Spatial Data Analysis Using Artificial Neural Networks, Part 2

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Spatial Data Analysis Using Artificial Neural Networks, Part 2

Richard E. Plant

Additional topic to accompany Spatial Data Analysis in Ecology and Agriculture using R, Second Edition Chapter and section references are contained in that text, which is referred to as SDA2. Data sets and R code are available in the Additional Topics through the link above.


This post originally appeared as an Additional Topic to my book as described above. Because it may be of interest to a wider community than just agricultural scientists and ecologists, I am posting it here as well. However, it is too long to be a single post. I have therefore divided it into two parts, posted successively. I have also removed some of the mathematical derivations. This is the second of these two posts. In the first post we discussed ANNs with a single hidden layer and constructed one to see how it functions. This second post discusses multilayer and radial basis function ANNs,  ANNs for multiclass problems, and the determination of predictor variable importance.  Some of the graphics and formulas became somewhat blurred when I transfered them from the original document to this post. I apologize for that, and again, if you want to see the clearer version you should go to the website listed above and download the pdf file.

3. The Multiple Hidden Layer ANN

Although artificial neural networks with a single hidden layer can provide quite general solution capabilities (Venables and Ripley, 2002, p. 245), preference is sometimes expressed for multiple hidden layers. The most widely used R ANN package providing multiple hidden layer capability is neuralnet (Fritsch et al., 2019). Let’s try it out on our training data set using two hidden layers with four cells each (Fig. 19).

> set.seed(1)
> mod.neuralnet <- neuralnet(QUDO ~ MAT +
+     Precip,
data = Set2.Train,
+     hidden = c(4, 4), act.fct = “logistic”,

+     linear.output = FALSE)
> Predict.neuralnet <-
+     predict(mod.neuralnet, Set2.Test)

> plotnet(mod.neuralnet, cex = 0.75) # Fig. 19a
> p <- plot.ANN(Set2.Test,
+  Predict.neuralnet, 0.5,
“neuralnet Predictions, Two Hidden Layers”) 

The argument hidden = c(4, 4) specifies two hidden layers of four cells each, and the argument linear.output = FALSE specifies that this is a classification and not a regression problem. As with the single layer ANNs of the previous section, the two layer ANNs of neuralnet() produce a variety of outputs, some tame and some not so tame (Exercise 6).

Figure 19. (a) Diagram of the neuralnet ANN with two hidden layers; (b) prediction map of the ANN.

Instead of using Newton’s method found in the function nlm() and approximated in nnet(),  it has become common in ANN work to use an alternative method called gradient descent. The neuralnet package permits the selection of various versions of the gradient descent algorithm. Suppose for definiteness that the objective function J is the sum of squared errors (everything works the same if it is, for example, the cross entropy, but the discussion is more complex). Denote the combined sets of biases and weights by wi, 1 = 1,…,nw,  (for our example of Section 2, with one hidden layer and M = 4, nw = 17). If the ANN returns an estimate of the vector Y of class labels then we can then write the sum of squared errors of the ANN as


The intent of this notation is to indicate explicitly that the vector of estimates  depends on the biases and weights. Minimizing  J(w1,…,wnw) is the unconstrained nonlinear minimization problem to which the gradient descent method is applied. The set of values of J(w1,…,wnw) describes a surface (technically a “hypersurface”) sitting “above” the nw dimensional space of the weights and biases. If you have trouble visualizing this, it might help to imagine the case in which nw = 2, in which case the surface of values of J(w1,w2)  lies above the plane of values of (w1,w2). The Wikipedia article discussing the gradient descent method is truly excellent, and I will not try to improve on it. From this article we can see that in order to find the minimum of the objective function J, starting from a particular point in the  space we move in the direction of the gradient


Here the ei are unit vectors in the space of the weights and biases, and the gradient points in the direction of steepest descent (and of steepest ascent – which would take us in the wrong direction) of J. As described in the Wikipedia article, the algorithm moves in a sequence of steps


