On the goodness of fit we performed an assessment of the mod.1 model which was a regression to predict soil CEC from clay content.

Usually it is more appropriate however to validate a model using data that was not included for model fitting. Model validation has a few different forms and we will cover the main ones on this page.

General background notes

For completely unbiased assessments of model quality, it is ideal to have an additional data set that is completely independent of the model data. When we are validating trained models with some sort of data sub-setting mechanism, always keep in mind that the validation statitsics will be biased. As Brus, Kempen, and Heuvelink (2011) explains, the sampling from the target mapping area to be used for DSM is more often than not from legacy soil survey, to which would not have been based on a probability sampling design. Therefore, that sample will be biased i.e not a true representation of the total population. Even though we may randomly select observations from the legacy soil survey sites, those vlidation points do not become a probaility sample of the target area, and consequently will only provide biased estimates of model quality. Thus an independent probaility sample is required. Further ideas on the statisical valdation of models can be found in Hastie, Tibshirani, and Friedman (2001). It is recommended that some flavor of random sampling from the target area be conducted, to which there are a few types such as simple random sampling and stratified simple random sampling. Further information regarding sampling, sampling desgings, their formulation and the relative advantages and constraints of each are described in Gruijter et al. (2006).

Usually from an operational perspective it is difficult to arrange the additional costs of organising and implementing some sort of probability sampling for determining unbiased model quality assessment. The alternative is to perform some sort of data sub-setting, such that with a data set we split it into a set for model calibration and another set for validation. This type of procedure can take different forms: the two main ones being random-hold back and leave-one-out-cross-validation (LOCV). Random-hold back (or sometimes k-fold validation) is where we may sample a data set of some pre-determined proportion (say 70%) for which is used for model calibration. We then validate the model using the other 30% of the data. This is usually done using sampling with replacement which is when a case has been selected, it is never offered up for sampling again. The alternative is sampling with replacement which is where a sample that has been selected is again put back into the main set, making it possible for this same case be sampled again. Sample with replacment does not change the underlying probabilities that a case will be sampled. This is not the case for sampling without replacment. Bootstrapping is the common term to describe the cross-validation technique whereby the sample size s3% (unique cases) of the data being selected for calibration (Efron and Tibshirani 1997).

For k-fold validation we divide the data set into equal sized partitions or folds, with all but one of the folds being used for the model calibration, the remaining fold is used for validation. We could repeat this k-fold process a number of times, each time using a different random sample from the data set for model calibration and validation. This allows one to efficently derive distributions of the validation statisics as a means of assessing the stability and sensitivity of the models and parameters. A variant of the k-fold cross-validation is spatial cross-validation (Lovelace, Nowosad, and Muenchow 2019) where instead of random subsets of data to act as the different folds, the data are clustered spatially into groups (which we can think of as folds to keep with the general idea). Randomly splitting spatial data can lead to training points that are neighbors in space with test points. Due to spatial autocorrelation there is a chance the calibration and validation datasets may not be independent, with the consequence that CV fails to detect a possible overfitting. We will explore spatial cross-validation once we start usng spatial data in further exercises.

For now lets just focus on conventional CV techniques to bed done some fundmental concepts. You may note the some R packages concerned with data modelling have some inbuilt techniques for cross-validation. The caret R package is one of these. Nonetheless it is always useful to know the mechanics of the approaches.

Now lets get the data orgranised.

data(USYD_soil1) <- USYD_soil1 <- na.omit([, c("clay", "CEC")])

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Leave-One-Out Cross Validation

LOCV follows the logic that if we had n number of data, we would subset n-1 of these data, and fit a model with these data. Using this model we would make a prediction for the single data that was left out of the model (and save the residual). This is repeated for all n. LOCV would be undertaken when there are very few data to work with. When we can sacrifice a few data points, the random-hold back or k-fold cross-validation or a bootstapping procedure would be acceptable.

At the most basic level, LOCV involves the use of a looping function or for loop. Essentially they can be used to great effect when we want to perform a particular analysis over-and-over which was done above for the repeated random subsetting. For example with LOCV, for each iteration or loop we take a subset of n-1 rows and fit a model to them, then use that model to predict for the point left out of the calibration. Computationally it will look something like below.

looPred <- numeric(nrow(
for (i in 1:nrow( {
    looModel <- lm(CEC ~ clay, data =[-i, ], y = TRUE, x = TRUE)
    looPred[i] <- predict(looModel, newdata =[i, ])

The i here is the counter, so for each loop it increases by 1 until we get to the end of the data set. As you can see, we can index the using the i, meaning that for each loop we will have selected a different calibration set. On each loop, the prediction on the point left out of the calibration is made onto the corresponding row position of the looPred object. Again we can assess the performance of the LOCV using the goof function.

goof(predicted = looPred, observed =$CEC)

##          R2 concordance      MSE     RMSE        bias
## 1 0.4064735   0.5790589 14.47653 3.804804 0.005758669

LOCV will generally be less sensitive to outliers, so overall these external validation results are not too different to those when we performed the internal validation. Make a plot of the LOCV results to visually compare against the internal validation.

