ProbabilityScalePlot[{x1,x2,…}]
生成样本 xi 的正态概率图.
ProbabilityScalePlot[{x1,x2,…},"dist"]
生成为分布 "dist" 缩放后的概率图.
ProbabilityScalePlot[{data1,data2,…},"dist"]
生成 data1、data2 … 的缩放后的概率图.
ProbabilityScalePlot
ProbabilityScalePlot[{x1,x2,…}]
生成样本 xi 的正态概率图.
ProbabilityScalePlot[{x1,x2,…},"dist"]
生成为分布 "dist" 缩放后的概率图.
ProbabilityScalePlot[{data1,data2,…},"dist"]
生成 data1、data2 … 的缩放后的概率图.
更多信息和选项
- ProbabilityScalePlot[data,"dist"] 使用依不同分布而定的缩放尺度,使得如果数据服从给定的分布,图线将位于一条直线上.
- 支持为以下分布制定的缩放尺度:
-
"Normal" 正态分布图 "Weibull" 韦伯分布图 "Exponential" 指数分布图 "LogNormal" 对数正态分布图 "Rayleigh" 瑞利分布图 "Frechet" Fréchet 分布图 "Gumbel" Gumbel 分布图 - 所绘制的位置对应于 {xi,yi},其中 yi 是由 Quantile[{x1,x2,…},
] 给出的均匀次序统计的中位数. - 数据由为不同分布专门制定的变换
和
进行缩放,这些变换为: -
"Exponential" 

"Frechet" 

"Gumbel" 

"LogNormal" 

"Normal" 

"Rayleigh" 

"Weibull" 

- 可以用下列形式给出数据集:
-
{x1,x2,…} 样本列表 {Quantity[x1,unit],Quantity[x2,unit],…} 带有单位的样本 <|k1e1,k2e2,…|> 键和样本构成的关联 WeightedData[…],EventData[…] 增广数据集 TimeSeries[…],EventSeries[…],TemporalData[…] 时间序列,事件序列和时态数据 w[{e1,e2,…},…] 应用于整个数据集的封装 w[{data1,data2,…}] 应用于所有数据集的封装 - 形式 w[data] 提供了一个封装 w 以应用在所得的图像基元中.
- ProbabilityScalePlot[Tabular[…]cspec] 用列指定 cspec 从表格对象中提取并绘制值.
- 可使用以下形式的列指定 cspec 来绘制表格数据:
-
colx 绘制列 x 的值 {colx1,colx2,…} 绘制列 x1、x2、… - 可以使用以下封装:
-
Annotation[e,label] 提供一个注解 Button[e,action] 定义元素被点击时要执行的操作 EventHandler[e,…] 定义元素的通用事件处理程序 Highlighted[datai,effect] 用某种效果动态突出显示 fi Highlighted[datai,Placed[effect,pos]] 在位置 pos 处用某种效果静态突出显示 fi Hyperlink[e,uri] 把元素变为超链接 PopupWindow[e,cont] 对元素附加一个弹出窗口 StatusArea[e,label] 当鼠标移到该元素时在状态栏中显示出来 Style[e,opts] 用指定样式显示元素 Tooltip[e,label] 对元素附加任意提示条 - ProbabilityScalePlot 具有和 Graphics 相同的选项,另外有以下添加和变动: [所有选项的列表]
-
AspectRatio 1/GoldenRatio 高宽比 ClippingStyle Automatic 在曲线被剪切的位置如何进行绘制 ColorFunction Automatic 如何确定曲线的着色 ColorFunctionScaling True 是否缩放 ColorFunction 的参数 Filling None 在每条曲线下插入的填充 FillingStyle Automatic 用于填充的样式 Joined Automatic 是否将点连接起来 Mesh None 在每条曲线上绘制多少个网格点 MeshFunctions {#1&} 如何确定网格点的位置 MeshShading None 怎样处理网格之间区域的色调 MeshStyle Automatic 网格点的样式 Method Automatic 要使用的方法 PerformanceGoal $PerformanceGoal 优化的目标 PlotHighlighting Automatic 曲线的突出显示效果 PlotLegends None 数据点的图例 PlotMarkers None 用来标明数据集的点的标记 PlotRange Automatic 要包括的数值的范围 PlotRangeClipping True 是否在绘图范围边界处进行剪切 PlotStyle Automatic 指定每个对象的样式的图形指令 PlotTheme $PlotTheme 绘图的整体主题 ReferenceLineStyle Automatic 参考线的样式 ScalingFunctions None 怎样缩放个别坐标 WorkingPrecision MachinePrecision 内部计算符号分布所用的精度 - 如果设置 Filling->Automatic,数据集和参考线之间的区域将被填充. 默认情况下,对数据集使用 "stems",对符号分布使用 "solid" 填充. 设置 Joined->True 将对数据集强制进行 "solid" 填充.
