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Novel tools tools for parametric and machine learning methods in regression and classification

Various tools for Prediction and/or Description goals in regression, classification and machine learning.

Some of them are associated with my book, From Linear Models to Machine Learning: Statistical Regression and Classification, N. Matloff, CRC, 2017 (recipient of the Technometrics Eric Ziegel Award for Best Book Reviewed in 2017), and with my forthcoming book, The Art of Machine Learning: Algorithms+Data+R, NSP, 2020 . But the tools are useful in general, independently of the books.


  • Advanced tool for tuning-parameter grid search, including plotting, Bonferroni intervals and smoothing.

  • Innovative graphical tools for assessing fit in linear and nonlinear parametric models, via nonparametric methods. Model evaluation, examination of quadratic effects, investigation of nonhomogeneity of variance.

  • Tools for multiclass classification to extend functions that only handle the binary case, e.g. glm(). One vs. All and All vs. All paradigms.

  • Novel adjustment for imbalanced data.

  • K-NN regression for general dimensions in predictor and response variables, using k-Nearest Neighbors (k-NN). Local-linear option to deal with edge aliasing. Allows for user-specified smoothing method. Allows for accelerated exploration of multiple values of k at once. Capability to give different variables different weights in the distance computation.

  • Extension to nonlinear parametric regression of Eicker-White technique to handle heteroscedasticity.

  • Utilities for conversion of time series data to rectangular form, enabling lagged prediction by lm() or other regression model.

  • Utilities for conversion between factor and dummy variable forms, useful since among various regression packages, some use factors while some others use dummies. (The glmnet package is an example of the latter case.)

  • Misc. tools, e.g. to reverse the effects of an earlier call to scale().

  • Nicer implementation of ridge regression, with more meaningful scaling and better plotting.

  • Linear regression, PCA and log-linear model estimation in missing-data setting, via the Available Cases method. (For handling missing values in Prediction contexts, see our toweranNA package.)

  • Interesting datasets.

A number of examples of use follow below.


See full function list by typing

> ?regtools

Here are the main categories:

  • Parametric modeling
  • Diagnostic plots
  • Classification
  • Machine learning
  • Dummies and R factors
  • Statistics
  • Matrix
  • Time series
  • Image processing
  • Text processing
  • Recommender systems
  • Misc.


Many statistical/machine learning methods have a number of tuning parameters (or hyperparameters). The idea of a grid search is to try many combinations of candidate values of the tuning parameters to find the "best" combination, say in the sense of highest rate of correct classification in a classification problem.

Which one is really best?

But why the quotation marks around the word best above? The fact is that, due to sampling variation, a naive grid search may not give you the best parameter combination:

  1. The full dataset is a sample from some target population.

  2. Most grid search functions use cross-validation, in which the data are (one or more times) split into a training set and a prediction set.

So the parameter combination that seems best in the grid search may not actually be best. It may do well just by accident, depending on the dataset size, the number of cross-validation folds and the number of features.

In other words:

Grid search is actually a form of p-hacking.

Thus one should not rely on the "best" combination found by a grid search. One should consider several combinations that did well. A good rule is that if several combinations yield about the same result, one should choose a combination with less-extreme parameters. Moreover, if the better combinations all have extreme parameter values, one should further search, in regions beyond these in the parameter space. In other words, after an initial parameter search, one may need to do a second search, based on refining the first one.

The regtools approach

The regtools grid search function fineTuning() is an advanced grid search tool, in two ways:

  1. It aims to counter p-hacking, by forming Bonferoni confidence intervals.

  2. It provides a plotting capability, so that the user can see at a glance which several combinations of parameters look promising, thereby providing guidance for further combinations to investigate.

Here is an example using the famous Pima diabetes data (stored in a data frame db in the example below). This is a binary problem in which we are predicting presence or absence of the disease. We'll do an SVM analysis, using the ksvm() function from the kernlab package, with a polynomial kernel. Our parameters are d, the degree of the polynomial, and C, the penalty for each datapoint inside the margin.

The grid search function fineTuning() calls a user-defined function that fits the user's model on the training data and then predicts on the test data.

