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SSN2 is an R package for spatial statistical modeling
and prediction on stream networks, including models based on in-stream
distance. Models are created using moving average constructions. Spatial
linear models, including explanatory variables, can be fit with
(restricted) maximum likelihood. Mapping and other graphical functions
are included. It is the successor to the SSN R package. See
the SSN2 website for more: https://usepa.github.io/SSN2/.
If you use SSN2 in a formal publication or report,
please cite it. Citing SSN2 lets us devote more resources
to it in the future. View the SSN2 citation by running
citation(package = "SSN2")#>
#> To cite SSN2 in publications use:
#>
#> Dumelle M, Peterson EE, Ver Hoef JM, Pearse A, Isaak DJ (2024). SSN2:
#> The next generation of spatial stream network modeling in R. Journal
#> of Open Source Software, 9(99), 6389,
#> https://doi.org/10.21105/joss.06389
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {{SSN2}: The next generation of spatial stream network modeling in {R}},
#> author = {Michael Dumelle and Erin E. Peterson and Jay M. {Ver Hoef} and Alan Pearse and Daniel J. Isaak},
#> journal = {Journal of Open Source Software},
#> year = {2024},
#> volume = {9},
#> number = {99},
#> pages = {6389},
#> doi = {10.21105/joss.06389},
#> url = {https://doi.org/10.21105/joss.06389},
#> publisher = {The Open Journal},
#> }Streams provide vital aquatic services that sustain wildlife, provide
drinking and irrigation water, and support recreational and cultural
activities. Data are often collected at various locations on a stream
network and used to characterize some scientific phenomenon in the
stream. Spatial stream network (SSN) models use a spatial statistical
modeling framework to describe unique and complex dependencies on a
stream network resulting from a branching network structure, directional
water flow, and differences in flow volume. SSN models relate a response
variable to one or more explanatory variables, a spatially independent
error term (i.e., nugget), and up to three spatially dependent error
terms: tail-down errors, tail-up errors, and Euclidean errors. Tail-down
errors restrict spatial dependence to flow-connected sites (i.e., water
flows from an upstream to a downstream site) and incorporate spatial
weights (i.e., additive function) to describe the branching network
between them. Tail-up errors describe spatial dependence between both
flow-connected and flow-unconnected (i.e., sites that share a common
downstream junction but not flow) sites, but spatial weights are not
required. Euclidean errors describe spatial dependence between sites
based on Euclidean distance and are governed by factors not confined to
the stream network like regional geology. The SSN2
R package is designed to help users fit SSN models to
their stream network data.
Install and load the most recent approved version from CRAN by running
# install the most recent approved version from CRAN
install.packages("SSN2")
# load the most recent approved version from CRAN
library(SSN2)Install and load the most recent version ofSSN2 from
GitHub by running
# Installing from GitHub requires you first install the remotes package
install.packages("remotes")
# install the most recent version from GitHub
remotes::install_github("USEPA/SSN2", ref = "main")
# load the most recent development version from GitHub
library(SSN2)Install and load the most recent development version
ofSSN2 from GitHub by running
# Installing from GitHub requires you first install the remotes package
install.packages("remotes")
# install the most recent version from GitHub
remotes::install_github("USEPA/SSN2", ref = "develop")
# load the most recent development version from GitHub
library(SSN2)SSN2We encourage users to report bugs and/or contribute to
SSN2. For more detail on how to do this, please see our
contributing guide (CONTRIBUTING.md).
There are several ways to get help with SSN2:
Below we provide a brief example showing how to use
SSN2. For a thorough introduction to the software, see our
introductory vignette linked
here. For a list of all functions available in SSN2,
see our function reference linked
here.