- 提供给 MeshFunctions 和 RegionFunction 中的函数的参数是
、
. 默认情况下,提供给 ColorFunction 中的函数的是这些参数缩放过的版本. - PlotLegends 的常见设置包括:
-
None 没有图例 Automatic 自动确定图例 {lbl1,lbl2,…} 用 lbl1、lbl2、… 作为图例标签 Placed[lspec,…] 指定图例的位置 - PlotStylesty 指定每条曲线使用的样式. 可能的设置包括:
-
{sty1,sty2,…} 针对各个数据集的一系列样式 <|"key"val,…|> 针对不同层级的数据的样式元素 - 接受的键有:
-
"Base" 所有 datai 的整体样式 "Lists" 针对每个 datai 的样式列表 styi - ColorData["DefaultPlotColors"] 给出 PlotStyle 使用的默认颜色序列.
- 如果设置 ReferenceLineStyle->None,则不绘制参考线.
- Highlighted 和 PlotHighlighting 可用的突出显示效果包括:
-

style 突出显示指定的数据 
"Ball" 突出显示并标注数据中指定的点 
"Dropline" 突出显示并标注数据中指定的点,同时绘制到坐标轴的准线 
"XSlice" 突出显示并标注垂直切片上的所有点 
"YSlice" 突出显示并标注水平切片上的所有点 
Placed[effect,pos] 静态突出显示给定位置 pos - 突出显示的位置指定 pos 包括:
-
x, {x} 在 {x,y} 处突出显示,自动选择 y {x,y} 在 {x,y} 处突出显示 {pos1,pos2,…} 多个位置 posi - ScalingFunctions 的可能设置包括:
-
sy 缩放 y 轴 {sx,sy} 缩放 x 和 y 轴 - 常见的内置缩放函数 s 包括:
-
"Reverse" 
反转坐标的方向
所有选项的列表
范例
打开所有单元 关闭所有单元基本范例 (3)
ProbabilityScalePlot[RandomVariate[NormalDistribution[1, 2], 100], "Normal"]ProbabilityScalePlot[RandomVariate[WeibullDistribution[2, 3], 100], "Weibull"]data1 = RandomVariate[NormalDistribution[2, 3], 100];
data2 = RandomVariate[StudentTDistribution[4, 2, 3], 200];ProbabilityScalePlot[{data1, data2}, "Normal", PlotLegends -> {"one", "two"}]范围 (29)
数据 (13)
ProbabilityScalePlot 适用于数值数据:
ProbabilityScalePlot[RandomVariate[UniformDistribution[{0, 1}], 100]]ProbabilityScalePlot 多数据集:
data1 = RandomVariate[NormalDistribution[1, 2], 100];
data2 = RandomVariate[UniformDistribution[{10, 20}], 100];ProbabilityScalePlot[{data1, data2}]data = RandomVariate[NormalDistribution[1, 2], 100];ProbabilityScalePlot[data, "Normal"]data1 = RandomVariate[WeibullDistribution[2, 3], 100];ProbabilityScalePlot[data1, "Weibull"]ProbabilityScalePlot[RandomVariate[ExponentialDistribution[1], 1000], "Exponential"]ProbabilityScalePlot[RandomVariate[LogNormalDistribution[0, 1], 200], "LogNormal"]ProbabilityScalePlot[RandomVariate[RayleighDistribution[1], 200], "Rayleigh"]ProbabilityScalePlot[RandomVariate[FrechetDistribution[1, 2], 200], "Frechet", PlotRange -> Automatic]ProbabilityScalePlot[RandomVariate[GumbelDistribution[1, 2], 200], "Gumbel"]Short[data = EntityValue[EntityList[EntityClass["Country", "Countries"]], "LifeExpectancy"]]ProbabilityScalePlot[data]ProbabilityScalePlot[<|"a" -> 2, "b" -> 3, "c" -> 5, "d" -> 7, "e" -> 11|>]data = WeightedData[RandomVariate[NormalDistribution[], 100], Abs[RandomVariate[NormalDistribution[], 100]]]ProbabilityScalePlot[data]data = TimeSeries[RandomReal[1, 50], {Automatic, Today}]ProbabilityScalePlot[data]表格数据 (1)
iris = ResourceData["Sample Tabular Data: Fisher Iris"]ProbabilityScalePlot[iris -> "SepalWidth"]ProbabilityScalePlot[iris -> {"SepalWidth", "PetalWidth"}]用 PivotToColumns 生成每个品种的 "SepalWidth" 列:
pivot = PivotToColumns[iris, "Species" -> "SepalWidth"]ProbabilityScalePlot[pivot -> {ExtendedKey["SepalWidth", "setosa"], ExtendedKey["SepalWidth", "versicolor"], ExtendedKey["SepalWidth", "virginica"]}]ProbabilityScalePlot[pivot -> {"setosa", "versicolor", "virginica"}]ProbabilityScalePlot[pivot -> {"setosa", "versicolor", "virginica"}, PlotLegends -> {"setosa", "versicolor", "virginica"}]演示 (15)
data = {RandomVariate[NormalDistribution[], 100], RandomVariate[WeibullDistribution[1, 2], 100]};ProbabilityScalePlot[data]data = {RandomVariate[NormalDistribution[], 100], RandomVariate[WeibullDistribution[1, 2], 100]};ProbabilityScalePlot[data, PlotStyle -> {Blue, Red}]data = {RandomVariate[NormalDistribution[], 100], RandomVariate[WeibullDistribution[1, 2], 100]};ProbabilityScalePlot[data, PlotLegends -> {"one", "two"}]用 Legended 为指定数据集提供图例:
normal = RandomVariate[NormalDistribution[], 100];weibull1 = RandomVariate[WeibullDistribution[1, 2], 100];weibull2 = RandomVariate[WeibullDistribution[2, 1], 100];ProbabilityScalePlot[{Legended[normal, "normal"], weibull1, weibull2}]ProbabilityScalePlot[RandomVariate[UniformDistribution[{0, 1}], 100], FrameLabel -> {"Theoretical Quantiles", "Empirical Quantiles"}, PlotLabel -> "Probability Scale Plot", PlotStyle -> Directive[PointSize[Medium], Purple]]ProbabilityScalePlot[Range[100], ReferenceLineStyle -> Directive[Dashing[{}], Red]]ProbabilityScalePlot[Range[100], ReferenceLineStyle -> None]ProbabilityScalePlot[Range[100], GridLines -> Automatic]为数据提供互动的 Tooltip:
data = RandomVariate[UniformDistribution[{0, 1}], 100];ProbabilityScalePlot[Tooltip[data], PlotStyle -> Directive[PointSize[Medium], Orange]]ProbabilityScalePlot[Tooltip[data, BoxWhiskerChart[data]], PlotStyle -> Blue]data1 = RandomReal[NormalDistribution[100, 400], 200];
data2 = RandomReal[UniformDistribution[{5, 400}], 500];ProbabilityScalePlot[{data1, data2}, Filling -> {1 -> {2}}, FillingStyle -> {Red, Blue}, Joined -> True]ProbabilityScalePlot[{{1, 2, 3, 5, 8}, {2, 3, 6, 9, 10}, {4, 5, 7, 10, 12}}, PlotMarkers -> Automatic]使用 Joined 用线连接数据集:
ProbabilityScalePlot[{1, 2, 4, 7, 3, 5, 8, 10, 9}, Joined -> True, PlotStyle -> Orange]ProbabilityScalePlot[{1, 2, 4, 7, 3, 5, 8, 10, 9}, PlotTheme -> "Monochrome"]ProbabilityScalePlot[{1, 2, 4, 7, 3, 5, 8, 10, 9}, ScalingFunctions -> {"Reverse", None}]ProbabilityScalePlot[{1, 2, 4, 7, 3, 5, 8, 10, 9}]ProbabilityScalePlot[Callout[{1, 2, 4, 7, 3, 5, 8, 10, 9}, "hello", 3]]{ProbabilityScalePlot[{1, 2, 4, 7, 3, 5, 8, 10, 9}, PlotHighlighting -> "Dropline"], ProbabilityScalePlot[{1, 2, 4, 7, 3, 5, 8, 10, 9}, PlotHighlighting -> "XSlice"]}选项 (78)
ClippingStyle (4)
ProbabilityScalePlot[RandomVariate[LogNormalDistribution[0, 1], 100], "Normal", PlotRange -> {10, 90}, Joined -> True, ClippingStyle -> None]ProbabilityScalePlot[RandomVariate[LogNormalDistribution[0, 1], 100], "Normal", PlotRange -> {10, 90}, Joined -> True, ClippingStyle -> Automatic, PlotStyle -> Thick]ProbabilityScalePlot[RandomVariate[LogNormalDistribution[0, 1], 100], "Normal", PlotRange -> {10, 90}, Joined -> True, ClippingStyle -> Red]ProbabilityScalePlot[RandomVariate[LogNormalDistribution[0, 1], 100], "Normal", PlotRange -> {10, 90}, Joined -> True, ClippingStyle -> Directive[Red, Thick]]ColorFunction (6)
