Introduction

The spmodel package is used to fit and summarize spatial models and make predictions at unobserved locations (Kriging). This vignette provides an overview of basic features in spmodel. We load spmodel by running

library(spmodel)

If you use spmodel in a formal publication or report, please cite it. Citing spmodel lets us devote more resources to it in the future. We view the spmodel citation by running

citation(package = "spmodel")

#> To cite spmodel in publications use:
#> 
#>   Dumelle M, Higham M, Ver Hoef JM (2023). spmodel: Spatial statistical
#>   modeling and prediction in R. PLOS ONE 18(3): e0282524.
#>   https://doi.org/10.1371/journal.pone.0282524
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {{spmodel}: Spatial statistical modeling and prediction in {R}},
#>     author = {Michael Dumelle and Matt Higham and Jay M. {Ver Hoef}},
#>     journal = {PLOS ONE},
#>     year = {2023},
#>     volume = {18},
#>     number = {3},
#>     pages = {1--32},
#>     doi = {10.1371/journal.pone.0282524},
#>     url = {https://doi.org/10.1371/journal.pone.0282524},
#>   }

There are three more spmodel vignettes available on our website at https://usepa.github.io/spmodel/:

1. A Detailed Guide to spmodel
2. Spatial Generalized Linear Models in `spmodel
3. Technical Details

Additionally, there are two workbooks that have accompanied recent spmodel workshops:

1. 2024 Society for Freshwater Science Conference: “Spatial Analysis and Statistical Modeling with R and spmodel available at https://usepa.github.io/spworkshop.sfs24/
2. 2023 Spatial Statistics Conference: spmodel workshop available at https://usepa.github.io/spmodel.spatialstat2023/

The Data

Many of the data sets we use in this vignette are sf objects. sf objects are data frames (or tibbles) with a special structure that stores spatial information. They are built using the sf (Pebesma 2018) package, which is installed alongside spmodel. We will use six data sets throughout this vignette:

• moss: An sf object with heavy metal concentrations in Alaska.
• sulfate: An sf object with sulfate measurements in the conterminous United States.
• sulfate_preds: An sf object with locations at which to predict sulfate measurements in the conterminous United States.
• caribou: A tibble (a special data.frame) for a caribou foraging experiment in Alaska.
• moose: An sf object with moose measurements in Alaska.
• moose_preds: An sf object with locations at which to predict moose measurements in Alaska.

We will create visualizations using ggplot2 (Wickham 2016), which we load by running

library(ggplot2)

ggplot2 is only installed alongside spmodel when dependencies = TRUE in install.packages(), so check that it is installed before reproducing any visualizations in this vignette.

Spatial Linear Models

Spatial linear models for a quantitative response vector y have spatially dependent random errors and are often parameterized as

y = Xβ + τ + ϵ,

where X is a matrix of explanatory variables (usually including a column of 1’s for an intercept), β is a vector of fixed effects that describe the average impact of X on y, τ is a vector of spatially dependent (correlated) random errors, and ϵ is a vector of spatially independent (uncorrelated) random errors. The spatial dependence of τ is explicitly specified using a spatial covariance function that incorporates the variance of τ, often called the partial sill, and a range parameter that controls the behavior of the spatial covariance. The variance of ϵ is often called the nugget.

Spatial linear models are fit in spmodel for point-referenced and areal data. Data are point-referenced when the elements in y are observed at point-locations indexed by x-coordinates and y-coordinates on a spatially continuous surface with an infinite number of locations. The splm() function is used to fit spatial linear models for point-referenced data (these are often called geostatistical models). Data are areal when the elements in y are observed as part of a finite network of polygons whose connections are indexed by a neighborhood structure. For example, the polygons may represent counties in a state who are neighbors if they share at least one boundary. The spautor() function is used to fit spatial linear models for areal data (these are often called spatial autoregressive models). This vignette focuses on spatial linear models for point-referenced data, though spmodel’s other vignettes discuss spatial linear models for areal data.

