inst/doc/workflow.R

In CedricMondy/ecodiag: Build Standardized Diagnostic Tool Models

## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  eval     = TRUE
)

knitr::knit_hooks$set(
   error = function(x, options) {
     paste('\n\n

', sep = '\n')
   },
   warning = function(x, options) {
     paste('\n\n

', sep = '\n')
   },
   message = function(x, options) {
     paste('\n\n

', sep = '\n')
   }
)


## ---- include=FALSE------------------------------------------------------
library(tidyverse)
library(ecodiag)

## ----import-packages, eval=FALSE-----------------------------------------
#  library(tidyverse)
#  library(ecodiag)

## ----import-data---------------------------------------------------------
# Import the data
data(metrics)
data(pressures)

# Define a learning and a test data sets
learning_set <- sample(x       = seq(nrow(metrics)),
                       size    = round(0.632 * nrow(metrics)),
                       replace = FALSE)

test_set <- setdiff(x = seq(nrow(metrics)), y = learning_set)

learning_metrics   <- metrics[learning_set, ]
learning_pressures <- pressures[learning_set, ]

test_metrics   <- metrics[test_set, ]
test_pressures <- pressures[test_set, ]

## ----no-calibration, eval=FALSE------------------------------------------
#  build_DT(metrics   = learning_metrics,
#           pressures = learning_pressures,
#           low       = c("high", "good"),
#           impaired  = c("moderate", "poor", "bad"),
#           pathDT    = "models/",
#           params    = list(num.trees       = 500,
#                            mtry            = 25,
#                            sample.fraction = 0.632,
#                            min.node.size   = 25),
#           nIter     = 4
#           )

## ----calibration, eval=FALSE---------------------------------------------
#  build_DT(metrics   = learning_metrics,
#           pressures = learning_pressures,
#           low       = c("high", "good"),
#           impaired  = c("moderate", "poor", "bad"),
#           pathDT    = "calibrated_models/",
#           params    = list(
#             num.trees       = c(500, 750, 1000, 1500),
#             mtry            = c(5, 10, 25, 50),
#             sample.fraction = 0.632,
#             min.node.size   = 25),
#           CVfolds   = 5
#           )

## ----iterations-no-calibration-------------------------------------------
build_DT(metrics   = learning_metrics, 
         pressures = learning_pressures,
         low       = c("high", "good"),
         impaired  = c("moderate", "poor", "bad"),
         pathDT    = "models/",
         params    = list(num.trees       = 500,
                          mtry            = 25, 
                          sample.fraction = 0.632, 
                          min.node.size   = 25),
         nIter     = 4,
         nCores    = 4
         )

## ----evaluation----------------------------------------------------------
modelPerformances <- 
  calculate_DT_performance(pathDT    = "models/",
                           metrics   = test_metrics,
                           pressures = test_pressures,
                           low       = c("high", "good"),
                           impaired  = c("moderate", "poor", "bad"),
                           smoothROC = TRUE
                           )

## ----AUC-table-----------------------------------------------------------
modelPerformances$AUC

## ----multiplot-----------------------------------------------------------
# Multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols:   Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will go in the upper left, 2 will go in the upper right, and
# 3 will go all the way across the bottom.
#
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
  library(grid)

  # Make a list from the ... arguments and plotlist
  plots <- c(list(...), plotlist)

  numPlots = length(plots)

  # If layout is NULL, then use 'cols' to determine layout
  if (is.null(layout)) {
    # Make the panel
    # ncol: Number of columns of plots
    # nrow: Number of rows needed, calculated from # of cols
    layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
                    ncol = cols, nrow = ceiling(numPlots/cols))
  }

 if (numPlots==1) {
    print(plots[[1]])

  } else {
    # Set up the page
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))

    # Make each plot, in the correct location
    for (i in 1:numPlots) {
      # Get the i,j matrix positions of the regions that contain this subplot
      matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))

      print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
                                      layout.pos.col = matchidx$col))
    }
  }
}

## ----ROC, fig.width=7, fig.height=4--------------------------------------
multiplot(plotlist = modelPerformances$ROC, cols = 2)

## ----evaluate_importance-------------------------------------------------
importance <- estimate_variable_importance(
  modelPath = "models/model_PESTICIDES.rda",
  methods   = c("anova.test", "auc", "chi.squared", "gain.ratio",
                "information.gain", "kruskal.test", "ranger.impurity",
                "ranger.permutation"),
  nVarToPlot = 10,
  nIter      = 8,
  nCores     = 4)

## ------------------------------------------------------------------------
importance$varImp

## ---- fig.height=7, fig.width=7------------------------------------------
importance$varImpPlot

## ------------------------------------------------------------------------
partialDependence <- estimate_partial_dependence(
  modelPath = "models/model_PESTICIDES.rda",
  variables = c("SPEAR_SENSIBILITE_MAX", "CSI_LOCOMOTION",
                "GROUPE_ECO_A_S", "GROUPE_ECO_A_Q"))

## ------------------------------------------------------------------------
as.tbl(partialDependence$data)

## ---- fig.width=7, fig.height=7------------------------------------------
partialDependence$plot

## ----run-DT--------------------------------------------------------------
predictions <- run_DT(pathDT  = "models/",
                      metrics = as.data.frame(test_metrics))

## ------------------------------------------------------------------------
as.tbl(predictions)

## ----plot-DT, fig.width=7------------------------------------------------
plot_DT(plot.data        = head(predictions), 
        plot.title       = "test DT model", 
        axis.label.size  = 3, 
        grid.label.size  = 6,
        legend.title     = "observations",
        legend.text.size = 12
        )

## ---- fig.width=7--------------------------------------------------------
plot_DT(plot.data        = head(predictions), 
        axis.label.size  = 3, 
        grid.label.size  = 6,
        byRow            = TRUE)