ColorFunction 要求至少一个数据集是 Joined 的:
{ProbabilityScalePlot[Range[0, 1, 0.025], "Normal", ColorFunction -> "Rainbow"], ProbabilityScalePlot[Range[0, 1, 0.025], "Normal", ColorFunction -> "Rainbow", Joined -> True]}Table[ProbabilityScalePlot[Range[0, 1, 0.025], "Normal", Joined -> True, ColorFunction -> Function[{x, y}, f], PlotLabel -> f, PlotStyle -> Thick], {f, {Hue[x], Hue[y]}}]ProbabilityScalePlot[Range[0, 1, 0.025], "Normal", Joined -> True, ColorFunction -> "DarkRainbow"]ProbabilityScalePlot[Range[0, 1, 0.025], "Normal", Joined -> True, ColorFunction -> Function[{x, y}, Hue[x]], Filling -> Automatic]对于曲线着色,ColorFunction 比 PlotStyle 具有更高的优先级:
ProbabilityScalePlot[Range[0, 1, 0.025], "Normal", Joined -> True, ColorFunction -> "DarkRainbow", PlotStyle -> Directive[Red, Thick]]利用 MeshShading 中的 Automatic 来使用 ColorFunction:
ProbabilityScalePlot[Range[0, 1, 0.025], "Normal", Joined -> True, ColorFunction -> "Rainbow", PlotStyle -> Directive[Red, Thick], Mesh -> 9, MeshShading -> {Automatic, StandardGray}, MeshStyle -> None]ColorFunctionScaling (2)
ProbabilityScalePlot[Range[0, 1, 0.025], "Normal", Joined -> True, ColorFunction -> Function[{x, y}, Hue[y]], PlotStyle -> Thick, ColorFunctionScaling -> True]ProbabilityScalePlot[Range[0, 1, 0.025], "Normal", Joined -> True, ColorFunction -> Function[{x, y}, Hue[y]], PlotStyle -> Thick, ColorFunctionScaling -> False]Filling (7)
ProbabilityScalePlot[RandomVariate[ParetoDistribution[1, 2], 50], Filling -> Automatic, Joined -> True]Table[ProbabilityScalePlot[RandomVariate[ParetoDistribution[1, 2], 50], "Normal", Filling -> c], {c, {Top, Bottom, Axis, 50}}]ProbabilityScalePlot[RandomVariate[ParetoDistribution[1, 2], 50], Filling -> Automatic]ProbabilityScalePlot[RandomVariate[ParetoDistribution[1, 2], 50], Filling -> Automatic, Joined -> True]ProbabilityScalePlot[{RandomVariate[NormalDistribution[], 14], RandomVariate[PoissonDistribution[1], 10], RandomVariate[WeibullDistribution[2, 2], 100]}, "Normal", Filling -> {3 -> Bottom}]ProbabilityScalePlot[{RandomVariate[WeibullDistribution[1, 2], 100], RandomVariate[WeibullDistribution[2, 2], 100]}, "Normal", Joined -> True, Filling -> {1 -> {{2}, Directive[Orange, Dashed]}}]ProbabilityScalePlot[{RandomVariate[WeibullDistribution[1, 2], 100], RandomVariate[WeibullDistribution[2, 2], 100]}, "Normal", Joined -> True, Filling -> {1 -> {{2}, {Yellow, Red}}}]ProbabilityScalePlot[{RandomVariate[WeibullDistribution[1, 2], 100], RandomVariate[WeibullDistribution[2, 2], 100]}, "Normal", Joined -> True, Filling -> {1 -> {{2}, Red}}]FillingStyle (2)
Table[ProbabilityScalePlot[{RandomVariate[ParetoDistribution[1, 10], 100]}, "Normal", Joined -> True, Filling -> Automatic, FillingStyle -> c], {c, {Red, Green, Blue, Yellow}}]ProbabilityScalePlot[Table[RandomVariate[ParetoDistribution[x, 4], 100], {x, 1, 2, 1}], "Normal", Joined -> True, Filling -> Bottom, FillingStyle -> Directive[Opacity[0.5], Orange]]GridLines (1)
