# Chapter 26 Regression when order matters: time series and spatial analysis

**Caution: in a highly developmental stage! See Section 1.1.**

## 26.1 Timeseries in (base) R

**To add**: `times()`

function to extract times from a `ts`

object. How to deal with the `start`

and `end`

arguments when declaring a `ts`

object. `tsibble`

.

This tutorial demonstrates `timeseries`

objects, and `stl`

decomposition.

Let’s make a periodic time series with a trend. The data can be contained in a vector:

It’s sometimes useful to make an object of type `timeseries`

. Do this with the `ts`

function in R. But, if there’s a cycle, we’ll need to indicate that in the `frequency`

argument, which is the number of observations per cycle. In this case, the period is 10.

```
## Time Series:
## Start = c(1, 1)
## End = c(20, 10)
## Frequency = 10
## [1] 105.93679 107.12367 107.10450 104.66889 101.85148 97.61401 98.46156
## [8] 98.37796 100.35422 101.31077 104.20251 107.12367 107.35454 108.17249
## [15] 104.58569 99.93998 99.41484 98.78727 102.06393 104.26951 107.77953
## [22] 107.61305 108.58878 107.26835 103.81317 100.60471 99.05478 100.83379
## [29] 102.31413 106.78493 108.35218 111.43316 110.53450 107.48284 105.20787
## [36] 102.36861 102.90923 102.29884 102.04310 106.99488 109.56236 109.47351
## [43] 111.86283 110.11625 106.04651 104.89458 102.97361 103.51116 103.88555
## [50] 106.77552 110.05782 111.73304 112.19821 110.22224 107.43132 103.74206
## [57] 103.04374 101.88210 104.16089 106.65454 110.16285 111.98773 112.89999
## [64] 110.55865 108.82549 103.84735 102.65400 104.63884 106.36905 109.09792
## [71] 109.25739 111.82278 113.48571 113.71421 109.06650 104.65420 104.57343
## [78] 103.96079 106.88677 109.23745 110.99903 113.94000 111.67382 112.83227
## [85] 110.77783 105.14801 105.96306 104.36689 106.38130 109.86790 109.89273
## [92] 115.93013 114.69626 111.38653 110.50749 106.24579 106.04142 105.64524
## [99] 106.51362 109.62323 113.02246 113.70224 115.17406 111.31060 110.40324
## [106] 106.83859 104.87913 106.15255 107.91620 111.07210 112.59874 115.70867
## [113] 115.12706 113.38885 109.80335 106.91143 106.79363 104.91440 108.19079
## [120] 109.75533 114.16076 116.23749 115.52902 114.25332 113.49286 108.84858
## [127] 106.60085 104.79073 107.15469 112.07755 115.51986 116.07387 115.83152
## [134] 114.31633 112.16524 108.22144 108.08308 108.53625 109.20326 111.05525
## [141] 114.98826 117.98996 115.87140 115.48505 111.20255 108.80626 107.72208
## [148] 109.10148 109.75413 110.98433 115.50723 117.10874 117.70151 114.99943
## [155] 111.29091 110.15535 107.90633 106.17662 109.27254 113.65775 116.22425
## [162] 116.70013 118.34150 114.64064 111.65284 111.28042 109.51509 108.46959
## [169] 110.05594 112.24702 114.36949 116.69164 117.93398 115.61049 113.24120
## [176] 110.02442 108.65953 109.44109 109.06466 113.95856 115.90800 119.01536
## [183] 117.19717 116.98781 113.28864 111.66947 107.19884 109.24039 111.48258
## [190] 115.28813 117.09972 118.27995 118.30307 115.79655 112.96102 111.05837
## [197] 111.71501 109.21837 111.98797 113.29548
```

You can plot this object too. You’ll get a nice looking time series plot:

And now you can decompose the trend, seasonal component, and error terms with `stl`

. Note that `stl`

requires a `timeseries`

object! Be sure to put `s.window="periodic"`

in the `stl`

function to use the periodicity of the `timeseries`

object. Notice that there are options to change the bandwidths of the loess estimation, along with the degree of the local polynomial, with the `_.degree`

and `_.window`

arguments.