# user provides a function stating the analysis to run on any parameter
# combination cmbi; here dtrn and dtst are the training and test sets,
# generated by fineTuning()
pdCall <- function(dtrn,dtst,cmbi) {
   # fit training set
   kout <- ksvm(diabetes ~ .,data=dtrn,kernel='polydot',
   # predict test set
   preds <- predict(kout,dtst)
   # find rate of correct classification
   mean(preds == dtst$diabetes)

db <- PimaIndiansDiabetes2

# below is the call; we'll use test data size of 50 in our
# cross-validation, with 100 folds
ft <- fineTuning(dataset=db,pars=list(d=c(2:6),C=seq(0.2,2,0.2)),

And the output:

> ft$outdf
   d   C meanAcc       seAcc    bonfAcc
30 6 1.2  0.6838 0.006160775 0.02027220
33 4 1.4  0.6874 0.005795540 0.01907038
14 5 0.6  0.6876 0.005719222 0.01881925
17 3 0.8  0.6878 0.005887120 0.01937173
4  5 0.2  0.7072 0.005245643 0.01726093
20 6 0.8  0.7118 0.006513979 0.02143442
31 2 1.4  0.7430 0.005396407 0.01775702
41 2 1.8  0.7450 0.005573204 0.01833878
16 2 0.8  0.7492 0.005758928 0.01894991
46 2 2.0  0.7506 0.005351900 0.01761057
26 2 1.2  0.7516 0.005997845 0.01973607
21 2 1.0  0.7562 0.005161591 0.01698435
6  2 0.4  0.7590 0.005666667 0.01864632
36 2 1.6  0.7612 0.005865978 0.01930216
11 2 0.6  0.7614 0.005615671 0.01847851
1  2 0.2  0.7622 0.005123880 0.01686026

Technically the best combination is (2,0.2), but several gave similar results, as can be seen by the Bonferroni column, which gives radii of approximate 95% confidence intervals.

It does seem that we should use degree 2 for the polynomial, but it the value of C seems to be of little consequence. It might be worth exploring other values, maybe smaller than 0.2 or larger than 2.0.

The corresponding plot function, called simply as


uses the notion of parallel coordinates. Here is a plot of the above analysis:


The three vertical axes represent d, C and meanAcc from the above output. Each polygonal line represents one row from outdf above, connecting the values of the three variables.

One sees that high values of the polynomial degree d tend to be associated with lower values of the classification accuracy. This can be made even clearer by dragging the d column to the right, past the C column (not shown here). One can also plot the top-performing parameter combinations.


The fit assessment techniques in regtools gauge the fit of parametric models by comparing to nonparametric ones. Since the latter are free of model bias, they are very useful in assessing the parametric models.

Let's take a look at the included dataset prgeng, some Census data for California engineers and programmers in the year 2000. The response variable in this example is wage income, and the predictors are age, gender, number of weeks worked, and dummy variables for MS and PhD degrees. You can read the details of the data by typing

> ?prgeng

One of the package's graphical functions for model fit assessment plots the parametric (e.g. lm()) values against the nonparametric fit via k-NN. Let's try this on the Census data.

The package includes three versions of the dataset: The original; a version with categorical variables in dummy form; and a version with categorical variables in R factor form. Since the k-NN routines require dummies, we'll use that first version, peDumms.

We need to generate the parametric and nonparametric fits, then call parvsnonparplot():

pe1 <- peDumms[c('age','educ.14','educ.16','sex.1','wageinc','wkswrkd')]
lmout <- lm(wageinc ~ .,data=pe1)
xd <- preprocessx(pe1[,-5],10)  # prep for k-NN, k <= 10
knnout <- knnest(pe1$wageinc,xd,10)


We see above how the k-NN code is used. We first call preprocessx() to determine the nearest neighbors of each data point. Here k is 10, so we can later compute various k-NN fits for k anywhere from 1 to 10. (A somewhat simpler function, kNN(), is also available.) The actual fit is done by knnest(). Then parvsnonparplot() plots the linear model fit against the nonparametric one.. Again, since the latter is model-free, it serves as a good assessment of the fit of the linear model.

There is quite a bit suggested in this picture:

  • There seems to be some overestimating near the low end, and quite substantial underestimating at the high end.

  • There are intriguing "streaks" or "tails" of points, suggesting the possible existence of small but important subpopulations arising from the dummy variables. Moreover, the plot suggests two separate large subpopulations, for wages less than or greater than about $40,000, possibly related to full- vs. part-time employment.

  • There appear to be a number of people with 0 wage income. Depending on the goals of our analysis, we might consider removing them.

