knitr::opts_chunk$set(echo = TRUE, message = FALSE, fig.width=7, fig.height=4) library(knitr) prop_train <- 0.2
With the R package varycoef
we enable the user to analyze spatial data and in a simple, yet versatile way. The underlying idea are spatially varying coefficients (SVC) that extend the linear model
$$ y_i = x_i^{(1)} \beta_1 + ... + x_i^{(p)} \beta_p + \varepsilon_i$$ by allowing the coefficients $\beta_j, j = 1, ..., p$ to vary over space. That is, for a location $s$ we assume the following model:
$$ y_i = x_i^{(1)} \beta_1(s) + ... + x_i^{(p)} \beta_p(s) + \varepsilon_i$$
In particular, we use so-called Gaussian processes (GP) to define the spatial structure of the coefficients. Therefore, our models are called GP-based SVC models.
In this article, we will show what SVC models are and how to define them. Afterwards, we give a short and illustrative example with synthetic data and show how to apply the methods provided in varycoef
. Finally, using the well known data set meuse
from the package sp
.
The analyses and results in this article are meant to introduce the package varycoef
and not to be a rigorous statistical analysis of a data set. As this article should make the usage of varycoef
as simple as possible, we skip over some of the technical or mathematical details and in some cases abuse notation. For a rigorous definition, please refer to the resources below. Further, the model estimation is performed on rather small data sets to ease computation ($n < 200$). We recommend to apply SVC models, particularly with many coefficients, on larger data sets.
Our package evolved over time and we present some highlights:
In Dambon et al. (2021a) we introduce the GP-based SVC model and the methodology on how to estimate them. Further, we provide a comparison on synthetic and real world data with other SVC methodologies.
In Dambon et al. (2022a) we present an in-depth analysis of Swiss real estate data using GP-based SVC models.
In Dambon et al. (2022b) we introduce a variable selection method. This is not covered by this article.
For more information on Gaussian processes and their application on spatial data, please refer to chapter 9 of the "STA330: Modeling Dependent Data" lecture notes by Reinhard Furrer.
Before we start, we want to give some prerequisites that you should know about in order to follow the analysis below. Beside a classical linear regression model, we require the knowledge of:
The varycoef
package is available via CRAN or Github. Latter one hosts the most recent version that is released to CRAN on a regular base.
# install from CRAN install.packages("varycoef") # install from Github (make sure that you installed the package "devtools") devtools::install_github("jakobdambon/varycoef")
Within the R package, you can use these resources:
# attach package library(varycoef) # general package help file help("varycoef") # where you find this vignette vignette("Introduction", package = "varycoef")
You can find this article on the Github repository, too. We continue to add more material and examples.
Let's dive into the analysis of some synthetic data. To ease the visualization, we will work with one-dimensional spatial data, i.e., any location $s$ is from the real line $\mathbb R$. We want to highlight that are package is not restricted to such analysis and that we can analyze spatial data from higher dimensions, i.e., $s \in \mathbb R^d$, where $d \geq 1$.
As mentioned before, an SVC model extends the linear model by allowing the coefficients to vary over space. Therefore, we can write:
$$ y_i = x_i^{(1)} \beta_1(s) + ... + x_i^{(p)} \beta_p(s) + \varepsilon_i,$$
for some location $s$ where $\beta_j(s)$ indicates the dependence of the $j$th coefficient on the space. The coefficients are defined by Gaussian processes, but we skip over this part for now and take a look at a data set that was sampled using the model above. In varycoef
, a data set named SVCdata
is provided:
str(SVCdata) help(SVCdata) # number of observations, number of coefficients n <- nrow(SVCdata$X); p <- ncol(SVCdata$X)
It consists of the response y
, the model matrix X
, the locations locs
, and the usually unknown true coefficients beta
, error eps
, and true parameters true_pars
. The model matrix is of dimension $r n
\times r p
$, i.e., we have $r n
$ observations and $p = r p
$ coefficients. The first column of X
is identical to 1 to model the intercept.
We will use r n*prop_train
observations of the data to train the model and leave the remaining r n*(1-prop_train)
out as a test sample, i.e.:
# create data frame df <- with(SVCdata, data.frame(y = y, x = X[, 2], locs = locs)) set.seed(123) idTrain <- sort(sample(n, n*prop_train)) df_train <- df[idTrain, ] df_test <- df[-idTrain, ]
We plot the part of the data that is usually available to us:
par(mfrow = 1:2) plot(y ~ x, data = df_train, xlab = "x", ylab = "y", main = "Scatter Plot of Response and Covariate") plot(y ~ locs, data = df_train, xlab = "s", ylab = "y", main = "Scatter Plot of Response and Locations") par(mfrow = c(1, 1))
We note that there is a clear linear dependency between the covariate and the response. However, there is not a clear spatial structure. We estimate a linear model and analyze the residuals thereof.