The estimates are contained in the `$time.series`

part of the output:

```
## seasonal trend remainder
## [1,] 2.65649907 102.3882 0.8921169
## [2,] 4.59964293 102.3618 0.1621814
## [3,] 4.62447521 102.3355 0.1445065
## [4,] 2.86001250 102.3233 -0.5144098
## [5,] 0.02560504 102.3111 -0.4851789
## [6,] -3.26586085 102.3072 -1.4272865
```

## 26.2 Spatial Example

Rocks were sampled at 54 sites along the river within a period of two days. The procedure to take one sample involves taking an underwater photo at a glide site along the river, and by using computer software, obtaining the lengths of the intermediate axes of each rock over 8mm in the photo area. To ensure accuracy of measurements, 25 photos of the same area are taken and combined.

## 26.3 A Model for River Rock Size

The variable \(x\) refers to a location of some distance downstream the river for example, distance downstream the Meadows campground. For the 54 sample sites, the locations are denoted by \(x_{1},\ldots,x_{54}\). There are three concepts related to rock size.

### 26.3.1 1. Average rock size:

This is the average size of sampled rocks at a location, had that location been sampled. For location \(x\), the value of the sample average rock size is denoted \(m\left(x\right)\). These values are known for 54 sample sites, which are denoted for brevity as \(m_{1},\ldots,m_{54}\), represented as the dots in Figure

### 26.3.2 2. Mean rock size:

At a particular river location, this can be thought of as the average rock size in the bed load in the hypothetical situation where the river flows forever in the same condition as during the sampling period. At any location, this quantity is unknown, and will thus be referred to as a “mean” instead of an “average” (an average is a known calculable quantity). At a location \(x\), the mean rock size will be denoted \(M\left(x\right)\), and is represented by the solid line in Figure

### 26.3.3 3. Downstream fining curve:

This can be thought of as an “overall trend” for rock size from upstream to downstream. The mean rock size \(M\left(x\right)\) should “on average” follow this curve. For location \(x\), the value of the downstream fining curve will be denoted \(T\left(x\right)\), represented as the dashed line in Figure . At any location, this is an unknown quantity.

## 26.4 Statistical Objectives

Using the three concepts of rock size in Section, the following statistical objectives can be pursued to address the scientific objectives:

Estimate the downstream fining curve \(T\left(x\right)\) (i.e. the dashed line in Figure) for the range of the study area;

Estimate the mean rock size \(M\left(x\right)\) (i.e. the solid line in Figure) for the range of the study area, along with confidence bands

These objectives are addressed in Sections, but first some preliminaries are needed, discussed in Section.

### 26.4.1 Preliminaries: Variance and Correlation

Most of the techniques introduced in Sections require three descriptions of rock size, which are defined in Section. The procedure for fitting these descriptions to the data is discussed in Section . For a detailed review of these concepts, see Chapter 2 in reference @Geostatistics, for example.

## 26.5 Three Concepts

### 26.5.1 Error Variance \(\sigma_{E}^{2}\left(x\right)\)

At location \(x\), the error variance is the variability of the average rock size \(m\left(x\right)\) in comparison to the true mean rock size \(M\left(x\right)\). In other words, it is the variance of \(\left(m\left(x\right)-M\left(x\right)\right)\).

### 26.5.2 Mean Variance \(\sigma_{M}^{2}\)

At location \(x\), the mean variance is the variability of the true mean \(M\left(x\right)\) around the downstream fining curve \(T\left(x\right)\) (i.e. how “tightly” \(M\left(x\right)\) follows \(T\left(x\right)\)). It may be reasonable to assume that this variance is constant, unless there is evidence that this variability changes significantly throughout the study area. In what follows we will make this assumption; denote the variance of the true mean rock size as \(\sigma_{M}^{2}\).

### 26.5.3 Mean Correlation \(\rho\left(d\right)\)

The mean correlation measures how closely related two mean rock sizes
are at different locations (and equivalently, the average rock sizes at
those locations). The mean rock sizes at two locations immediately next
to each other are expected to be closely related, whereas mean rock
sizes at two locations that are far apart may not be related. A key
assumption used in Sections is *isotropy*, that is, the correlation between mean rock sizes
at two sites only depends on the distance between the sites, and not on
the actual locations along the river. This assumption is of course not
exactly true. For example, the relationship between rocks at sites
upstream and downstream the dam should be different than the
relationship between two equally-spaced sites located without the dam
between them. However, the assumption of isotropy should be viewed as an
approximation to reality.

If \(d\) is the distance between two sites, then \(\rho\left(d\right)\) denotes the correlation between mean rock sizes at those sites. In technical terms, \(\rho\left(d\right)=\text{Corr}\left(M\left(x\right),M\left(x+d\right)\right)\) for all \(x\), \(x+d\) in the study area.

