Tidy Resampling Redux with Agricultural Economics Data

March 12, 2018

(This article was first published on Blog - Applied Predictive Modeling, and kindly contributed to R-bloggers)


(No statistical graphs in this one. This is what my dog Artemis looks like when she wants my attention during work hours.)

Mindy L. Mallory (@ace_prof) wrote a blog post on Machine Learning and Econometrics: Model Selection and Assessment Statistical Learning Style where she has a great description of the variance-bias tradeoff, resampling, and model complexity using some data from agricultural economics. I asked if I could take her code and make a more tidy version of the resampling part. Here it is…

Here are the R packages that I’ll be using:


First, duplicate the process of reading in the data and adding two new columns:

stocks  <- read.csv('http://blog.mindymallory.com/wp-content/uploads/2018/02/stocks.csv') %>% 
  as_tibble() %>%
    USStockUse = USEndingStocks/USTotalUse, 
    WorldStockUse = ROWEndingStocks/WorldTotalUse
## # A tibble: 43 x 8
##     Year USEndingStocks ROWEndingStocks USTotalUse
##  1  1975           633.           36411      5767.
##  2  1976          1136.           39491      5789.
##  3  1977          1436.           40833      6207.
##  4  1978          1710.           47957      6995.
##  5  1979          2034.           59481      7604.
##  6  1980          1392.           67180      7282.
##  7  1981          2537.           62725      6975.
##  8  1982          3523.           60273      7249.
##  9  1983          1006.           63421      6693.
## 10  1984          1648.           76287      7032.
## # ... with 33 more rows, and 4 more variables:
## #   WorldTotalUse , PriceRecievedFarmers ,
## #   USStockUse , WorldStockUse 

The blog post has an excellent description of cross-validation and looked at five different models that encoded the US and World stock-use predictors. Either a log- or inverse-transformation was applied and then polynomial basis functions were used on these features to demonstrate overfitting.

The blog post has some for loops to do the resampling and I volunteered to show how to do it with some tidy modeling packages.

Tidy Cross-Validation

First, let’s take the easy part. Instead of using for loops, we can use the new infrastructure in the tidyverse to resample the model. The rsample package has some functions for different types of resampling and we will use the same procedure as the original post:

resamp_info <- vfold_cv(stocks, v = 5)
## #  5-fold cross-validation 
## # A tibble: 5 x 2
##   splits       id   
## 1  Fold1
## 2  Fold2
## 3  Fold3
## 4  Fold4
## 5  Fold5

The first column in the tibble is a set of “rsplit” objects that define how the data are split for each fold of cross-validation. Each one fully and efficiently encapsulates everything what is needed to get the two divisions of the original data. In rsample, to avoid naming confusion, we label the two resulting data sets as:

  • The analysis data are those that we selected in the resample. For a bootstrap, this is the sample with replacement. For 5-fold cross-validation, this is the 80% of the data. These data are often used to fit a model or calculate a statistic in traditional bootstrapping.

  • The assessment data are usually the section of the original data not covered by the analysis set. Again, in 5-fold CV, this is the 20% held out. These data are often used to evaluate the performance of a model that was fit to the analysis data.

To get these partitions for the first split there are functions analysis and assessment that return the appropriate data frames when given an rsplit:

# printing just shows the #rows per analysis/assessment/overall
## <34/9/43>
# data used for modeling:
## # A tibble: 34 x 8
##     Year USEndingStocks ROWEndingStocks USTotalUse
##  1  1975           633.           36411      5767.
##  2  1976          1136.           39491      5789.
##  3  1977          1436.           40833      6207.
##  4  1979          2034.           59481      7604.
##  5  1980          1392.           67180      7282.
##  6  1981          2537.           62725      6975.
##  7  1982          3523.           60273      7249.
##  8  1983          1006.           63421      6693.
##  9  1984          1648.           76287      7032.
## 10  1985          4040.           75069      6494.
## # ... with 24 more rows, and 4 more variables:
## #   WorldTotalUse , PriceRecievedFarmers ,
## #   USStockUse , WorldStockUse 

The first model for the data contained the US stock-use data with an inverse transformation. Let’s side-step the polynomial model tuning for now and just fit a quadratic model. To make things easier, I’ll define a function that can be used to fit the model when given an rsplit object and return the holdout mean squared error (MSE):

glm_results <- function(split, ...) {
  # Get the data used ot fit the model aka the "analysis" set
  # and fit the model with a formula given in the ...
  mod <- glm(data = analysis(split), ...)

