Intro to spind
spind is a package
dedicated to removing the spectre of spatial autocorrelation in your
spatial models. It contains many of the tools you need to make
predictions, assess model performance, and conduct multimodel inference
for 2-D gridded data sets using methods that are robust to spatial
autocorrelation.
The theory underlying the use of GEEs, WRMs, and many of the other tools in this package is covered elsewhere in the literature, and for the purposes of this vignette, we assume that you have already read those papers. If you haven’t, citations are included in the footnotes of this vignette as well as in the documentation of each function. We also assume that you have a working knowledge of how to use R. This vignette will focus on demonstrating how to utilize this package to create a robust model from spatially referenced data and subsequently assess its accuracy. Along the way, we will use a couple different data sets to examine how these functions work and investigate how one might use them to create a spatially robust model. This particular demonstration will focus on species distribution models (hereafter referred to as SDMs), but this general framework can be applied to any data set that is spatially structured (e.g. economic, sociological).
Generalized Estimating Equations (GEEs) for species distribution modeling 1
This package utilizes the functions already written for GEEs from the
packages gee2 and geepack3 and adapts them for
easy use in the context of an SDM. Let’s start with a fairly simple GEE
using the simulated musdata data set included in the
package. Before we get started though, note that GEE
requires that predictor variables be continuous
variables.
data(musdata)
data(carlinadata)
# Examine the structure to familiarize yourself with the data
?musdata
head(musdata)
?carlinadata
head(carlinadata)# Next, fit a simple GEE and view the output
coords <- musdata[ ,4:5]
mgee <- GEE(musculus ~ pollution + exposure, family = "poisson", data = musdata,
coord = coords, corstr = "fixed", scale.fix = FALSE)
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> ℹ The deprecated feature was likely used in the spind package.
#> Please report the issue at <https://github.com/levisc8/spind/issues>.
#> This warning is displayed once per session.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
summary(mgee, printAutoCorPars = TRUE)
#>
#> Call:
#> GEE(formula = musculus ~ pollution + exposure, family = "poisson",
#> data = musdata, coord = coords, corstr = "fixed", scale.fix = FALSE)
#> ---
#> Coefficients:
#> Estimate Std.Err z value Pr(>|z|)
#> (Intercept) -1.90475 1.31091 -1.4530 0.1462252
#> pollution 3.36216 0.91416 3.6779 0.0002352 ***
#> exposure -1.46348 0.88010 -1.6629 0.0963410 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---
#> QIC: 1139.159
#> ---
#> Autocorrelation of GLM residuals
#> [1] 0.685338504 0.509680590 0.363021118 0.247398654 0.144726020
#> [6] 0.084220961 0.050228656 0.022369044 -0.001985639 -0.027296083
#>
#> Autocorrelation of GEE residuals
#> [1] -0.001277974 -0.004261554 0.045280260 0.022738750 0.005821352
#> [6] 0.004289166 0.008311357 0.003437398 0.001030847 -0.010359040
#> ---
#> Autocorrelation parameters from fixed model
#> [1] "a=alpha^(d^v) , alpha=0.685 , v=1.093"
plot(mgee)
predictions <- predict(mgee, newdata = musdata)
# you can modify the plot itself by extracting it from the plot object and
# treating it as any other ggplot object.
library(ggplot2)
my_plot <- mgee$plot
# more of a base-R graphic feel
my_plot +
theme(plot.background = element_rect(fill = NA,
color = 'black',
size = 1.25))
#> Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
#> ℹ Please use the `linewidth` argument instead.
#> This warning is displayed once per session.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
As you can see, this package includes methods for plot,
summary and predict. These are useful in
evaluating model fit and autocorrelation of residuals compared to a
non-spatial model (in this case, a GLM with the same family as the GEE).
The plot.GEE method generates a plot of the autocorrelation
of the residuals from the fitted GEE and a GLM
of the same family for comparison. Note that a QIC (Quasi-information
criterion) score is reported as opposed to AIC. This is calculated based
on the method described in Hardin & Hilbe4,5 and is
implemented using the function qic.calc.
Also note that trying to fit GEEs with corstr = "fixed"
to large data sets (i.e. number of observations is approximately
sqrt(.Machine$integer.max)) will produce errors as the
resulting variance-covariance matrices will be too large to be handled
in R (you may well run into problems before this point
depending on how much RAM you have available). This is where fitting
clustered models can come in handy, as they work with smaller, more
manageable matrices. These can be specified by changing the
corstr to either "quadratic" or
"exchangeable".
