Students in any basic statistics class are taught linear regression, which is one of the simplest forms of a statistical model. The basic idea is that a ‘response’ variable can be mathematically related to one or any number of ‘explanatory’ variables through a linear equation and a normally distributed error term. With any statistical tool, linear regression comes with a set of assumptions that have to be met in order for the model to be valid. These assumptions include^{1}:

- model residuals are individually normally distributed
- constant variance or homogeneity (i.e., even spread of residuals)
- explanatory variables are deterministic
- independence of observations or no pattern in residuals

When I first learned about linear regression I was overly optimistic about how easy it was to satisfy each of these assumptions. Once I learned to be more careful I started using techniques to verify if each of these assumptions were met. However, no one bothered to tell me what actually happens when these assumptions are violated, at least not in a quantitative sense. Most textbooks will tell you that if your model violates these assumptions the statistics or coefficients describing relationships among variables will be biased. In other words, your model will tell you information that is systematically different from relationships describing the true population.

Of course the bias in your model depends on what rule you’ve violated and it would be impractical (for the purpose of this blog) to conduct a thorough analysis of each assumption. However, I’ve always wondered what would happen if I ignored the fourth point above regarding independence. I’ve had the most difficulty dealing with auto-correlation among observations and I’d like to get a better idea of how a model might be affected if this assumption is ignored. For example, how different would the model coefficients be if we created a regression model without accounting for a correlation structure in the data? In this blog I provide a trivial but potentially useful example to address this question. I start by simulating a dataset and show how the model estimates are affected by ignoring spatial dependency among observations. I also show how these dependencies can be addressed using an appropriate model. Before I continue, I have to give credit to the book ‘Mixed Effects Models and Extensions in Ecology with R’^{1}, specifically chapters 2 and 7. The motivation for this blog comes primarily from these chapters and interested readers should consult them for more detail.

Let’s start by importing the packages `gstat`

, `sp`

, and `nlme`

. The first two packages have some nifty functions to check model assumptions and the `nlme`

package provides models that can be used to account for various correlation structures.

```
```#import relevant packages
library(gstat)
library(sp)
library(nlme)

Next we create a simulated dataset, which allows us to verify the accuracy of our model if we know the ‘true’ parameter values. The dataset contains 400 observations that are each spatially referenced by the ‘lat’ and ‘lon’ objects. The coordinates were taken from a uniform distribution (from -4 to 4) using the `runif`

function. There are two explanatory variables that are both taken from the standard normal distribution using the `rnorm`

function. I’ve used the `set.seed`

function so the results can be reproduced. R uses pseudo-random number generation, which can be handy for sharing examples.

```
```#create simulated data, lat and lon from uniformly distributed variable
#exp1 and exp2 from random normal
set.seed(2)
samp.sz<-400
lat<-runif(samp.sz,-4,4)
lon<-runif(samp.sz,-4,4)
exp1<-rnorm(samp.sz)
exp2<-rnorm(samp.sz)

The response variable is a linear combination of the explanatory variables and follows the form of a standard linear model. I’ve set the parameters for the linear model as 1, 4, -3, and -2 for the intercept and slope values. The error term is also normally distributed. Note that I’ve included latitude in the model to induce a spatial correlation structure.

```
```#resp is linear combination of explanatory variables
#plus an error term that is normally distributed
resp<-1+4*exp1-3*exp2-2*lat+rnorm(samp.sz)

Setting the parameter values is key because we hope that any model we use to relate the different variables will return values that are very close to the actual and show minimal variation. In the real world, we do not know the actual parameter values so we have to be really careful that we are using the models correctly.

Next, we can plot the variables and run some diagnostics to verify we’ve created appropriate data. Plotting the data frame produces scatterplots that indicate correlation among the simulated variables.

```
```#pair plots of variables
plot(data.frame(resp,exp1,exp2,lat))

The `cor`

function verifies the info in the figure by returning Pearson correlation coefficients. Note the sign and magnitude of the correlations of the response variable with all other variables. The explanatory variables and latitude are not correlated with each other.

```
```#correlation between variables
cor(data.frame(resp,exp1,exp2,lat))
resp exp1 exp2 lat
resp 1.0000000 0.53416042 -0.44363892 -0.70131965
exp1 0.5341604 1.00000000 0.01703620 0.03419987
exp2 -0.4436389 0.01703620 1.00000000 0.04475536
lat -0.7013196 0.03419987 0.04475536 1.00000000

Now that we’ve simulated our data, we can create a linear model to see if the parameter coefficients are similar to the actual. In this example, we’re ignoring the spatial correlation structure. If we were savvy modelers, we would check for spatial correlation prior to building our model. Spatial correlation structures in actual data may also be more complex than a simple latitudinal gradient, so we need to be extra careful when evaluating the independence assumption. We create the model using the basic `lm`

function.

```
```mod<-lm(resp~exp1+exp2)
coefficients(summary(mod))
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.298600 0.2523227 5.146587 4.180657e-07
exp1 3.831385 0.2533704 15.121673 4.027448e-41
exp2 -3.237224 0.2561525 -12.637879 5.276878e-31

