# extractParam
#
-# Extract successive values of a projection of the parameter(s)
+# Extract successive values of a projection of the parameter(s).
+# The method works both on a list of lists of results,
+# or on a single list of parameters matrices.
#
# @inheritParams plotHist
#
-extractParam <- function(mr, x=1, y=1)
+.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])
- } )
+ if (is.list(mr[[1]]))
+ {
+ # Obtain L vectors where L = number of res lists in mr
+ return ( lapply( mr, function(mr_list) {
+ sapply(mr_list, function(m) m[x,y])
+ } ) )
+ }
+ sapply(mr, function(m) m[x,y])
}
#' plotHist
#'
-#' Plot histogram
+#' Plot compared histograms of a single parameter (scalar)
#'
#' @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
+#' @param ... Additional graphical parameters (xlab, ylab, ...)
#'
#' @examples
#' \donttest{
#' β <- matrix(c(1,-2,3,1),ncol=2)
-#' mr <- multiRun(...) #see bootstrap example in ?multiRun : return lists of mu_hat
+#' mr <- multiRun(...) #see bootstrap example in ?multiRun
+#' #mr[[i]] is a list of estimated parameters matrices
#' 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)
+plotHist <- function(mr, x, y, ...)
{
- params <- extractParam(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))
+ args <- list(...)
for (i in 1:L)
- hist(params[[i]], breaks=40, freq=FALSE, xlab="Parameter value", ylab="Density")
+ {
+ hist(params[[i]], breaks=40, freq=FALSE,
+ xlab=ifelse("xlab" %in% names(args), args$xlab, "Parameter value"),
+ ylab=ifelse("ylab" %in% names(args), args$ylab, "Density"))
+ }
}
#' plotBox
#'
-#' Draw boxplot
+#' Draw compared boxplots of a single parameter (scalar)
#'
#' @inheritParams plotHist
#'
#' @examples
-#' #See example in ?plotHist
+#' \donttest{
+#' β <- matrix(c(1,-2,3,1),ncol=2)
+#' mr <- multiRun(...) #see bootstrap example in ?multiRun
+#' #mr[[i]] is a list of estimated parameters matrices
+#' μ <- normalize(β)
+#' for (i in 1:2)
+#' mr[[i]] <- alignMatrices(res[[i]], ref=μ, ls_mode="exact")
+#' plotBox(mr, 2, 1) #second row, first column}
+#'
#' @export
-plotBox <- function(mr, x, y, xtitle="")
+plotBox <- function(mr, x, y, ...)
{
- params <- extractParam(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))
+ args <- list(...)
for (i in 1:L)
- boxplot(params[[i]], xlab=xtitle, ylab="Parameter value")
+ {
+ boxplot(params[[i]],
+ ifelse("ylab" %in% names(args), args$ylab, "Parameter value"))
+ }
}
#' plotCoefs
#'
-#' Draw coefs estimations + standard deviations
+#' Draw a graph of (averaged) coefficients estimations with their standard,
+#' deviations ordered by mean values.
+#' Note that the drawing does not correspond to a function; it is just a
+#' convenient way to visualize the estimated parameters.
#'
-#' @inheritParams plotHist
-#' @param params True value of parameters matrix
-#' @param idx List index to process in mr
+#' @param mr List of parameters matrices
+#' @param params True value of the parameters matrix
+#' @param ... Additional graphical parameters
#'
#' @examples
-#' #See example in ?plotHist
+#' \donttest{
+#' β <- matrix(c(1,-2,3,1),ncol=2)
+#' mr <- multiRun(...) #see bootstrap example in ?multiRun
+#' #mr[[i]] is a list of estimated parameters matrices
+#' μ <- normalize(β)
+#' for (i in 1:2)
+#' mr[[i]] <- alignMatrices(res[[i]], ref=μ, ls_mode="exact")
+#' params <- rbind( c(.5,.5), β, c(0,0) ) #p, β, b stacked in a matrix
+#' plotCoefs(mr[[1]], params)}
+#'
#' @export
-plotCoefs <- function(mr, params, idx, xtitle="Parameter")
+plotCoefs <- function(mr, params, ...)
{
- L <- nrow(mr[[1]][[1]])
- K <- ncol(mr[[1]][[1]])
+ d <- nrow(mr[[1]])
+ K <- ncol(mr[[1]])
- params_hat <- matrix(nrow=L, ncol=K)
- stdev <- matrix(nrow=L, ncol=K)
- for (x in 1:L)
+ params_hat <- matrix(nrow=d, ncol=K)
+ stdev <- matrix(nrow=d, ncol=K)
+ for (x in 1:d)
{
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 ) )
+ estims <- .extractParam(mr, x, y)
+ params_hat[x,y] <- mean(estims)
+ # Another way to compute stdev: using distances to true params
+# stdev[x,y] <- sqrt( mean( (estims - 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]]) ] )
+ stdev[x,y] <- sd(estims) #[ estims < max(estims) & estims > min(estims) ] )
}
}
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=xtitle, ylab="")
+ args <- list(...)
+ 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=ifelse("xlab" %in% names(args), args$xlab, "Parameter index"),
+ ylab=ifelse("ylab" %in% names(args), args$ylab, "") )
#print(o) #not returning o to avoid weird Jupyter issue... (TODO:)
}
-
-#' plotQn
-#'
-#' Draw 3D map of objective function values
-#'
-#' @param N Number of starting points
-#' @param n Number of points in sample
-#' @param p Vector of proportions
-#' @param b Vector of biases
-#' @param β Regression matrix (target)
-#' @param link Link function (logit or probit)
-#'
-#' @export
-plotQn <- function(N, n, p, β, b, link)
-{
- d <- nrow(β)
- K <- ncol(β)
- io <- generateSampleIO(n, p, β, b, link)
- op <- optimParams(K, link, list(X=io$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...
-}
projects / morpheus.git / blobdiff