GridLinesStyle (1)
Joined (1)
Mesh (4)
ProbabilityScalePlot[RandomVariate[NormalDistribution[], 100], "Normal", Joined -> True, Mesh -> 20]data = RandomVariate[UniformDistribution[{1, 10}], 100];ProbabilityScalePlot[data, Joined -> True, Mesh -> {Quantile[data, Range[0.1, 0.9, 0.1]]}]ProbabilityScalePlot[RandomVariate[UniformDistribution[{1, 10}], 100], Joined -> True, Mesh -> {Range[0, 12, 2]}, MeshStyle -> PointSize[Medium]]ProbabilityScalePlot[RandomVariate[UniformDistribution[{0, 1}], 100], Joined -> True, Mesh -> {Table[{x, Hue[x]}, {x, 0., 1, .2}]}]MeshFunctions (2)
Table[ProbabilityScalePlot[{RandomVariate[ParetoDistribution[1, 10], 100]}, "Normal", Joined -> True, MeshFunctions -> {Function[{x, y}, Evaluate[f]]}, Mesh -> 9, PlotLabel -> f], {f, {x, y}}]在
方向上显示5个网格点水平(红色);在
方向上显示10个网格点水平(蓝色):
ProbabilityScalePlot[{RandomVariate[ParetoDistribution[1, 10], 100]}, "Normal", Joined -> True, Mesh -> {5, 10}, MeshFunctions -> {#1&, #2&}, MeshStyle -> {Directive[PointSize[Medium], Red], Blue}]MeshShading (6)
ProbabilityScalePlot[RandomVariate[NormalDistribution[], 100], "Normal", Joined -> True, Mesh -> 10, MeshShading -> {Red, Blue}]使用 None 删除某些部分的线条:
ProbabilityScalePlot[RandomVariate[NormalDistribution[], 100], "Normal", Joined -> True, Mesh -> 10, MeshShading -> {Red, None}]MeshShading 可以与 PlotStyle 联合使用:
ProbabilityScalePlot[RandomVariate[NormalDistribution[], 100], "Normal", Joined -> True, Mesh -> 10, PlotStyle -> Thick, MeshFunctions -> {#1&}, MeshShading -> {Red, Blue}]对曲线样式化而言,MeshShading 具有比 PlotStyle 更高的优先级:
ProbabilityScalePlot[RandomVariate[NormalDistribution[], 100], "Normal", Joined -> True, Mesh -> 10, PlotStyle -> Green, MeshFunctions -> {#1&}, MeshShading -> {Red, Blue}]通过把 MeshShading 设置为 Automatic,对某些线条使用 PlotStyle:
ProbabilityScalePlot[RandomVariate[NormalDistribution[], 100], "Normal", Joined -> True, Mesh -> 10, PlotStyle -> Directive[Thick, Yellow], MeshFunctions -> {#1&}, MeshShading -> {Red, Automatic}]MeshShading 可以与 ColorFunction 联合使用:
ProbabilityScalePlot[RandomVariate[NormalDistribution[], 100], "Normal", Joined -> True, Mesh -> 10, PlotStyle -> Thick, MeshFunctions -> {#1&}, MeshShading -> {StandardGray, Automatic},
ColorFunction -> Function[{x, y}, Hue[x]]]MeshStyle (4)
ProbabilityScalePlot[RandomVariate[NormalDistribution[], 100], "Normal", Joined -> True, Mesh -> 9, MeshStyle -> Automatic]ProbabilityScalePlot[RandomVariate[NormalDistribution[], 100], "Normal", Joined -> True, Mesh -> 9, MeshStyle -> Red]ProbabilityScalePlot[RandomVariate[NormalDistribution[], 100], Joined -> True, Mesh -> 9, MeshStyle -> {Red, Blue}, MeshFunctions -> {#1&, #2&}]ProbabilityScalePlot[RandomVariate[NormalDistribution[], 100], Joined -> True, Mesh -> 10, MeshStyle -> Directive[PointSize[Large], Red]]Method (3)
