Bootstrapping is a non-parametric approach for quantifying prediction uncertainties (Efron and Tibshirani 1993). Bootstrapping involves repeated random sampling with replacement of the available data. With the bootstrap sample, a model is fitted, and can then be applied to generate a digital soil map. By repeating the process of random sampling and applying the model, we are able to generate probability distributions of the prediction realizations from each model at each pixel. A robust estimate may be determined by taking the average of all the simulated predictions at each pixel. By being able to obtain probability distributions of the outcomes, one is also able to quantify the uncertainty of the modeling by computing a prediction interval given a specified level of confidence. While the bootstrapping approach is relatively straightforward, there is a requirement to generate x number of maps, where x is the number of bootstrap samples. This obviously could be prohibitive from a computational and data storage point of view, but not altogether impossible (given parallel processing capabilities etc.) as was demonstrated by both Viscarra Rossel et al. (2015) and Liddicoat et al. (2015) whom both performed bootstrapping for quantification of uncertainties across very large mapping extents. In the case of Viscarra Rossel et al. (2015) this for for the entire Australian continent at 100m resolution.

In the example below, the bootstrap method is demonstrated. We will be using Cubist modeling for the model structure and perform 50 bootstrap samples. We will do the bootstrap model using 70% of the available data. The remaining 30% will be used for an external validation and therefore independent of any model calibration function (although that a completely independent design-based sample).

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Defining the model parameters

For the first step, we do the random partitioning of the data into calibration and validation data sets. Again we are using the HV_subsoilpH data from the ithir package and the associated hunterCovariates_sub raster data stack.

## DATA Point data

## 'data.frame':    506 obs. of  14 variables:
##  $ X                       : num  340386 340345 340559 340483 340734 ...
##  $ Y                       : num  6368690 6368491 6369168 6368740 6368964 ...
##  $ pH60_100cm              : num  4.47 5.42 6.26 8.03 8.86 ...
##  $ Terrain_Ruggedness_Index: num  1.34 1.42 1.64 1.04 1.27 ...
##  $ AACN                    : num  1.619 0.281 2.301 1.74 3.114 ...
##  $ Landsat_Band1           : int  57 47 59 52 62 53 47 52 53 63 ...
##  $ Elevation               : num  103.1 103.7 99.9 101.9 99.8 ...
##  $ Hillshading             : num  1.849 1.428 0.934 1.517 1.652 ...
##  $ Light_insolation        : num  1689 1701 1722 1688 1735 ...
##  $ Mid_Slope_Positon       : num  0.876 0.914 0.844 0.848 0.833 ...
##  $ MRVBF                   : num  3.85 3.31 3.66 3.92 3.89 ...
##  $ NDVI                    : num  -0.143 -0.386 -0.197 -0.14 -0.15 ...
##  $ TWI                     : num  17.5 18.2 18.8 18 17.8 ...
##  $ Slope                   : num  1.79 1.42 1.01 1.49 1.83 ...

# Raster data

## class      : RasterStack 
## dimensions : 249, 210, 52290, 11  (nrow, ncol, ncell, nlayers)
## resolution : 25, 25  (x, y)
## extent     : 338422.3, 343672.3, 6364203, 6370428  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=56 +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs 
## names      : Terrain_Ruggedness_Index,        AACN, Landsat_Band1,   Elevation, Hillshading, Light_insolation, Mid_Slope_Positon,       MRVBF,        NDVI,         TWI,       Slope 
## min values :                 0.194371,    0.000000,     26.000000,   72.217499,    0.000677,      1236.662840,          0.000009,    0.000002,   -0.573034,    8.224325,    0.001708 
## max values :                15.945321,  106.665482,    140.000000,  212.632507,   32.440960,      1934.199950,          0.956529,    4.581594,    0.466667,   20.393652,   21.809752

# subset data for modeling
training <- sample(nrow(HV_subsoilpH), 0.7 * nrow(HV_subsoilpH))
cDat <- HV_subsoilpH[training, ]
vDat <- HV_subsoilpH[-training, ]

The nbag variable below holds the value for the number of bootstrap models we want to fit. Here it is 50. Essentially the bootstrap can can be contained within a for loop, where upon each loop a sample of the available data is taken of n size where the n is the same number of cases in the model frame. This sampling is done with replacment however, which works out to be about 66% of the model frame in terms of unique cases. The other 34% of the data are used to assess the model goodness of fit for each bootstrap iteration in terms of out-of-bag assessment, which just means data not included in the model. Note below the use of the replace parameter to indicate we want random sample with replacement. After a model is fitted, we save the model to file and will come back to it later. The modelFile variable shows the extensive use of the paste0 function in order to provide the pathway and file name for the model that we want to save on each loop iteration. The saveRDS function allows us to save each of the model objects as rds files to the location specified. An alternative to save the models individually to file is to save them to elements within a list. When dealing with very large numbers of models and additionally are complex in terms of their parameterizations, the save to list elements alternative could run into computer memory limitation issues. The last section of the script below just demonstrates the use of the list.files functions to confirm that we have saved those models to file and they are ready to use.