Here w(k) is the estimate of the vector w of biases and weights at the kth gradient descent step, and  is the length of the step taken in the direction of the negative gradient (sometimes called the learning rate). All of the partial derivatives in Equation (8) must be estimated numerically, so it is evident that although the problem is much smaller than that involving the computation of the Hessian matrix of second partial derivatives, it still involves a lot of computation. For that reason, ANN algorithms often use an approximate form of gradient descent. The most common of these is stochastic gradient descent. In this method, instead of estimating all nw partial derivatives at each step, a random subset of a fixed size (possibly as small as one) is chosen and the direction is determined based on minimizing this subset. If you have read the Wikipedia article above then this short description provides a good explanation of stochastic gradient descent, and this discussion provides more detail. In particular, you can see from these articles that it is even possible that the stochastic gradient descent method can actually move past a local minimum and find the global minimum. In fact, the gradient in Equation (9) is not actually computed directly. Instead, an algorithm called back propagation is generally used to estimate the partial derivatives. The Additional Topic on which this post is based includes a mathematical description of back propagation, but I have decided to remove that from this post, both to bring the post to a reasonable length and to reduce the mathematical complexity. The basic idea of back propagation is to compute the partial derivatives in Equation (8) by repeated applications of the chain rule of differential calculus. You can see the full derivation by downloading the Additional Topic. The neuralnet() function has available several forms of back propagation. As a default it uses the resilient propagation algorithm (Riedmiller et al., 1993). This algorithm recognizes that the size of the step from w(k to w(k+1 depends not only on the size of  but also on the sizes of the components of , and that this latter dependence may be disadvantageous. For this reason resilient propagation develops a step size that is less dependent on the magnitudes of the components of , using these values primarily to determine the direction of the step. There are other methods available and you can see the neuralnet package documentation for details. The package also makes available the cross-entropy error function in addition to the default error sum of squares. Now that we have an idea of how the function neuralnet() works, let’s try it out on the full QUDO data set. We will use two hidden layers of four cells each, similar to Fig 19a. When I worked with on this problem it turned out that neuralnet() failed to converge in several attempts when applied to the full data set. The largest training set for which convergence was obtained had n = 200 data records evenly divided between QUDO = 0 and QUDO = 1. Fig. 20a shows the result of applying neuralnet() to this training data set using a cutoff of 0.73, which was determined from a ROC curve analysis (code not shown). The error rate is 0.225, which is not as good as that obtained in Section 2 using nnet().

                                       (a)                                                      (b)
Figure 20. (a) Plot of prediction regions of neuralnet() with two hidden layers and a training data set of 200 randomly selected records; (b) application of the resulting model to the full set.

  Sections 2 and 3 have given us a general idea of how a multilayer perceptron works. Although the MLP is probably the most commonly used ANN for the type of classification problem discussed here, several alternatives exist. The next section discusses one of them, the radial basis function ANN.

4. The Radial Basis Function ANN

The seemingly obscure term radial basis function ANN is actually not hard to parse out. We will start with word basis. As you may remember from linear algebra, a basis is a set of vectors in the vector space that have the property that any vector in the vector space can be described as a linear combination of elements of this set. For discussion and a picture see here. If you had a course in Fourier series, you may remember that the Fourier series functions form a basis for a set of functions on an infinite dimensional vector space. Radial basis functions do the same sort of thing. A radial function is a function of the form
                  , (10)
where c is the center point of a vector space. In other words, a radial function is radially symmetric and depends only on the distance of the vector x from the center. Again we are using the terms “point” and “vector” synonymously. The radial basis function commonly used in ANNs is the Gaussian function (i.e., the normal distribution). When x is a two dimensional quantity, as it is in our case (MAT and Precip), the function  can be visualized, and a picture of it is shown here. Thus, as with a Fourier series, a radial basis function forms a basis with which to approximate other functions – in particular, in our case, functions associated with the process of classification via an ANN.        
Figure 21. Architecture of the radial basis function ANN.

  The way a radial basis function (RBF) works is as follows. Suppose again that the cost function J is based on the sum of squared errors. Referring to Fig. 21, which shows a schematic of a radial basis function ANN with a single hidden layer, the input activations for the ith data record are, as with the multilayer perceptron, simply the values Xi,j. We will describe the combined output of all of the hidden cells in terms of the basis function . Specifically, for a fixed i define a function f(X) by
. (11)
Here cm is the center of the mth radial basis function and jm measures the size of the contribution of this component to the overall function . If our objective function is the sum of squared errors then we plug Equation (11) into the definition of J to get
. (12)
Each of the M elements of the sum  forms one of the cells in the hidden layer of the ANN (Fig. 19) (note that the order of the summation has been reversed from Section 2 so that first the summation over i takes place first in each cell and then second over the m hidden cells). The links between the hidden cells and the output cell hold the weights jm. The weighted outputs of the m cells, given by , are summed to determine the activation a(3) of the output cell. The architecture described here and shown in Fig. 21 is the simplest form of a radial basis function ANN. More bells and whistles can be added; for example, biases can be incorporated as constant terms in the sums of Equation (11). In addition, a logistic or other function can be interposed between the output cell level internal value  and the activation a(3), similar to that of the MLP (Fig. 8).
The first step in the RBF computational process is to determine a set of centers cm in Equation (11). The determination is made by trying to place the centers so that their location matches as closely as possible the distribution in the data space of values . Some RBF systems do this by carrying out a k-means clustering (SDA2 Sec. 12.6.1) of the space of data records, where the number of means k equals the number M of hidden cells (e.g. Nunes da Silva et al., 2017, p.121). Another possibility is to carry out a Kohonen training process. This is basically an unsupervised clustering algorithm analogous to k-means clustering, although, as is often the case with methods related to ANNs, the terminology is a bit more pretentious: in this case the result is called a “self-organizing map.” We will be using the function rbf() of the RSNNS package (Bergmeir and Benitez, 2012). It does not use k-means clustering, but the use of the Kohonen training process is available as an option. The default method, which in my experience works the best, is called the “rbf weights” method. Fundamentally, the rbf weights method is, in the words of the creators of the package (Zell et al, 1998, p. 177), “rather simple.” Essentially, the weights are distributed in the data space to match a random sample of the values of the data records. There are a few adjustments that can be used, but this description captures the basics. Once the values of the centers cm are established the final step of the training process is to determine the values of the link weights jm along with the values of the biases if there are any. This is accomplished in rbf() by gradient descent. Similarly to the multilayer perceptron ANNs described in Section 3, the gradient descent algorithm carries out an iterative process in which the link weights are adjusted by a series of small steps of the form