Wavelet Revised Models (WRMs) 6
Next, we’ll examine the other main model that is introduced in this
package - the Wavelet Revised Model. These are implemented using wavelet
transforms from the waveslim package.7 Let’s start with a
fairly simple WRM using the same musdata data set as above.
As with GEE, WRM also requires that predictor
variables are continuous.
mwrm <- WRM(musculus ~ pollution + exposure, family = "poisson",
data = musdata, coord = coords, level = 1)
plot(mwrm)
summary(mwrm)
#>
#> Call:
#> WRM(formula = musculus ~ pollution + exposure, family = "poisson",
#> data = musdata, coord = coords, level = 1)
#>
#> Pearson Residuals:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -1.6135878 -0.3057118 0.0047507 -0.0003873 0.3038946 3.0620363
#> ---
#> Coefficients:
#> Estimate Std.Err z value Pr(>|z|)
#> (Intercept) -1.9360 1.9177 -1.0095 0.312717
#> pollution 3.1841 1.2251 2.5991 0.009348 **
#> exposure -1.2286 1.5063 -0.8156 0.414723
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---
#> Number of observations n: 400 , n.eff: 300 , AIC: 1110.845
#>
#> Number of iterations: 7
#> ---
#> Autocorrelation of glm.residuals
#> [1] 0.685338504 0.509680590 0.363021118 0.247398654 0.144726020
#> [6] 0.084220961 0.050228656 0.022369044 -0.001985639 -0.027296083
#> Autocorrelation of wavelet.residuals
#> [1] 0.024855393 -0.086311686 0.007820356 0.024501828 -0.016578686
#> [6] 0.002798656 -0.002977017 -0.004611334 0.018150352 -0.008727321
predictions <- predict(mwrm, newdata = musdata)WRM has many of the same features as GEE.
The plot.WRM allows you to visually examine the
autocorrelation of residuals from a GLM of the same error family as your
WRM. Methods for predict and summary allow you
to examine outputs from the model using the same code as you might use
for a GLM. However, note that this reports an AIC score, rather than a
QIC score as in the GEE. You can extract and modify the
ggplot output from WRM as well using the
plot slot in the WRM class.
Other features specific to WRMs
WRM has a number of other model-specific functions that
you may find useful in diagnosing model fit and understanding your
results. For example, you might want to plot the variance and/or
covariance of each of your variables as a function of
level. The covar.plot function allows you to
visually examine the wavelet relationships from your model. You can
select whether to plot the variance or covariance using
plot argument which accepts "var" or
"covar" as inputs. Note that we are going to switch to the
carlinadata data set now.
coords <- carlinadata[ ,4:5]
wave_covariance <- covar.plot(carlina.horrida ~ aridity + land.use - 1,
data = carlinadata, coord = coords, wavelet = "d4",
wtrafo = 'modwt', plot = 'covar')
wave_variance <- covar.plot(carlina.horrida ~ aridity + land.use - 1,
data = carlinadata, coord = coords, wavelet = "d4",
wtrafo = 'modwt', plot = 'var')
wave_variance$result
#> [,1] [,2] [,3] [,4] [,5]
#> carlina.horrida 0.7235 0.1792 0.0628 0.0242 0.0093
#> aridity 0.0691 0.1025 0.2028 0.3588 0.2657
#> land.use 0.7556 0.1851 0.0420 0.0119 0.0044
wave_covariance$result
#> [,1] [,2] [,3] [,4] [,5]
#> carlina.horrida-aridity 0.0368 0.0450 0.0623 0.0780 0.0466
#> carlina.horrida-land.use 0.4782 0.1191 0.0332 0.0126 0.0055
# view plots side by side
library(gridExtra)
grid.arrange(wave_variance$plot, wave_covariance$plot)
You may also want to view the smooth components of your wavelets at
different scales. For this, we offer the
upscale function. upscale allows you to
visually examine your data at a number of different levels of
scale which controls the resolution of the grid cells in
your data set.8 It also offers the option to adjust padding
settings so you can see how that influences your smooth components. The
default padding level is the mean value of your input vector, but it can
be easily switched using the pad argument, which works the
same way as in the other WRM functions. A quick example
below using carlinadata data set.
NOTE: The color.maps argument is
soft-deprecated from spind >= v2.2.
