NEWS.md
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., $).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.