where   is a small constant. This brief introduction is sufficient to allow us to try out an RBF ANN. The package RSNNS is a port to R of the Stuttgart Neural Network Simulator (Zell et al, 1998), or SNNS, which contains implementations of many ANNs. As a segue to the use of the function rbf(), which creates a radial basis function ANN, we will first test the function mlp() from the same package. This provides a multilayer perceptron solution that can be compared with those of Sections 2 and 3. The use of functions with the SNNS package is slightly different from what we have seen in Sections 2 and 3. Working initially with the 50 record Training data set, we first prepare its data records to serve as arguments for the ANN functions.

Train.Values <- Set2.Train[,c(“MAT”, “Precip”)]
Train.Targets <-
+   decodeClassLabels(as.character(Set2.Train$QUDO))

We will start by testing the RSNNS multilayer perceptron using a single hidden layer with four cells. The package has a very nice function called plotIterativeError() that permits one to visualize how the error declines as the iterations proceed. We will first run the ANN and check out the output. These are slight differences in how the polymorphic R functions are implemented from those of the previous sections.

> set.seed(1)
> mod.SNNS <- mlp(Train.Values, Train.Targets,
+    size = 4)

> Predict.SNNS <- predict(mod.SNNS,
+     Set2.Test[,1:2])

> head(Predict.SNNS)
         [,1]      [,2]
[1,] 0.6385500 0.3543552
[2,] 0.6247426 0.3684126
[3,] 0.6107571 0.3826691
[4,] 0.5966275 0.3970889
[5,] 0.5823891 0.4116347
[6,] 0.5680778 0.4262682

For this binary classification problem the output of the function predict() is in the form of a matrix whose columns sum to 1, reflecting the alternative predictions. Using this information, we can plot the prediction region (Fig. 22a).


Figure 22. Plots of (a) the predicted regions and (b) the iterative error with the default iteration limit.

  Next we plot the iterative error (Fig. 22b).

> plotIterativeError(mod.SNNS) # Fig. 22b

The error clearly declines, and it may or may not be leveling off. To check, we will set the argument maxit to a very large value and see what happens.

> mod.SNNS <- mlp(Train.Values, Train.Targets,
+    size = 4,
maxit = 50000)
> stop_time <- Sys.time()
> stop_time – start_time
Time difference of 5.016404 secs
> Predict.SNNS <- predict(mod.SNNS,
+    Set2.Test[,1:2])

> p <- plot.ANN(Set2.Test, Predict.SNNS[,2],
+    0.5,

+    “RSNNS mlp Prediction, 50,000 Iterations”)
> plotIterativeError(mod.SNNS) # Fig. 23b

                                  (a)                                            (b)
Figure 23. Plots of (a) the predicted regions and (b) the iterative error with an iteration limit of 50,000.

  The iteration error clearly declines some more. The prediction region does not look like most of those that we have seen earlier, and does not look very intuitive, but of course the machine doesn’t know that. The time to run 50,000 iterations for this problem is not bad at all.
Now we can move on to the radial basis function ANN, implemented via the function rbf(). First we will try it with all arguments set to their default values (Fig. 24).

> set.seed(1)
> mod.SNNS <- rbf(Train.Values, Train.Targets)
> Predict.SNNS <- predict(mod.SNNS, Set2.Test)
> p <- plot.ANN(Set2.Test, Predict.SNNS[,2],
+   0.5,
“RSNNS rbf Prediction,
+   Default Values”) 

Figure 24. Prediction regions for the model developed using rbf() with default values.

The “radial” part of the term radial basis function becomes very apparent! A glance at the documentation via ?rbf reveals that the default value for the number of hidden cells is 5. Many sources discussing radial basis function ANNs point out that a large number of hidden cells is often necessary to correctly model the data. We will try another run with the number of hidden cells increased to 40.

> set.seed(1)
> mod.SNNS <- rbf(Train.Values, Train.Targets,
+    size = 40)
The rest of the code is identical to what we have seen before and is not shown. The prediction region is similar to that of Fig. 25a below and is not shown. The error rate is about what we have seen from the other methods.

> length(which(Set2.Pred$TrueVal !=
+   Set2.Pred$PredVal)) /
[1] 0.22
                                       (a)                                      (b)
Figure 25. (a) Prediction regions and (b) error estimate for the model developed using rbf() with the full data set.

  The performance of rbf() on the full data set is not particularly spectacular.