The splm() function has similar syntax and output as the commonly used lm() function used to fit non-spatial linear models. splm() generally requires at least three arguments:

• formula: a formula that describes the relationship between the response variable and explanatory variables.
  • formula uses the same syntax as the formula argument in lm()
• data: a data.frame or sf object that contains the response variable, explanatory variables, and spatial information.
• spcov_type: the spatial covariance type ("exponential", "spherical", "matern", etc).

If data is an sf object, the coordinate information is taken from the object’s geometry. If data is a data.frame (or tibble), then xcoord and ycoord are required arguments to splm() that specify the columns in data representing the x-coordinates and y-coordinates, respectively. spmodel uses the spatial coordinates “as-is,” meaning that spmodel does not perform any projections. To project your data or change the coordinate reference system, use sf::st_transform(). If an sf object with polygon geometries is given to splm(), the centroids of each polygon are used to fit the spatial linear model.

Next we show the basic features and syntax of splm() using the Alaskan moss data. We study the impact of log distance to the road (log_dist2road) on log zinc concentration (log_Zn). We view the first few rows of the moss data by running

moss

#> Simple feature collection with 365 features and 7 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -445884.1 ymin: 1929616 xmax: -383656.8 ymax: 2061414
#> Projected CRS: NAD83 / Alaska Albers
#> # A tibble: 365 × 8
#>    sample field_dup lab_rep year  sideroad log_dist2road log_Zn
#>    <fct>  <fct>     <fct>   <fct> <fct>            <dbl>  <dbl>
#>  1 001PR  1         1       2001  N                 2.68   7.33
#>  2 001PR  1         2       2001  N                 2.68   7.38
#>  3 002PR  1         1       2001  N                 2.54   7.58
#>  4 003PR  1         1       2001  N                 2.97   7.63
#>  5 004PR  1         1       2001  N                 2.72   7.26
#>  6 005PR  1         1       2001  N                 2.76   7.65
#>  7 006PR  1         1       2001  S                 2.30   7.59
#>  8 007PR  1         1       2001  N                 2.78   7.16
#>  9 008PR  1         1       2001  N                 2.93   7.19
#> 10 009PR  1         1       2001  N                 2.79   8.07
#> # ℹ 355 more rows
#> # ℹ 1 more variable: geometry <POINT [m]>

We can visualize the distribution of log zinc concentration (log_Zn) by running

ggplot(moss, aes(color = log_Zn)) +
  geom_sf() +
  scale_color_viridis_c()

Log zinc concentration appears highest in the middle of the spatial domain, which has a road running through it. We fit a spatial linear model regressing log zinc concentration on log distance to the road using an exponential spatial covariance function by running

spmod <- splm(log_Zn ~ log_dist2road, data = moss, spcov_type = "exponential")

The estimation method is specified via the estmethod argument, which has a default value of "reml" for restricted maximum likelihood. Other estimation methods are "ml" for maximum likelihood, "sv-wls" for semivariogram weighted least squares, and "sv-cl" for semivariogram composite likelihood.

Printing spmod shows the function call, the estimated fixed effect coefficients, and the estimated spatial covariance parameters. de is the estimated variance of τ (the spatially dependent random error), ie is the estimated variance of ϵ (the spatially independent random error), and range is the range parameter.

print(spmod)

#> 
#> Call:
#> splm(formula = log_Zn ~ log_dist2road, data = moss, spcov_type = "exponential")
#> 
#> 
#> Coefficients (fixed):
#>   (Intercept)  log_dist2road  
#>        9.7683        -0.5629  
#> 
#> 
#> Coefficients (exponential spatial covariance):
#>        de         ie      range  
#> 3.595e-01  7.897e-02  8.237e+03

Next we show how to obtain more detailed summary information from the fitted model.

summary(spmod)

