当我们创建直方图并将其保存为对象名称时,我们可以通过调用该对象来提取频率作为中间值或中断的计数。我们可以认为由对象获得的中间值或中断是频率在直方图上绘制的实际值。
> x1<-rpois(1000,5) > Histogram1<-hist(x1) > Histogram1
输出结果
$breaks [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 $counts [1] 45 82 150 156 172 142 113 62 43 20 9 5 1 $density [1] 0.045 0.082 0.150 0.156 0.172 0.142 0.113 0.062 0.043 0.020 0.009 0.005 [13] 0.001 $mids [1] 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 $xname [1] "x1" $equidist [1] TRUE attr(,"class") [1] "histogram"
> x2<-rpois(100000,5) > Histogram2<-hist(x2) > Histogram2
输出结果
$breaks [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 $counts [1] 4068 8293 14117 17627 17451 14671 10471 6604 3545 1793 810 322 [13] 152 49 12 10 4 1 $density [1] 0.04068 0.08293 0.14117 0.17627 0.17451 0.14671 0.10471 0.06604 0.03545 [10] 0.01793 0.00810 0.00322 0.00152 0.00049 0.00012 0.00010 0.00004 0.00001 $mids [1] 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 [16] 15.5 16.5 17.5 $xname [1] "x2" $equidist [1] TRUE attr(,"class") [1] "histogram"
> x3<-runif(10000,5,10) > Histogram3<-hist(x3) > Histogram3
输出结果
$breaks [1] 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 $counts [1] 1062 1037 959 987 1010 970 942 1019 1045 969 $density [1] 0.2124 0.2074 0.1918 0.1974 0.2020 0.1940 0.1884 0.2038 0.2090 0.1938 $mids [1] 5.25 5.75 6.25 6.75 7.25 7.75 8.25 8.75 9.25 9.75 $xname [1] "x3" $equidist [1] TRUE attr(,"class") [1] "histogram"
> x4<-rnorm(10000,5,10) > Histogram4<-hist(x4) > Histogram4
输出结果
$breaks [1] -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50 $counts [1] 1 15 47 155 459 939 1458 1924 1922 1511 938 406 153 55 13 [16] 1 3 $density [1] 0.00002 0.00030 0.00094 0.00310 0.00918 0.01878 0.02916 0.03848 0.03844 [10] 0.03022 0.01876 0.00812 0.00306 0.00110 0.00026 0.00002 0.00006 $mids [1] -32.5 -27.5 -22.5 -17.5 -12.5 -7.5 -2.5 2.5 7.5 12.5 17.5 22.5 [13] 27.5 32.5 37.5 42.5 47.5 $xname [1] "x4" $equidist [1] TRUE attr(,"class") [1] "histogram"