> length(which(Set2.Pred$TrueVal !=
+   Set2.Pred$PredVal)) /
[1] 0.2324658  

Now that we have had a chance to compare several ANNs on a binary selection problem, an inescapable conclusion is that, for the problem we are studying at least, the old workhorse nnet() acquits itself pretty well. In Section 5 we will try out various ANNs on a multi-category classification process. In this case each of the ANNs we are considering has an output cell for each class label.  

5. A Multicategory Classification Problem

The multicategory classification problem that we will study in this chapter is the same one that we studied in the Additional Topic on Comparison of Supervised Classification Methods. A detailed introduction is given there. In brief, the data set is the augmented Data Set 2 from SDA2. It is based on the Wieslander survey of oak species in California (Wieslander, 1935) and contains records of the presence or absence of blue oak (Quercus douglasii, QUDO), coast live oak (Quercus agrifolia, QUAG), canyon oak (Quercus chrysolepis, QUCH), black oak (Quercus kelloggii, QUKE), valley oak (Quercus lobata, QULO), and interior live oak (Quercus wislizeni, QUWI). As implemented it is the data frame data.Set2U; until now we have grouped all of the non-QUDO species together and assigned them the value QUDO = 0. In this section we will create the class label species and assign the appropriate character value to each record.

> species <- character(nrow(data.Set2U))
> species[which(data.Set2U$QUAG == 1)] <- “QUAG”
> species[which(data.Set2U$QUWI == 1)] <- “QUWI”
> species[which(data.Set2U$QULO == 1)] <- “QULO”
> species[which(data.Set2U$QUDO == 1)] <- “QUDO”
> species[which(data.Set2U$QUKE == 1)] <- “QUKE”
> species[which(data.Set2U$QUCH == 1)] <- “QUCH”
> data.Set2U$species <- as.factor(species)
> table(data.Set2U$species)
551   99  731  717   47  122

As can be seen, the data set is highly unbalanced. This property always poses a severe challenge for classification algorithms. The data are also highly autocorrelated spatially. In the Additional Topic on Method Comparison we found that 83% of the data records have the same species classification as their nearest neighbor. As in other Additional Topics, we want to be able to take advantage of locational information both through spatial relationships and by including Latitude and Longitude in the set of predictors. I won’t repeat the discussion about this since it is covered in the other Additional Topics. We will use the same spatialPointsDataFrame (Bivand et al, 2011) D of rescaled predictors and species class labels that was used in the Additional Topic on method comparison. Again, if you are not familiar with the spatialPointsDataFrame object, don’t worry about it: you can easily understand what is going anyway.

> library(spdep)
> coordinates(data.Set2U) <-
+   c(“Longitude”, “Latitude”)
> proj4string(data.Set2U) <-
+    CRS(“+proj=longlat +datum=WGS84”)
> D <- cbind(coordinates(data.Set2U),
+    data.Set2U@data[,c(2:19, 28:34, 43)])
> for (i in 1:2)) D[,i] <- rescale(D[,i]) 

  In this section we will use the predictors selected as optimal in the variable selection process for support vector machine analysis. This established as optimal the predictors Precip, CoastDist, the distance from the Pacific Coast, TempR, the average annual temparature range, Elevation, and Latitude. We will first try out nnet() using these predictors (Fig. 26). After playing around a bit with the number of hidden and skip layers, here is a result.

> set.seed(1)
> modf.nnet <- nnet(species ~ Precip +
+    CoastDist + TempR + Elevation + Latitude,

+    data = D, size = 10, skip = 4,
+    maxit = 10000)
# weights:  156
initial  value 3635.078808
iter2190 value 925.764437
     *     *     *     *
final  value 925.760573
> plotnet(modf.nnet, cex = 0.6)

> Predict.nnet <- predict(modf.nnet, D)
> pred.nnet <- apply(Predict.nnet, 1,
+      which.max)

> 1 – (sum(diag(conf.mat)) / nrow(D))
[1] 0.1433613
> print(conf.mat <- table(pred.nnet, D$species))

        1  514    9   29    7   16   24
        2    1   27    2    6    2    1
        3   27    7  661   19   11   22
        4    1   54   27  673    4   20
        5    1    0    4    0   12    0
        6    7    2    8   12    2   55
> print(diag(conf.mat) /
+   table(data.Set2U$species), digits = 2)

0.93 0.27 0.90 0.94 0.26 0.45

The overall error rate is about 0.14, and, as usual, the rare species are not predicted particularly well.   Figure 26. Schematic of the multicategory ANN.

  In the Additional Topic on method comparison it was noted that, owing to the high level of spatial autocorrelation of these data, one can obtain an error rate of about 0.18 by simply predicting that a particular data record will be the same species as that of its nearest neighbor. We will try using that to our advantage by adding the species of the nearest and second nearest geographic neighbors to the set of predictors.