We load SSN2, copy the .ssn object that
comes with SSN2 to the temporary directory, and create
stream distance matrices used for modeling by running
library(SSN2)
copy_lsn_to_temp()
path <- paste0(tempdir(), "/MiddleFork04.ssn")
mf04p <- ssn_import(path, predpts = "pred1km")
ssn_create_distmat(mf04p, predpts = "pred1km", overwrite = TRUE)We fit and summarize an SSN model explaining summer water temperatue
(Summer_mn) as a function of elevation
(ELEV_DEM) and precipitation (AREAWTMAP) with
a exponential, spherical, and Gaussian structures for the tail-up,
tail-down, and Euclidean errors, respectively, by running
ssn_mod <- ssn_lm(
formula = Summer_mn ~ ELEV_DEM + AREAWTMAP,
ssn.object = mf04p,
tailup_type = "exponential",
taildown_type = "spherical",
euclid_type = "gaussian",
additive = "afvArea"
)
summary(ssn_mod)#>
#> Call:
#> ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,
#> tailup_type = "exponential", taildown_type = "spherical",
#> euclid_type = "gaussian", additive = "afvArea")
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -3.6393 -2.0646 -0.5952 0.2143 0.7497
#>
#> Coefficients (fixed):
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 76.195041 7.871574 9.680 < 2e-16 ***
#> ELEV_DEM -0.026905 0.003646 -7.379 1.6e-13 ***
#> AREAWTMAP -0.009099 0.004461 -2.040 0.0414 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Pseudo R-squared: 0.6124
#>
#> Coefficients (covariance):
#> Effect Parameter Estimate
#> tailup exponential de (parsill) 3.800e+00
#> tailup exponential range 4.194e+06
#> taildown spherical de (parsill) 4.480e-01
#> taildown spherical range 1.647e+05
#> euclid gaussian de (parsill) 1.509e-02
#> euclid gaussian range 4.496e+03
#> We tidy, glance at, and augment (with diagnostics) the fitted model by running
tidy(ssn_mod, conf.int = TRUE)#> # A tibble: 3 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 76.2 7.87 9.68 0 60.8 91.6
#> 2 AREAWTMAP -0.00910 0.00446 -2.04 4.14e- 2 -0.0178 -0.000356
#> 3 ELEV_DEM -0.0269 0.00365 -7.38 1.60e-13 -0.0341 -0.0198glance(ssn_mod)#> # A tibble: 1 × 9
#> n p npar value AIC AICc logLik deviance pseudo.r.squared
#> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 45 3 7 59.3 73.3 76.3 -29.6 41.9 0.612head(augment(ssn_mod))#> Simple feature collection with 6 features and 9 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -1515032 ymin: 2529461 xmax: -1512690 ymax: 2531883
#> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version
#> # A tibble: 6 × 10
#> Summer_mn ELEV_DEM AREAWTMAP .fitted .resid .hat .cooksd .std.resid pid
#> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 11.4 1977 940. 14.4 -3.07 0.0915 0.0962 -1.78 1
#> 2 10.7 1984 1087. 12.9 -2.20 0.114 0.00471 -0.352 2
#> 3 10.4 1993 1087. 12.7 -2.25 0.0372 0.00724 -0.764 3
#> 4 10.1 2007 1087. 12.3 -2.18 0.0251 0.00153 -0.427 4
#> 5 10.1 2009 1087. 12.3 -2.13 0.0374 0.000583 -0.216 5
#> 6 9.81 2012 1109. 12.0 -2.16 0.0602 0.0150 -0.863 6
#> # ℹ 1 more variable: geometry <POINT [m]>
We make predictions at the prediction sites (pred1km) by
running predict() (or augment():
preds <- predict(ssn_mod, newdata = "pred1km", interval = "prediction")
head(preds)#> fit lwr upr
#> 1 14.64383 14.27138 15.01627
#> 2 15.00608 14.65017 15.36198
#> 3 14.79235 14.27414 15.31057
#> 4 14.96884 14.45492 15.48276
#> 5 15.15182 14.73770 15.56595
#> 6 15.12783 14.76358 15.49208SSN2 imports the following R
packages:
plot()).ssn_create_distmat()).randcov_initial()) and generic functions (e.g.,
loocv()).confint()).tidy()).SSN2 suggests the following R
packages:
SSN objects from the SSN
R package compatible with SSN2.This project is licensed under the GNU General Public License, GPL-3.
The United States Environmental Protection Agency (EPA) GitHub project code is provided on an “as is” basis and the user assumes responsibility for its use. EPA has relinquished control of the information and no longer has responsibility to protect the integrity , confidentiality, or availability of the information. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by EPA. The EPA seal and logo shall not be used in any manner to imply endorsement of any commercial product or activity by EPA or the United States Government.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.