One type per plot for coefs

diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index 614f5c5..8acd2c0 100644 (file)
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -64,48 +64,37 @@ plotBox <- function(mr, x, y)
 #'
 #' @inheritParams plotHist
 #' @param params True value of parameters matrix
+#' @param idx List index to process in mr
 #'
 #' @examples
 #' #See example in ?plotHist
 #' @export
-plotCoefs <- function(mr, params)
+plotCoefs <- function(mr, params, idx)
 {
-       nf <- length(mr)
        L <- nrow(mr[[1]][[1]])
        K <- ncol(mr[[1]][[1]])
 
-       params_hat <- vector("list", nf)
-       stdev <- vector("list", nf)
-       for (i in 1:nf)
-       {
-               params_hat[[i]] <- matrix(nrow=L, ncol=K)
-               stdev[[i]] <- matrix(nrow=L, ncol=K)
-       }
+       params_hat <- matrix(nrow=L, ncol=K)
+       stdev <- matrix(nrow=L, ncol=K)
        for (x in 1:L)
        {
                for (y in 1:K)
                {
                        estims <- extractParam(mr, x, y)
-                       for (i in 1:nf)
-                       {
-                               params_hat[[i]][x,y] <- mean(estims[[i]])
-#                              stdev[[i]][x,y] <- sqrt( mean( (estims[[i]] - params[x,y])^2 ) )
-                               # HACK remove extreme quantile in estims[[i]] before computing sd()
-                               stdev[[i]][x,y] <- sd( estims[[i]] [ estims[[i]] < max(estims[[i]]) & estims[[i]] > min(estims[[i]]) ] )
-                       }
+                       params_hat[x,y] <- mean(estims[[idx]])
+#                      stdev[x,y] <- sqrt( mean( (estims[[idx]] - params[x,y])^2 ) )
+                       # HACK remove extreme quantile in estims[[i]] before computing sd()
+                       stdev[x,y] <- sd( estims[[idx]] ) #[ estims[[idx]] < max(estims[[idx]]) & estims[[idx]] > min(estims[[idx]]) ] )
                }
        }
 
-       par(mfrow=c(1,nf), cex.axis=1.5, cex.lab=1.5, mar=c(4.7,5,1,1))
+       par(cex.axis=1.5, cex.lab=1.5, mar=c(4.7,5,1,1))
        params <- as.double(params)
        o <- order(params)
-       for (i in 1:nf)
-       {
-               avg_param <- as.double(params_hat[[i]])
-               std_param <- as.double(stdev[[i]])
-               matplot(cbind(params[o],avg_param[o],avg_param[o]+std_param[o],avg_param[o]-std_param[o]),
-                       col=1, lty=c(1,5,2,2), type="l", lwd=2, xlab="param", ylab="value")
-       }
+       avg_param <- as.double(params_hat)
+       std_param <- as.double(stdev)
+       matplot(cbind(params[o],avg_param[o],avg_param[o]+std_param[o],avg_param[o]-std_param[o]),
+               col=1, lty=c(1,5,3,3), type="l", lwd=2, xlab="Parameter", ylab="Value")
 
        #print(o) #not returning o to avoid weird Jupyter issue... (TODO:)
 }