## 26.6 Estimation

The quantities introduced in Section \(\sigma_{E}^{2}\left(x\right)\), \(\sigma_{M}^{2}\), and \(\rho\left(d\right)\) are unknown and need to be estimated, because most of the techniques in Sections use them. There is a relatively simple way to do this estimation in the case that the error variance, \(\sigma_{E}^{2}\left(x\right)\), is constant, which is discussed in Section . To account for the changing error variance, an estimation technique is suggested in Section, which may require some manual computations depending on the capabilities of the software you end up using.

### 26.6.1 Constant Error Variance

Although the error variance \(\sigma_{E}^{2}\left(x\right)\) is influenced by sample size \(n\left(x\right)\) and distribution variance \(\sigma^{2}\left(x\right)\), approximating the error variance as a constant should be acceptable if the error variances are small compared to the mean variance, \(\sigma_{M}^{2}\). Then, any differences in \(\sigma_{E}^{2}\left(x\right)\) amongst different locations would be minuscule relative to \(\sigma_{M}^{2}\). Because your sample sizes are quite large (at least 100), the assumption of small error variances might be reasonable. However, if that assumption is not close to the truth, then the error bars described in Section would be overly wide at locations where the error variance is small (and mean rock size is small), and vice-versa. Since the error variance is assumed constant here, we will denote it \(\sigma_{E}^{2}\), without the \(\left(x\right)\), since it is assumed not to change with \(x\).

One tool that incorporates all the quantities in Section
is the *variogram*, often
denoted by \(\gamma\left(d\right)\). For locations separated by a distance
of \(d\), it is defined as half the variance of the difference of the
sample averages at those sites. Using symbols, the variogram is defined
as
\[\gamma\left(d\right)=\frac{1}{2}\text{Var}\left\{ m\left(x\right)-m\left(x+d\right)\right\} ,\]
where “Var” means “variance of”. Working out the math, the variogram can
be expressed as
\[\gamma\left(d\right)=\sigma_{M}^{2}\left[1-\rho\left(d\right)\right]+\sigma_{E}^{2},\label{eq:Variogram}\]
which contains all the quantities we need to estimate. Thus, estimating
\(\sigma_{E}^{2}\), \(\sigma_{M}^{2}\), and \(\rho\left(d\right)\) amounts to
estimating the variogram. A plot of the variogram against \(d\) might look
something like that in Figure . The bottom dashed line
is called a *nugget*, and the top dashed line is called a *sill*. The
nugget equals \(\sigma_{E}^{2}\) and the difference between the sill and
the nugget equals \(\sigma_{M}^{2}\).

To estimate a variogram, an *empirical variogram* is typically used (a
“data version” of the variogram). An empirical variogram can be viewed
as a scatterplot, where one point is plotted for each pair of sample
sites. In particular, for two different site numbers \(i\) and \(j\), a
point is plotted with a vertical value of
\(0.5\left(m_{i}-m_{j}\right)^{2}\) and a horizontal value of
\(\left|x_{i}-x_{j}\right|\), where \(\left|\cdot\right|\) refers to the
absolute value. Then nonlinear regression is used to fit a variogram
model to the data in this scatterplot.

In R, you will need to specify the “model type”. The model type refers to the form of the mean correlation, \(\rho\left(d\right)\). In Figure , the form is exponential, but there are many other forms one can choose, including gaussian, matern, or spherical (see Section 2.5 in Reference @Geostatistics for a discussion of these models). There is no such thing as a “correct” model, but some may be good approximations to the truth. You can assess whether a model is a good approximation by visually checking whether a plot of the fitted variogram is “close” to the empirical (data) variogram. For a more formal assessment, you can choose the model that results in the smallest Akaike Information Criterion (AIC) value, which quantifies a compromise between model simplicity and goodness-of-fit. Yet another option is to assess the residual plot, where a “residual” here is the difference between a point on the empirical variogram and the theoretical variogram. Although this residual plot is slightly different from “traditional” residual plots (since the points are not independent), ensuring the residuals are roughly centered around zero is still useful for assessing the model fit (you were wondering where an “analysis of residuals” could be used in your analysis this is one place). If several models seem to be a good approximation, then it is best to choose the simplest one (the exponential form is one of the simplest, and most popular).

### 26.6.2 Non-Constant Error Variance

If you fit a variogram model to your data as discussed in Section , and you find that the nugget is fairly large in comparison to the sill, then it is probably not realistic to approximate the error variance by a constant. This is because the fluctuations in the error variance \(\sigma_{E}^{2}\left(x\right)\) (which we know exist) would no longer be small relative to the mean variance \(\sigma_{M}^{2}\).