# Number of bootstraps
nbag <- 50

# Fit cubist models for each bootstrap
for (i in 1:nbag) {
    # sample with replacement
    trainingREP <-, replace = TRUE)
    # unique cases
    trainingREP <- unique(trainingREP)
    fit_cubist <- cubist(x = cDat[trainingREP, c("Terrain_Ruggedness_Index", "AACN", "Landsat_Band1", 
        "Elevation", "Hillshading", "Light_insolation", "Mid_Slope_Positon", "MRVBF", "NDVI", "TWI", 
        "Slope")], y = cDat$pH60_100cm[trainingREP], cubistControl(rules = 5, extrapolation = 5), committees = 1)
    ### Note you will likely have different file path names ###
    modelFile <- paste0(root, "models/bootMod_", i, ".rds")
    saveRDS(object = fit_cubist, file = modelFile)

# list all files in directory Note you will likely have different file path names ###
c.models <- list.files(path = paste0(root, "models/"), pattern = "\\.rds$", full.names = TRUE)

## [1] "~/uncertaintyanalysis/bootstrap/models//bootMod_1.rds" 
## [2] "~/uncertaintyanalysis/bootstrap/models//bootMod_10.rds"
## [3] "~/uncertaintyanalysis/bootstrap/models//bootMod_11.rds"
## [4] "~/uncertaintyanalysis/bootstrap/models//bootMod_12.rds"
## [5] "~/uncertaintyanalysis/bootstrap/models//bootMod_13.rds"
## [6] "~/uncertaintyanalysis/bootstrap/models//bootMod_14.rds"

We can then assess the goodness of fit and validation statistics of the bootstrap models. This is done using the goof function as in previous examples. This time we incorporate that function within a for loop. For each loop, we read in the model via the radRDS function and then save the diagnostics to the cubiMat matrix object. After the iterations are completed, we use the colMeans function to calculate the means of the diagnostics over the 50 model iterations. You could also assess the variance of those means by a command such as var(cubiDat[,1]), which would return the variance of the R2 values.

# calibration data
cubiMat <- matrix(NA, nrow = nbag, ncol = 5)
for (i in 1:nbag) {
    fit_cubist <- readRDS(c.models[i])
    cubiMat[i, ] <- as.matrix(goof(observed = cDat$pH60_100cm, predicted = predict(fit_cubist, newdata = cDat)))
cubiDat <-
names(cubiDat) <- c("R2", "concordance", "MSE", "RMSE", "bias")

##          R2 concordance         MSE        RMSE        bias 
##   0.2317208   0.3966600   1.4491123   1.2036320  -0.1056484

# Validation data
cubPred.V <- matrix(NA, ncol = nbag, nrow = nrow(vDat))
cubiMat <- matrix(NA, nrow = nbag, ncol = 5)
for (i in 1:nbag) {
    fit_cubist <- readRDS(c.models[i])
    cubPred.V[, i] <- predict(fit_cubist, newdata = vDat)
    cubiMat[i, ] <- as.matrix(goof(observed = vDat$pH60_100cm, predicted = predict(fit_cubist, newdata = vDat)))
cubPred.V_mean <- rowMeans(cubPred.V)

cubiDat <-
names(cubiDat) <- c("R2", "concordance", "MSE", "RMSE", "bias")

##          R2 concordance         MSE        RMSE        bias 
##  0.13384917  0.35375158  1.37133056  1.17048402  0.06049847

# Average validation MSE
avGMSE <- mean(cubiDat[, 3])

## [1] 1.371331

For the validation data, in addition to deriving the model diagnostic statistics, we are also saving the actual model predictions for these data for each iteration to the cubPred.V object. These will be used further on for validating the prediction uncertainties.

The last line of the script above saves the mean of the mean square error (MSE) estimates from the validation data. The independent MSE estimator, accounts for both systematic and random errors in the modeling. This estimate of the MSE is needed for quantifying the uncertainties, as this error is in addition to that which are accounted for by the bootstrap model, which are specifically those associated with the deterministic model component i.e. the model relationship between target variable and the covariates. Subsequently an overall prediction variance (at each point or pixel) will be the sum of the random error component (MSE) and the bootstrap prediction variance (as estimated from the mean of the realisations from the bootstrap modeling).

