Revert "version bump 0.8.3"

This reverts commit 128a349.

Author: grlloyd

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: structToolbox
 Type: Package
 Title: Some tools bult using the struct package
-Version: 0.8.3
+Version: 0.8.2
 Author: Gavin Rhys Lloyd
 Maintainer: Gavin Rhys Lloyd <[email protected]>
 Description: Extends the class templates provided by the struct package to provide methods for training PCA, PLS models with cross-validation, permutation testing etc.
@@ -44,7 +44,6 @@ Collate:
     'glog_class.R'
     'grid_search_1d_class.R'
     'hca_class.R'
-    'kfold_xval2_class.R'
     'kfold_xval_class.R'
     'kfold_xval_charts.R'
     'knn_impute_class.R'
@@ -76,7 +75,7 @@ Collate:
     'vec_norm_class.R'
     'wilcox_test_class.R'
     'zzz.R'
-Depends: struct (>= 0.4.1), struct(< 0.5.0)
+Depends: struct (== 0.4.1)
 Imports: ggplot2,
  pmp,
  gridExtra,
@@ -104,7 +103,7 @@ Suggests:
  sbcms,
  Rtsne
 Remotes: computational-metabolomics/pmp,
-  computational-metabolomics/struct,
+  computational-metabolomics/struct@v0.4.1,
   computational-metabolomics/sbcms
 VignetteBuilder: knitr
 biocViews: WorkflowStep

NAMESPACE

@@ -42,7 +42,6 @@ export(grid_search_1d)
 export(gs_line)
 export(hca_dendrogram)
 export(kfold_xval)
-export(kfold_xval2)
 export(kfoldxcv_grid)
 export(kfoldxcv_metric)
 export(knn_impute)

R/PCA_class.R

@@ -19,8 +19,7 @@ PCA<-setClass(
         outputs.eigenvalues='data.frame',
         outputs.ssx='numeric',
         outputs.correlation='data.frame',
-        outputs.that='dataset',
-        outputs.xhat='dataset'
+        outputs.that='dataset'
     ),
     prototype = list(name='Principal Component Analysis (PCA)',
         description='PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that describe the maximum variation present in the dataset.',
@@ -108,12 +107,6 @@ setMethod(f="model.predict",
         dataset.data(S)=that
         output.value(M,'that')=S
 
-        xhat=as.matrix(that)%*%as.matrix(t(P))
-        xhat=as.data.frame(xhat)
-        rownames(that)=rownames(X)
-        colnames(xhat)=colnames(X)
-        M$xhat=dataset(data=xhat,sample_meta=D$sample_meta,variable_meta=D$variable_meta)
-
         return(M)
     }
 )

R/forward_selection_by_rank_class.R

@@ -167,6 +167,7 @@ eval_loess=function(x,X,Y,k=10,p=0.66)
     # Y = observed values
     # k = number of replicates
     # p = proportion in training
+
     residual=numeric(k)
     for (i in 1:k)
     {
@@ -180,16 +181,9 @@ eval_loess=function(x,X,Y,k=10,p=0.66)
         yy2=Y[X %in% xx2]
 
 
-        loessMod <- loess(yy ~ xx, span=x)
-
-        # check for NaN
-        if (any(is.nan(loessMod$fitted))){
-            residual[i]=99999
-        } else {
-
-            smoothed=stats::predict(loessMod,newdata=xx2)
-            residual[i]=sum((smoothed-yy2)^2)
-        }
+        loessMod <- loess(yy ~ xx, span=x) # 25% smoothing span
+        smoothed=stats::predict(loessMod,newdata=xx2)
+        residual[i]=sum((smoothed-yy2)^2)
     }
     return(sqrt(mean(residual)))
 }

R/kfold_xval2_class.R

@@ -11,8 +11,9 @@ permutation_test2<-setClass(
     slots=c(
         params.number_of_permutations='numeric',
         params.collect='character',
-        outputs.metric_permuted='data.frame',
-        outputs.metric_unpermuted='data.frame',
+        outputs.results.permuted='data.frame',
+        outputs.results.unpermuted='data.frame',
+        outputs.metric='data.frame',
         outputs.collected='entity'
     ),
     prototype = list(name='permutation test',
@@ -39,6 +40,9 @@ setMethod(f="run",
               WF=models(I)
               n=param.value(I,'number_of_permutations')
 
+              all_results_permuted=data.frame('actual'=rep(y[,1],n),'predicted'=rep(y[,1],n),'permutation'=0)
+              all_results_unpermuted=data.frame('actual'=rep(y[,1],n),'predicted'=rep(y[,1],n),'permutation'=0)
+
               collected=list(permuted=list(),unpermuted=list())
 
               for (i in 1:n)
@@ -138,8 +142,8 @@ setMethod(f="run",
 
               }
               # store results
-              output.value(I,'metric_permuted')=all_results_permuted
-              output.value(I,'metric_unpermuted')=all_results_unpermuted
+              output.value(I,'results.permuted')=all_results_permuted
+              output.value(I,'results.unpermuted')=all_results_unpermuted
               return(I)
           }
 )

R/permutation_test2_class.R

@@ -19,14 +19,9 @@ setMethod(f="calculate",
     signature=c('r_squared'),
     definition=function(obj,Y,Yhat)
     {
-
-        M=matrix(colMeans(Y),nrow=1)
-        O=matrix(1,nrow=nrow(Y),ncol=1)
-        M=O %*% M
-
-        SSR  = sum(sum((Yhat-M)^2))
-        SSE  = sum(sum((Y-Yhat)^2))
-        SSTO = sum(sum((Y-M)^2))
+        SSR  = sum((Yhat-mean(Y))^2)
+        SSE  = sum((Y-Yhat)^2)
+        SSTO = sum((Y-mean(Y))^2)
 
         R2=1-(SSE/SSTO)