The user can choose from the set of characteristics associated with each particle and use the capabilities of the GIS to selectively trace the movement of water discharging from specific cells in the model grid. ![]() MODTOOLS uses particle information recorded by MODPATH such as the row, column, or layer of the model grid, to generate a set of characteristics associated with each particle. MODTOOLS uses the particle data calculated by MODPATH to construct several types of GIS output. MODPATH is used to calculate ground-water flow paths using the results of MODFLOW and MODPATH-PLOT can be used to display the flow paths in various ways. Geological Survey Three-Dimensional Particle Tracking Post-Processing Programs. MODPATH and its companion program, MODPATH-PLOT, are collectively called the U.S. ![]() MODTOOLS can also be used to translate data from MODPATH into GIS files. MODTOOLS uses the data arrays input to or output by MODFLOW during a ground-water flow simulation to construct several types of GIS output files. Geological Survey Modular Three-Dimensional Finite-Difference Ground-Water Model. MODFLOW is the recognized name for the U.S. MODTOOLS translates data into a GIS software called ARC/INFO. Learning with continuous classes.MODTOOLS is a set of computer programs for translating data of the ground-water model, MODFLOW, and the particle-tracker, MODPATH, into a Geographic Information System (GIS). SVM, GLM, Multinom: There are no implementations for these models so far.Ī data frame with class c("ain", "ame") for Neural Networks: The method used here is "Garson weights". Number of times each predictor was involved in a split by using the The underlying function can also return the The same strategy is applied to rule-based models and boosted Of training set samples, the importance scores may be close to Other predictors may be usedįrequently in splits, but if the terminal nodes cover only a handful For example, the predictor in the first splitĪutomatically has an importance measurement of 100 percent since all Percentage of training set samples that fall into all the terminal ![]() Rpart, Random Forest: VarImp.rpart and VarImp.randomForest are wrappers around the importance functions from the rpart or randomForest packages, respectively.Ĭ5.0: C5.0 measures predictor importance by determining the Linear Models: For linear models there's a fine package relaimpo available on CRAN containing several interesting approaches for quantifying the variable importance. The number of digits for printing the "VarImp" table Linear models accept one of "lmg", "pmvd", "first", "last", "betasq", "pratt". Some models have more than one type available to produce a variable importance. The maximum number of rows to be reported The name of the column, the importance table should be ordered after Parameters to pass to the specific VarImp methods Logical, should the importance values be scaled to 0 and 100? ) # S3 method for class 'VarImp' print ( x, digits = 3. ) # S3 method for class 'VarImp' plot ( x, sort = TRUE, maxrows = NULL, main = "Variable importance". ) # Default S3 method: VarImp ( x, scale = FALSE, sort = TRUE. ) # S3 method for class 'FitMod' VarImp ( x, scale = FALSE, sort = TRUE, type = NULL. VarImp ( x, scale = FALSE, sort = TRUE.
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