How to select a single graph from multiple plot output

I'm attemtping to select a single grpah from multiple plot output with "glmnet" function (especially elastic net) in R.

I'm currently dealing with multinomial case; the response variable has 3 or more categories. So I fit the model with explanatory variable X and the response variable y with 3 types of categories. Assume that we already have alpha and lambda values from cross validation using cv.glmnet. I didn't use the argument type.multinomial = "grouped".

fit <- glmnet(X, y, alpha = alpha, lambda = lambda, standardize = TRUE, family = "multinomial")
plot(fit, xvar = "lambda")

fit is then the elastic net model. I know that plot(fit, xvar = "lambda") shows the coefficients path something like this:

However, when it is multinomial case, R generates multiple coefficient plots at the same time: for example when y has three categories, then R shows 3 coefficient plots. But I need to select only one graph out of them. I tried to find a way in google, but failed. Is there any way that I can try?

We could modify the glmnet:::plot.multnet method for this and add a which= option similar to that in stats:::plot.lm.

plot.multnet <- function(x, xvar=c("norm", "lambda", "dev"), label=FALSE, 
          type.coef=c("coef", "2norm"), which=x$classnames, ...) {
  xvar <- match.arg(xvar)
  type.coef <- match.arg(type.coef)
  if (!all(which %in% x$classnames)) {
    warning(sprintf('`which="%s"` not in classnames, defaulting to all', which))
    which <- x$classnames
  }
  beta <- x$beta
  if (xvar == "norm") {
    cnorm1 <- function(beta) {
      whichnz <- nonzeroCoef(beta)
      beta <- as.matrix(beta[whichnz, ])
      apply(abs(beta), 2, sum)
    }
    norm <- apply(sapply(x$beta, cnorm1), 1, sum)
  } else {
    norm <- NULL
  }
  dfmat <- x$dfmat
  if (type.coef == "coef") {
    ncl <- nrow(dfmat)
    clnames <- rownames(dfmat)
    lapply(which, function(z) glmnet:::plotCoef(beta[[z]], norm, x$lambda, dfmat[z, ], x$dev.ratio, 
                                label=label, xvar=xvar, ylab=paste("Coefficients: Response", 
                                                                   grep(z, clnames, value=TRUE))))
  } else {
    dfseq <- round(apply(dfmat, 2, mean), 1)
    glmnet:::plotCoef(coefnorm(beta, 2), norm, x$lambda, dfseq, x$dev.ratio, 
             label=label, xvar=xvar, ylab="Coefficient 2Norms", 
             ...)
  }
}

Usage

fit1 <- glmnet::glmnet(x, y, family="multinomial")

plot(fit1, xvar='lambda', which='c')

I implemented a warning if a certain layer is not present, then by default all layers are plotted as before.

plot(fit1, xvar='lambda', which='foo')
# Warning message:
# In plot.multnet(fit1, xvar = "lambda", which = 1) :
#   `which="foo"` not in classnames, defaulting to all

Data:

set.seed(42)
x <- matrix(rnorm(100 * 20), 100, 20)
y <- sample(letters[1:3], 100, replace=TRUE)