lvplot.rrvglm(object,
A = TRUE, C = TRUE, scores = FALSE, show.plot = 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 = rownames(Cmat), 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()$col,
slabels = rownames(x2mat), ...)"rrvglm".TRUE then
C is represented by arrows emenating from the origin.FALSE, no plot is produced
and the matrix of scores ($n$ latent variable values) is returned.
If TRUE, the rank of object need not be 2.multinomialpar.par.adj argument of par.cex argument of par.col argument of par.par.
The pch argument can be of length $M$, the number of speadj argument of par.cex argument of par.col argument of par.lty argument of par.lwd argument of par.group argument).cex argument of par.col argument of par.lty argument of par.lwd argument of par.par.
The spch argument can be of length $M$, the number of spcex argument of par.col argument of par.plot function
when setting up the entire plot. Useful arguments here include
xlim and ylim.length(unique(groups))),
and ``s'' to scores (of length $n$).As the result is a biplot, its interpretation is based on the inner product.
lvplot,
par,
rrvglm,
Coef.rrvglm,
rrvglm.control.nn <- nrow(pneumo) # x1, x2 and x3 are some unrelated covariates
pneumo <- transform(pneumo, slet = scale(log(exposure.time)),
x1 = rnorm(nn), x2 = rnorm(nn), x3 = rnorm(nn))
fit <- rrvglm(cbind(normal, mild, severe) ~ slet + x1 + x2 + x3,
multinomial, pneumo, Rank=2, Corner=FALSE, Uncorrel=TRUE)
lvplot(fit, chull = TRUE, scores = TRUE, clty = 2, ccol = "blue", scol = "red",
Ccol = "darkgreen", Clwd = 2, Ccex = 2,
main = "Biplot of some fictitional data")Run the code above in your browser using DataLab