Changeset 973

Timestamp:

04/30/12 20:46:05 (13 months ago)

Author:

jjr8

Message:

Fixed #545: Fit GLM and Predict GLM From Rasters tools fail with RPy_Exception: could not find function "..."

Location:

MGET/Branches/Jason/PythonPackage/src/GeoEco/Statistics

Files:

• ClevelandPlotForDataframe.r (modified) (1 diff)
• DensityHistogramForDataframe.r (modified) (1 diff)
• FitGAMForDataframe.r (modified) (1 diff)
• FitGLMForDataframe.r (modified) (1 diff)
• FitLMEForDataframe.r (modified) (1 diff)
• FitTreeModelForDataframe.r (modified) (1 diff)
• PerfPlotForBinaryModel.r (modified) (1 diff)
• PredictLMForArcGISRasters.r (modified) (1 diff)
• ROCPlotForBinaryModel.r (modified) (1 diff)
• ScatterplotMatrixForDataframe.r (modified) (1 diff)

MGET/Branches/Jason/PythonPackage/src/GeoEco/Statistics/ClevelandPlotForDataframe.r

@@ -20 +20 @@
 ClevelandPlot <- function(df, transforms=NULL)
 {
+    library(stats)
+    
     # Apply the transforms (if any).
     

MGET/Branches/Jason/PythonPackage/src/GeoEco/Statistics/DensityHistogramForDataframe.r

@@ -20 +20 @@
 DensityHistogram <- function(df, densityField, transform=NULL, categoryField=NULL, .legend=NULL)
 {
+    library(stats)
+    
     ylim <- c(0, 0)
     

MGET/Branches/Jason/PythonPackage/src/GeoEco/Statistics/FitGAMForDataframe.r

@@ -20 +20 @@
 FitGAMForDataframe <- function(f, d, fam, rPackage, outputModelFile, method=NULL, optimizer=NULL, xVar=NULL, yVar=NULL, zVar=NULL, mVar=NULL, coordinateSystem=NULL, selectionMethod=NULL, logSelectionDetails=TRUE, writeSummaryFile=TRUE, writeDiagnosticPlots=TRUE, writeTermPlots=TRUE, partial.resid=FALSE, xAxis=TRUE, commonScale=TRUE, plotFileFormat="png", res=1000.0, width=3000.0, height=3000.0, pointSize=10.0, bg="white")
 {
+    library(stats)
+    
     message(sprintf("Fitting the GAM using the %s package...", rPackage))
     library(rPackage, character.only=TRUE)

MGET/Branches/Jason/PythonPackage/src/GeoEco/Statistics/FitGLMForDataframe.r

@@ -27 +27 @@
 
         # Fit the model.
-        
+
+        library(stats)
         message("Fitting the GLM...")
         model <- glm(f, data=na.omit(d), family=fam)

MGET/Branches/Jason/PythonPackage/src/GeoEco/Statistics/FitLMEForDataframe.r

@@ -20 +20 @@
 FitLMEForDataframe <- function(fixed, data, outputModelFile, random, correlation="NULL", method=NULL, xVar=NULL, yVar=NULL, zVar=NULL, mVar=NULL, coordinateSystem=NULL, writeSummaryFile=TRUE, writeDiagnosticPlots=TRUE, plotFileFormat="png", res=1000.0, width=3000.0, height=3000.0, pointSize=10.0, bg="white")
 {
+    library(stats)
+    
     # Fit the model.
 

MGET/Branches/Jason/PythonPackage/src/GeoEco/Statistics/FitTreeModelForDataframe.r

@@ -20 +20 @@
 FitTreeModelForDataframe <- function(f, d, outputModelFile, method, allowMissingCovariates=TRUE, minSplit=20, minBucket=7, cp=0.01, maxCompete=4, maxSurrogate=5, useSurrogate=2, surrogateStyle=0, xval=10, maxDepth=30, pruningMethod=NULL, pruningCP=NULL, xVar=NULL, yVar=NULL, zVar=NULL, mVar=NULL, coordinateSystem=NULL, writeSummaryFile=TRUE, writeDiagnosticPlots=TRUE, writeTreePlot=TRUE, writePrunedTreePlot=TRUE, plotFileFormat="png", res=1000, width=3000, height=3000, pointSize=10, bg="white", treePlotType=0, extra=1, percentage=TRUE, under=TRUE, clipRightLabels=TRUE, fallenLeaves=FALSE, branchType=0, branch=0.2, uniform=TRUE, digits=2, varlen=0, faclen=0, cex=NULL, tweak=1, compress=TRUE, ycompress=TRUE)
 {
+    library(stats)
     library(rpart)
     library(rpart.plot)

MGET/Branches/Jason/PythonPackage/src/GeoEco/Statistics/PerfPlotForBinaryModel.r

@@ -20 +20 @@
 PerfPlotForBinaryModel <- function(predictedValues, actualValues, measure1, measure2="cutoff", summaryStats=NULL, .title=NULL)
 {
+    library(stats)
+    
     # Create the prediction object.
     

MGET/Branches/Jason/PythonPackage/src/GeoEco/Statistics/PredictLMForArcGISRasters.r

@@ -20 +20 @@
 PredictLMForArcGISRasters <- function(model, rPackage, rastersForPredictors, outputResponseFile, outputErrorFile=NULL, ignoreOutOfRangeValues=FALSE, chunks=NULL)
 {
+    library(stats)
+    
     # Look up the min and max values for each predictor from the data
     # that was used to fit the model.

MGET/Branches/Jason/PythonPackage/src/GeoEco/Statistics/ROCPlotForBinaryModel.r

@@ -20 +20 @@
 ROCPlotForBinaryModel <- function(predictedValues, actualValues, cutoff="automatic", cutoffValue=NULL, colorize=TRUE, .title=NULL, summaryFile=NULL, bg="white")
 {
+    library(stats)
+    
     # Create the prediction object.
     

MGET/Branches/Jason/PythonPackage/src/GeoEco/Statistics/ScatterplotMatrixForDataframe.r

@@ -20 +20 @@
 ScatterplotMatrix <- function(df, transforms=NULL, .diag=NULL, lower=NULL, upper=NULL)
 {
+    library(stats)
+    
     panel.hist <- function(x, ...)
     {