ProbabilityScalePlot[RandomVariate[UniformDistribution[{0, 1}], 100]]ProbabilityScalePlot[RandomVariate[UniformDistribution[{0, 1}], 100], Method -> {"ReferenceLineMethod" -> "Fit"}]ProbabilityScalePlot[RandomVariate[NormalDistribution[0, 1], 100], Method -> {"ReferenceLineMethod" -> "Reference"}]PlotHighlighting (9)
当设置为默认的 PlotHighlightingAutomatic 时,绘图有交互式坐标标注:
ProbabilityScalePlot[{1, 2, 4, 7, 3, 5, 8, 10, 9}]用 PlotHighlightingNone 禁止整个绘图的突出显示:
ProbabilityScalePlot[{1, 2, 4, 7, 3, 5, 8, 10, 9}, PlotHighlighting -> None]ProbabilityScalePlot[{{1, 2, 4, 7, 3, 5, 8, 10, 9}, {1, 2, 4, 5, 4, 7, 3, 6, 8, 10, 9}}, PlotHighlighting -> Directive[Red, AbsolutePointSize[10], DropShadowing[]]]ProbabilityScalePlot[{1, 2, 4, 5, 4, 7, 3, 6, 8, 10, 9}, PlotHighlighting -> "Dropline"]ProbabilityScalePlot[{1, 2, 4, 5, 4, 7, 3, 6, 8, 10, 9}, PlotHighlighting -> "Dropline"]ProbabilityScalePlot[{1, 2, 4, 5, 4, 7, 3, 6, 8, 10, 9}, PlotHighlighting -> "XSlice"]ProbabilityScalePlot[{{1, 2, 7, 7, 6, 5, 8, 10, 9}, {1, 2, 4, 5, 4, 7, 3, 6, 8, 10, 9}}, PlotHighlighting -> "YSlice"]ProbabilityScalePlot[{{1, 2, 7, 7, 6, 5, 8, 10, 9}, {1, 2, 4, 5, 4, 7, 3, 6, 8, 10, 9}}, PlotHighlighting -> "XNearestPoint"]ProbabilityScalePlot[{{1, 2, 7, 7, 6, 5, 8, 10, 9}, {1, 2, 4, 5, 4, 7, 3, 6, 8, 10, 9}}, PlotHighlighting -> {"XNearestPoint", <|"Style" -> Red|>}]ProbabilityScalePlot[{{1, 2, 7, 7, 6, 5, 8, 10, 9}, {1, 2, 4, 5, 4, 7, 3, 6, 8, 10, 9}}, PlotHighlighting -> "XYLabel"]用 Callout 的选项改变标签的外观:
ProbabilityScalePlot[{{1, 2, 7, 7, 6, 5, 8, 10, 9}, {1, 2, 4, 5, 4, 7, 3, 6, 8, 10, 9}}, PlotHighlighting -> {"XYLabel", <|"Appearance" -> "Corners", "CalloutMarker" -> "Circle"|>}]ProbabilityScalePlot[{{1, 2, 7, 7, 6, 5, 8, 10, 9}, {1, 2, 4, 5, 4, 7, 3, 6, 8, 10, 9}}, PlotHighlighting -> {{"XNearestPoint", <|"Style" -> Magenta|>}, {"XYLabel", <|"Appearance" -> "Corners", "CalloutMarker" -> "Circle"|>}}]PlotLegends (7)
ProbabilityScalePlot[{Sqrt[Range[40]], Log[Range[40]]}]ProbabilityScalePlot[{Sqrt[Range[40]], Log[Range[40]]}, PlotLegends -> {"sqrt", "log"}]ProbabilityScalePlot[{Sqrt[Range[40]], Log[Range[40]]}, PlotLegends -> Automatic]ProbabilityScalePlot[{Sqrt[Range[40]], Log[Range[40]]}, PlotStyle -> {Red, Blue}, PlotLegends -> {"sqrt", "log"}]使用 Placed 指定图例位置:
ProbabilityScalePlot[{Sqrt[Range[40]], Log[Range[40]]}, PlotLegends -> Placed[{"sqrt", "log"}, Below]]ProbabilityScalePlot[{Sqrt[Range[40]], Log[Range[40]]}, PlotLegends -> Placed[{"sqrt", "log"}, {0.25, 0.75}]]使用 LineLegend 来改变图例外观:
ProbabilityScalePlot[{Sqrt[Range[40]], Log[Range[40]]}, PlotLegends -> LineLegend[{"sqrt", "log"}, LegendFunction -> Frame, LegendMarkers -> None]]PlotMarkers (7)
QuantilePlot 通常使用不同的颜色来区分不同的数据集:
data = Table[RandomVariate[ParetoDistribution[p[[1]], p[[2]]], 20], {p, {{1, 2}, {4, 9}, {5, 7}, {7, 9}}}];ProbabilityScalePlot[data, "Normal", PlotMarkers -> None]data = Table[RandomVariate[ParetoDistribution[p[[1]], p[[2]]], 20], {p, {{1, 2}, {4, 9}, {5, 7}, {7, 9}}}];ProbabilityScalePlot[ReleaseHold /@ {data[[#]]& /@ Range[Length@data]}, "Normal", PlotMarkers -> Automatic]data = Table[RandomVariate[ParetoDistribution[p[[1]], p[[2]]], 20], {p, {{1, 2}, {4, 9}, {5, 7}, {7, 9}}}];ProbabilityScalePlot[ReleaseHold /@ {data[[#]]& /@ Range[Length@data]}, "Normal", PlotMarkers -> Automatic, PlotStyle -> StandardBlue]data = Table[RandomVariate[ParetoDistribution[p[[1]], p[[2]]], 20], {p, {{1, 2}, {4, 9}, {5, 7}, {7, 9}}}];Table[ProbabilityScalePlot[ReleaseHold /@ {data[[#]]& /@ Range[Length@data]}, "Normal", PlotMarkers -> {Automatic, s}], {s, {Small, Medium}}]data = Table[RandomVariate[ParetoDistribution[p[[1]], p[[2]]], 20], {p, {{1, 2}, {4, 9}, {5, 7}, {7, 9}}}];ProbabilityScalePlot[ReleaseHold /@ {data[[#]]& /@ Range[Length@data]}, "Normal", PlotMarkers -> {"1", "2", "3", "4"}]data = Table[RandomVariate[ParetoDistribution[p[[1]], p[[2]]], 20], {p, {{1, 2}, {4, 9}, {5, 7}, {7, 9}}}];ProbabilityScalePlot[ReleaseHold /@ {data[[#]]& /@ Range[Length@data]}, "Normal", PlotMarkers -> {[image], [image], [image], [image]}]data = Table[RandomVariate[ParetoDistribution[p[[1]], p[[2]]], 20], {p, {{1, 2}, {4, 9}, {5, 7}, {7, 9}}}];ProbabilityScalePlot[ReleaseHold /@ {data[[#]]& /@ Range[Length@data]}, "Normal", PlotMarkers -> {"●"}]data = Table[RandomVariate[ParetoDistribution[p[[1]], p[[2]]], 20], {p, {{1, 2}, {4, 9}, {5, 7}, {7, 9}}}];ProbabilityScalePlot[ReleaseHold /@ {data[[#]]& /@ Range[Length@data]}, "Normal", PlotMarkers -> "α"]PlotRange (3)
自动计算 PlotRange:
ProbabilityScalePlot[RandomVariate[LogNormalDistribution[2, 6], 100], "Normal"]ProbabilityScalePlot[RandomVariate[LogNormalDistribution[2, 6], 100], "Normal", PlotRange -> All]显示
位于1和3之间的分布,以及
位于 90 和 99 之间的分布:
ProbabilityScalePlot[RandomVariate[NormalDistribution[], 100], "Normal", PlotRange -> {{1, 3}, {90, 99}}]PlotStyle (3)
Table[ProbabilityScalePlot[{RandomVariate[ParetoDistribution[1, 10], 100]}, "Normal", Joined -> True, PlotStyle -> ps], {ps, {Red, Thick, Dashed, Directive[Red, Thick]}}]ProbabilityScalePlot[Table[RandomVariate[ParetoDistribution[x, 10], 100], {x, 1, 4, 1}], "Normal"]ProbabilityScalePlot[Table[RandomVariate[ParetoDistribution[x, 10], 100], {x, 1, 4, 1}], "Normal", PlotStyle -> {Red, Green, Blue, Brown}]PlotTheme (1)
ReferenceLineStyle (3)
默认情况下,ReferenceLineStyle 使用 PlotStyle 的 Dotted 形式:
ProbabilityScalePlot[RandomVariate[WeibullDistribution[2, 4], 100]]ProbabilityScalePlot[RandomVariate[WeibullDistribution[2, 4], 100], "Normal", ReferenceLineStyle -> Red]ProbabilityScalePlot[RandomVariate[WeibullDistribution[2, 4], 100], "Normal", ReferenceLineStyle -> Directive[Red, Dashing[0]]]利用 None 来关闭参照线:
ProbabilityScalePlot[RandomVariate[WeibullDistribution[2, 4], 100], "Normal", ReferenceLineStyle -> None]ScalingFunctions (2)
默认情况下,ProbabilityScalePlot 为两个坐标轴自动设定刻度:
ProbabilityScalePlot[RandomVariate[UniformDistribution[{0, 1}], 100]]ProbabilityScalePlot[RandomVariate[UniformDistribution[{0, 1}], 100], ScalingFunctions -> {"Reverse", None}]应用 (2)
