Convert network precision score to the edge weight.

precision_to_edge-weight.Rmd

I used the precision curve only to decide the cutoff value. I chose a precision cutoff 0.2, then the correspondent normalized ranking is *~0.01*. The meaning of the normalized ranking is explained [here](https://github.com/takayasaito/precrec/issues/12).

Therefore, to calculate the **weight** value for precision 0.2, we got the following calculation: (rank-1)/(n-1) = 0.01. In this case, `n=nrow(dfg_ortho_label)`, so the rank is *649*. We went back to the *649* row of the `dfg_ortho_label`, the `weight` is 1.57. Therefore, we kept the edge that has weight higher than 1.57.

```{r, fig.width=5, fig.height=5, fig.align="center"}
scurve_os_b <- evalmod(scores = dfg_ortho_label$weight, labels = dfg_ortho_label$label, 
                     mode = "basic")
sos_df_b <- fortify(scurve_os_b)
p2 <- ggplot(subset(sos_df_b, curvetype == "precision"), aes(x = x, y = y))+
   geom_point(color = "blue", size = 0.4)+ ylim(0:1)
p2 + geom_hline(yintercept = 0.2, color = "black", linetype = "dashed") + 
  geom_vline(xintercept = 0.022, color = "black", linetype = "dashed") +
  xlab("normalized rank") +
  ylab("Precision") +
  geom_vline(xintercept = 0.015, color = "black", linetype = "dashed")

```