View source: R/plot.multidim.R
plot_multidim | R Documentation |
Plot Confidence Regions obtained with Split Conformal
plot_multidim(out, same.scale = FALSE)
out |
The output of a prediction function. |
same.scale |
Should I force the same scale for all the y-axis ? Default is FALSE. |
It exploits the package ggplot2
, gridExtra
and hrbrthemes
to better visualize the results.
g_list A list of ggplots (output[[i]] is the i-th observation confidence region).
n=50 p=4 q=2 mu=rep(0,p) x = mvtnorm::rmvnorm(n, mu) beta<-sapply(1:q, function(k) c(mvtnorm::rmvnorm(1,mu))) y = x%*%beta + t(mvtnorm::rmvnorm(q,1:n)) x0=x[ceiling(0.9*n):n,] y0=y[ceiling(0.9*n):n,] n0<-nrow(y0) q<-ncol(y) fun=mean_multi() final.point = conformal.multidim.split(x,y,x0, fun$train.fun, fun$predict.fun, alpha=0.1, split=NULL, seed=FALSE, randomized=FALSE,seed.rand=FALSE, verbose=FALSE, rho=0.5,score ="l2",s.type="st-dev") ppp2<-plot_multidim(final.point) n=25 p=4 q=2 mu=rep(0,p) x = mvtnorm::rmvnorm(n, mu) beta<-sapply(1:q, function(k) c(mvtnorm::rmvnorm(1,mu))) y = x%*%beta + t(mvtnorm::rmvnorm(q,1:n)) x0=x[ceiling(0.9*n):n,] y0=y[ceiling(0.9*n):n,] n0<-nrow(y0) q<-ncol(y) fun=mean_multi() #################################### FULL CONFORMAL final.full=conformal.multidim.full(x, y, x0, fun$train.fun, fun$predict.fun, score="l2", num.grid.pts.dim=5, grid.factor=1.25, verbose=FALSE) ppp<-plot_multidim(final.full)
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