#> 
#> Call:
#> splm(formula = log_Zn ~ log_dist2road, data = moss, spcov_type = "exponential")
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -2.6801 -1.3606 -0.8103 -0.2485  1.1298 
#> 
#> Coefficients (fixed):
#>               Estimate Std. Error z value Pr(>|z|)    
#> (Intercept)    9.76825    0.25216   38.74   <2e-16 ***
#> log_dist2road -0.56287    0.02013  -27.96   <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Pseudo R-squared: 0.683
#> 
#> Coefficients (exponential spatial covariance):
#>        de        ie     range 
#> 3.595e-01 7.897e-02 8.237e+03

tidy(spmod)

#> # A tibble: 2 × 5
#>   term          estimate std.error statistic p.value
#>   <chr>            <dbl>     <dbl>     <dbl>   <dbl>
#> 1 (Intercept)      9.77     0.252       38.7       0
#> 2 log_dist2road   -0.563    0.0201     -28.0       0

glance(spmod)

#> # A tibble: 1 × 10
#>       n     p  npar value   AIC  AICc   BIC logLik deviance pseudo.r.squared
#>   <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl>
#> 1   365     2     3  367.  373.  373.  385.  -184.      363            0.683

lmod <- splm(log_Zn ~ log_dist2road, data = moss, spcov_type = "none")
glances(spmod, lmod)

#> # A tibble: 2 × 11
#>   model     n     p  npar value   AIC  AICc   BIC logLik deviance
#>   <chr> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
#> 1 spmod   365     2     3  367.  373.  373.  385.  -184.      363
#> 2 lmod    365     2     1  634.  636.  636.  640.  -317.      363
#> # ℹ 1 more variable: pseudo.r.squared <dbl>

augment(spmod)

#> Simple feature collection with 365 features and 7 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -445884.1 ymin: 1929616 xmax: -383656.8 ymax: 2061414
#> Projected CRS: NAD83 / Alaska Albers
#> # A tibble: 365 × 8
#>    log_Zn log_dist2road .fitted .resid   .hat .cooksd .std.resid
#>  *  <dbl>         <dbl>   <dbl>  <dbl>  <dbl>   <dbl>      <dbl>
#>  1   7.33          2.68    8.26 -0.928 0.0200 0.0142       1.18 
#>  2   7.38          2.68    8.26 -0.880 0.0200 0.0186       1.35 
#>  3   7.58          2.54    8.34 -0.755 0.0225 0.00482      0.647
#>  4   7.63          2.97    8.09 -0.464 0.0197 0.0305       1.74 
#>  5   7.26          2.72    8.24 -0.977 0.0215 0.131        3.45 
#>  6   7.65          2.76    8.21 -0.568 0.0284 0.0521       1.89 
#>  7   7.59          2.30    8.47 -0.886 0.0300 0.0591       1.96 
#>  8   7.16          2.78    8.20 -1.05  0.0335 0.00334      0.439
#>  9   7.19          2.93    8.12 -0.926 0.0378 0.0309       1.26 
#> 10   8.07          2.79    8.20 -0.123 0.0314 0.00847      0.723
#> # ℹ 355 more rows
#> # ℹ 1 more variable: geometry <POINT [m]>

ggplot(sulfate, aes(color = sulfate)) +
  geom_sf(size = 2) +
  scale_color_viridis_c(limits = c(0, 45))

sulfmod <- splm(sulfate ~ 1, data = sulfate, spcov_type =  "spherical")

sulfate_preds$preds <- predict(sulfmod, newdata = sulfate_preds)

ggplot(sulfate_preds, aes(color = preds)) +
  geom_sf(size = 2) +
  scale_color_viridis_c(limits = c(0, 45))

sulfate_preds$preds <- NULL

augment(sulfmod, newdata = sulfate_preds)