> set.seed(1)
> start_time <- Sys.time()
> modf.nnet <- nnet(species ~ Precip +
+    CoastDist + TempR +
Elevation + Latitude +
+    nearest1 + nearest2,
data = D, size = 12,
+    skip = 2, maxit = 10000)

> stop_time <- Sys.time()
> stop_time – start_time
Time difference of 11.64763 secs
> plotnet(modf.nnet, cex = 0.6)

> Predict.nnet <- predict(modf.nnet, D)
> pred.nnet <- apply(Predict.nnet, 1,
+    which.max)

> print(conf.mat <- table(pred.nnet,
+    D$species))

        1  527    3   13    4   11   12
        2    1   48    0    3    1    0
        3   12    2  695    7    7   18
        4    1   45   17  698    1   16
        5    6    0    2    0   27    0
        6    4    1    4    5    0   76
> 1 – (sum(diag(conf.mat)) / nrow(D))

[1] 0.08645787
> print(diag(conf.mat) /
+     table(data.Set2U$species), digits = 2)

0.96 0.48 0.95 0.97 0.57 0.62

The error rate is a little under 9%, which is really quite good. The improvement is particularly good for the rare species. Of course, this only works if one knows the species of the nearest neighbors! Nevertheless, it does show that incorporation of spatial information, if possible, can work to one’s advantage. The neuralnet ANN was not as successful in my limited testing. The best results were obtained when nearest neighbors were not used. The code is not shown, but here are the results.

> stop_time – start_time
Time difference of 2.726899 mins
> print(conf.mat <- table(pred.neuralnet,
+    D$species))

             1  499   22   31   16   21   35
             2    0    8    2    2    0    0
             3   51    5  654   27   22   47
             4    1   62   34  672    4   23
             6    0    2   10    0    0   17
> 1 – (sum(diag(conf.mat[1:4,1:4])) +

+         conf.mat[5,6]) / nrow(D)
[1] 0.1839435  

No records were predicted to be QULO. The performance of the SNNS MLP was about the same. The RBF ANN performed even worse.

> print(conf.mat <- table(pred.SNNS,
+   D$species))
        1  471   24   42   18   13   33
        3   80   15  665   63   31   68
        4    0   60   24  636    3   21
> 1 – (conf.mat[1:1] +conf.mat[2,3] +
+      conf.mat[3,4]) / nrow(D)

[1] 0.2183502  

Once again, despite being the oldest and arguably the simplest package, nnet has performed the best in our analysis. There may, of course, be problems for which it does not do as well as the alternatives. Nevertheless, we will limit our remaining work to this package alone. The last topic we need to cover is variable selection for ANNs.

6. Variable Selection for ANNs

There have been numerous papers published on variable selection for ANNs (try googling the topic!). Olden and Jackson (2002), Gevery et al. (2003), and Olden et al. (2004) provide, in my opinion, particularly useful ones for the practitioner. Gevery et al. (2003) discuss some stepwise selection methods in which variables enter and leave the model based on the mean squared error (see SDA2 Sec. 8.2.1 for a discussion of stepwise methods in linear regression). These methods actually have much to recommend them, but they are not much different from stepwise selection methods we have discussed in SDA2 and in other Additional Topics and we won’t consider them further here. The unique distinguishing feature of ANNs in terms of variable selection is their connection weights, and several methods for estimating variable importance based on these weights have been developed. The most commonly used methods for ANNs involve a single output variable, implying only binary classification or regression. We will therefore discuss them by returning to the QUDO classification problem of Sections 2-4. We will carry over the data frame D from Section 5. Our model will include three predictors. Two of them are MAT and Precip, as used in Sections 2-4. The third, called Dummy, is just that: a dummy variable consisting of a uniformly distributed set of random numbers having no predictive power at all. A good method should identify Dummy as a prime candidate for elimination.

> set.seed(1)
> D$Dummy <- runif(nrow(D))
> size <- 2
> set.seed(1)  

For simplicity our demonstration ANN will have only two hidden cells (Fig. 27). We will first select the random number seed with the best performance, using the same procedure as that carried out in Section 2. Here is the result.

> seeds [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20> sort(error.rate)
[1] 0.2077636 0.2077636 0.2082047 0.2254080 0.2271725 0.2271725 0.2271725
[8] 0.2271725 0.2271725 0.2271725 0.2271725 0.2271725 0.2276136 0.2280547
[15] 0.2284958 0.2289369 0.3224526 0.3224526 0.3224526 0.3224526
> print(best.seed <- which.min(error.rate))

[1] 10
> mod.nnet <- nnet(X.t, Y.t, size = size,
+     maxit = 10000)

> plotnet(mod.nnet, cex_val = 0.75)

  Fig. 27 shows the plotnet() plot of this ANN. Here are the numerical values of the biases and weights

> mod.nnet$wts [1] -80.82405907   0.87215226  85.41369339 108.51778012  -4.8336825
[6]   0.04523967   2.83945431  -0.84126067  -2.81207163  -2.34322065
[11]  71.37131603

The order of biases and weights is {B1H1, I1H1, I2H1, I3H1, B1H2, I1H2 I2H2, I3H2, B2O1, H1O1, H2O1}.

Figure 27. plotnet() diagram of the simple ANN.