Multi-model inference with GEEs and WRMs
spind provides a couple of frameworks for conducting
multi-model inference analyses and some helper functions to assist you
when examining the results. The first that we’ll examine here is the
step.spind function, which implements step-wise model
selection. The process is loosely based on MASS::stepAIC
and stats::step, but is specific to classes
GEE and WRM. For GEEs, step.spind
uses models with the lowest QIC scores to determine what the next step
will be. For WRMs, you have the option of using AIC or AICc (AIC
corrected for small sample sizes) using the logical AICc
argument.
Currently, the function only supports backwards model selection. In
other words, you have to start with all of the variables in your model
formula and remove them in a stepwise fashion. We hope to add forward
model selection methods shortly. Additionally, step.spind
is written to always respect the hierarchy of variables in the model and
currently the user cannot directly override this. For example,
step.spind would not remove race while
retaining I(race^2). We may change that in the future, but
it will remain like this at least until the next major release.
Currently, it recognizes polynomial variables by matching variable names
located inside of I(var^some_power) and interaction terms
by searching for var1:var2 in the model terms. If you want
to use a higher order polynomial variable and are not worried about the
variable hierarchy, you can create a separate variable
(i.e. race_2) and use that in the model.
We’ll go through an example of step.spind using a GEE on
the birthwt data set in the MASS package
below. The data in birthwt aren’t at all related to SDMs
and are not spatially structured, but we hope that in using this data
set, we will demonstrate how this function can work with many types of
data sets.
# For demonstration only. We are artificially imposing a grid structure
# on data that is not actually spatial data
library(MASS)
data(birthwt)
x <- rep(1:14, 14)
y <- as.integer(gl(14, 14))
coords <- cbind(x[-(190:196)], y[-(190:196)])
formula <- formula(low ~ age + lwt + race + smoke + ftv + bwt + I(race^2))
mgee <- GEE(formula, family = "gaussian", data = birthwt,
coord = coords, corstr = "fixed",scale.fix = TRUE)
mwrm <- WRM(formula, family = "gaussian", data = birthwt,
coord = coords, level = 1)
ssgee <- step.spind(mgee, birthwt)
#> Iteration: 1
#> Single term deletions
#> Deleted Term: age
#> --------------------
#> Deleted.Vars Quasi.Lik QIC
#> 1 <none> -52.68206 112.4177
#> 2 age -52.67027 111.9314
#> 3 lwt -52.74725 112.3100
#> 4 race -52.68782 112.1267
#> 5 smoke -52.68652 112.1349
#> 6 ftv -52.70359 112.1632
#> 7 bwt -121.62394 299.1973
#> 8 I(race^2) -52.69176 112.1329
#>
#> Iteration: 2
#> Single term deletions
#> Deleted Term: race
#> --------------------
#> Deleted.Vars Quasi.Lik QIC
#> 1 <none> -52.67027 111.9314
#> 2 lwt -52.72965 111.8146
#> 3 race -52.67038 111.6474
#> 4 smoke -52.67100 111.6566
#> 5 ftv -52.72503 111.7364
#> 6 bwt -121.56174 299.0924
#> 7 I(race^2) -52.67607 111.6567
#>
#> -----
#> Model hierarchy violated by last removal
#> New Deleted Term: smoke
#> Previously deleted term added back into model
#> -----
#> Iteration: 3
#> Single term deletions
#> Deleted Term: race
#> --------------------
#> Deleted.Vars Quasi.Lik QIC
#> 1 <none> -52.67100 111.6566
#> 2 lwt -52.72809 111.5311
#> 3 race -52.67366 111.3802
#> 4 ftv -52.72790 111.4711
#> 5 bwt -123.16568 300.0954
#> 6 I(race^2) -52.67890 111.3877