#> Simple feature collection with 100 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -2283774 ymin: 582930.5 xmax: 1985906 ymax: 3037173
#> Projected CRS: NAD83 / Conus Albers
#> # A tibble: 100 × 2
#>    .fitted            geometry
#>  *   <dbl>         <POINT [m]>
#>  1    1.40  (-1771413 1752976)
#>  2   24.5    (1018112 1867127)
#>  3    8.99 (-291256.8 1553212)
#>  4   16.4    (1274293 1267835)
#>  5    4.91 (-547437.6 1638825)
#>  6   26.7    (1445080 1981278)
#>  7    3.00  (-1629090 3037173)
#>  8   14.3    (1302757 1039534)
#>  9    1.49  (-1429838 2523494)
#> 10   14.4    (1131970 1096609)
#> # ℹ 90 more rows

augment(sulfmod, newdata = sulfate_preds, interval = "prediction")

#> Simple feature collection with 100 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -2283774 ymin: 582930.5 xmax: 1985906 ymax: 3037173
#> Projected CRS: NAD83 / Conus Albers
#> # A tibble: 100 × 4
#>    .fitted .lower .upper            geometry
#>  *   <dbl>  <dbl>  <dbl>         <POINT [m]>
#>  1    1.40  -6.62   9.42  (-1771413 1752976)
#>  2   24.5   17.0   32.0    (1018112 1867127)
#>  3    8.99   1.09  16.9  (-291256.8 1553212)
#>  4   16.4    8.67  24.2    (1274293 1267835)
#>  5    4.91  -2.80  12.6  (-547437.6 1638825)
#>  6   26.7   19.2   34.2    (1445080 1981278)
#>  7    3.00  -4.92  10.9   (-1629090 3037173)
#>  8   14.3    6.76  21.8    (1302757 1039534)
#>  9    1.49  -6.34   9.32  (-1429838 2523494)
#> 10   14.4    6.74  22.1    (1131970 1096609)
#> # ℹ 90 more rows

caribou

#> # A tibble: 30 × 5
#>    water tarp      z     x     y
#>    <fct> <fct> <dbl> <dbl> <dbl>
#>  1 Y     clear  2.42     1     6
#>  2 Y     shade  2.44     2     6
#>  3 Y     none   1.81     3     6
#>  4 N     clear  1.97     4     6
#>  5 N     shade  2.38     5     6
#>  6 Y     none   2.22     1     5
#>  7 N     clear  2.10     2     5
#>  8 Y     clear  1.80     3     5
#>  9 Y     shade  1.96     4     5
#> 10 Y     none   2.10     5     5
#> # ℹ 20 more rows

cariboumod <- splm(z ~ water + tarp, data = caribou,
                   spcov_type = "exponential", xcoord = x, ycoord = y)

anova(cariboumod)

#> Analysis of Variance Table
#> 
#> Response: z
#>             Df    Chi2 Pr(>Chi2)    
#> (Intercept)  1 43.4600 4.327e-11 ***
#> water        1  1.6603 0.1975631    
#> tarp         2 15.4071 0.0004512 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Spatial Generalized Linear Models

When building spatial linear models, the response vector y is typically assumed Gaussian (given X). Relaxing this assumption on the distribution of y yields a rich class of spatial generalized linear models that can describe binary data, proportion data, count data, and skewed data. Spatial generalized linear models are parameterized as g(μ) = η = Xβ + τ + ϵ, where g(⋅) is called a link function, μ is the mean of y, and the remaining terms X, β, τ, ϵ represent the same quantities as for the spatial linear models. The link function, g(⋅), “links” a function of μ to the linear term η, denoted here as Xβ + τ + ϵ, which is familiar from spatial linear models. Note that the linking of μ to η applies element-wise to each vector. Each link function g(⋅) has a corresponding inverse link function, g⁻¹(⋅). The inverse link function “links” a function of η to μ. Notice that for spatial generalized linear models, we are not modeling y directly as we do for spatial linear models, but rather we are modeling a function of the mean of y. Also notice that η is unconstrained but μ is usually constrained in some way (e.g., positive). Next we discuss the specific distributions and link functions used in spmodel.

