@@ -1,7 +1,7 @@

 Package: structToolbox

 Type: Package

 Title: Data processing & analysis tools for Metabolomics and other omics

-Version: 1.18.1

+Version: 1.18.2

 Authors@R: c(

     person(

         c("Gavin","Rhys"),

@@ -1,3 +1,6 @@

+Changes 1.18.2

++ fix pca_biplot

+

 Changes 1.81.1

 + fix missing interactions for ANOVA outputs

@@ -277,29 +277,10 @@ setMethod(f="chart_plot",

         P=output_value(dobj,'loadings')

         Ev=output_value(dobj,'eigenvalues')

-        # eigenvalues were square rooted when training PCA

-        Ev=Ev[,1]

-        Ev=Ev^2

-        

-        ## unscale the scores

-        #ev are the norms of scores

-        Ts=as.matrix(Ts) %*% diag(1/Ev) # these are normalised scores

-        

-        # scale scores and loadings by alpha

-        Ts=Ts %*% diag(Ev^(1-opt$scale_factor))

-        P=as.matrix(P) %*% diag(Ev^(opt$scale_factor))

-        

-        # additionally scale the loadings

-        sf=min(max(abs(Ts[,opt$components[1]]))/max(abs(P[,opt$components[1]])),

-            max(abs(Ts[,opt$components[2]]))/max(abs(P[,opt$components[2]])))

-        Ts=as.data.frame(Ts)

-        

-        rownames(Ts)=rownames(dobj$scores) # fix dimnames for SE object

-        colnames(Ts)=colnames(dobj$scores)

-        dobj$scores$data=as.data.frame(Ts) # nb object not returned, so only temporary scaling

+        sf = max(abs(Ts)) / max(abs(P)) * opt$scale_factor

         # plot

-        A=data.frame("x"=P[,opt$components[1]]*sf*0.8,"y"=P[,opt$components[2]]*sf*0.8)

+        A=data.frame("x"=P[,opt$components[1]]*sf,"y"=P[,opt$components[2]]*sf)

         C=pca_scores_plot(points_to_label=obj$points_to_label,xcol=obj$components[1],ycol=obj$components[2],factor_name=obj$factor_name)

         out=chart_plot(C,dobj)