X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2Fplot.R;h=35e89ff40d096a91f368ef1c3d7438fff94e0aaf;hb=476a79d4b332d378716e320fdfd95fa3e1d7be9f;hp=f94f19a6da41b54f854de4d0ad0d359707ed8d1c;hpb=cbd88fe5729bf206a784238a2637aa60e697fcdc;p=morpheus.git

diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index f94f19a..35e89ff 100644
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -18,10 +18,10 @@ extractParam <- function(mr, x=1, y=1)
 #'
 #' @param mr Output of multiRun(), list of lists of functions results
 #' @param x Row index of the element inside the aggregated parameter
-#' @param y Colomn index of the element inside the aggregated parameter
+#' @param y Column index of the element inside the aggregated parameter
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' β <- matrix(c(1,-2,3,1),ncol=2)
 #' mr <- multiRun(...) #see bootstrap example in ?multiRun : return lists of mu_hat
 #' μ <- normalize(β)
@@ -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, xtitle="Parameter")
 {
-	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=c(2,1,1,1), lty=c(1,1,2,2), 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=xtitle, ylab="")
 
 	#print(o) #not returning o to avoid weird Jupyter issue... (TODO:)
 }
@@ -115,6 +104,9 @@ plotCoefs <- function(mr, params)
 #' Draw 3D map of objective function values
 #'
 #' @param N Number of starting points
+#' @param n Number of points in sample
+#' @param p Vector of proportions
+#' @param b Vector of biases
 #' @param β Regression matrix (target)
 #' @param link Link function (logit or probit)
 #'