In the case that you would like to account for the differences in error variance, the variogram then depends on the actual locations being compared, as opposed to just the distance between the locations. For sites \(x\) and \(y\), the variogram can be expressed as

\[\gamma\left(x,y\right)=\sigma_{M}^{2}\left[1-\rho\left(\left|x-y\right|\right)\right]+\frac{1}{2}\left[\sigma_{E}^{2}\left(x\right)+\sigma_{E}^{2}\left(y\right)\right].\]

Compared to Equation , the term on the right is no longer constant. This means that each point in the empirical variogram (as described at the end of Section ) would have a different nugget and sill; in other words, they do not come from a common variogram. As such, direct regression cannot be used to estimate the parameters. However, if we knew the values of \(\sigma_{E}^{2}\left(x\right)\) at the sample sites, then you could follow these steps to estimate \(\sigma_{M}^{2}\) and \(\rho\left(d\right)\):

For all (different) pairs of sites \(i\) and \(j\), create a modified empirical variogram with dependent variable \(0.5\left(m_{i}-m_{j}\right)^{2}-0.5\left[\sigma_{E}^{2}\left(x_{i}\right)-\sigma_{E}^{2}\left(x_{j}\right)\right]\) and independent variable \(\left|x_{i}-x_{j}\right|\) ;

Choose a form for \(\rho\left(d\right)\) this could be exponential, gaussian, etc. (see Section 2.5 in Reference @Geostatistics for a discussion of these models);

Fit a nonlinear regression model using the mean structure \(\gamma\left(d\right)=\sigma_{M}^{2}\left[1-\rho\left(d\right)\right]\) (see Reference @Nonlinear [@Regression] for details on nonlinear regression);

Nonlinear regression will provide estimates for \(\sigma_{M}^{2}\) and \(\rho\left(d\right)\). As before, an analysis of residuals would be useful here to assess the goodness-of-fit of the regression.

It only remains to estimate \(\sigma_{E}^{2}\left(x\right)\) at the sample sites. The simplest case is to use the sample variance of the individual rock sizes at a site, then dividing by the total number of rocks counted in that sample to get the estimate.

One might believe that the variances of rock size at two sites with equal means are approximately equal. If this belief is close to the truth, then there is a more efficient way to estimate the error variances by “pooling” all of the data. This can be done by simple regression of the sample variances of the individual rock sizes (computed by the excel functions or ) against the sample averages. Then \(\sigma_{E}^{2}\left(x\right)\) can be estimated as the value of the regression curve evaluated at \(x\), then dividing by the sample size at that site.

## 26.7 Statistical Objective 1: Downstream Fining Curve

regression of \(m_{1},\ldots,m_{54}\) against \(x_{1},\ldots,x_{54}\). there are two other things you might want to change in the regression.

### 26.7.1 Regression Form

there might be a theoretical form that more accurately describes the curve, such as an exponential model: \[T\left(x\right)=ae^{-bx}.\] If such a theoretical model exists, you may need to consider nonlinear regression, as discussed in Reference @Nonlinear [@Regression]. If there is no theoretical form, then using a straight line here is probably a reasonable choice as a rough approximation.

## 26.8 Statistical Objective 2: River Profile

The goal of this section is to estimate the mean rock size \(M\left(x\right)\) at a location \(x\) along the study site of the river. Doing this for all locations in the study site, you can obtain a “river profile” of mean rock size.

*Kriging* is a method of estimating mean rock size \(M\left(x\right)\)
that takes the variances and correlation of the data into consideration.
In the case when there is no error variance, Kriging will smoothly
“connect the dots” of your data \(m_{1},\ldots,m_{54}\). There are many
types of Kriging, but there are two that are directly relevant for this
project Simple Kriging and Universal Kriging. These are discussed in
Sections and when
approximating the error variance \(\sigma_{E}^{2}\left(x\right)\) by a
constant, and in Section when allowing
the error variance to be non-constant. For a summary of many types of
Kriging, see Reference @arcGIS_Kriging. For an extensive overview of
Kriging, see Chapter 3 in Reference @Geostatistics.

