ternary_str | R Documentation |

Data structure with the projected and boundary by node and class.

ternary_str(ppf, id, sp, dx, dy)

`ppf` |
is a PPforest object |

`id` |
is a vector with the selected projection directions |

`sp` |
is the simplex dimensions, if k is the number of classes sp = k - 1 |

`dx` |
first direction included in id |

`dy` |
second direction included in id |

Data frame needed to visualize a ternary plot

#crab data set with all the observations used as training pprf.crab <- PPforest(data = crab, std =TRUE, class = "Type", size.tr = 1, m = 100, size.p = .5, PPmethod = 'LDA') require(dplyr) pl_ter <- function(dat, dx, dy ){ p1 <- dat[[1]] %>% dplyr::filter(pair %in% paste(dx, dy, sep = "-") ) %>% dplyr::select(Class, x, y) %>% ggplot2::ggplot(ggplot2::aes(x, y, color = Class)) + ggplot2::geom_segment(data = dat[[2]], ggplot2::aes(x = x1, xend = x2, y = y1, yend = y2), color = "black" ) + ggplot2::geom_point(size = I(3), alpha = .5) + ggplot2::labs(y = " ", x = " ") + ggplot2::theme(legend.position = "none", aspect.ratio = 1) + ggplot2::scale_colour_brewer(type = "qual", palette = "Dark2") + ggplot2::labs(x = paste0("T", dx, " "), y = paste0("T", dy, " ")) + ggplot2::theme(aspect.ratio = 1) p1 } #ternary plot in tree different selected dierections pl_ter(ternary_str(pprf.crab, id = c(1, 2, 3), sp = 3, dx = 1, dy = 2), 1, 2 )

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