#>
#> -----
#> Model hierarchy violated by last removal
#> New Deleted Term: I(race^2)
#> Previously deleted term added back into model
#> -----
#> Iteration: 4
#> Single term deletions
#> Deleted Term: ftv
#> --------------------
#> Deleted.Vars Quasi.Lik QIC
#> 1 <none> -52.67890 111.3877
#> 2 lwt -52.72805 111.2494
#> 3 race -52.76311 111.3122
#> 4 ftv -52.73630 111.2088
#> 5 bwt -123.33017 298.6147
#>
#> Iteration: 5
#> Single term deletions
#> Deleted Term: lwt
#> --------------------
#> Deleted.Vars Quasi.Lik QIC
#> 1 <none> -52.73630 111.2088
#> 2 lwt -52.78793 111.0717
#> 3 race -52.82335 111.1415
#> 4 bwt -123.35038 298.6351
#>
#> Iteration: 6
#> Single term deletions
#> Deleted Term: race
#> --------------------
#> Deleted.Vars Quasi.Lik QIC
#> 1 <none> -52.78793 111.0717
#> 2 race -52.86072 110.9656
#> 3 bwt -123.12477 295.8817
#>
#> Iteration: 7
#> Single term deletions
#> Deleted Term: <none>
#> --------------------
#> Deleted.Vars Quasi.Lik QIC
#> 1 <none> -52.86072 110.9656
#> 2 bwt -123.98743 296.2879
#>
#>
#> ---------------
#> Best model found:
#> low ~ bwt
sswrm <- step.spind(mwrm, birthwt, AICc = TRUE)
#> Iteration: 1
#> Single term deletions
#> Deleted Term: race
#> --------------------
#> Deleted.Vars LogLik AIC AICc
#> 1 <none> -36.27411 90.54822 91.55381
#> 2 age -36.27965 88.55930 89.35930
#> 3 lwt -36.30071 88.60143 89.40143
#> 4 race -36.03376 88.06752 88.86752
#> 5 smoke -36.13543 88.27087 89.07087
#> 6 ftv -36.25766 88.51532 89.31532
#> 7 bwt -88.97789 193.95579 194.75579
#> 8 I(race^2) -36.03879 88.07758 88.87758
#>
#> -----
#> Model hierarchy violated by last removal
#> New Deleted Term: I(race^2)
#> Previously deleted term added back into model
#> -----
#> Iteration: 2
#> Single term deletions
#> Deleted Term: smoke
#> --------------------
#> Deleted.Vars LogLik AIC AICc
#> 1 <none> -36.03879 88.07758 88.87758
#> 2 age -36.04158 86.08317 86.70195
#> 3 lwt -36.09072 86.18144 86.80022
#> 4 race -36.11776 86.23552 86.85430
#> 5 smoke -35.90087 85.80174 86.42052
#> 6 ftv -36.02003 86.04007 86.65885
#> 7 bwt -88.71971 191.43941 192.05820
#>
#> Iteration: 3
#> Single term deletions
#> Deleted Term: ftv
#> --------------------
#> Deleted.Vars LogLik AIC AICc
#> 1 <none> -35.90087 85.80174 86.42052
#> 2 age -35.90797 83.81593 84.27747
#> 3 lwt -35.95683 83.91366 84.37520
#> 4 race -35.97398 83.94797 84.40950
#> 5 ftv -35.88997 83.77995 84.24149
#> 6 bwt -90.79731 193.59462 194.05616
#>
#> Iteration: 4
#> Single term deletions
#> Deleted Term: age
#> --------------------
#> Deleted.Vars LogLik AIC AICc
#> 1 <none> -35.88997 83.77995 84.24149
#> 2 age -35.89084 81.78169 82.10956
#> 3 lwt -35.94335 81.88670 82.21457
#> 4 race -35.96301 81.92602 82.25389
#> 5 bwt -90.81649 191.63298 191.96085
#>
#> Iteration: 5
#> Single term deletions
#> Deleted Term: lwt
#> --------------------
#> Deleted.Vars LogLik AIC AICc
#> 1 <none> -35.89084 81.78169 82.10956
#> 2 lwt -35.94877 79.89754 80.11494
#> 3 race -35.95486 79.90972 80.12711
#> 4 bwt -91.14883 190.29766 190.51505
#>
#> Iteration: 6
#> Single term deletions
#> Deleted Term: race
#> --------------------
#> Deleted.Vars LogLik AIC AICc
#> 1 <none> -35.94877 79.89754 80.11494
#> 2 race -36.00167 78.00334 78.13307
#> 3 bwt -91.88479 189.76958 189.89931
#>
#> Iteration: 7
#> Single term deletions
#> Deleted Term: <none>
#> --------------------
#> Deleted.Vars LogLik AIC AICc
#> 1 <none> -36.00167 78.00334 78.13307
#> 2 bwt -92.26134 188.52268 188.58719
#>
#>
#> ---------------