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Random holdback subsetting

We will do the random-back validation using 70% of the data for calibration. A random sample of the data will be performed using the sample function.

training <- sample(nrow(, 0.7 * nrow(, replace = FALSE)

##   [1]  14  50 118  43 146 144  90  91 139  92  99  72  26   7  78  81 143 103
##  [19] 117  76  15  32 106 109 133   9  41  74  23  27  60  53 122 115 128  96
##  [37]  38  89  34  93  69 135 127  63  13  82  97 138  25 110  21  79 120  47
##  [55] 140 116  16   6 105  86  87  39  31 131 145 108   4 102 124  98  52  22
##  [73] 125  77  35  40  30  12  84  70  64 142  29 119 114   3 141  54  58 126
##  [91] 111  37   8  51  10 101  42  44  83 104  75 132

These values correspond to row numbers which will correspond to the row which we will use for the calibration data. We subset these rows out of and fit a new linear model.

mod.rh <- lm(CEC ~ clay, data =[training, ], y = TRUE, x = TRUE)

So lets evaluate the calibration model with goof:

goof(predicted = mod.rh$fitted.values, observed =$CEC[training])

##          R2 concordance      MSE    RMSE bias
## 1 0.3658025   0.5304079 15.55893 3.94448    0

But we are more interested in how this model performs when we use the validation data. Here we use the predict function to predict upon this data.

mod.rh.V <- predict(mod.rh,[-training, ])
goof(predicted = mod.rh.V, observed =$CEC[-training])

##          R2 concordance      MSE     RMSE       bias
## 1 0.5395843   0.6613833 10.83252 3.291279 -0.1277488

A good indicator of model generalisation is that the validation statisitcs are near or better than that found for model calibration. Model overfitting is evident when there is a large discrepancy. Set the parameter to TRUE and re-run the script above and you will see a plot like the figure below.

Observed vs. predicted plot of CEC model (validation data set) with line of concordance (red line).

The mod.rh model does not appear to perform too bad after all. A few of the high observed values contribute greatly to the validation diagnostics. A couple of methods are available to assess the sensitivity of these results. The first is to remove what could potentially be outliers from the data. The second is to perform a sensitivity analysis which would include interating the data sub-setting procedure and evaluate the validation statistics each time to get a sense how much they vary.

In the example below this is demstrated by repeating the random subsetting 5 times. Note that you could do this many more times over. Notes also the removal of the set.seed function as we need to take a new subset for each iteration. This will also mean that the final results shown below and what you generate will be slightly different.

# repeated random subsetting place to store results
validation.outs <- matrix(NA, nrow = 5, ncol = 6)

# repeat subsetting and model fitting
for (i in 1:5) {
    training <- sample(nrow(, 0.7 * nrow(, replace = FALSE)
    mod.rh <- lm(CEC ~ clay, data =[training, ], y = TRUE, x = TRUE)
    mod.rh.V <- predict(mod.rh,[-training, ])
    validation.outs[i, 1] <- i
    validation.outs[i, 2:6] <- as.matrix(goof(predicted = mod.rh.V, observed =$CEC[-training]))

validation.outs <-
names(validation.outs) <- c("iteration", "R2", "concordance", "MSE", "RMSE", "bias")

# print outputs

##   iteration        R2 concordance       MSE     RMSE       bias
## 1         1 0.5403760   0.6735857 13.467984 3.669875  0.6320056
## 2         2 0.3707397   0.5200623 17.925045 4.233798 -1.1412116
## 3         3 0.6174596   0.7442635  5.734524 2.394687  0.2961916
## 4         4 0.4390767   0.5909275  9.697983 3.114159  0.2976717
## 5         5 0.2138451   0.4567065 21.587716 4.646258 -1.8841137

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K-fold cross-validation

As the name suggests, k-fold cross-validation is about creating a defined number of folds or subsets in the whole data, fitting the model with a given number of folds and then vlidating the model with the other remaining folds. For example if we impose four folds in the available data, we could fit the model with three of the folds and validate on the fourth one. In the example below the folds are randomly assigned to each case, but could be imposed differently for example with a sptatial clustering in order to implement a spatial cross-validation procedure. In the example below we repeat the four fold cross-validation 1000 times, in which case we would call the a repeated four fold cross-validation.

