One type per plot for coefs

[morpheus.git] / pkg / R / plot.R

# extractParam
#
# Extract successive values of a projection of the parameter(s)
#
# @inheritParams plotHist
#
extractParam <- function(mr, x=1, y=1)
{
	# Obtain L vectors where L = number of res lists in mr
	lapply( mr, function(mr_list) {
		sapply(mr_list, function(m) m[x,y])
	} )
}

#' plotHist
#'
#' Plot histogram
#'
#' @param mr Output of multiRun(), list of lists of functions results
#' @param x Row index of the element inside the aggregated parameter
#' @param y Column index of the element inside the aggregated parameter
#'
#' @examples
#' \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)
#' for (i in 1:2)
#'   mr[[i]] <- alignMatrices(res[[i]], ref=μ, ls_mode="exact")
#' plotHist(mr, 2, 1) #second row, first column}
#' @export
plotHist <- function(mr, x, y)
{
	params <- extractParam(mr, x, y)
	L = length(params)
	# Plot histograms side by side
	par(mfrow=c(1,L), cex.axis=1.5, cex.lab=1.5, mar=c(4.7,5,1,1))
	for (i in 1:L)
		hist(params[[i]], breaks=40, freq=FALSE, xlab="Parameter value", ylab="Density")
}

#' plotBox
#'
#' Draw boxplot
#'
#' @inheritParams plotHist
#'
#' @examples
#' #See example in ?plotHist
#' @export
plotBox <- function(mr, x, y)
{
	params <- extractParam(mr, x, y)
	L = length(params)
	# Plot boxplots side by side
	par(mfrow=c(1,L), cex.axis=1.5, cex.lab=1.5, mar=c(4.7,5,1,1))
	for (i in 1:L)
		boxplot(params[[i]], ylab="Parameter value")
}

#' plotCoefs
#'
#' Draw coefs estimations + standard deviations
#'
#' @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, idx)
{
	L <- nrow(mr[[1]][[1]])
	K <- ncol(mr[[1]][[1]])

	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)
			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(cex.axis=1.5, cex.lab=1.5, mar=c(4.7,5,1,1))
	params <- as.double(params)
	o <- order(params)
	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:)
}

#' plotQn
#'
#' 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)
#'
#' @export
plotQn <- function(N, n, p, β, b, link)
{
	d <- nrow(β)
	K <- ncol(β)
	io <- generateSampleIO(n, p, β, b, link)
	op <- optimParams(K, link, list(X=io$X, Y=io$Y))
	# N random starting points gaussian (TODO: around true β?)
	res <- matrix(nrow=d*K+1, ncol=N)
	for (i in seq_len(N))
	{
		β_init <- rnorm(d*K)
		par <- op$run( c(rep(1/K,K-1), β_init, rep(0,K)) )
		par <- op$linArgs(par)
		Qn <- op$f(par)
		res[,i] = c(Qn, par[K:(K+d*K-1)])
	}
	res #TODO: plot this, not just return it...
}