spmodel allows fitting of spatial generalized linear models when y is a binomial (or Bernoulli), beta, Poisson, negative binomial, gamma, or inverse Gaussian random vector. For binomial and beta y, the logit link function is defined as $g(\boldsymbol{\mu}) = \ln(\frac{\boldsymbol{\mu}}{1 - \boldsymbol{\mu}}) = \boldsymbol{\eta}$, and the inverse logit link function is defined as $g^{-1}(\boldsymbol{\eta}) = \frac{\exp(\boldsymbol{\eta})}{1 + \exp(\boldsymbol{\eta})} = \boldsymbol{\mu}$. For Poisson, negative binomial, gamma, and inverse Gaussian y, the log link function is defined as g(μ) = ln (μ) = η, and the inverse log link function is defined as g⁻¹(η) = exp (η) = μ.

As with spatial linear models, spatial generalized linear models are fit in spmodel for point-referenced and areal data. The spglm() function is used to fit spatial generalized linear models for point-referenced data, and the spgautor() function is used to fit spatial generalized linear models for areal data. Though this vignette focuses on point-referenced data, spmodel’s other vignettes discuss spatial generalized linear models for areal data.

The spglm() function is quite similar to the splm() function, though one additional argument is required:

• family: the generalized linear model family (i.e., the distribution of y). family can be binomial, beta, poisson, nbinomial, Gamma, or inverse.gaussian.
  • family uses similar syntax as the family argument in glm().
  • One difference between family in spglm() compared to family in glm() is that the link function is fixed in spglm().

Next we show the basic features and syntax of spglm() using the moose data. We study the impact of elevation (elev) on the presence of moose (presence) observed at a site location in Alaska. presence equals one if at least one moose was observed at the site and zero otherwise. We view the first few rows of the moose data by running

moose

#> Simple feature collection with 218 features and 4 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 269085 ymin: 1416151 xmax: 419976.2 ymax: 1541763
#> Projected CRS: NAD83 / Alaska Albers
#> # A tibble: 218 × 5
#>     elev strat count presence           geometry
#>    <dbl> <chr> <dbl> <fct>           <POINT [m]>
#>  1  469. L         0 0        (293542.6 1541016)
#>  2  362. L         0 0        (298313.1 1533972)
#>  3  173. M         0 0        (281896.4 1532516)
#>  4  280. L         0 0        (298651.3 1530264)
#>  5  620. L         0 0        (311325.3 1527705)
#>  6  164. M         0 0        (291421.5 1518398)
#>  7  164. M         0 0        (287298.3 1518035)
#>  8  186. L         0 0        (279050.9 1517324)
#>  9  362. L         0 0        (346145.9 1512479)
#> 10  430. L         0 0        (321354.6 1509966)
#> # ℹ 208 more rows

We can visualize the distribution of moose presence by running

ggplot(moose, aes(color = presence)) +
  scale_color_viridis_d(option = "H") +
  geom_sf(size = 2) 

One example of a generalized linear model is a binomial (e.g., logistic) regression model. Binomial regression models are often used to model presence data such as this. To quantify the relationship between moose presence and elevation, we fit a spatial binomial regression model (a specific spatial generalized linear model) by running

binmod <- spglm(presence ~ elev, family = "binomial",
                data  = moose, spcov_type = "exponential")

The estimation method is specified via the estmethod argument, which has a default value of "reml" for restricted maximum likelihood. The other estimation method is "ml" for maximum likelihood.

Printing binmod shows the function call, the estimated fixed effect coefficients (on the link scale), the estimated spatial covariance parameters, and a dispersion parameter. The dispersion parameter is estimated for some spatial generalized linear models and changes the mean-variance relationship of y. For binomial regression models, the dispersion parameter is not estimated and is always fixed at one.

print(binmod)

#> 
#> Call:
#> spglm(formula = presence ~ elev, family = "binomial", data = moose, 
#>     spcov_type = "exponential")
#> 
#> 
#> Coefficients (fixed):
#> (Intercept)         elev  
#>   -0.874038     0.002365  
#> 
#> 
#> Coefficients (exponential spatial covariance):
#>        de         ie      range  
#> 3.746e+00  4.392e-03  3.203e+04  
#> 
#> 
#> Coefficients (Dispersion for binomial family):
#> dispersion  
#>          1

summary(binmod)