fit_lm <- lm(y ~ x, data = df_train) coef(fit_lm) # residual plots par(mfrow = 1:2) plot(x = fitted(fit_lm), y = resid(fit_lm), xlab = "Fitted Values", ylab = "Residuals", main = "Residuals vs Fitted") abline(h = 0, lty = 2, col = "grey") plot(x = df_train$locs, y = resid(fit_lm), xlab = "Location", ylab = "Residuals", main = "Residuals vs Locations") abline(h = 0, lty = 2, col = "grey") par(mfrow = c(1, 1))
Discarding the spatial information, we observe no structure within the residuals (Figure above, LHS). However, if we plot the residuals against there location, we clearly see some spatial structure. Therefore, we will apply the SVC model next.
To estimate an SVC model, we can use the SVC_mle()
function almost like the lm()
function from above. As the name suggests, we use a maximum likelihood estimation (MLE) for estimating the parameters. For now, we only focus on the mean coefficients, which we obtain by the coef()
method.
fit_svc <- SVC_mle(y ~ x, data = df_train, locs = df_train$locs) coef(fit_svc)
The only additional argument that we have to provide explicitly when calling SVC_mle()
are the coordinates, i.e., the observation locations. The output is an object of class r class(fit_svc)
. We can apply most of the methods that exist for lm
objects to out output, too. For instance methods of summary()
, fitted()
, or resid()
. We give the summary()
output but do not go into details for now. Further, use the fitted()
and residuals()
methods to analyze the SVC model. Contrary to a fitted()
method for an lm
object, the output does not only contain the fitted response, but is a data.frame
that contains the spatial deviations from the mean named SVC_1
, SVC_2
, and so on (see section Model Interpretation below), the response y.pred
, and the respective locations loc_1
, loc_2
, etc. In our case, there is only one column since the locations are from a one-dimensional domain.
# summary output summary(fit_svc) # fitted output head(fitted(fit_svc)) # residual plots par(mfrow = 1:2) plot(x = fitted(fit_svc)$y.pred, y = resid(fit_svc), xlab = "Fitted Values", ylab = "Residuals", main = "Residuals vs Fitted") abline(h = 0, lty = 2, col = "grey") plot(x = df_train$locs, y = resid(fit_svc), xlab = "Location", ylab = "Residuals", main = "Residuals vs Locations") abline(h = 0, lty = 2, col = "grey") par(mfrow = c(1, 1))
Compared to the output and residuals of the linear models, we immediately see that the residuals of the SVC model are smaller in range and do not have a spatial structure. They are both with respect to fitted values and locations distributed around zero and homoscedastic.
We end this synthetic data example by comparing the linear with the SVC model. We already saw some advantages of the SVC model when investigating the residuals. Now, we take a look at the quality of the model fit, the model interpretation, and the predictive performance.
We can compare the models by the log likelihood, Akaike's or the Bayesian information criterion (AIC and BIC, respectively). Again, the corresponding methods are available:
kable(data.frame( Model = c("linear", "SVC"), # using method logLik `log Likelihood` = round(as.numeric(c(logLik(fit_lm), logLik(fit_svc))), 2), # using method AIC AIC = round(c(AIC(fit_lm), AIC(fit_svc)), 2), # using method BIC BIC = round(c(BIC(fit_lm), BIC(fit_svc)), 2) ))
In all four metrics the SVC model outperforms the linear model, i.e., the log likelihood is larger and the two information criteria are smaller.
While the linear model estimates constant coefficients $\beta_j$, contrary and as the name suggests, the SVC model's coefficients vary over space $\beta_j(s)$. That is why the fitted()
method does not only return the fitted response, but also the fitted coefficients and at their given locations:
head(fitted(fit_svc))
Therefore, the SVC mentioned above is the sum of the mean value from the method coef()
which we name $\mu_j$ and the zero-mean spatial deviations from above which we name $\eta_j(s)$, i.e., $\beta_j(s) = \mu_j + \eta_j(s)$. We visualize the coefficients at their respective locations:
mat_coef <- cbind( # constant coefficients from lm lin1 = coef(fit_lm)[1], lin2 = coef(fit_lm)[2], # SVCs svc1 = coef(fit_svc)[1] + fitted(fit_svc)[, 1], svc2 = coef(fit_svc)[2] + fitted(fit_svc)[, 2] ) matplot( x = df_train$locs, y = mat_coef, pch = c(1, 2, 1, 2), col = c(1, 1, 2, 2), xlab = "Location", ylab = "Beta", main = "Estimated Coefficients") legend("topright", legend = c("Intercept", "covariate x", "linear model", "SVC model"), pch = c(1, 2, 19, 19), col = c("grey", "grey", "black", "red"))
We use the entire data set to compute the in- and out-of-sample rooted mean square error (RMSE) for the response for both the linear and SVC model. Here we rely on the predict()
methods. Further, we can compare the predicted coefficients with the true coefficients provided in SVCdata
, something that we usually cannot do.
