Update README.md

Author: guokai8

README.md

@@ -9,11 +9,6 @@ _o2plsda_ provides functions to do O2PLS-DA analysis for multiple omics integrat
 In order to avoid overfitting of the model, the optimal number of latent variables for each model structure was estimated using group-balanced MCCV. The package could use the group information when we select the best paramaters with cross-validation. In cross-validation (CV) one minimizes a certain measure of error over some parameters that should be determined a priori. Here, we have three parameters: (nc, nx, ny). A popular measure is the prediction error ||Y - \hat{Y}||, where \hat{Y} is a prediction of Y. In our case the O2PLS method is symmetric in X and Y, so we minimize the sum of the prediction errors: 
 ||X - \hat{X}||+||Y - \hat{Y}||. 
 
-And we also calculate the the average Q^2 values:
-
-Q^2 = 1 - err / Var_{total};    
-
-err = Var_{expected} - Var_{estimated};   
 
 Here nc should be a positive integer, and nx and ny should be non-negative. The best integers are then the minimizers of the prediction error.
 
@@ -48,38 +43,38 @@ set.seed(123)
 ## ncores : parallel paramaters for large datasets
 cv <- o2cv(X,Y,1:5,1:3,1:3,group=group,nr_folds = 10)
 #####################################
-The best parameters are nc = 5, nx = 3, ny = 3
+The best parameters are nc = 1, nx = 2, ny = 3
 #####################################
-The Qxy is 0.0741821432027437 and the RMSE is: 2.02464376258545
+The the RMSE is: 1.98186790425324
 #####################################
 ```
 
-Then we can do the O2PLS analysis with nc = 5, nx = 3, ny =3. You can also select the best paramaters by looking at the cross validation results.
+Then we can do the O2PLS analysis with nc = 1, nx = 2, ny =3. You can also select the best paramaters by looking at the cross validation results.
 ```{r}
-fit <- o2pls(X,Y,5,2,3)
+fit <- o2pls(X,Y,1,2,3)
 summary(fit)
 ######### Summary of the O2PLS results #########
-### Call o2pls(X, Y, nc= 5 , nx= 2 , ny= 3 ) ###
+### Call o2pls(X, Y, nc= 1 , nx= 2 , ny= 3 ) ###
 ### Total variation 
 ### X: 4900 ; Y: 4900  ###
-### Total modeled variation ### X: 0.261 ; Y: 0.314  ###
+### Total modeled variation ### X: 0.12 ; Y: 0.16  ###
 ### Joint, Orthogonal, Noise (proportions) ###
                X     Y
-Joint      0.186 0.199
-Orthogonal 0.075 0.115
-Noise      0.739 0.686
-### Variation in X joint part predicted by Y Joint part: 0.901 
-### Variation in Y joint part predicted by X Joint part: 0.902 
+Joint      0.041 0.047
+Orthogonal 0.079 0.113
+Noise      0.880 0.840
+### Variation in X joint part predicted by Y Joint part: 0.934 
+### Variation in Y joint part predicted by X Joint part: 0.934 
 ### Variation in each Latent Variable (LV) in Joint part: 
-    LV1   LV2   LV3   LV4   LV5
-X 0.039 0.040 0.040 0.034 0.033
-Y 0.049 0.043 0.036 0.037 0.033
+    LV1
+X 0.041
+Y 0.047
 ### Variation in each Latent Variable (LV) in X Orthogonal part: 
-   LV1   LV2
-X 0.04 0.036
+    LV1   LV2
+X 0.044 0.035
 ### Variation in each Latent Variable (LV) in Y Orthogonal part: 
     LV1   LV2   LV3
-Y 0.045 0.037 0.034
+Y 0.046 0.035 0.033
 
 ############################################
 
@@ -96,7 +91,7 @@ plot(fit,type="loading",var="Xjoint", group=group,repel=F,rotation=TRUE)
 
 Do the OPLSDA based on the O2PLS results
 ```{r}
-res <- oplsda(fit,group, nc=5)
+res <- oplsda(fit,group, nc=1)
 plot(res,type="score", group=group)
 vip <- vip(res)
 plot(res,type="vip", group = group, repel = FALSE,order=TRUE)