生态学家调查组调查了一个岛屿的鸟类物种数量. 并记载了岛屿中的每个物种所观察到的数量. LogNormalDistribution 经常用于模拟物种的丰度:
spCnts = {8, 44, 91, 32, 4, 33, 8, 115, 61, 136, 18, 54, 43, 28, 56, 36, 137, 26, 53, 21, 69, 12, 13, 42, 10};ProbabilityScalePlot[spCnts, "LogNormal", GridLines -> Automatic, GridLinesStyle -> "Classic", ImageSize -> Medium]使用最大似然性估计求最佳拟合的 LogNormalDistribution:
𝒟 = EstimatedDistribution[spCnts, LogNormalDistribution[μ, σ]]data = RandomVariate[WienerProcess[0, 3][7], 10 ^ 3];ProbabilityScalePlot[data, "Normal"]属性和关系 (8)
Table[ProbabilityScalePlot[Range[1, 100, 0.25], ref, PlotLabel -> ref], {ref, {"Weibull", "Exponential", "LogNormal", "Rayleigh", "Frechet", "Gumbel"}}]QuantilePlot[Range[1, 100, 0.25]]ProbabilityPlot[Range[1, 100, 0.25]]BoxWhiskerChart 和 DistributionChart 可用于可视化数据的分布:
data = RandomVariate[ExponentialDistribution[2], 10 ^ 3];{BoxWhiskerChart[data], DistributionChart[data]}SmoothHistogram 和 Histogram 可用于可视化数据的分布:
data = RandomVariate[ExponentialDistribution[2], 10 ^ 3];{SmoothHistogram[data, PlotRange -> All], Histogram[data]}DiscretePlot 可用于可视化离散分布:
{ProbabilityScalePlot[RandomVariate[PoissonDistribution[3], 100]], DiscretePlot[PDF[PoissonDistribution[3], x], {x, 0, 6}, PlotStyle -> PointSize[Medium]]}使用 ListPlot 查看数据:
data = RandomVariate[ExponentialDistribution[1], 100];data = FindClusters[data];ListPlot[data]当输入为 TimeSeries 时,ProbabilityScalePlot 忽略时间戳:
data = TemporalData[TimeSeries, {{{-1.2851063764241863, 1.8324042559834568, 4.644950609531538,
1.509265945417493, 1.8128859551454304, 3.1813846692402996, 2.171122557373735,
1.5336042672985486, 1.419469763349254, -0.2663059484359207, 0.3409179135 ... 18797615790018, -1.2241389048830675,
2.059880322321172, -0.36363794430027485, 0.4890752692327103}}, {{0, 999, 1}}, 1,
{"Continuous", 1}, {"Discrete", 1}, 1,
{ResamplingMethod -> {"Interpolation", InterpolationOrder -> 1}}}, False, 10.1];{ProbabilityScalePlot[data], ProbabilityScalePlot[data["Values"]]}文本
Wolfram Research (2010),ProbabilityScalePlot,Wolfram 语言函数,https://reference.wolfram.com/language/ref/ProbabilityScalePlot.html (更新于 2026 年).
CMS
Wolfram 语言. 2010. "ProbabilityScalePlot." Wolfram 语言与系统参考资料中心. Wolfram Research. 最新版本 2026. https://reference.wolfram.com/language/ref/ProbabilityScalePlot.html.
APA
Wolfram 语言. (2010). ProbabilityScalePlot. Wolfram 语言与系统参考资料中心. 追溯自 https://reference.wolfram.com/language/ref/ProbabilityScalePlot.html 年
BibTeX
@misc{reference.wolfram_2026_probabilityscaleplot, author="Wolfram Research", title="{ProbabilityScalePlot}", year="2026", howpublished="\url{https://reference.wolfram.com/language/ref/ProbabilityScalePlot.html}", note=[Accessed: 04-November-2026]}
BibLaTeX
@online{reference.wolfram_2026_probabilityscaleplot, organization={Wolfram Research}, title={ProbabilityScalePlot}, year={2026}, url={https://reference.wolfram.com/language/ref/ProbabilityScalePlot.html}, note=[Accessed: 04-November-2026]}