Let's now check the classical assumption of homoscedasticity, meaning that the conditional variance of Y given X is constant. The function nonparvarplot() plots the estimated conditional variance against the estimated conditional mean, both computed nonparametrically:


Though we ran the plot thinking of the homoscedasticity assumption, and indeed we see larger variance at large mean values, this is much more remarkable, confirming that there are interesting subpopulations within this data. These may correspond to different occupations, something to be investigated.

The package includes various other graphical diagnostic functions.

By the way, violation of the homoscedasticity assumption won't invalidate the estimates in our linear model. They still will be statistically consistent. But the standard errors we compute, and thus the statistical inference we perform, will be affected. This is correctible using the Eicker-White procedure, which for linear models is available in the car and sandwich packages. Our package here also extends this to nonlinear parametric models, in our function nlshc() (the validity of this extension is shown in the book).


A very popular prediction method in 2-class problems is to use logistic (logit) regression. In analyzing click-through patterns of Web users, for instance, we have 2 classes, Click and Nonclick. We might fit a logistic model for Click, given user Web history, demographics and so on. Note that logit actually models probabilities, e.g. the probability of Click given the predictor variables.

But the situation is much less simple in multiclass settings. Suppose our application is recognition of hand-written digits (a famous machine learning example). The predictor variables are pixel patterns in images. There are two schools of thought on this:

  • One vs. All (OVA): We would run 10 logistic regression models, one for predicting '0' vs. non-'0', one for '1' vs. non-'1', and so on. For a particular new image to be classified, we would thus obtain 10 estimated conditional probabilities. We would then guess the digit for this image to be the digit with the highest estimated conditional probability.

  • All vs. All (AVA): Here we would run C(10,2) = 45 logit analyses, one for each pair of digits. There would be one for '0' vs. '1', one for '0' vs. '2', etc., all the way up through '8' vs. '9'. In each case there is a "winner" for our new image to be predicted, and in the end we predict the new image to be whichever digit has the most winners.

Many in the machine learning literature recommend AVA over OVA, on the grounds that there might be linear separability (in the statistical sense) in pairs but not otherwise. My book counters by noting that such a situation could be remedied under OVA by adding quadratic terms to the logit models.

At any rate, the regtools package gives you a choice, OVA or AVA, for both parametric and nonparametric methods. For example, avalogtrn() and avalogpred() do training and prediction operations for logit with AVA.

Let's look at an example, again using the Census data from above. We'll predict occupation from age, sex, education (MS, PhD, other) wage income and weeks worked.

pef <- peFactors 
pef1 <- pef[,c('age','educ','sex','wageinc','wkswrkd','occ')] 
# "Y" must be in last column, class ID 0,1,2,...; convert from factor
pef1$occ <- as.numeric(pef1$occ) 
pef1$occ <- pef1$occ - 1
pef2 <- pef1 
# create the education, gender dummy varibles
pef2$ms <- as.integer(pef2$educ == 14) 
pef2$phd <- as.integer(pef2$educ == 16) 
pef2$educ <- NULL 
pef2$sex <- as.integer(pef2$sex == 1) 
pef2 <- pef2[,c(1,2,3,4,6,7,5)] 
ovaout <- ovalogtrn(6,pef2) 
# estimated coefficients, one set ofr each of the 6 classes
# prints
                        0             1             2
# (Intercept) -9.411834e-01 -6.381329e-01 -2.579483e-01
# xage         9.090437e-03 -3.302790e-03 -2.205695e-02
# xsex        -5.187912e-01 -1.122531e-02 -9.802006e-03
# xwageinc    -6.741141e-06 -4.609168e-06  5.132813e-06
# xwkswrkd     5.058947e-03 -2.247113e-03  2.623924e-04
# xms         -5.201286e-01 -4.272846e-01  5.280520e-01
# xphd        -3.302821e-01 -8.035287e-01  3.531951e-01
#                         3             4             5
# (Intercept) -3.370758e+00 -3.322356e+00 -4.456788e+00
# xage        -2.193359e-03 -1.206640e-02  3.323948e-02
# xsex        -7.856923e-01  5.173516e-01  1.175657e+00
# xwageinc    -4.076872e-06  2.033175e-06  1.831774e-06
# xwkswrkd     1.311084e-02  5.517912e-04  2.794453e-03
# xms         -1.797544e-01  9.947253e-02  2.705293e-01
# xphd        -3.883463e-01  4.967115e-01  4.633907e-01
# predict the occupation of a woman, age 35, no MS/PhD, inc 60000, 52
# weeks worked
# outputs class 2, Census occupation code 102
[1] 2

With the optional argument probs=TRUE, the call to ovalogpred() will also return the conditional probabilities of the classes, given the predictor values, in the R attribute 'probs'.

