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#' Plot coefficients from a \code{clogitLasso} object
#'
#' Plot the parameter profile associated \code{clogitLasso} object
#'
#' @param x an objet of type \code{clogitLasso}
#' @param logLambda Set to TRUE if the horizontal axis is on log scale
#' @param add.legend Take the value TRUE if legend should be printed in top right hand corner
#' @param add.labels set to TRUE if labels are to be added to curves at leftmost side
#' @param lty Same to \code{lty} parameter of plot function
#' @param col Same to \code{col} parameter of plot function
#' @param ... additional arguments to plot function
#' @author Marta Avalos, Helene Pouyes, Marius Kwemou and Binbin Xu
#' @references Avalos, M., Pouyes, H., Grandvalet, Y., Orriols, L., & Lagarde, E. (2015). \emph{Sparse conditional logistic
#' regression for analyzing large-scale matched data from epidemiological studies: a simple algorithm.} BMC bioinformatics, 16(6), S1. \doi{10.1186/1471-2105-16-S6-S1}.
#' @importFrom graphics matplot legend text
#' @examples
#' \dontrun{
#' # generate data
#' y <- rep(c(1,0), 100)
#' X <- matrix (rnorm(20000, 0, 1), ncol = 100) # pure noise
#' strata <- sort(rep(1:100, 2))
#'
#' fitLasso <- clogitLasso(X,y,strata,log=TRUE)
#' # plot
#' plot(fitLasso)
#' }
#' @export
plot.clogitLasso <- function (x,
logLambda = TRUE,
add.legend = FALSE,
add.labels = TRUE,
lty = 1:ncol(x$beta),
col = 1:ncol(x$beta),
...)
{
if (logLambda)
horiz <- log(x$fraction)
else
horiz <- x$fraction
matplot(
x = horiz,
y = x$beta,
type = "l",
xlab = "Regularisation parameter",
ylab = "Parameter estimate",
lty = lty,
col = col,
...
)
if (add.legend) {
if (is.null(dimnames(x$x_rec))) {
var.names <- paste("Variable", 1:ncol(x$beta))
}
else {
var.names <- dimnames(x$x_rec)[[2]]
}
legend("topright",
legend = var.names,
lty = lty,
col = col,
...)
}
if (add.labels) {
if (is.null(dimnames(x$x_rec))) {
plot.names <- 1:ncol(x$beta)
}
else {
plot.names <- dimnames(x$x_rec)[[2]]
}
text(
x = min(horiz),
y = x$beta[nrow(x$beta),],
labels = plot.names,
lty = lty,
col = col,
...
)
}
}
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