fixing dataset

Author: muschellij2

DESCRIPTION

@@ -1,12 +1,11 @@
 Package: oasis
 Type: Package
-Title: OASIS Lesion Segmentation 
+Title: Multiple Sclerosis Lesion Segmentation using Magnetic Resonance Imaging (MRI)
 Version: 1.0
 Date: 2015-09-02
 Author: Elizabeth Sweeney, John Muschelli
 Maintainer: Elizabeth Sweeney <[email protected]>
-Description:  This package trains and makes predictions from the OASIS method, described in detail in the paper "OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI" (http://www.sciencedirect.com/science/article/pii/S2213158213000235). OASIS is a method for multiple sclerosis (MS) lesion segmentation on structural magnetic resonance image (MRI) studies.  OASIS creates probability maps of lesion presence using the FLAIR, T2, T1, and PD structural MRI volumes. This packages allows for training of the OASIS model and prediction of OASIS probability maps from a trained model with user supplied studies that have a gold standard lesion segmentation masks. The package will also create OASIS probaility maps for MRI studies using the OASIS model from the OASIS paper if no gold standard lesion segmentation masks are avaliable. 
-Citation: 
+Description:  Trains and makes predictions from the OASIS method, described in detail in the paper "OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI" (http://www.sciencedirect.com/science/article/pii/S2213158213000235). OASIS is a method for multiple sclerosis (MS) lesion segmentation on structural magnetic resonance image (MRI) studies.  OASIS creates probability maps of lesion presence using the FLAIR, T2, T1, and PD structural MRI volumes. This packages allows for training of the OASIS model and prediction of OASIS probability maps from a trained model with user supplied studies that have a gold standard lesion segmentation masks. The package will also create OASIS probaility maps for MRI studies using the OASIS model from the OASIS paper if no gold standard lesion segmentation masks are avaliable. 
 Depends:
   fslr,
   oro.nifti

R/oasis_predict.R

@@ -89,7 +89,7 @@ oasis_predict <- function(flair, ##flair volume of class nifti
 
   ## make the model predictions 
   if(is.null(model)){
-  predictions <- predict(oasis_model, newdata = oasis_dataframe, type = 'response')    
+  predictions <- predict( oasis::oasis_model, newdata = oasis_dataframe, type = 'response')    
   } else { 
   predictions <- predict(model, newdata = oasis_dataframe, type = 'response')    
   }

R/oasis_training.R

@@ -21,7 +21,7 @@ oasis_training <- function(..., ##dataframes from function
   oasis_model <- glm(formula = GoldStandard ~ FLAIR_10 *FLAIR  +
                        FLAIR_20*FLAIR + PD_10 *PD  + PD_20 *PD 
                      + T2_10 *T2 +  T2_20 *T2 + T1_10 *T1 
-                     + T1_20 *T1 , data = oasis_dataframe, family=binomial)
+                     + T1_20 *T1 , data = train_vectors_multi, family=binomial)
 
   ##clean up the oasis model 
   oasis_model$y = c()

oasis.Rproj

@@ -16,4 +16,4 @@ BuildType: Package
 PackageUseDevtools: Yes
 PackageInstallArgs: --no-multiarch --with-keep.source
 PackageCheckArgs: --as-cran
-PackageRoxygenize: rd,collate,namespace
+PackageRoxygenize: rd,collate,namespace,vignette