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")