  # Get predictions on the other data (aka the "assessment" set 
  # and compute some metrics
  holdout <- assessment(split)

  # Compute performance using the yardstick package
  rmse <- holdout %>%
    mutate(pred = predict(mod, holdout)) %>%
    rmse(truth = PriceRecievedFarmers, estimate = pred)

We can use this with any formula since it is just passed to glm using the ellipses. For example to get the holdout MSE estimate for the first fold:

glm_results(resamp_info$splits[[1]], formula = PriceRecievedFarmers ~ poly(1 / USStockUse, 2))
## [1] 1.67

To get these statistics for all folds, purrr::map_dbl is used to add another column:

resamp_info <- resamp_info %>%
    model_1_deg_2 = 
        PriceRecievedFarmers ~ poly(1 / USStockUse, 2)
## #  5-fold cross-validation 
## # A tibble: 5 x 3
##   splits       id    model_1_deg_2
## *                 
## 1  Fold1         1.67 
## 2  Fold2         0.558
## 3  Fold3         0.596
## 4  Fold4         9.58 
## 5  Fold5         0.248

That’s a lot of variation in the outcome! The mean value is fairly consistent with the blog post though:

resamp_info %>% select(model_1_deg_2) %>% colMeans()
## model_1_deg_2 
##          2.53
# MSE = 2.675 in the blog post

K-fold cross-validation is one of the noisiest resampling methods so this difference isn’t too surprising.

This same process could be repeated for each polynomial degree to get new columns for this model (we’ll discuss this below). The good things about doing things this way:

  • It is a lot cleaner (so far) than doing for loops.
  • Other tidyverse infrastructure can be used. For example, tidyposterior is a great way to do model comparisons with resampling and Bayesian analysis.
  • It is simple to change resampling methods. Suppose you wanted to change to a larger number of bootstrap resamples (given the variance shown above). The same infrastructure can be easily exchanged; resample::bootstraps is used in place of rsample::vfold_cv.

Tidy Model Specification (maybe)

The model specification part is, for me, a lot more difficult to tidy. It would be good to be able to state what predictors that we want, specify the polynomial degree, and have a function to generate the appropriate formula. The original post sensibly just types the terms out.

I spent some time thinking about how we could use expressions and tidy evaluation (video from Hadley) to make it a little less script-like. The problem is that the solution took me a while to write and, arguably, it doesn’t really buy you much more than the original code (apart from the potential copy/paste duplication errors).

In any case, the function uses rlang to manipulate expressions to make the formula. The inputs are expressions of how the predictors are used (i.e. log, inverse, etc.) and the degree. It captures the expression (without evaluating it), substitutes it into the polynomial function, then creates the formula:

make_formula <- function(..., degree = 1) {
  # Capture the expression so it is not evaluated
  var_expr <- exprs(...)

  # Create a template expression
  inv_poly <- quote(poly(x = x, degree = degree))

  # Use a wrapper around rlang::call_modify to reverse the
  # order of the arguments so that we can map over the
  # predictor expressions
  add_args <- function(arg, call, ...)
    call_modify(call, x = arg, ...)

  # Add the variables and the degree into the template
  poly_expr <- map(var_expr, add_args, call = inv_poly, degree = degree)

  # Convert to character
  poly_char <- map_chr(poly_expr, deparse)

  # Convert to a formula
  poly_char <- paste(poly_char, collapse = " + ")
  as.formula(paste("PriceRecievedFarmers ~", poly_char))
# For example:
make_formula(1/USStockUse, log(WorldStockUse), degree = 3)
## PriceRecievedFarmers ~ poly(x = 1/USStockUse, degree = 3) + poly(x = log(WorldStockUse), 
##     degree = 3)

From here, we could use a bunch of mutate commands like the one shown above or write a slightly smaller for loop to work across the polynomial degrees. While the function above works well, the overall approach to working across models isn’t particularly satisfying.

Any suggestions? I’m on twitter @topepos!

(edit – fix fixed the formula call!)

To leave a comment for the author, please follow the link and comment on their blog: Blog - Applied Predictive Modeling.

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