  Since the biases act on all predictors equally, they are not considered in the algorithm. One of the earliest ANN methods for determining the relative importance of each predictor was developed by Garson (1991) and Goh (1995). We will present this method following the discussion of Olden and Jackson (2002). The following replicates for our current case the set of tables displayed in Box 1 of that paper. First, we compute the matrix of weights.

> print(n.wts <- length(mod.nnet$wts))
[1] 11
> # Remove H1O1, H2O1
> print(M.seq <- seq(1,(n.wts-size)))

[1] 1 2 3 4 5 6 7 8 9
> n.X <- 3

> print(M.out <-  seq(1,(n.wts-size),
+    by = n.X + 1))

[1] 1 5 9
> print(M1 <- matrix(mod.nnet$wts[M.seq[-M.out]],

+   n.X, size), digits = 2)
      [,1]   [,2]
[1,]   0.87  0.045
[2,]  85.41  2.839
[3,] 108.52 -0.841

Next we compute the output weights.

> print(O <- matrix(mod.nnet$wts
+   [seq(n.wts-size+1,n.wts)],

+   1, size), digits = 2)
    [,1] [,2]
[1,] -2.3   71

You may want to compare the numerical values with the sign and shade of the links in Fig. 27. Table 4 shows the weights and output in in tabular form, analogous to the first table in Box 1 of Olden and Jackson (2002) (from this you can see the reason for the convention involving the arrangement of subscripts described in Section 2).  
Table 4. Input – hidden – output link weights.
  Hidden H1 Hidden H2
Input I1 w(1)11 = 0.87 w(1)12 = 0.045
Input I2 w(1)21 = 84.41 w(1)22 = 2.839
Input I3 w(1)31 = 108.52 w(1)32 = –0.841
Output w(2)1 = -2.3 w(2)2 = 71
    If your output is different from mine then of course your table will have different values, but you should be able to check them by comparing your output with mine. Next we compute the matrix C.m, which contains the contribution of each input cell to the output cell (C is a reserved word in R and can’t be used).

> C.m <- matrix(0, n.X, size)
> for (i in 1:n.X) C.m[i,] <- M1[i,] * O[1,]
> print(C.m, digits = 2)
    [,1]  [,2]
[1,]   -2   3.2
[2,] -200 202.7
[3,] -254 -60.0

These row sums represent the total weight that each input applies to the computation of the output. Next, we compute the matrix R, which is defined the ratio of the absolute value of each element of C.m to the column sum. This is considered to be the relative contribution of each input cell to the activation of each output cell. We won’t be generalizing this code, and it is easier to write it using the fixed values of n.X and size.

> R <- matrix(0, 3, 2)
> for (i in 1:3) R[i,] <-
+    abs(C.m[i,]) / (abs(C.m[1,]) +
+    abs(C.m[2,]) + abs(C.m[3,]))
> print(R, digits = 2)
      [,1]  [,2]
[1,] 0.0045 0.012
[2,] 0.4385 0.762
[3,] 0.5571 0.226

Next, we compute the matrix S, which is the sum of the relative contributions of each input cell to the activation of each output cell.

> S <- matrix(0, 3, 1)
> for (i in 1:3) S[i] <- R[i,1] + R[i,2]
> print(S, digits = 2)
[1,] 0.017
[2,] 1.201
[3,] 0.783

Finally, we compute the matrix RI, which is the relative importance of each input cell to the activation of each output cell.

> RI <- matrix(0, 3, 1)
> for (i in 1:3) RI[i,1] <- S[i,1] / sum(S)
> print(RI, 2)
[1,] 0.0083
[2,] 0.6003
[3,] 0.3914

The package NeuralNetTools contains the function garson(), which carries out these computations.

> print(garson(mod.nnet, bar_plot = FALSE),
+   digits = 2)

Dummy   0.0083
MAT     0.6003
Precip  0.3914
The function garson() can also be used to create a barplot of these values (Fig. 29a).

                     (a)                                             (b)
Fig. 29. a) barplot of Garson importance values; b) barplot of Olden-Jackson importance values.

  Olden and Jackson (2002) criticized Garson’s algorithm on the grounds that by computing absolute values of link weights it ignores the possibility that a large positive input-hidden link weight followed by a large negative hidden-output link weight might cancel each other out. In a comparison of various methods for evaluating variable importance, Olden et al. (2004) found that Garson’s algorithm fared the worst among alternatives tested. Olden and Jackson (2002) suggest a permutation test (SDA2 Sec. 3.4) as an alternative to Garson’s algorithm. In reality, this method provides useful information even without employing a permutation test, and we will present it in that way. The test involves the following steps (we will use the same numbering as Olden and Jackson).

Step 1. Test several random seeds and select the one giving the smallest error. This step has already been carried out.
Step 2a
. Compute the matrix C.m above for the selected weight set. Again, this step has already been carried out.

> print(C.m, digits = 2)
[,1]  [,2]
[1,]   -2   3.2
[2,] -200 202.7
[3,] -254 -60.0

Step 2b
. Compute the row sums of C.m.

