These tools 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).

The tools are useful in general, **independently of the book**.

**NOTE:** See also our **qeML** package – Quick and Easy Machine Learning.

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, parametric and nonparametric, for any number of classes. One vs. All and All vs. All paradigms. Novel adjustment for artificially balanced (or undesirably imbalanced) data.

Nonparametric 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. Tool to aid in choosing**k**.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.Linear regression, PCA and log-linear model estimation in missing-data setting, via the Available Cases method. (For Prediction contexts, see our toweranNA package.)

Utilities for conversion between factor and dummy variable forms, useful since among various regression packages, some use factors while some others use dummies. (The

**lars**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.

Interesting datasets.

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()**:

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

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. 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 overfitting near the low end, and quite substantial underfitting at the high end.

There are intriguing “streaks” or “tails” of points, suggesting the possible existence of small but important subpopulations. 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, 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** packagers. 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.

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

```
<- avalogtrn(6,pef2)
avaout
avaout# prints
1,2 1,3 1,4 1,5
-1.914000e-01 -4.457460e-01 2.086223e+00 2.182711e+00
(Intercept) 8.551176e-03 2.199740e-02 1.017490e-02 1.772913e-02
xijage -3.643608e-01 -3.758687e-01 3.804932e-01 -8.982992e-01
xijsex -1.207755e-06 -9.679473e-06 -6.967489e-07 -4.273828e-06
xijwageinc 4.517229e-03 4.395890e-03 -9.535784e-03 -1.543710e-03
xijwkswrkd -9.460392e-02 -7.509925e-01 -2.702961e-01 -5.466462e-01
xijms 3.983077e-01 -5.389224e-01 7.503942e-02 -7.424787e-01
xijphd 1,6 2,3 2,4 2,5
3.115845e+00 -2.834012e-01 2.276943e+00 2.280739e+00
(Intercept) -2.139193e-02 1.466992e-02 1.950032e-03 1.084527e-02
xijage -1.458056e+00 3.720012e-03 7.569766e-01 -5.130827e-01
xijsex -5.424842e-06 -9.709168e-06 -1.838009e-07 -4.908563e-06
xijwageinc -2.526987e-03 9.884673e-04 -1.382032e-02 -3.290367e-03
xijwkswrkd -6.399600e-01 -6.710261e-01 -1.448368e-01 -4.818512e-01
xijms -6.404008e-01 -9.576587e-01 -2.988396e-01 -1.174245e+00
xijphd 2,6 3,4 3,5 3,6
3.172786e+00 2.619465e+00 2.516647e+00 3.486811e+00
(Intercept) -2.908482e-02 -1.312368e-02 -3.051624e-03 -4.236516e-02
xijage -1.052226e+00 7.455830e-01 -5.051875e-01 -1.010688e+00
xijsex -5.336828e-06 1.157401e-05 1.131685e-06 1.329288e-06
xijwageinc -3.792371e-03 -1.804920e-02 5.606399e-04 -3.217069e-03
xijwkswrkd -5.987265e-01 4.873494e-01 2.227347e-01 5.247488e-02
xijms -1.140915e+00 6.522510e-01 -2.470988e-01 -1.971213e-01
xijphd 4,5 4,6 5,6
-9.998252e-02 6.822355e-01 9.537969e-01
(Intercept) 1.055143e-02 -2.273444e-02 -3.906653e-02
xijage -1.248663e+00 -1.702186e+00 -4.195561e-01
xijsex -4.986472e-06 -7.237963e-06 6.807733e-07
xijwageinc 1.070949e-02 8.097722e-03 -5.808361e-03
xijwkswrkd -1.911361e-01 -3.957808e-01 -1.919405e-01
xijms -8.398231e-01 -8.940497e-01 -2.745368e-02
xijphd # predict the occupation of a woman, age 35, no MS/PhD, inc 60000, 52
# weeks worked
avalogpred(6,ovaout,matrix(c(35,0,60000,52,0,0),nrow=1))
# outputs class 2, Census occupation code 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 term *priors* here does refer to a subjective Bayesian analysis. It is merely a standard term for the class probabilities.)

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.) Let’s continue the above example on predicting occupation, using k-NN.

The three components of k-NN analysis in **regtools** are:

**preprocessx()**: This finds the sets of nearest neighbors in the training set, for all values of**k**up to a user-specified maximum. This facilitates the user’s trying various values of**k**.**knnest()**: This fits the regression model.**knnpred()**: This does prediction on the user’s desired set of points of new cases.

Since k-NN involves finding distances between points, our data must be numeric, not factors. This means that in **pef2**, we’ll need to replace the **occ** column by a matrix of dummy variables. Utilities in the **regtools** package make this convenient:

```
<- factorToDummies(as.factor(pef2$occ),'occ',omitLast=FALSE)
occDumms <- cbind(pef2[,-7],occDumms) pef3
```

Note that in cases in which “Y” is multivariate, **knnest()** requires it in multivariate form. Here “Y” is 6-variate, so we’ve set the last 6 columns of **pef3** to the corresponding dummies.

Many popular regression packages, e.g. **lars** for the LASSO, require data in numeric form, so the **regtools**’ conversion utilities are quite handy.

Now fit the regression model:

`<- knnest(pef3[, -(1:6)],xd,10) kout `

One of the components of **kout** is the matrix of fitted values:

```
> head(kout$regest)
.0 occ.1 occ.2 occ.3 occ.4 occ.5
occ1,] 0.2 0.4 0.2 0 0.0 0.2
[2,] 0.2 0.5 0.2 0 0.0 0.1
[3,] 0.5 0.1 0.3 0 0.1 0.0
[4,] 0.3 0.4 0.1 0 0.0 0.2
[5,] 1.0 0.0 0.0 0 0.0 0.0
[6,] 0.2 0.4 0.2 0 0.0 0.2 [
```

So for example the conditional probability of Occupation 4 for the third observation is 0.1.

Now let’s do the same prediction as above:

```
> predict(kout,matrix(c(35,0,60000,52,0,0),nrow=1),TRUE)
.0 occ.1 occ.2 occ.3 occ.4 occ.5
occ0.1 0.4 0.5 0.0 0.0 0.0
```

These are conditional probabilities. The most likely one is Occupation 2.

The TRUE argument was to specify that we need to scale the new cases in the same way the original data were scaled.

By default, our k-NN routines find the mean Y in the neighborhood. Another option is to do local linear smoothing. Among other things, this may remedy aliasing at the edges of the data. This should be done with a value of **k** much larger than the number of predictor variables.

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.

```
<- TStoX(Nile,5)
xy head(xy)
# [,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
head(Nile,36)
# [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()**:

```
<- lm(xy[,6] ~ xy[,1:5])
lmout
lmout
...:
Coefficients:
Coefficients1:5]1 xy[, 1:5]2 xy[, 1:5]3 xy[, 1:5]4 xy[, 1:5]5
(Intercept) xy[, 307.84354 0.08833 -0.02009 0.08385 0.13171 0.37160
```

Predict the 101st observation:

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