# using method predict with whole data and corresponding locations df_svc_pred <- predict(fit_svc, newdata = df, newlocs = df$locs) # combining mean values and deviations mat_coef_pred <- cbind( svc1_pred = coef(fit_svc)[1] + df_svc_pred[, 1], svc2_pred = coef(fit_svc)[2] + df_svc_pred[, 2] ) # plot matplot(x = df$locs, y = mat_coef_pred, xlab = "Location", ylab = "Beta", main = "Predicted vs Actual Coefficients", col = c(1, 2), lty = c(1, 1), type = "l") points(x = df$locs, y = SVCdata$beta[, 1], col = 1, pch = ".") points(x = df$locs, y = SVCdata$beta[, 2], col = 2, pch = ".") legend("topright", legend = c("Intercept", "Covariate", "Actual"), col = c("black", "red", "black"), pch = c(NA, NA, "."), lty = c(1, 1, NA))
The linear model is constant and the same for each location, i.e.:
$$y = \beta_1 + \beta_2\cdot x + \varepsilon = r round(coef(fit_lm)[1], 2)
+ r round(coef(fit_lm)[2], 2)
\cdot x + \varepsilon$$
Here, the SVC model can be interpreted in a similar way where on average, it is simply the mean coefficient value with some location specific deviation $\eta_j(s)$, i.e.:
$$y = \beta_1(s) + \beta_2(s)\cdot x + \varepsilon = \bigl(r round(coef(fit_svc)[1], 2)
+ \eta_1(s)\bigr) + \bigl(r round(coef(fit_svc)[2], 2)
+ \eta_2(s) \bigr) \cdot x + \varepsilon$$
Say we are interested in a particular position, like:
(s_id <- sample(n, 1))
The coordinate is $s_{r s_id
} = r round(df$locs[s_id], 2)
$. We can extract the deviations $\eta_j(s)$ and simply add them to the model. We receive the following location specific model.
$$y = \beta_1(s_{r s_id
}) + \beta_2(s_{r s_id
})\cdot x + \varepsilon = \bigl(r round(coef(fit_svc)[1], 2)
r round(df_svc_pred[s_id, 1], 2)
\bigr) + \bigl(r round(coef(fit_svc)[2], 2)
+ r round(df_svc_pred[s_id, 2], 2)
\bigr) \cdot x + \varepsilon = r round(coef(fit_svc)[1] + df_svc_pred[s_id, 1], 2)
+ r round(coef(fit_svc)[2] + df_svc_pred[s_id, 2], 2)
\cdot x + \varepsilon$$
The remaining comparison of the two model at the given location is as usual. Between the both models we see that the intercept of the linear model is larger and the coefficient of $x$ is smaller than the
SVC model's intercept and coefficient of $x$, respectively.
Finally, we compare the prediction errors of the model. We compute the rooted mean squared errors (RMSE) for both models and both the training and testing data.
SE_lm <- (predict(fit_lm, newdata = df) - df$y)^2 # df_svc_pred from above SE_svc <- (df_svc_pred$y.pred - df$y)^2 kable(data.frame( model = c("linear", "SVC"), `in-sample RMSE` = round(sqrt(c(mean(SE_lm[idTrain]), mean(SE_svc[idTrain]))), 3), `out-of-sample RMSE` = round(sqrt(c(mean(SE_lm[-idTrain]), mean(SE_svc[-idTrain]))), 3) ))
We notice a significant difference in both RMSE between the models. On the training data, i.e., the in-sample RMSE, we observe and improvement by more than 50%. This is quite common since the SVC model has a higher flexibility. Therefore, it is very pleasing to see that even on the testing data, i.e., the out-of-sample RMSE, the SVC model still improves the RMSE by almost 30% compared to the linear model.
We now turn to a real world data set, the meuse
data from the package sp
.
library(sp) # attach sp and load data data("meuse") # documentation help("meuse") # overview summary(meuse) dim(meuse)
Our goal is to model the log cadmium
measurements using the following independent variables:
dist
, i.e. the normalized distance to the river Meuse. lime
, which is a 2-level factor indicating the presence of lime. elev
, i.e. the relative elevation above the local river bed.This provides us a model with "cheap" covariates and we can regress our variable of interest on them.
df_meuse <- meuse[, c("dist", "lime", "elev")] df_meuse$l_cad <- log(meuse$cadmium) df_meuse$lime <- as.numeric(as.character(df_meuse$lime)) locs <- as.matrix(meuse[, c("x", "y")])
First, we plot the log Cadmium measurements at their respective locations. The color ranges from yellow (high Cadmium measurements) to black (low Cadmium measurements).