Here is the AVA version:

avaout <- avalogtrn(6,pef2) 
# prints
                      1,2           1,3           1,4           1,5
(Intercept) -1.914000e-01 -4.457460e-01  2.086223e+00  2.182711e+00
xijage       8.551176e-03  2.199740e-02  1.017490e-02  1.772913e-02
xijsex      -3.643608e-01 -3.758687e-01  3.804932e-01 -8.982992e-01
xijwageinc  -1.207755e-06 -9.679473e-06 -6.967489e-07 -4.273828e-06
xijwkswrkd   4.517229e-03  4.395890e-03 -9.535784e-03 -1.543710e-03
xijms       -9.460392e-02 -7.509925e-01 -2.702961e-01 -5.466462e-01
xijphd       3.983077e-01 -5.389224e-01  7.503942e-02 -7.424787e-01
                      1,6           2,3           2,4           2,5
(Intercept)  3.115845e+00 -2.834012e-01  2.276943e+00  2.280739e+00
xijage      -2.139193e-02  1.466992e-02  1.950032e-03  1.084527e-02
xijsex      -1.458056e+00  3.720012e-03  7.569766e-01 -5.130827e-01
xijwageinc  -5.424842e-06 -9.709168e-06 -1.838009e-07 -4.908563e-06
xijwkswrkd  -2.526987e-03  9.884673e-04 -1.382032e-02 -3.290367e-03
xijms       -6.399600e-01 -6.710261e-01 -1.448368e-01 -4.818512e-01
xijphd      -6.404008e-01 -9.576587e-01 -2.988396e-01 -1.174245e+00
                      2,6           3,4           3,5           3,6
(Intercept)  3.172786e+00  2.619465e+00  2.516647e+00  3.486811e+00
xijage      -2.908482e-02 -1.312368e-02 -3.051624e-03 -4.236516e-02
xijsex      -1.052226e+00  7.455830e-01 -5.051875e-01 -1.010688e+00
xijwageinc  -5.336828e-06  1.157401e-05  1.131685e-06  1.329288e-06
xijwkswrkd  -3.792371e-03 -1.804920e-02  5.606399e-04 -3.217069e-03
xijms       -5.987265e-01  4.873494e-01  2.227347e-01  5.247488e-02
xijphd      -1.140915e+00  6.522510e-01 -2.470988e-01 -1.971213e-01
                      4,5           4,6           5,6
(Intercept) -9.998252e-02  6.822355e-01  9.537969e-01
xijage       1.055143e-02 -2.273444e-02 -3.906653e-02
xijsex      -1.248663e+00 -1.702186e+00 -4.195561e-01
xijwageinc  -4.986472e-06 -7.237963e-06  6.807733e-07
xijwkswrkd   1.070949e-02  8.097722e-03 -5.808361e-03
xijms       -1.911361e-01 -3.957808e-01 -1.919405e-01
xijphd      -8.398231e-01 -8.940497e-01 -2.745368e-02
# predict the occupation of a woman, age 35, no MS/PhD, inc 60000, 52
# weeks worked
# outputs class 2, Census occupation code 102


In addition to use in linear regression graphical diagnostics, k-NN can be very effective as a nonparametric regression/machine learning tool. I would recommend it in cases in which the number of predictors is moderate and there are nonmonotonic relations. (See also our polyreg package.)

The regtools package has two k-NN functions to choose from, kNN() and knnest(). The latter is more efficient once a model has been settled on, as it greatly reduces computation in continuing prediction of new cases. However, it is more complex, and kNN() is recommended for ordinary use.


Our example here will be day1 from the famous bike sharing dataset from the UC Irvine Machine Learning Repository. It is included in regtools by permission of the data curator. A detailed description of the data is available on the UCI site.

(The original data is in day. It has some transformed values for the weather variables; here we'll use day1, which retains the original scale.)