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Spatial mapping

Our initial purpose here is to derive the mean and the variance of the predictions from each bootstrap sample. This requires loading in each bootstrap model, applying into the covariate data, then saving the predicted map to file or R memory. In the case below the predictions are saved to file. This is illustrated in the following script:

### Note you will likely have different file path names ###
for (i in 1:nbag){
  fit_cubist<- readRDS(c.models[i])
  mapFile<- paste0(root,"maps/bootMap_", i, ".tif")
  predict(hunterCovariates_sub, # raster stack 
          fit_cubist, # model
          filename=mapFile, # export file
          format="GTiff", # file format

To evaluate the mean at each pixel from each of the created maps, the base function mean can be applied to a given stack of rasters. First we need to get the path location of the rasters. Notice from the list.files function and the pattern parameter, we are restricting the search of rasters that contain the string “bootMap”. Next we make a stack of those rasters, followed by the calculation of the mean, which is also written directly to file.

# Pathway to rasters Note you will likely have different file path names ###
files <- list.files(path = paste0(root, "maps/"), pattern = "bootMap", full.names = TRUE)

# Raster stack
r1 <- raster(files[1])
for (i in 2:length(files)) {
    r1 <- stack(r1, files[i])

# Calculate mean
meanFile <- paste0(root, "maps/meanPred.tif")
bootMap.mean <- writeRaster(mean(r1), filename = meanFile, format = "GTiff", overwrite = TRUE)

There is not a simple R function to use in order to estimate the variance at each pixel from the prediction maps. Therefore we resort to estimating it directly from the standard equation:

$Var(X) = \frac{1}{1-n}\sum_{i=1}^{n}(x_{i}-\mu)^{2}$

The symbol μ in this case is the mean bootstrap prediction, and xi is the ith bootstrap map. In the first step below, we estimate the square differences and save the maps to file. Then we calculate the sum of those squared differences, before deriving the variance prediction. The last step is to add the variance of the bootstrap predictions to the averaged MSE estimated from the validation data.

# Square differences
for (i in 1:length(files)) {
    r1 <- raster(files[i])
    diffFile <- paste0(root, "maps/bootAbsDif_", i, ".tif")
    jj <- (r1 - bootMap.mean)^2
    writeRaster(jj, filename = diffFile, format = "GTiff", overwrite = TRUE)

# calculate the sum of square differences Look for files with the bootAbsDif character string in file
# name
files2 <- list.files(path = paste0(root, "maps/"), pattern = "bootAbsDif", full.names = TRUE)
# stack
r2 <- raster(files2[1])
for (i in 2:length(files2)) {
    r2 <- stack(r1, files2[i])

sqDiffFile <- paste0(root, "maps/sqDiffPred.tif")
bootMap.sqDiff <- writeRaster(sum(r2), filename = sqDiffFile, format = "GTiff", overwrite = TRUE)

# Variance
varFile <- paste0(root, "maps/varPred.tif")
bootMap.var <- writeRaster(((1/(nbag - 1)) * bootMap.sqDiff), filename = varFile, format = "GTiff", overwrite = TRUE)

# Overall prediction variance
varFile2 <- paste0(root, "maps/varPredF.tif")
bootMap.varF <- writeRaster((bootMap.var + avGMSE), filename = varFile, format = "GTiff", overwrite = TRUE)

To derive to 90% prediction interval we take the square root of the variance estimate and multiply that value by the quantile function value that corresponds to a 90% probability. The z value is obtained using the qnorm function. The result is then either added or subtracted to the mean prediction in order to generate the upper and lower prediction limits respectively.

# Standard deviation
sdFile <- paste0(root, "maps/sdPred.tif") <- writeRaster(sqrt(bootMap.varF), filename = sdFile, format = "GTiff", overwrite = TRUE)

# standard error
seFile <- paste0(root, "maps/sePred.tif") <- writeRaster(( * qnorm(0.95)), filename = seFile, format = "GTiff", overwrite = TRUE)

# upper prediction limit
uplFile <- paste0(root, "maps/uplPred.tif")
bootMap.upl <- writeRaster((bootMap.mean +, filename = uplFile, format = "GTiff", overwrite = TRUE)

# lower prediction limit
lplFile <- paste0(root, "maps/lplPred.tif") <- writeRaster((bootMap.mean -, filename = lplFile, format = "GTiff", overwrite = TRUE)

# prediction interval range
pirFile <- paste0(root, "maps/pirPred.tif")
bootMap.pir <- writeRaster((bootMap.upl -, filename = pirFile, format = "GTiff", overwrite = TRUE)