> print(C.sum <- apply(C.m, 1, sum),
+      digits = 2)

[1]    1.2    2.5 -314.3  

The package NeuralNetTools has a function olden() to carry out these computations and to plot a barplot (Fig. 29b).

> print(olden(mod.nnet, bar_plot = FALSE), digits = 2)
Dummy         1.2
MAT           2.5
Precip     -314.3

In this particular example, one might say that the Garson method acquits itself better than the Olden-Jackson method. This shows that one must keep an open mind when playing with these toys. Lek (1996) developed a method that is also discussed by Gevery et al. (2003). We will follow the discussion in this latter publication as well as the Details section of ?lekprofile. It is easiest to present the method via an example. The package NeuralNetTools contains the function lekprofile() that does the computations and sets up the graphical output. The default output of the function is a ggplot (Wickham, 2016) object.

> library(ggplot2)
> lekprofile(mod, steps = 5,
+    group_vals = seq(0, 1, by = 0.5))   

The figure is shown below after the explanation. The procedure divides the range of each predictor into (steps – 1) equally spaced segments. In our case, steps = 5, so there are 4 segments and 5 steps (the default steps value is 100). It is also possible to obtain the numerical values to compute the values represented in the plot. Here is a portion of it.

> lekprofile(mod.nnet, steps = 5,
+    group_vals = seq(0, 1, by = 0.5),
+    val_out = TRUE)

[[1]] Explanatory     resp_name    Response Groups exp_name
1  0.0006052661      QUDO 0.095468020      1    Dummy
2  0.2504365980      QUDO 0.096018057      1    Dummy
3  0.5002679299      QUDO 0.096577123      1    Dummy
4  0.7500992618      QUDO 0.097145396      1    Dummy
5  0.9999305937      QUDO 0.097723053      1    Dummy
6  0.0006052661      QUDO 0.189228401      2    Dummy
7  0.2504365980      QUDO 0.195418722      2    Dummy
8  0.5002679299      QUDO 0.201826448      2    Dummy
9  0.7500992618      QUDO 0.208457333      2    Dummy
10 0.9999305937      QUDO 0.215317020      2    Dummy
11 0.0006052661      QUDO 0.231719048      3    Dummy
         *         *            *           *
30 1.0000000000      QUDO 0.263814961      3      MAT
31 0.0000000000      QUDO 0.095468020      1   Precip
32 0.2500000000      QUDO 0.086684129      1   Precip
33 0.5000000000      QUDO 0.080092615      1   Precip
34 0.7500000000      QUDO 0.017896195      1   Precip
35 1.0000000000      QUDO 0.007309402      1   Precip
36 0.0000000000      QUDO 0.867860787      2   Precip
37 0.2500000000      QUDO 0.213604259      2   Precip
38 0.5000000000      QUDO 0.119027224      2   Precip
39 0.7500000000      QUDO 0.070552909      2   Precip
40 1.0000000000      QUDO 0.045104426      2   Precip
41 0.0000000000      QUDO 0.977067535      3   Precip
42 0.2500000000      QUDO 0.902834305      3   Precip
43 0.5000000000      QUDO 0.720770997      3   Precip
44 0.7500000000      QUDO 0.468084251      3   Precip
45 1.0000000000      QUDO 0.263814961      3   Precip
            Dummy       MAT    Precip
0%   0.0006052661 0.0000000 0.0000000
50%  0.4781180343 0.7601916 0.2747424
100% 0.9999305937 1.0000000 1.0000000

The numerical output is a list with two elements. The first element is a data frame and the second is a matrix. The explanation is clearest if we start with the second element. The components of the matrix are percentile values of each of the predictors. For example, the fiftieth percentile value of MAT is 0.7601916. Note that the 0 and 100 percentile values of MAT and Precip are 0 and 1 because these variables have been rescaled. The number of percentiles is specified via the argument group_vals, which has the default value seq(0, 1, by = 0.2). In the example we use a coarser sequence to make the interpretation easier.
Moving to the first element of the list, the data frame, the first data field is the column labeled explanatory. Its first five values represent the subdivision into five (as specified by the value of steps) equal segments of the range of values of Dummy from the 0% to the 100% percentile. For each explanatory variable, the data field exp_name, there are three Groups; the 0, 50 and 100 percentile groups, as specified by the group_vals argument. The values of the input to the mod.nnet ANN object are cycled as follows. Starting with MAT, this variable is first set to its 0 percentile value, which is 0. This forms Group 1 for MAT. Holding MAT at this value, the other two inputs are both set successively to the values specified by steps: 0, 0.25, 0.5, 0.75, and 1. The output of the ANN is calculated for each of these combinations of values and recorded in the Response data field. Thus, for example, the second data record has Response = 0.096018057, which is the value of QUDO computed by the ANN for the input vector {Dummy, MAT, Precip} = {0, 0.25, 0.25}. This value of Response is encircled by the small black circle.

Figure 30. a) Lek plot of the three-variable model. The response to {Dummy, MAT, Precip} = {0, 0.25, 0.25} is encircled by the small black circle; b) plot of the data.