` `

The parameter estimates are not incredibly different form the actual values. Additionally, the standard errors of the estimates are not large enough to exclude the true parameter values. We can verify this using a t-test with the null hypothesis for each parameter set at the actual (the default null in `lm`

is zero). I’ve created a function for doing this that takes the observed mean value as input rather than the normal `t.test`

function that uses a numeric vector of values as input. The arguments for the custom function are the observed mean (x.bar), null mean (mu), standard error (se), degrees of freedom (deg.f), and return.t which returns the t-statistic if set as true.

```
```#function for testing parameter values against actual
t_test<-function(x.bar,mu,se,deg.f,return.t=F){
if(return.t) return(round((x.bar-mu)/se,3))
pt(abs((x.bar-mu)/se),deg.f,lower.tail=F)*2
}
#for first explanatory variable
t_test(3.8314, 4, 0.2534, 397)
[1] 0.5062122

` `

The p-value indicates that the estimated parameter value for the first explanatory variable is not different from the actual. We get similar results if we test the other parameters. So far we’ve shown that if we ignore the spatial correlation structure we get fairly reasonable parameter estimates. Can we improve our model if we account for the spatial correlation structure?

Before we model spatial correlation we first have to identify its existence (remember, we’re assuming we don’t know the response is correlated with latitude). Let’s dig a little deeper by first describing some tools to evaluate the assumption of independence. The easiest way to evaluate this assumption in a linear model is to plot the model residuals by their spatial coordinates. We would expect to see some spatial pattern in the residuals if we have not accounted for spatial correlation. The `bubble`

plot function in the `sp`

package can be used to plot residuals.

```
```dat<-data.frame(lon,lat,resids=resid(mod))
coordinates(dat)<-c('lon','lat')
bubble(dat,zcol='resids')

` `

A nice feature of the `bubble`

function is that it color codes the residuals based on positive and negative values and sets the size of the points in proportion to the magnitude. An examination of the plot shows a clear residual pattern with latitude such that smaller values are seen towards the north.

The bubble plot provides a qualitative means of evaluating spatial correlation. A more quantitative approach is to evaluate semivariance, which provides a measure of spatial correlation between points at different distances. Points closer to one another are more likely to be similar if observations in our dataset are spatially correlated. The variety of statistics that can be applied to spatial data is staggering and evaluations of semivariance are no exception. For this example, it is sufficient to know that points closer to one another will have smaller values of semivariance (more similar) and those farther apart will have larger values (less similar) if points are spatially correlated. We would also expect calculated values of semivariance to vary depending on what direction we are measuring (i.e., anisotropy). For example, we might not detect spatial correlation if we estimate semivariance strictly in an east to west direction, whereas semivariance would vary quite dramatically along a north to south direction. We can apply the `variogram`

function (`gstat`

package) to estimate semivariance and also account for potential differences depending on direction.

```
```var.mod<-variogram(resids~1,data=dat,alpha=c(0,45,90,135))
plot(var.mod)

` `

When we use the `variogram`

function we can specify directions in which semivariance is measured using the alpha argument. Here we’ve specified that we’d like to measure semivariance in the residuals in a northern, north-eastern, eastern, and south-eastern direction. This is the same as estimating semivariance in directions 180 degrees from what we’ve specified, so it would be redundant if we looked at semivariance in a southern, south-western, western, and north-western direction. The plot shows that spatial correlation is strongest in a north-south direction and weakest along an east-west direction, which confirms the latitudinal change in our response variable.

Our analyses have made it clear that the model does not account for spatial correlation, even though our parameter estimates are not significantly different from the actual values. If we account for this spatial correlation can we improve our model? An obvious solution would be to include latitude as an explanatory variable and obtain a parameter estimate that describes the relationship. Although this would account for spatial correlation, it would not provide any explanation for why our response variable changes across a latitudinal gradient. That is, we would not gain new insight into causation, rather we would only create a model with more precise parameters. If we wanted to account for spatial correlation by including an additional explanatory variable we would have to gather additional data based on whatever we hypothesized to influence our response variable. Temperature, for example, may be a useful variable because it would likely be correlated with latitude and its relationship with the response variable could be biologically justified.

In the absence of additional data we can use alternative models to account for spatial correlation. The `gls`

function (`nlme`

package) is similar to the `lm`

function because it fits linear models, but it can also be used to accommodate errors that are correlated. The implementation is straightforward with the exception that you have specify a form of the spatial correlation structure. Identifying this structure is non-trivial and the form you pick will affect how well your model accounts for spatial correlation. There are six options for the spatial correlation structure. Picking the optimal correlation structure requires model comparison using AIC or similar tools.

For our example, I’ve shown how we can create a model using only the Gaussian correlation structure. Also note the form argument, which can be used to specify a covariate that captures the spatial correlation.

```
```#refit model with correlation structure
mod.cor<-gls(resp~exp1+exp2,correlation=corGaus(form=~lat,nugget=TRUE))
summary(mod.cor)
Coefficients:
Value Std.Error t-value p-value
(Intercept) 154.49320 393.3524 0.39276 0.6947
exp1 3.99840 0.0486 82.29242 0.0000
exp2 -3.01688 0.0491 -61.38167 0.0000

` `

The `gls`

function seems to have trouble estimating the intercept. If we ignore this detail, the obvious advantage of accounting for spatial correlation is a reduction in the standard error for the slope estimates. More precisely, we’ve reduced the uncertainty of our estimates by over 500% from those obtained using `lm`

, even though our initial estimates were not significantly different from the actual.

