在R中旋转直方图或在条形图中覆盖密度

Question

我想在R中旋转直方图,由hist()绘制.这个问题并不新鲜,在一些论坛上我发现这是不可能的.但是,所有这些答案可以追溯到2010年甚至更晚.

有没有人找到解决方案？

解决问题的一种方法是通过barplot()绘制直方图,提供选项"horiz = TRUE".该情节工作正常但我未能在条形图中覆盖密度.问题可能在于x轴,因为在垂直图中,密度以第一个仓为中心,而在水平图中,密度曲线混乱.

很感谢任何形式的帮助!

谢谢,

尼尔斯

码:

require(MASS)
Sigma <- matrix(c(2.25, 0.8, 0.8, 1), 2, 2)
mvnorm <- mvrnorm(1000, c(0,0), Sigma)

scatterHist.Norm <- function(x,y) {
 zones <- matrix(c(2,0,1,3), ncol=2, byrow=TRUE)
 layout(zones, widths=c(2/3,1/3), heights=c(1/3,2/3))
 xrange <- range(x) ; yrange <- range(y)
 par(mar=c(3,3,1,1))
 plot(x, y, xlim=xrange, ylim=yrange, xlab="", ylab="", cex=0.5)
 xhist <- hist(x, plot=FALSE, breaks=seq(from=min(x), to=max(x), length.out=20))
 yhist <- hist(y, plot=FALSE, breaks=seq(from=min(y), to=max(y), length.out=20))
 top <- max(c(xhist$counts, yhist$counts))
 par(mar=c(0,3,1,1))
 plot(xhist, axes=FALSE, ylim=c(0,top), main="", col="grey")
 x.xfit <- seq(min(x),max(x),length.out=40)
 x.yfit <- dnorm(x.xfit,mean=mean(x),sd=sd(x))
 x.yfit <- x.yfit*diff(xhist$mids[1:2])*length(x)
 lines(x.xfit, x.yfit, col="red")
 par(mar=c(0,3,1,1))
 plot(yhist, axes=FALSE, ylim=c(0,top), main="", col="grey", horiz=TRUE)
 y.xfit <- seq(min(x),max(x),length.out=40)
 y.yfit <- dnorm(y.xfit,mean=mean(x),sd=sd(x))
 y.yfit <- y.yfit*diff(yhist$mids[1:2])*length(x)
 lines(y.xfit, y.yfit, col="red")
}
scatterHist.Norm(mvnorm[,1], mvnorm[,2])


scatterBar.Norm <- function(x,y) {
 zones <- matrix(c(2,0,1,3), ncol=2, byrow=TRUE)
 layout(zones, widths=c(2/3,1/3), heights=c(1/3,2/3))
 xrange <- range(x) ; yrange <- range(y)
 par(mar=c(3,3,1,1))
 plot(x, y, xlim=xrange, ylim=yrange, xlab="", ylab="", cex=0.5)
 xhist <- hist(x, plot=FALSE, breaks=seq(from=min(x), to=max(x), length.out=20))
 yhist <- hist(y, plot=FALSE, breaks=seq(from=min(y), to=max(y), length.out=20))
 top <- max(c(xhist$counts, yhist$counts))
 par(mar=c(0,3,1,1))
 barplot(xhist$counts, axes=FALSE, ylim=c(0, top), space=0)
 x.xfit <- seq(min(x),max(x),length.out=40)
 x.yfit <- dnorm(x.xfit,mean=mean(x),sd=sd(x))
 x.yfit <- x.yfit*diff(xhist$mids[1:2])*length(x)
 lines(x.xfit, x.yfit, col="red")
 par(mar=c(3,0,1,1))
 barplot(yhist$counts, axes=FALSE, xlim=c(0, top), space=0, horiz=TRUE)
 y.xfit <- seq(min(x),max(x),length.out=40)
 y.yfit <- dnorm(y.xfit,mean=mean(x),sd=sd(x))
 y.yfit <- y.yfit*diff(yhist$mids[1:2])*length(x)
 lines(y.xfit, y.yfit, col="red")
}
scatterBar.Norm(mvnorm[,1], mvnorm[,2])

#

具有边缘直方图的散点图的来源(点击"改编自......后"的第一个链接):

http://r.789695.n4.nabble.com/newbie-scatterplot-with-marginal-histograms-done-and-axes-labels-td872589.html