As for the Universal kriging example, we can plot the associated maps of the predictions and quantified uncertainties (Figure below)

phCramp <- c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#ffffbf", "#e6f598", "#abdda4", "#66c2a5", 
    "#3288bd", "#5e4fa2", "#542788", "#2d004b")
brk <- c(2:14)
par(mfrow = c(2, 2))
plot(, main = "90% Lower prediction limit", breaks = brk, col = phCramp)
plot(bootMap.mean, main = "Prediction", breaks = brk, col = phCramp)
plot(bootMap.upl, main = "90% Upper prediction limit", breaks = brk, col = phCramp)
plot(bootMap.pir, main = "Prediction limit range", col = terrain.colors(length(seq(0, 6.5, by = 1)) - 
    1), axes = FALSE, breaks = seq(0, 6.5, by = 1))
Soil pH predictions and prediction limits derived using bootstrapping.

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Validating the quantification of uncertainty

You will recall the bootstrap model predictions on the validation data were saved to the cubPred.V object. We want estimate the standard deviation of those predictions for each point. Also recall that the prediction variance is the sum of the MSE and the bootstrap models prediction variance. Taking the square root of that summation results in standard deviation estimate. <- matrix(NA, ncol = 1, nrow = nrow(cubPred.V))
for (i in 1:nrow(cubPred.V)) {[i, 1] <- sqrt(var(cubPred.V[i, ]) + avGMSE)}

We then need to multiply the standard deviation by the corresponding percentile of the standard normal distribution in order to express the prediction limits at each level of confidence. Note the use of the for loop and the associated cycling through of the different percentile values.

# Percentiles of normal distribution
qp <- qnorm(c(0.995, 0.9875, 0.975, 0.95, 0.9, 0.8, 0.7, 0.6, 0.55, 0.525))

# zfactor multiplication
vMat <- matrix(NA, nrow = nrow(cubPred.V), ncol = length(qp))
for (i in 1:length(qp)) {
    vMat[, i] <- * qp[i]}

Now we add or subtract the limits to/from the averaged model predictions to derive to prediction limits for each level of confidence.

# upper prediction limit
uMat <- matrix(NA, nrow = nrow(cubPred.V), ncol = length(qp))
for (i in 1:length(qp)) {
    uMat[, i] <- cubPred.V_mean + vMat[, i]}

# lower prediction limit
lMat <- matrix(NA, nrow = nrow(cubPred.V), ncol = length(qp))
for (i in 1:length(qp)) {
    lMat[, i] <- cubPred.V_mean - vMat[, i]}

Now we assess the PICP for each level confidence. Recalling that we are simply assessing whether the observed value is encapsulated by the corresponding prediction limits, then calculating the proportion of agreement to total number of observations.

bMat <- matrix(NA, nrow = nrow(cubPred.V), ncol = length(qp))
for (i in 1:ncol(bMat)) {
    bMat[, i] <- as.numeric(vDat$pH60_100cm <= uMat[, i] & vDat$pH60_100cm >= lMat[, i])


##  [1] 0.98684211 0.97368421 0.96710526 0.92105263 0.84868421 0.63157895 0.43421053 0.20394737
##  [9] 0.10526316 0.05263158

As can be seen on the Figure below that there is an indication that the prediction uncertainties could be a little too liberally defined, where particularly at the higher level of confidence the associated PICP is higher.

# make plot
cs <- c(99, 97.5, 95, 90, 80, 60, 40, 20, 10, 5)  # confidence level
plot(cs, ((colSums(bMat)/nrow(bMat)) * 100))
Plot of PICP and confidence level based on validation of bootstrapping model.

Quantiles of the distribution of the prediction limit range are express below for the validation data (in terms of the 90% level of confidence). Compared to the universal kriging approach, the uncertainties quantified from the bootstrapping approach are higher in general.

cs <- c(99, 97.5, 95, 90, 80, 60, 40, 20, 10, 5)  # confidence level
colnames(lMat) <- cs
colnames(uMat) <- cs
quantile(uMat[, "90"] - lMat[, "90"])

##       0%      25%      50%      75%     100% 
## 3.871776 3.905907 3.930499 3.985198 4.300110

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Efron, B., and R. Tibshirani. 1993. An Introduction to the Bootstrap. London: Chapman; Hall.

Liddicoat, C., D. Maschmedt, D. Clifford, R. Searle, T. Herrmann, L.M. Macdonald, and J. Baldock. 2015. “Predictive Mapping of Soil Organic Carbon Stocks in South Australia’s Agricultural Zone.” Soil Research 53: 956–73.

Viscarra Rossel, R. A., C. Chen, M. J. Grundy, R. Searle, D. Clifford, and P. H. Campbell. 2015. “The Australian Three-Dimensional Soil Grid: Australia’s Contribution to the Globalsoilmap Project.” Soil Research 53: 845–64.

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