The advantage of accounting for spatial correlation is a huge improvement in the precision of the parameter estimates. Additionally, we do not sacrifice degrees of freedom if we use the `gls`

function to model spatial correlation. Hopefully this example has been useful for emphasizing the need to account for model assumptions. I invite further discussion on methods for modeling spatial correlation.

Get the code here:

^{1}Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A., Smith, G.M., 2009. Mixed Effects Models and Extensions in Ecology with R. Springer, New York.

Cross-blogging on R | The Cons Bio Blog

Hi. Thanks for the post. I was just wondering why try to solve a potential problem cause by spatial correlation if none of your predictors are significant. I mean, you would not even have a problem with type I error. Shouldn’t you just stop at the moment you noticed there is no relationship between your response and predictor variables?

Hi TS,

Thanks for reading. I suppose a spatial correlation structure could mask potential relationships with more relevant explanatory variables if the correlation is unaccounted for. In this example, we still see significant relationships w/o accounting for spatial correlation but perhaps this may not be observed in extreme cases. Conversely, you may induce Type II error by not accounting for spatial correlation. I’m no expert, but that’s my belief.

-Marcus

In the absence of simulated data (and ‘true’ or otherwise known parameter values), and assuming at least some knowledge by thy analyst of the system under consideration, wouldn’t the unexpectedly large estimate for the intercept (and esp. the large standard error for this estimate) call this gls fit into question? And if we had concerns about the intercept, would there be cause for concern about the other parameter estimates. Just curious. Thanks, and great post BTW! (P.S. did you have any issues with false convergence in fitting the gls model — I seem to be running into this issue.)

Hi,

Yea I think the model fit should come into question with the very large intercept… I’m not sure how to address the issue since I couldn’t find any literature on the topic. Not to mention convergence issues, which is why I included the seed for the data generation. From what I understand, spatial correlation is notoriously difficult to deal with… even tools like gls seem to have issues.

-Marcus

I got same issue running this code

mod.cor<- gls(resp~exp1+exp2,correlation=corGaus(form=~lat,nugget=TRUE))

Error in gls(resp ~ exp1 + exp2, correlation = corGaus(form = ~lat, nugget = TRUE)) : false convergence (8)

Hi, nice post! I have ran gls on my dataset using two different coordinate systems. I would expect to find the same optimal spatial correlation structure for a dataset no matter the choice of coordinate system (since the relative distance between sites are the same), but that is not the case for my data – different correlation structures get the lowest AIC depending on which coordinate system i use. The outcome is, however, the same with only small differences in p-value etc. Have you ran into this or have any thoughts about it?

Hm, that is strange. Are you comparing geographic vs. projected coordinate systems? The former is 2D whereas the latter is 3D. I suspect there might be minor differences comparing the two.

Hi,

I’ve got an error trying to replicate your GLS example:

> mod.cor w10nn mod.sem summary(mod.sem)

Call:errorsarlm(formula = formula(mod), data = dat, listw = w10nn)

Residuals:

Min 1Q Median 3Q Max

-4.13604 -0.92707 -0.04603 0.81982 4.87872

Type: error

Coefficients: (asymptotic standard errors)

Estimate Std. Error z value Pr(>|z|)

(Intercept) 1.624766 0.474148 3.4267 0.000611

exp1 3.992947 0.064015 62.3747 < 2.2e-16

exp2 -2.989956 0.064694 -46.2167 < 2.2e-16

Lambda: 0.9347, LR test value: 979.9, p-value: < 2.22e-16

Asymptotic standard error: 0.0069681

z-value: 134.14, p-value: < 2.22e-16

Wald statistic: 17993, p-value: < 2.22e-16

Log likelihood: -723.1156 for error model

ML residual variance (sigma squared): 1.7871, (sigma: 1.3368)

Number of observations: 400

Number of parameters estimated: 5

AIC: 1456.2, (AIC for lm: 2434.1)

Hi Maksym, bump up the sample size to 500. I was having similar issues using n = 400. You might also try changing the optimization algorithm from ‘nlminb’ to ‘optim’ (see the help file for gls and glsControl):

The ‘false convergence’ error seems to be not uncommon with these types of models. A think a good indicator of questionable convergence (even if you don’t get an error) is really wacky estimates for the parameters. Although the point of the blog was to show greater precision in the parameter estimates for exp1 and exp2, you’ll notice the the intercept estimate is very odd. I’ve no idea what causes the issue but it’s not good practice to use model results if there is no convergence in the parameter estimates. I guess it’s up to you if you care about the intercept… it wasn’t important for this blog. You might get more info by looking at the link here: https://www.researchgate.net/post/Can_someone_help_with_function_gls_Error_false_convergence_8