title: Filter partial dependencies by effect size
body: <p>I fitted a model and want to take a look (and plot) at the partial dependencies.
  For this task, I use the <code>mlr</code> package. However, since I have 80 features I only want to look at those with the highest effect on the target variable. Is there a way to calculate or show the partial dependence only for the features with the highest influence?</p>
  
  <p>Here is an example: I just fitted 4 values. Let´s assume I only want to look or calculate the partial dependence for the 2 most influential features. </p>
  
  <pre><code>library(mlr)
  pd = generatePartialDependenceData(mod, train_task, c("diveyTrue",  "dinnerTrue","BikeParkingTrue", "latenightTrue"))
  
  pd
  PartialDependenceData
  Task: dat
  Features: diveyTrue, dinnerTrue, BikeParkingTrue, latenightTrue
  Target: diveyTrue, dinnerTrue, BikeParkingTrue, latenightTrue
  Derivative: FALSE
  Interaction: FALSE
  Individual: FALSE
     review_count diveyTrue dinnerTrue BikeParkingTrue latenightTrue
  1:     73.92993 0.0000000         NA              NA            NA
  2:     73.68386 0.1111111         NA              NA            NA
  3:     73.68386 0.2222222         NA              NA            NA
  4:     73.68386 0.3333333         NA              NA            NA
  5:     73.68386 0.4444444         NA              NA            NA
  6:     63.56335 0.5555556         NA              NA            NA
  ... (#rows: 40, #cols: 5)
  </code></pre>
  
  <p>The task is a regression and the first column is the target variable. All other variables are dummies. Therefore, the target variable stays constant until the value of "diveyTrue" is greater than 0.5. </p>
  
  <p>Here is a small <code>dput()</code>: </p>
  
  <pre><code> structure(list(data = structure(list(review_count = c(73.9299260484918, 
  73.6838552698629, 73.6838552698629, 73.6838552698629, 73.6838552698629, 
  63.5633491608329, 63.5633491608329, 63.5633491608329, 63.5633491608329, 
  63.5633491608329, 44.123492893074, 44.0855985404284, 44.0855985404284, 
  44.0855985404284, 44.0855985404284, 67.9185575263356, 67.9185575263356, 
  67.9185575263356, 67.9185575263356, 67.9185575263356, 64.1248331786005, 
  64.1243679505065, 64.1243679505065, 64.1243679505065, 64.1243679505065, 
  64.9177431842816, 64.9177431842816, 64.9177431842816, 64.9177431842816, 
  64.9177431842816, 58.2709529252224, 58.2709529252224, 58.2709529252224, 
  58.2709529252224, 58.2709529252224, 89.8281204749236, 89.8281204749236, 
  89.8281204749236, 89.8281204749236, 89.8281204749236), diveyTrue = c(0, 
  0.111111111111111, 0.222222222222222, 0.333333333333333, 0.444444444444444, 
  0.555555555555556, 0.666666666666667, 0.777777777777778, 0.888888888888889, 
  1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
  NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), 
      dinnerTrue = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 
      0.111111111111111, 0.222222222222222, 0.333333333333333, 
      0.444444444444444, 0.555555555555556, 0.666666666666667, 
      0.777777777777778, 0.888888888888889, 1, NA, NA, NA, NA, 
      NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
      NA), BikeParkingTrue = c(NA, NA, NA, NA, NA, NA, NA, NA, 
      NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0.111111111111111, 
      0.222222222222222, 0.333333333333333, 0.444444444444444, 
      0.555555555555556, 0.666666666666667, 0.777777777777778, 
      0.888888888888889, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
      NA), latenightTrue = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, 
      NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
      NA, NA, NA, NA, NA, NA, 0, 0.111111111111111, 0.222222222222222, 
      0.333333333333333, 0.444444444444444, 0.555555555555556, 
      0.666666666666667, 0.777777777777778, 0.888888888888889, 
      1)), row.names = c(NA, -40L), class = c("data.table", "data.frame"
  ), .internal.selfref = &lt;pointer: 0x0000000002521ef0&gt;), task.desc = structure(list(
      id = "dat", type = "regr", target = "review_count", size = 9943L, 
      n.feat = c(numerics = 79L, factors = 0L, ordered = 0L, functionals = 0L
      ), has.missings = TRUE, has.weights = FALSE, has.blocking = FALSE, 
      has.coordinates = FALSE), class = c("RegrTaskDesc", "SupervisedTaskDesc", 
  "TaskDesc")), target = c("diveyTrue", "dinnerTrue", "BikeParkingTrue", 
  "latenightTrue"), features = c("diveyTrue", "dinnerTrue", "BikeParkingTrue", 
  "latenightTrue"), derivative = FALSE, interaction = FALSE, individual = FALSE), class = "PartialDependenceData")
  </code></pre>