#> Best model found:
#> low ~ bwt
best.mgee <- GEE(ssgee$model, family = "gaussian", data = birthwt,
coord = coords, corstr = "fixed",scale.fix = TRUE)
best.wrm <- WRM(sswrm$model, family = "gaussian", data = birthwt,
coord = coords, level = 1)
summary(best.mgee, printAutoCorPars = FALSE)
#>
#> Call:
#> GEE(formula = ssgee$model, family = "gaussian", data = birthwt,
#> coord = coords, corstr = "fixed", scale.fix = TRUE)
#> ---
#> Coefficients:
#> Estimate Std.Err t value Pr(>|t|)
#> (Intercept) 1.2492e+00 4.9121e-01 2.5430 0.01099 *
#> bwt -3.0919e-04 6.5913e-05 -4.6909 2.72e-06 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---
#> QIC: 110.9656
#> ---
#> Autocorrelation of GLM residuals
#> [1] 0.837748633 0.724407532 0.602588671 0.500754270 0.387294592
#> [6] 0.275433941 0.147728669 0.008716423 -0.130798183 -0.268641655
#>
#> Autocorrelation of GEE residuals
#> [1] 0.43453709 0.35186795 0.27457621 0.21231229 0.10255028 0.08028419
#> [7] 0.07174312 0.04070057 0.02919975 -0.06904364
summary(best.wrm)
#>
#> Call:
#> WRM(formula = sswrm$model, family = "gaussian", data = birthwt,
#> coord = coords, level = 1)
#>
#> Pearson Residuals:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.32375 -0.03533 -0.01311 0.00000 0.03328 0.42378
#> ---
#> Coefficients:
#> Estimate Std.Err t value Pr(>|t|)
#> (Intercept) 1.2809e+00 2.3089e-09 5.5477e+08 < 2.2e-16 ***
#> bwt -3.4305e-04 7.4898e-06 -4.5802e+01 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---
#> Number of observations n: 189 , n.eff: 142 , AIC: 78.00334
#>
#> Number of iterations: 2
#> ---
#> Autocorrelation of glm.residuals
#> [1] 0.837748633 0.724407532 0.602588671 0.500754270 0.387294592
#> [6] 0.275433941 0.147728669 0.008716423 -0.130798183 -0.268641655
#> Autocorrelation of wavelet.residuals
#> [1] 0.427865614 0.201246818 0.139896894 0.103437367 0.046485859 0.045779123
#> [7] 0.025277466 0.034175107 0.005430915 0.055360212Additionally, we offer multimodel inference tools for GEEs and WRMs
which are loosely based on the MuMIn package. These are
implemented in mmiWMRR and mmiGEE. They enable
you to examine the effect that the grid resolution and variable
selection have on the resulting regressions, and then select the
appropriate model for subsequent analyses. Note that
mmiWMRR has two more arguments than mmiGEE
that must be specified.
# Example for WRMs
data(carlinadata)
coords <- carlinadata[ ,4:5]
wrm <- WRM(carlina.horrida ~ aridity + land.use, family = "poisson",
data = carlinadata, coord = coords, level = 1, wavelet = "d4")
ms1 <- scaleWMRR(carlina.horrida ~ aridity + land.use, family = "poisson",
data = carlinadata, coord = coords, scale = 1,
wavelet = 'd4', trace = FALSE)
mmi <- mmiWMRR(wrm, data = carlinadata, scale = 1, detail = TRUE)
# Example for GEEs
library(MASS)
data(birthwt)
# impose an artificial (not fully appropriate) grid structure
x <- rep(1:14, 14)
y <- as.integer(gl(14, 14))
coords <- cbind(x[-(190:196)], y[-(190:196)])
formula <- formula(low ~ race + smoke + bwt)
mgee <- GEE(formula, family = "gaussian", data = birthwt,
coord = coords, corstr = "fixed", scale.fix = TRUE)
mmi <- mmiGEE(mgee, birthwt)Finally, we offer one further model selection procedure specific to
WRMs. rvi.plot uses mmiWMRR and creates a plot
of the relative importance of each explanatory variable as a function of
the resolution of the grid (in other words, as a function of the
scale argument in mmiWMRR). It will also print
the resulting model selection tables to the console.
