Package index
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AIC(<ssn_lm>)AIC(<ssn_glm>)AICc(<ssn_lm>)AICc(<ssn_glm>) - Compute AIC and AICc of fitted model objects
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anova(<ssn_lm>)anova(<ssn_glm>)tidy(<anova.ssn_lm>)tidy(<anova.ssn_glm>) - Compute analysis of variance and likelihood ratio tests of fitted model objects
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augment(<ssn_lm>)augment(<ssn_glm>) - Augment data with information from fitted model objects
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coef(<ssn_lm>)coefficients(<ssn_lm>)coef(<ssn_glm>)coefficients(<ssn_glm>) - Extract fitted model coefficients
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confint(<ssn_lm>)confint(<ssn_glm>) - Confidence intervals for fitted model parameters
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cooks.distance(<ssn_lm>)cooks.distance(<ssn_glm>) - Compute Cook's distance
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copy_lsn_to_temp() - Copy LSN to temporary directory
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covmatrix(<ssn_lm>)covmatrix(<ssn_glm>) - Create a covariance matrix
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create_netgeom() - Create netgeom column in SSN object
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deviance(<ssn_lm>)deviance(<ssn_glm>) - Fitted model deviance
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fitted(<ssn_lm>)fitted.values(<ssn_lm>)fitted(<ssn_glm>)fitted.values(<ssn_glm>) - Extract model fitted values
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formula(<ssn_lm>)formula(<ssn_glm>) - Model formulae
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glance(<ssn_lm>)glance(<ssn_glm>) - Glance at a fitted model object
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glances(<ssn_lm>)glances(<ssn_glm>) - Glance at many fitted model objects
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hatvalues(<ssn_lm>)hatvalues(<ssn_glm>) - Compute leverage (hat) values
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influence(<ssn_lm>)influence(<ssn_glm>) - Regression diagnostics
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labels(<ssn_lm>)labels(<ssn_glm>) - Find labels from object
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logLik(<ssn_lm>)logLik(<ssn_glm>) - Extract log-likelihood
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loocv(<ssn_lm>)loocv(<ssn_glm>) - Perform leave-one-out cross validation
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mf04p - Imported SSN object from the MiddleFork04.ssn data folder
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MiddleFork04.ssn - MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset
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model.frame(<ssn_lm>)model.frame(<ssn_glm>) - Extract the model frame from a fitted model object
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model.matrix(<ssn_lm>)model.matrix(<ssn_glm>) - Extract the model matrix from a fitted model object
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plot(<ssn_lm>)plot(<ssn_glm>) - Plot fitted model diagnostics
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plot(<Torgegram>) - Plot Torgegram
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predict(<ssn_lm>)predict(<ssn_glm>) - Model predictions (Kriging)
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print(<SSN>) - Print SSN object
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print(<ssn_lm>)print(<ssn_glm>)print(<summary.ssn_lm>)print(<summary.ssn_glm>)print(<anova.ssn_lm>)print(<anova.ssn_glm>) - Print values
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pseudoR2(<ssn_lm>)pseudoR2(<ssn_glm>) - Compute a pseudo r-squared
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residuals(<ssn_lm>)resid(<ssn_lm>)rstandard(<ssn_lm>)residuals(<ssn_glm>)resid(<ssn_glm>)rstandard(<ssn_glm>) - Extract fitted model residuals
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ssn_create_distmat() - Calculate Hydrologic Distances for an
SSNobject
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ssn_get_data() - Get a data.frame from an SSN, ssn_lm, or ssn_glm object
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ssn_get_netgeom() - Extract netgeom column
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ssn_get_stream_distmat() - Get stream distance matrices from an
SSNobject
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ssn_glm() - Fitting Generalized Linear Models for Spatial Stream Networks
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ssn_import() - Import
SSNobject
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ssn_import_predpts() - Import prediction points into an SSN, ssn_lm, or ssn_glm object
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tailup_initial()taildown_initial()euclid_initial()nugget_initial() - Create a covariance parameter initial object
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ssn_lm() - Fitting Linear Models for Spatial Stream Networks
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ssn_names() - Return names of data in an SSN object
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tailup_params()taildown_params()euclid_params()nugget_params() - Create covariance parameter objects.
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ssn_put_data() - Put an sf data.frame in an SSN object
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ssn_simulate()ssn_rbeta()ssn_rbinom()ssn_rgamma()ssn_rinvgauss()ssn_rnbinom()ssn_rnorm()ssn_rpois() - Simulate random variables on a stream network
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ssn_split_predpts() - Split a prediction dataset in an
SSNobject
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ssn_subset() - Subset an
SSNobject
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SSN_to_SSN2() - Convert object from
SpatialStreamNetworkclass toSSNclass
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ssn_update_path() - Update path in an SSN object
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ssn_write() - write an SSN object
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summary(<SSN>) - Summarize an SSN object
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summary(<ssn_lm>)summary(<ssn_glm>) - Summarize a fitted model object
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tidy(<ssn_lm>)tidy(<ssn_glm>) - Tidy a fitted model object
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Torgegram() - Compute the empirical semivariogram
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varcomp(<ssn_lm>)varcomp(<ssn_glm>) - Variability component comparison
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vcov(<ssn_lm>)vcov(<ssn_glm>) - Calculate variance-covariance matrix for a fitted model object