# load meuse river outlines data("meuse.riv") # create spatial object to create spplot sp_meuse <- df_meuse coordinates(sp_meuse) <- ~ locs # visualize log Cadmium measurements along river spplot( sp_meuse, zcol = "l_cad", main = "Meuse River and Log Cadmium Measurements" ) + latticeExtra::layer(panel.lines(meuse.riv))
Generally, the values for l_cad
are highest close to the river Meuse. However, there is some spatial structure comparing the center of all observations to the Northern part. Therefore, we expect the dist
covariate to be an important regressor. Omitting the spatial structure, we can also look at a pairs
plot.
pairs(df_meuse)
Indeed, we note linear relationships between l_cad
on the one hand and elev
as well as dist
on the other hand. Further, when lime
is equal to 1, i.e., there is lime present in the soil, the Cadmium measurements are higher.
As a baseline, we start with a linear model:
fit_lm <- lm(l_cad ~ ., data = df_meuse) coef(fit_lm)
The residual analysis shows:
oldpar <- par(mfrow = c(1, 2)) plot(fit_lm, which = 1:2) par(oldpar)
The spatial distribution of the residuals is the following:
# add residuals to spatial object sp_meuse$res_lm <- resid(fit_lm) # visualize linear model residuals along river spplot(sp_meuse, zcol = "res_lm", main = "Meuse River and Residuals of Linear Model" ) + latticeExtra::layer(panel.lines(meuse.riv))
One can observe that there is a spatial structure in the residuals. This motivates us to use an SVC model.
The call to estimate the SVC model is again quite similar to the lm()
call from above. However, we specify further arguments for the MLE using the control
argument. First, we are using an optimization over the profiled likelihood (profileLik = TRUE
) and second, we apply parameter scaling (parscale = TRUE
) in the numeric optimization. Please check the corresponding help file help("SVC_mle_control")
for more details.
fit_svc <- SVC_mle(l_cad ~ ., data = df_meuse, locs = locs, control = SVC_mle_control( profileLik = TRUE, parscale = TRUE )) coef(fit_svc)
The obtained mean coefficient values of the linear and SVC model are quite similar, but this does not come as a surprise.
kable(t(data.frame( round(cbind(`linear` = coef(fit_lm), `SVC`= coef(fit_svc)), 3) )))
Additionally to the meuse
data the sp
package also contains another data set called meuse.grid
that contains a 40 by 40 meter spaced grid of the entire study area along the Meuse river. We can use the locations to predict the SVCs. However, the covariates elev
and lime
are missing. Therefore, we cannot predict the response. Again, the fitted SVC values are only the deviations from the mean. We observe that the SVC for elev
is in fact constant.
# study area data("meuse.grid") # prediction df_svc_pred <- predict(fit_svc, newlocs = as.matrix(meuse.grid[, c("x", "y")])) colnames(df_svc_pred)[1:4] <- c("Intercept", "dist", "lime", "elev") head(df_svc_pred)
The attentive reader might have noticed that the elev
column only contains 0. Therefore, there are no deviations and the respective coefficient is constant and our method is capable to estimate constant coefficients within the MLE. There exists a selection method to not only select the varying coefficients, but to also select the mean value, i.e., the fixed effect. Please refer to Dambon et al. (2022b) for further information.
SVC models are a powerful tool to analyze spatial data. With the R package varycoef
, we provide an accessible and easy way to apply GP-based SVC models that is very close to the classical lm
experience. If you have further questions or issues, please visit our Github repository.
Dambon, J. A., Sigrist, F., Furrer, R. (2021) Maximum likelihood estimation of spatially varying coefficient models for large data with an application to real estate price prediction, Spatial Statistics, 41 (100470). doi:10.1016/j.spasta.2020.100470
Dambon, J.A., FahrlĂ¤nder, S.S., Karlen, S. et al. (2022a) Examining the vintage effect in hedonic pricing using spatially varying coefficients models: a case study of single-family houses in the Canton of Zurich, Swiss Journal Economics Statistics 158(2). doi:10.1186/s41937-021-00080-2
Dambon, J. A., Sigrist, F., Furrer, R. (2022b) Joint variable selection of both fixed and random effects for Gaussian process-based spatially varying coefficient models, International Journal of Geographical Information Science doi:10.1080/13658816.2022.2097684
Furrer, R. (2022) Modeling Dependent Data, Lecture notes accessed on August 15, 2022
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