We'll predict total ridership. Let's start out using as features just the dummy for working day, and the numeric weather variables, columns 8, 10-13 and 16:

day1 <- day1[,c(8,10:13,16)]
# prints
#   workingday     temp     atemp      hum windspeed  tot
# 1          0 8.175849  7.999250 0.805833 10.749882  985
# 2          0 9.083466  7.346774 0.696087 16.652113  801
# 3          1 1.229108 -3.499270 0.437273 16.636703 1349
# 4          1 1.400000 -1.999948 0.590435 10.739832 1562
# 5          1 2.666979 -0.868180 0.436957 12.522300 1600
# 6          1 1.604356 -0.608206 0.518261  6.000868 1606

In its simplest version, the call form is


where the arguments are:

  • x: the "X" matrix, i.e. matrix of predictor values, for the training set (it cannot be a data frame, as nearest-neighbor distances between rows must be computed); in this case, it's the entire data except for the tot column

  • y: the "Y" vector for the training set, i.e. the response values, in this case the tot column

  • newx: a vector of feature values for a new case to be predicted

  • k: the number of nearest neighbors we wish to use

So, say you are the manager this morning, and the day is a working day, with temperature 12.0, atemp 11.8, humidity 23% and wind at 5 miles (80.6 km) per hour. What is your prediction for the ridership?

tot <- day1$tot
knnout <- kNN(day1,tot,c(1,12.0,11.8,0.23,5),5)
# prints
# $whichClosest
#      [,1] [,2] [,3] [,4] [,5]
# [1,]  459  481  469  452   67
# $regests
# [1] 5320.2

The output shows which rows in the training set were closest to the point to be predicted --- rows 459, 481 and so on --- and the prediction itself. Our prediction would then be about 5320 riders.


In the above Census data, let's predict the occupation of a college graduate, age 32, male, with income $67,500 and having worked 52 weeks.

x <- peDumms[,c(1,8:22,29,31,32)] 
y <- peDumms[,c(23:28)] 
# prints
# $whichClosest
#      [,1] [,2] [,3] [,4] [,5]  [,6] [,7] [,8]  [,9] [,10] [,11] [,12] [,13]
# [1,] 1437 8922 3437 5743 1576 17371 3565 7197 16513  7734  4023  4741 19143
#      [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
# [1,] 15858  3648   988 19728 19345 15441  3470  7724 19005 16029  4259  4115
# $regests
#         [,1]
# occ.100 0.16
# occ.101 0.24
# occ.102 0.44
# occ.106 0.00
# occ.140 0.04
# occ.141 0.12

We guess Occupation 102.


The LetterRecognition dataset in the mlbench package lists various geometric measurements of capital English letters, thus another image recognition problem. One problem is that the frequencies of the letters in the dataset are not similar to those in actual English texts. The correct frequencies are given in the ltrfreqs dataset included here in the regtools package.

In order to adjust the analysis accordingly, the ovalogtrn() function has an optional truepriors argument. For the letters example, we could set this argument to ltrfreqs. The function knntrn() also has such an argument. (The term priors here does NOT refer to a subjective Bayesian analysis. It is merely a standard term for the class probabilities.)

In an example in the book associated with this package, the use of correct priors increased the rate of correct classification from 75 to 88%.


This allows use of ordinary tools like lm() for prediction in time series data. Since the goal here is prediction rather than inference, an informal model can be quite effective, as well as convenient.

The basic idea is that x[i] is predicted by x[i-lg], x[i-lg+1], x[i-lg+2], i... x[i-1], where lg is the lag.

xy <- TStoX(Nile,5)
#      [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] 1120 1160  963 1210 1160 1160
# [2,] 1160  963 1210 1160 1160  813
# [3,]  963 1210 1160 1160  813 1230
# [4,] 1210 1160 1160  813 1230 1370
# [5,] 1160 1160  813 1230 1370 1140
# [6,] 1160  813 1230 1370 1140  995
#  [1] 1120 1160  963 1210 1160 1160  813 1230 1370 1140  995  935 1110  994 1020
# [16]  960 1180  799  958 1140 1100 1210 1150 1250 1260 1220 1030 1100  774  840
# [31]  874  694  940  833  701  916

Try lm():

lmout <- lm(xy[,6] ~ xy[,1:5])
(Intercept)   xy[, 1:5]1   xy[, 1:5]2   xy[, 1:5]3   xy[, 1:5]4   xy[, 1:5]5  
  307.84354      0.08833     -0.02009      0.08385      0.13171      0.37160  

Predict the 101st observation:

cfs <- coef(lmout)
cfs %*% c(1,Nile[96:100])
#          [,1]
# [1,] 784.4925


Various tools for linear, nonlinear and nonparametric regression.



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