# Set up matrix to store goodness of fit statistics for each model 4 fold
# repeated 1000 times = 4000 models
validation.outs <- matrix(NA, nrow = 4000, ncol = 6)

# repeat subsetting and model fitting
cnt <- 1
for (j in 1:1000) {
    # set up folds
    folds <- rep(1:4, length.out = nrow(
    # random permutation
    rs <- sample(1:nrow(, replace = F)
    rs.folds <- folds[order(rs)]
    # model fitting for each combination of folds
    for (i in 1:4) {
        training <- which(rs.folds != i)
        mod.rh <- lm(CEC ~ clay, data =[training, ], y = TRUE, x = TRUE)
        mod.rh.V <- predict(mod.rh,[-training, ])
        validation.outs[cnt, 1] <- cnt
        validation.outs[cnt, 2:6] <- as.matrix(goof(predicted = mod.rh.V, observed =$CEC[-training]))
        cnt <- cnt + 1

validation.outs <-
names(validation.outs) <- c("iteration", "R2", "concordance", "MSE", "RMSE", "bias")

# averaged goodness of fit meansures
apply(validation.outs[, 2:6], 2, mean)

##           R2  concordance          MSE         RMSE         bias 
##  0.398731466  0.572562985 14.530480367  3.744664092  0.006906522

# standard deviation of goodness of fit meansures
apply(validation.outs[, 2:6], 2, sd)

##          R2 concordance         MSE        RMSE        bias 
##  0.12564317  0.09799382  5.54250995  0.71281009  0.72051238

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If the size of a data set has n number of cases, as described earlier, the bootstrap cross validation involves selecting n number of cases with replacement. However because sampling s done with replacment only about 63% of cases are selected. The other 37% can therefore be used as a validation dataset (Efron and Tibshirani 1997). Below we repeat the bootstrapping 4000 times and then estimate the avraged goodness of fit measures and the standard deviation of those too.

# Set up matrix to store goodness of fit statistics for each model of the 4000
# models
validation.outs <- matrix(NA, nrow = 4000, ncol = 6)

# repeat subsetting and model fitting repeat subsetting and model fitting
for (j in 1:4000) {
    # sample with replacement
    rs <- sample(1:nrow(, replace = T)
    # get the unique cases
    urs <- unique(rs)
    # calibration data
    cal.dat <-[urs, ]
    # validation data
    val.dat <-[-urs, ]
    # model fitting
    mod.rh <- lm(CEC ~ clay, data = cal.dat, y = TRUE, x = TRUE)
    mod.rh.V <- predict(mod.rh, val.dat)
    validation.outs[j, 1] <- cnt
    validation.outs[j, 2:6] <- as.matrix(goof(predicted = mod.rh.V, observed = val.dat$CEC))

validation.outs <-
names(validation.outs) <- c("iteration", "R2", "concordance", "MSE", "RMSE", "bias")

# averaged goodness of fit meansures
apply(validation.outs[, 2:6], 2, mean)

##           R2  concordance          MSE         RMSE         bias 
##  0.398374260  0.574861262 14.587025983  3.779626669  0.006002626

# standard deviation of goodness of fit meansures
apply(validation.outs[, 2:6], 2, sd)

##          R2 concordance         MSE        RMSE        bias 
##  0.09757849  0.07662582  4.19643649  0.54911165  0.64825113

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Brus, D., B. Kempen, and G.B.M. Heuvelink. 2011. “Sampling for Validation of Digital Soil Maps.” European Journal of Soil Science 62 (3): 394–407.

Efron, Bradley, and Robert Tibshirani. 1997. “Improvements on Cross-Validation: The .632+ Bootstrap Method.” Journal of the American Statistical Association 92 (438). [American Statistical Association, Taylor & Francis, Ltd.]: 548–60.

Gruijter, J. de, D.J. Brus, M.F.P. Bierkens, and M. Knotters. 2006. Sampling for Natural Resource Monitoring. Berlin Heidelberg: Springer-Verlag.

Hastie, T., R. Tibshirani, and J Friedman. 2001. The Elements of Statistical Learning. New York, NY: Springer.

Lovelace, R., J Nowosad, and J Muenchow. 2019. Geocomputation with R. Chapman; Hall/CRC.