The idea behind ** Kriging is to estimate mean rock size \(M\left(x\right)\) by using a “weighted average” of \(m_{1},\ldots,m_{54}\). That is, instead of using the regular average \(\frac{1}{54}m_{1}+\cdots+\frac{1}{54}m_{54}\), Kriging uses \(w_{1}\left(x\right)m_{1}+\cdots+w_{54}\left(x\right)m_{54}\) to estimate \(M\left(x\right)\), where the weights \(w_{1}\left(x\right),\ldots,w_{54}\left(x\right)\) are nonnegative numbers adding to 1 that depend on the location \(x\). The weights are chosen to minimize variability in the estimate (technically, the “mean squared error”).

### 26.8.1 Simple Kriging

Simple Kriging is Kriging when the downstream fining curve, \(T\left(x\right)\), is known. Of course, the downstream fining curve is unknown, but you could get an estimate of it, as discussed in Section . Regarding the implementation of Kriging, if you are not prompted to specify \(T\left(x\right)\), then whichever software you use will likely assume \(T\left(x\right)=0\). In this case, you would need to adjust for this by subtracting \(T\left(x_{1}\right),\ldots,T\left(x_{54}\right)\) from your data \(m_{1},\ldots,m_{54}\) before running the Kriging procedure, then add \(T\left(x\right)\) to your result.

### 26.8.2 Universal Kriging

Universal Kriging is Kriging when the downstream fining curve, \(T\left(x\right)\), is a straight line. In this case, there is no need to estimate \(T\left(x\right)\) separately as in Section the estimation is “built-in” to the procedure. It is best to use Universal Kriging if you choose \(T\left(x\right)\) to be linear and if you do not estimate \(T\left(x\right)\) using GLS.

### 26.8.3 Kriging under Non-Constant Error Variance

To implement Kriging, it is likely that most software would use the variogram estimate in place of the estimates of \(\sigma_{E}^{2}\left(x\right)\), \(\sigma_{M}^{2}\), and \(\rho\left(d\right)\). However, if you want to account for \(\sigma_{E}^{2}\left(x\right)\) being different for different locations, then the Kriging procedure would need to use the individual estimates of \(\sigma_{E}^{2}\left(x\right)\), \(\sigma_{M}^{2}\), and \(\rho\left(d\right)\) instead of the variogram itself (recall Section for estimating these quantities under the non-constant error variance consideration). See Chapter 3 in Reference @Geostatistics to find formulas for the Kriging estimator.

## 26.9 Confidence Intervals of the River Profile

Various software that have Kriging capabilities most likely also have the ability to construct confidence intervals when prompted. A confidence interval at a location \(x\) is an interval that covers the true mean rock size, \(M\left(x\right)\), with approximately some pre-specified chance (such as 95%). When the intervals are plotted over a range of locations, they form a confidence “band” around the river profile.

However, be aware that the confidence intervals in the case of Simple Kriging will be “too narrow”, unless the estimate of the downstream fining curve is quite precise. A confidence interval that is “too narrow” has the implication that the amount of confidence you claim (such as 95%) is actually more than the actual confidence level. This is because the Simple Kriging procedure treats the downstream fining curve \(T\left(x\right)\) as known, and does not incorporate the uncertainty involved in estimating \(T\left(x\right)\).

It is likely that the confidence intervals provided by the software are based on the assumption that the data are normally distributed. Since your data \(m_{1},\ldots,m_{54}\) are averages, and there are many rocks in each site, this assumption of normality is probably reasonable due to the Central Limit Theorem even though the original rock size distributions are not normally distributed. However, this relies on the assumption that the presence of each rock from a sampling site is not influenced from other rocks (i.e. they are independent). It is not clear whether this is true, but the influence from other rocks might be small enough to make independence a reasonable assumption.

As usual, if you decide to account for the non-constant nature of the error variance \(\sigma_{E}^{2}\left(x\right)\), then you may need to compute the variance of the mean estimate. These formulas are found in Chapter 3 of Reference @Geostatistics. You can compute a 95% confidence interval using the variance of the mean estimate (denoting this variance as \(V\left(x\right)\) and the estimated mean as \(\hat{M}\left(x\right)\)) by \(\hat{M}\left(x\right)\pm1.96\sqrt{V\left(x\right)}\).

1 Chilès, J.P., and Delfiner, P. (2012) Geostatistics: Modeling Spatial Uncertainty. Second Edition. Wiley.

http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html\#//00310000003q000000

http://www2.sas.com/proceedings/sugi27/p213-27.pdf

Huet, S., Bouvier, A., Poursat, M.-A., and Jolivet, E. (2003) Statistical Tools for Nonlinear Regression: A Practical Guide with S-Plus and R Examples. Second Edition. Springer.

Anton, H. (2010) Elementary Linear Algebra. Tenth Edition. Wiley.