如何使用R包``pls‘运行具有多个响应的` `pls::plsr( )’

Question

如果caret::train()是多列的矩阵，则y似乎不接受y。

谢谢你的帮助！

Answer 1

是这样的。也许你想要tidymodels包？库恩曾表示，其中将支持多元反应。以下是支持我的建议的证据：https://www.tidymodels.org/learn/models/pls/

搜索该文档以获取plsr

library(tidymodels)
library(pls)

get_var_explained <- function(recipe, ...) {
  
  # Extract the predictors and outcomes into their own matrices
  y_mat <- bake(recipe, new_data = NULL, composition = "matrix", all_outcomes())
  x_mat <- bake(recipe, new_data = NULL, composition = "matrix", all_predictors())
  
  # The pls package prefers the data in a data frame where the outcome
  # and predictors are in _matrices_. To make sure this is formatted
  # properly, use the `I()` function to inhibit `data.frame()` from making
  # all the individual columns. `pls_format` should have two columns.
  pls_format <- data.frame(
    endpoints = I(y_mat),
    measurements = I(x_mat)
  )
  # Fit the model
  mod <- plsr(endpoints ~ measurements, data = pls_format)
  
  # Get the proportion of the predictor variance that is explained
  # by the model for different number of components. 
  xve <- explvar(mod)/100 

  # To do the same for the outcome, it is more complex. This code 
  # was extracted from pls:::summary.mvr. 
  explained <- 
    drop(pls::R2(mod, estimate = "train", intercept = FALSE)$val) %>% 
    # transpose so that components are in rows
    t() %>% 
    as_tibble() %>%
    # Add the predictor proportions
    mutate(predictors = cumsum(xve) %>% as.vector(),
           components = seq_along(xve)) %>%
    # Put into a tidy format that is tall
    pivot_longer(
      cols = c(-components),
      names_to = "source",
      values_to = "proportion"
    )
}
#We compute this data frame for each resample and save the results in the different columns.

folds <- 
  folds %>%
  mutate(var = map(recipes, get_var_explained),
         var = unname(var))
#To extract and aggregate these data, simple row binding can be used to stack the data vertically. Most of the action happens in the first 15 components so let’s filter the data and compute the average proportion.

variance_data <- 
  bind_rows(folds[["var"]]) %>%
  filter(components <= 15) %>%
  group_by(components, source) %>%
  summarize(proportion = mean(proportion))

这可能不是一个可复制的代码块。可能需要额外的数据或包。