  Now we can interpret Fig. 30a. For each of the three predictors, the colored lines represent the ANN response as that predictor is held fixed and the other two predictors are successively increased in value. For Dummy there is, unsurprisingly, very little response. Discounting the effect of Dummy, at low values of MAT (Group 1), the response QUDO is high until Precip reaches a high value. Fig 30b shows (again) the data, helping to interpret the plots in Fig. 30a. The Lek profile allows us to interpret how the predictors interact to generate the value of the class label. In terms of variable selection, we can see that the Lek plot would indeed suggest that we eliminate Dummy, since the values of QUDO are virtually unresponsive to it. Depending on how we implement it, the Lek method can be used for stepwise selection, by bringing explanatory variables in and out of the model depending on how they line up in plots such as Fig. 30, or one can simply carry out comparison of all of the explanatory variables at once. The Details section of ?lekprofile describes other uses of the results of an analysis based on the Lek method.

7. Further Reading

Bishop (1995) is probably the most widely cited introductory book on ANNs. The text by Nunes da Silva et al. (2017) is also a good resource. Venables and Ripley (2002) provide an excellent discussion at a more advanced and theoretical level. A blog by Hatwell (2016) provides a nice discussion of the variable selection methods in a regression context. The manual by Zell et al. (1998), although specifically developed for the SNNS library, gives a good practical discussion of many aspects of ANNs. Finally, this site, developed by Daniel Smilkov and Shan Carter, is a “neural net playground” where you can try out various ANN configurations and see the results (I am grateful to Kevin Ryan for pointing this site out to me).

8. Exercises

1) Repeat the analysis of Section 2 using nnet(), but apply it to a data set in which MAT and Precip are normalized rather than rescaled. Determine the difference in predicted value of the class labels.   2) Explore the use of the homemade ANN ANN.1() with differently sized hidden layers in terms of the stability of the prediction.   3) By computing the sum of squared errors for a set of predictions of nnet(), determine whether it is converging to different local minima for different random starting values and number of hidden cells.   4) Using a set of predictions of nnet(), compare the error rates when entropy is TRUE with the default of FALSE.   5) If you have access to Venables and Ripley (2002), read about the softmax option. Repeat Exercise (4) for this option.   6) Generate a “bestiary” of predictions of neuralnet() analogous to that of Fig. 9 for ANN1.().

7) Look up the word “beastiary” in the dictionary.

9. References

Beck, M. W. (2018). NeuralNetTools: Visualization and Analysis Tools for Neural Networks. _Journal of Statistical Software 85: 1-20. doi: 10.18637/jss.v085.i11 (URL:   Bishop, C.M. (1995). Neural Networks for Pattern Recognition. Clarendon Press, Oxford, UK.   Bergmeir, C. and J. M. Benitez (2012). Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS. Journal of Statistical Software, 46(7): 1-26. URL   Bivand, R. (with contributions by M. Altman, L. Anselin, R. Assunçăo, O. Berke, A. Bernat, G. Blanchet, E. Blankmeyer, M. Carvalho, B. Christensen, Y. Chun, C. Dormann, S. Dray, R. Halbersma, E. Krainski, N. Lewin-Koh, H. Li, J. Ma, G. Millo, W. Mueller, H. Ono, P. Peres-Neto, G. Piras, M. Reder, M. Tiefelsdorf and and D. Yu) (2011). spdep: Spatial dependence: weighting schemes, statistics and models.   Du, K.-L. and M.N.S. Swamy (2014). Neural Networks and Statistical Learning. Springer, London   Fritsch, S., F. Guenther and M. N. Wright (2019). neuralnet: Training of Neural Networks. R package version 1.44.2.   Gevery, M. I. Dimopoulos and S. Lek (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling 160: 249-264. Guenther, F. and S. Fritsch (2010). neuralnet: Training of Neural Networks. The R Journal 2:30-38.   Hatwell, J. (2016) Artificial Neural Networks in R.   Meyer, D, E. Dimitriadou, K. Hornik, A. Weingessel and F. Leisch (2019). e1071: Misc Functions  of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-3.   Nunes DaSilva, I.N, D. Hernane Spatti, R. Andrade Flauzino, L.H. Bartocci Liboni and S.F. dos Reis Alves (2017). Artificial Neural Networks: A Practical Course. Springer International, Switzerland.   Riedmiller M. and H. Braun (1993) A direct adaptive method for faster backpropagation learning: The RPROP algorithm. Proceedings of the IEEE International Conference on Neural Networks: 586-591. San Francisco, CA.   Ripley, B.D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, UK   Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408.   Venables, W. N. and B.D. Ripley (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York.   Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.   Wieslander, A. E. (1935). A vegetation type map of California. Madroño 3: 140-144.   Zell, A. and 27 others (1998), SNNS Stuttgart Neural Network Simulator User Manual, Version 4.2. IPVR, University of Stuttgart and WSI, University of Tubingen. URL        
Spatial Data Analysis Using Artificial Neural Networks, Part 2 was first posted on October 14, 2020 at 2:55 pm.
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