#> 
#> Call:
#> spglm(formula = presence ~ elev, family = "binomial", data = moose, 
#>     spcov_type = "exponential")
#> 
#> Deviance Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -1.5249 -0.8114  0.5600  0.8306  1.5757 
#> 
#> Coefficients (fixed):
#>              Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -0.874038   1.140953  -0.766    0.444
#> elev         0.002365   0.003184   0.743    0.458
#> 
#> Pseudo R-squared: 0.00311
#> 
#> Coefficients (exponential spatial covariance):
#>        de        ie     range 
#> 3.746e+00 4.392e-03 3.203e+04 
#> 
#> Coefficients (Dispersion for binomial family):
#> dispersion 
#>          1

tidy(binmod)

#> # A tibble: 2 × 5
#>   term        estimate std.error statistic p.value
#>   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
#> 1 (Intercept) -0.874     1.14       -0.766   0.444
#> 2 elev         0.00237   0.00318     0.743   0.458

glance(binmod)

#> # A tibble: 1 × 10
#>       n     p  npar value   AIC  AICc   BIC logLik deviance pseudo.r.squared
#>   <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>            <dbl>
#> 1   218     2     3  692.  698.  698.  708.  -346.     190.          0.00311

glmod <- spglm(presence ~ elev, family = "binomial", data = moose, spcov_type = "none")
glances(binmod, glmod)

#> # A tibble: 2 × 11
#>   model      n     p  npar value   AIC  AICc   BIC logLik deviance
#>   <chr>  <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
#> 1 binmod   218     2     3  692.  698.  698.  708.  -346.     190.
#> 2 glmod    218     2     0  715.  715.  715.  715.  -358.     302.
#> # ℹ 1 more variable: pseudo.r.squared <dbl>

augment(binmod)

#> Simple feature collection with 218 features and 7 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 269085 ymin: 1416151 xmax: 419057.4 ymax: 1541016
#> Projected CRS: NAD83 / Alaska Albers
#> # A tibble: 218 × 8
#>    presence  elev .fitted .resid    .hat  .cooksd .std.resid
#>  * <fct>    <dbl>   <dbl>  <dbl>   <dbl>    <dbl>      <dbl>
#>  1 0         469.   -1.73 -0.571 0.0500  0.00904      -0.586
#>  2 0         362.   -2.12 -0.477 0.0168  0.00198      -0.481
#>  3 0         173.   -2.15 -0.468 0.00213 0.000235     -0.469
#>  4 0         280.   -2.27 -0.444 0.00616 0.000615     -0.445
#>  5 0         620.   -1.40 -0.664 0.136   0.0402       -0.714
#>  6 0         164.   -1.90 -0.528 0.00260 0.000364     -0.528
#>  7 0         164.   -1.86 -0.538 0.00269 0.000392     -0.539
#>  8 0         186.   -1.61 -0.603 0.00332 0.000607     -0.604
#>  9 0         362.   -1.60 -0.606 0.0245  0.00474      -0.614
#> 10 0         430.   -1.24 -0.714 0.0528  0.0150       -0.734
#> # ℹ 208 more rows
#> # ℹ 1 more variable: geometry <POINT [m]>

moose_preds$preds <- predict(binmod, newdata = moose_preds, type = "response")

ggplot(moose_preds, aes(color = preds)) + 
  geom_sf(size = 2) +
  scale_color_viridis_c(limits = c(0, 1), option = "H")

moose_preds$preds <- NULL
augment(binmod, newdata = moose_preds, type.predict = "response", interval = "prediction")

