linearRegEvaluate(modelObj, newdata, origRespName, newRespName, origPredNames, newPredNames, modelName)
modelObj | an object of class "lm". |
newdata | a data frame to use for the evaluations. It must contain columns with names newRespName and newPredNames. |
origRespName | a character string naming the response variable used in modelObj. |
newRespName | a character string naming the response variable in newdata. This is often the same as origRespName. |
origPredNames | a single character string containing the predictor variables used in modelObj. The values must be tab delimited. |
newPredNames | a single character string naming the predictor variable in newdata. This is often the same as origPredNames. The values must be tab delimited. |
modelName | a character string containing the name of the model in Spotfire. This is used to construct the names of the components in the return list. |
evalSummaryTable | a single column data frame containing summary information for the model evaluation. This includes R^2 and the mean square error. |
evalPlotData | a data frame containing the predictions, residuals and Normal quantiles of the residuals, all computed from newdata. |
evalPlotDesc |
a character matrix containing a description of the visualizations that can
be created in Spotfire using the data in evalPlotData.
The columns of the matrix are:
MenuName
the text to appear in the Spotfire menu.
PlotType
the type of visualization to create
Xdatatable
the name of the data table for the x-axis variable.
If the data table is generated by this function (i.e. fitPlotData) the
name will have the prefix modelName_.
Xcolumn
the name of the x-axis column in Xdatatable.
Ydatatable
the name of the data table for the y-axis variable.
If the data table is generated by this function (i.e. fitPlotData) the
name will have the prefix modelName_.
Ycolumn
the name of the y-axis column in Ydatatable.
Title
the title for the visualization.
|
zlm <- linearRegFit(ozone ~ wind + temperature, data=Sdatasets::air, modelName="lmExample") respName <- zlm$fitSummaryTable[1,1] predNamesString <- zlm$fitSummaryTable[2, 1] predNames <- strsplit(predNamesString, "\t", fixed=TRUE)[[1]] ap <- Sdatasets::air[1:20, c(respName, predNames)] zEval <- linearRegEvaluate(zlm$modelObj, newdata=ap, respName, respName, predNamesString, predNamesString, modelName="lmExample")