SSN2 website
titled “Adding Explanatory Variables to ‘SSN’ Objects Directly in R
Using StreamCat”.SSN2 website
titled “Fitting ‘SSN’ Models to Large Data Sets and Making Predictions
(i.e., Kriging)”size argument to the
local argument in ssn_lm() and
ssn_glm() from 100 to 200. This enhances the
approximation’s accuracy but can slightly increase computational
complexity.size argument to the
local argument in predict() and
augment() from 2000 to 4000. This enhances the
approximation’s accuracy but can slightly increase computational
complexity.local was specified (for large data sets) in
ssn_lm() or ssn_glm().local_index in
ssn_lm() or ssn_glm() model objects.spmodel version at least
0.7.0 so that AUROC() methods are properly
called.partition_factor from working properly when
non-NULL (#32).emmeans R
package for estimating marginal means of ssn_lm() and
ssn_glm() models.ssn_create_bigdist() function was added to create large
distance matrices using the filematrix R
package. Estimation for large data sets is performed by leveraging the
local argument to ssn_lm() and
ssn_glm(). Prediction for large data sets is performed by
leveraging the local argument to predict()
(and augment()). When local is used,
SSN2 looks for distance matrices created using
ssn_create_bigdist().ssn_import() so that it does not force an
overwrite of the netgeom column when it already
exists.verbose argument to ssn_import(),
ssn_import_predpts(), and createBinaryID() to
control whether warning messages are printed to the R
console.na.action argument to
predict.ssn_lm() and predict.ssn_glm()
functions to clarify that missing values in newdata return
an error.type argument in augment() for
ssn_glm() models to type.predict to match
broom::augment.glm().augment() for ssn_glm() models now returns
fitted values on the link scale by default to match
broom::augment.glm().type.residuals argument for
ssn_glm() models to match
broom::augment.glm().logLik() to match lm() and
glm() behavior. logLik() now returns a vector
with class logLik and attributes nobs and
df.AIC() and BIC()
from stats and removed SSN2-specific
AIC() methods.warning argument to glances() that
determines whether relevant warnings should be displayed or not.glances() about interpreting
likelihood-based statistics (e.g., AIC, AICc, BIC) when a one model has
estmethod = "ml" and another model has
estmethod = "reml".glances() about interpreting
likelihood-based statistics (e.g., AIC, AICc, BIC) when two models with
estmethod = "reml" have distinct formula
arguments.glances() about interpreting
likelihood-based statistics (e.g., AIC, AICc, BIC) when two models have
different sample sizes.glances() about interpreting
likelihood-based statistics (e.g., AIC, AICc, BIC) when two models have
different family supports (which can happen with ssn_glm()
models).cloud argument to Torgegram() to
return a cloud Torgegram.cex to
plot.Torgegram().Torgegram(); see
the robustargument to Torgegram()AUROC() function to compute the area under the
receiver operating characteristic (AUROC) curve for ssn_glm
models when family is "binomial" and the
response is binary (i.e., represents a single success or failure).type argument to loocv() when
cv_predict = TRUE and using ssn_glm() models
so that predictions may be obtained on the link or response scale."terms" prediction for
ssn_lm() and ssn_glm() models.scale and df arguments to
predict() for ssn_lm() models.dispersion argument to predict() for
ssn_glm() models.anova(model1, model2)) when
estmethod is "ml" for both models #25.anova(object1, object2) when the name of
object1 had special characters (e.g., $).ssn_glm() models when family = "beta" (#23).reexport.Rd to reflect changes in
spmodel v0.8.0’s handling of AIC() and
AICc().README.md) updates as part of a submission to Journal
of Open Source Software. Relevant issues associated with the review
are available at #11, #12, #13, #14, #15, #16, #17, #20, #21. The review is linked
here..ssn
folder that is accessed when importing SSN objects via
ssn_import().ssn_names() to return column names in the
edges, obs, and preds elements of
an SSN object.Matrix::rankMatrix(X, method = "tolNorm2") to
Matrix::rankMatrix(X, method = "qr") to enhance stability
when determining linear independence in X, the design
matrix of explanatory variables.X
has perfect collinearities (i.e., is not full rank).format_additive argument from
ssn_import() because of transition to geopackage support,
which eliminates the need to convert additive function values to
text.create_netgeom() function to create the
network geometry column for the edges, obs,
and preds elements in an SSN object.SSN_to_SSN2() that caused an error using
ssn_write() with no prediction sites.names.SSN() with ssn_names(), as
names.SSN() prevented proper naming of elements in the SSN
object.netgeometry to
netgeom to avoid exceeding the 10 character limit for
column/field names while writing to shapefiles (#2).family is missing in
ssn_glm() (#8).SSN_to_SSN2().Torgegram() that prevented intended
computation when cutoff was specified.plot.Torgegram() that occasionally
prevented proper spacing of the legend.ssn_glm() model objects (and their
summaries) when all covariance parameters were known.euclid_type
was "none".taildown_type was "spherical".ssn_glm() objects.
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