#> Simple feature collection with 100 features and 5 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 269386.2 ymin: 1418453 xmax: 419976.2 ymax: 1541763
#> Projected CRS: NAD83 / Alaska Albers
#> # A tibble: 100 × 6
#>     elev strat .fitted .lower .upper           geometry
#>  * <dbl> <chr>   <dbl>  <dbl>  <dbl>        <POINT [m]>
#>  1  143. L       0.705 0.248   0.946 (401239.6 1436192)
#>  2  324. L       0.336 0.0373  0.868 (352640.6 1490695)
#>  3  158. L       0.263 0.0321  0.792 (360954.9 1491590)
#>  4  221. M       0.243 0.0360  0.734 (291839.8 1466091)
#>  5  209. M       0.742 0.270   0.957 (310991.9 1441630)
#>  6  218. L       0.191 0.0196  0.736 (304473.8 1512103)
#>  7  127. L       0.179 0.0226  0.673 (339011.1 1459318)
#>  8  122. L       0.241 0.0344  0.738 (342827.3 1463452)
#>  9  191  L       0.386 0.0414  0.902 (284453.8 1502837)
#> 10  105. L       0.494 0.114   0.882 (391343.9 1483791)
#> # ℹ 90 more rows

Function Glossary

Here we list some commonly used spmodel functions.

• AIC(): Compute the AIC.
• AICc(): Compute the AICc.
• anova(): Perform an analysis of variance.
• augment(): Augment data with diagnostics or new data with predictions.
• AUROC(): Compute the area under the receiver operating characteristic curve for binary spatial generalized linear models.
• BIC(): Compute the BIC.
• coef(): Return coefficients.
• confint(): Compute confidence intervals.
• cooks.distance(): Compute Cook’s distance.
• covmatrix(): Return covariance matrices.
• deviance(): Compute the deviance.
• esv(): Compute an empirical semivariogram.
• fitted(): Compute fitted values.
• glance(): Glance at a fitted model.
• glances(): Glance at multiple fitted models.
• hatvalues(): Compute leverage (hat) values.
• logLik(): Compute the log-likelihood.
• loocv(): Perform leave-one-out cross validation.
• model.matrix(): Return the model matrix (X).
• plot(): Create fitted model plots.
• predict(): Compute predictions and prediction intervals.
• pseudoR2(): Compute the pseudo r-squared.
• residuals(): Compute residuals.
• spautor(): Fit a spatial linear model for areal data (i.e., spatial autoregressive model).
• spautorRF(): Fit a random forest spatial residual model for areal data.
• spgautor(): Fit a spatial generalized linear model for areal data (i.e., spatial generalized autoregressive model).
• splm(): Fit a spatial linear model for point-referenced data (i.e., geostatistical model).
• splmRF(): Fit a random forest spatial residual model for point-referenced data.
• spglm(): Fit a spatial generalized linear model for point-referenced data (i.e., generalized geostatistical model).
• sprbeta(): Simulate spatially correlated beta random variables.
• sprbinom(): Simulate spatially correlated binomial (Bernoulli) random variables.
• sprgamma(): Simulate spatially correlated gamma random variables.
• sprinvgauss(): Simulate spatially correlated inverse Gaussian random variables.
• sprnbinom(): Simulate spatially correlated negative binomial random variables.
• sprnorm(): Simulate spatially correlated normal (Gaussian) random variables.
• sprpois(): Simulate spatially correlated Poisson random variables.
• summary(): Summarize fitted models.
• tidy(): Tidy fitted models.
• varcomp(): Compare variance components.
• vcov(): Compute variance-covariance matrices of estimated parameters.

For a full list of spmodel functions alongside their documentation, see the documentation manual. Documentation for methods of generic functions that are defined outside of spmodel can be found by running help("generic.spmodel", "spmodel") (e.g., help("summary.spmodel", "spmodel"), help("predict.spmodel", "spmodel"), etc.). Note that ?generic.spmodel is shorthand for help("generic.spmodel", "spmodel").

Support for Additional R Packages

spmodel provides support for:

1. The tidy(), glance() and augment() functions from the broom R package (Robinson, Hayes, and Couch 2021).
2. The emmeans R package (Lenth 2024) applied to splm(), spautor(), spglm(), and spgautor() model objects from spmodel.