grasp.in {grasp}R Documentation

Generalized Regression Analysis and Spatial Prediction, initializing session

Description

This function is used only when beginning with new data or when modifying names of dimensions of YYY, XXX, XXXpred

Usage

grasp.in(Ymat = YYY, Xmat = XXX, Xpred = XXXpred, Xlut = NULL)

Arguments

Ymat is the dataframe containing the response variables (RV): Each column represents a variable and each row an observation. Columns must have unique names not included in other variable names. Data type: single, double.
Xmat is the dataframe containing the predictor variables (PV): Each column represents a variable and each rows an observation. The number of rows must be the same as for YYY. Columns must have unique names not included in other variable names. Data type: single, double, integer, factor.
Xpred is the dataframe containing the explanatory data (PV) to predict from: Each column represents a variable and each rows a new observation to predict from. Columns must have unique names identical to those in XXX and missing data must be avoided
Xlut If predictions are made on large datasets (more than 250000 pixels) it is better to use lookup tables to describe models and build predictions in Arcview. XXXlut must contain 2 rows (rows 1 and 2) with the minimum and maximum values for each variable in the prediction dataset built as Grids in Arcview.

Author(s)

Anthony.Lehmann@unige.ch

References

  • A. Lehmann, J.R. Leathwick & J.McC. Overton, 2002. GRASP. Ecological Modelling, 157: 189-207
  • HOMEPAGE: http://www.unige.ch/ia/climate/grasp
  • RELATED: http://www.spacedoor.net

    See Also

    grasp

    Examples

    data(YYY) # reads in YYY,XXX and XXXpred demo dataset
    data(XXX)
    data(XXXpred)
    
    grasp.in(YYY,XXX,XXXpred) # initialize a new grasp session
    
    grasp( 2:3,c(4:6,8:9), title = "GRASP: ", path = "", gr.fam = "binomial", weights = TRUE, make.summary = TRUE, plot.maps = TRUE, plot.distry = TRUE, 
    plot.histograms = TRUE, plot.respvspred = TRUE, plot.xpred = TRUE,plot.correlation = TRUE, stepwise.models = TRUE, test = "AIC", contributions = TRUE, plot.contributions = TRUE, plot.models = TRUE, model.anova = TRUE, validate.models = TRUE, predictions = TRUE, plot.predictions = TRUE) # run a full grasp analysis on 2 responses with 5 predictors
    
    

    [Package grasp version 2.5-7 Index]