-axis. See par . |
ylab |
Caption for the y-axis. See par . |
Alabels |
Character vector to label A. Must be of length M. |
Aadj |
Justification of text strings for labelling A.
See the adj argument of par . |
Acex |
Numeric. Character expansion of the labelling of A.
See the cex argument of par . |
Acol |
Line color of the arrows representing C.
See the col argument of par . |
Apch |
Either an integer specifying a symbol or a single character
to be used as the default in plotting points.
See par .
The pch argument can be of length M, the number of species. |
Clabels |
Character vector to label C. Must be of length p2. |
Cadj |
Justification of text strings for labelling C.
See the adj argument of par . |
Ccex |
Numeric. Character expansion of the labelling of C.
See the cex argument of par . |
Ccol |
Line color of the arrows representing C.
See the col argument of par . |
Clty |
Line type of the arrows representing C.
See the lty argument of par . |
Clwd |
Line width of the arrows representing C.
See the lwd argument of par . |
chull.arg |
Logical. Plot the convex hull of the scores? This is
done for each group (see the group argument). |
ccex |
Numeric. Character expansion of the labelling of the convex hull.
See the cex argument of par . |
ccol |
Line color of the convex hull.
See the col argument of par . |
clty |
Line type of the convex hull.
See the lty argument of par . |
clwd |
Line width of the convex hull.
See the lwd argument of par . |
spch |
Either an integer specifying a symbol or a single character
to be used as the default in plotting points.
See par .
The spch argument can be of length M, the number of species. |
scex |
Numeric. Character expansion of the labelling of the scores.
See the cex argument of par . |
scol |
Line color of the arrows representing C.
See the col argument of par . |
slabels |
Character vector to label the scores. Must be of length n. |
... |
Arguments passed into the plot function
when setting up the entire plot. Useful arguments here include
xlim and ylim .
|
For RR-VGLMs, a biplot and a latent variable plot coincide.
In general, many of the arguments starting with
``A'' refer to A (of length M),
``C'' to C (of length p2),
``c'' to the convex hull (of length length(unique(groups))
),
and ``s'' to scores (of length n).
As the result is a biplot, its interpretation is based on the inner product.
The matrix of scores (n latent variable values) is returned regardless of whether a plot was produced or not.
The functions lvplot.rrvglm
and
biplot.rrvglm
are equivalent.
In the example below the predictor variables are centered, which is a good idea.
Thomas W. Yee
Yee, T. W. and Hastie, T. J. (2003) Reduced-rank vector generalized linear models. Statistical Modelling, 3, 15–41.
lvplot
,
par
,
rrvglm
,
Coef.rrvglm
,
rrvglm.control
.
data(pneumo) n = nrow(pneumo) # x1, x2 and x3 are some unrelated covariates pneumo = transform(pneumo, slet=scale(log(exposure.time)), x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n)) fit = rrvglm(cbind(normal, mild, severe) ~ slet + x1 + x2 + x3, multinomial, pneumo, Rank=2, Corner=FALSE, Uncor=TRUE) ## Not run: lvplot(fit, chull=TRUE, scores=TRUE, clty=2, ccol="blue", scol="red", Ccol="darkgreen", Clwd=2, Ccex=2, main="Biplot of some fictitional data") ## End(Not run)