散点图中的密度来源:

http://www.statmethods.net/graphs/density.html

Answer 1

scatterBarNorm <- function(x, dcol="blue", lhist=20, num.dnorm=5*lhist, ...){
    ## check input
    stopifnot(ncol(x)==2)
    ## set up layout and graphical parameters
    layMat <- matrix(c(2,0,1,3), ncol=2, byrow=TRUE)
    layout(layMat, widths=c(5/7, 2/7), heights=c(2/7, 5/7))
    ospc <- 0.5 # outer space
    pext <- 4 # par extension down and to the left
    bspc <- 1 # space between scatter plot and bar plots
    par. <- par(mar=c(pext, pext, bspc, bspc),
                oma=rep(ospc, 4)) # plot parameters
    ## scatter plot
    plot(x, xlim=range(x[,1]), ylim=range(x[,2]), ...)
    ## 3) determine barplot and height parameter
    ## histogram (for barplot-ting the density)
    xhist <- hist(x[,1], plot=FALSE, breaks=seq(from=min(x[,1]), to=max(x[,1]),
                                     length.out=lhist))
    yhist <- hist(x[,2], plot=FALSE, breaks=seq(from=min(x[,2]), to=max(x[,2]),
                                     length.out=lhist)) # note: this uses probability=TRUE
    ## determine the plot range and all the things needed for the barplots and lines
    xx <- seq(min(x[,1]), max(x[,1]), length.out=num.dnorm) # evaluation points for the overlaid density
    xy <- dnorm(xx, mean=mean(x[,1]), sd=sd(x[,1])) # density points
    yx <- seq(min(x[,2]), max(x[,2]), length.out=num.dnorm)
    yy <- dnorm(yx, mean=mean(x[,2]), sd=sd(x[,2]))
    ## barplot and line for x (top)
    par(mar=c(0, pext, 0, 0))
    barplot(xhist$density, axes=FALSE, ylim=c(0, max(xhist$density, xy)),
            space=0) # barplot
    lines(seq(from=0, to=lhist-1, length.out=num.dnorm), xy, col=dcol) # line
    ## barplot and line for y (right)
    par(mar=c(pext, 0, 0, 0))
    barplot(yhist$density, axes=FALSE, xlim=c(0, max(yhist$density, yy)),
            space=0, horiz=TRUE) # barplot
    lines(yy, seq(from=0, to=lhist-1, length.out=num.dnorm), col=dcol) # line
    ## restore parameters
    par(par.)
}

require(mvtnorm)
X <- rmvnorm(1000, c(0,0), matrix(c(1, 0.8, 0.8, 1), 2, 2))
scatterBarNorm(X, xlab=expression(italic(X[1])), ylab=expression(italic(X[2])))

Answer 2

知道该hist()函数可以使用更简单的绘图函数无形地返回所需的所有信息,这可能会有所帮助rect().

    vals <- rnorm(10)
    A <- hist(vals)
    A
    $breaks
    [1] -1.5 -1.0 -0.5  0.0  0.5  1.0  1.5

    $counts
    [1] 1 3 3 1 1 1

    $intensities
    [1] 0.2 0.6 0.6 0.2 0.2 0.2

    $density
    [1] 0.2 0.6 0.6 0.2 0.2 0.2

    $mids
    [1] -1.25 -0.75 -0.25  0.25  0.75  1.25

    $xname
    [1] "vals"

    $equidist
    [1] TRUE

    attr(,"class")
    [1] "histogram"

您可以手动创建相同的直方图,如下所示:

    plot(NULL, type = "n", ylim = c(0,max(A$counts)), xlim = c(range(A$breaks)))
    rect(A$breaks[1:(length(A$breaks) - 1)], 0, A$breaks[2:length(A$breaks)], A$counts)

使用这些部件,您可以随意翻转轴:

    plot(NULL, type = "n", xlim = c(0, max(A$counts)), ylim = c(range(A$breaks)))
    rect(0, A$breaks[1:(length(A$breaks) - 1)], A$counts, A$breaks[2:length(A$breaks)])

对于类似的自己动手density(),请参阅: R直方图和密度图中的轴标记; 多个密度图叠加