data(carlinadata)
coords <- carlinadata[ ,4:5]
rvi <- rvi.plot(carlina.horrida ~ aridity + land.use, family = "poisson",
data = carlinadata, coord = coords, maxlevel = 4,
detail = TRUE, wavelet = "d4")
rvi$rvi
#> level=1 level=2 level=3 level=4
#> aridity 0.005 1 1 1
#> land.use 1.000 1 1 0
rvi$plot
Goodness of fit and model performance
You may also find that a model not implemented by this package works
best for your data. We’ve implemented some spatially corrected accuracy
measures that you can use to assess goodness of model fit. The first two
of these are categorized according to whether or not their outputs are
dependent on the chosen threshold and first appeared in
spind v1.0 9. th.dep (threshold dependent)
and th.indep (threshold independent) are designed to work
on any number of model types, all you need is a set of actual values,
predictions, and their associated coordinates. We’ll use the
hook data set to see how these work.
df <- hook[ ,1:2]
coords <- hook[ ,3:4]
# Threshold dependent metrics
th.dep.indices <- th.dep(data = df, coord = coords, spatial = TRUE)
# Confusion Matrix
th.dep.indices$cm
#> [,1] [,2] [,3] [,4]
#> [1,] 5 2 0 0
#> [2,] 3 1 1 3
#> [3,] 2 0 0 8
#> [4,] 2 3 0 70
# Kappa statistic
th.dep.indices$kappa
#> [1] 0.628529
# Threshold independent metrics
th.indep.indices <- th.indep(data = df, coord = coords,
spatial = TRUE)
# AUC
th.indep.indices$AUC
#> [1] 0.9424119
# TSS
th.indep.indices$TSS
#> [1] 0.7425474
# AUC plot
th.indep.indices$plot
Finally, many analyses require a calculation of spatial
autocorrelation. To that end, we include the function acfft
(AutoCorrelation Fast Fourier Transform) to calculate spatial
autocorrelation using Moran’s I statistic. While many other packages
include functions to perform this analysis, ours provides improved
efficiency by harnessing the power of fast Fourier transforms which
reduce the time needed to compute the statistic. A quick example below
using a GLM and the musdata data set.
coords <- musdata[ ,4:5]
mglm <- glm(musculus ~ pollution + exposure, family = "poisson",
data = musdata)
ac <- acfft(coords, resid(mglm, type = "pearson"),
lim1 = 0, lim2 = 1, dmax = 10)
ac
#> [1] 0.685338504 0.509680590 0.363021118 0.247398654 0.144726020
#> [6] 0.084220961 0.050228656 0.022369044 -0.001985639 -0.027296083Note that you can adjust the number of distance bins to examine in
acfft using the dmax argument. The default is
10. Finally, you can adjust the size of the distance bins by adjusting
the values of lim1 and lim2 to meet your
needs.
Wrapping up
Hopefully, you are now ready to utilize GEEs and WRMs to conquer the world of spatial modeling. However, if this vignette has not served its purpose and you still have questions about how to use these tools, please let us know. Of course, no package is complete without bugs and we are always trying to improve our code. If you find any bugs that need squashing, have suggestions for additional functionality or improvements to existing functionality (or this vignette), please don’t hesitate to contact us10.
Carl G & Kuehn I, 2007. Analyzing Spatial Autocorrelation in Species Distributions using Gaussian and Logit Models, Ecol. Model. 207, 159 - 170]↩︎
Carey, V. J., 2006. Ported to R by Thomas Lumley (versions 3.13, 4.4, version 4.13)., B. R. gee: Generalized Estimation Equation solver. R package version 4.13-11.↩︎
Yan, J., 2004. geepack: Generalized Estimating Equation Package. R package version 0.2.10.↩︎
Hardin, J.W. & Hilbe, J.M. (2003) Generalized Estimating Equations. Chapman and Hall, New York.↩︎
Barnett et al. Methods in Ecology & Evolution 2010, 1, 15-24.↩︎
Carl, G., Kuehn, I. (2010): A wavelet-based extension of generalized linear models to remove the effect of spatial autocorrelation. Geographical Analysis 42 (3), 323 - 337↩︎
Whitcher, B. (2005) Waveslim: basic wavelet routines for one-, two- and three-dimensional signal processing. R package version 1.5.↩︎
Carl G, Doktor D, Schweiger O, Kuehn I (2016) Assessing relative variable importance across different spatial scales: a two-dimensional wavelet analysis. Journal of Biogeography 43: 2502-2512.↩︎
Carl G, Kuehn I (2017) Spind: a package for computing spatially corrected accuracy measures. Ecography 40: 675-682. doi: 10.1111/ecog.02593↩︎
Contact email - [email protected] or visit the Github repo and create an issue at https://github.com/levisc8/spind/issues.↩︎