在Vega Lite（Deneb）中，如何将一个回归类型参数列表应用于计算的回归线？

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英文:

In Vega lite (Deneb) how do I apply a parameter list of regression types to a calculated regression line

问题

I have created a "poly" regression line for a scatterplot in vega lite similar to the below.

我已经为散点图创建了一个“poly”回归线，类似于下面的图表。

I have also created a parameter list, listing the types of regression (linear, log, etc.)

我还创建了一个参数列表，列出了回归的类型（线性、对数等）。

However, I am unable to correctly map the list so that I am able to switch between different types of regression calcs. So I am looking for some advice.

然而，我无法正确地映射该列表，以便能够在不同类型的回归计算之间切换。所以我正在寻求一些建议。

在Vega Lite（Deneb）中，如何将一个回归类型参数列表应用于计算的回归线？

在下面的代码中，我尝试替换:
"method": "poly",

with

"method": {"expr": "regressionline"}

where "regressionline" is the name of my parameter list, but I dont think it is correct.

在下面的代码中，我尝试将:
"method": "poly",

替换为

"method": {"expr": "regressionline"}

其中"regressionline"是我的参数列表的名称，但我认为这不正确。

I was wondering if it is actually possible, and if there is another method.

我在想这是否真的可能，并且是否有其他方法。

在Vega Lite（Deneb）中，如何将一个回归类型参数列表应用于计算的回归线？

Many thanks

非常感谢

英文:

I have created a "poly" regression line for a scatterplot in vega lite similar to the below.

I have also created a parameter list, listing the types of regression (linear, log, etc.)

However, I am unable to correctly map the list so that I am able to switch between different types of regression calcs. So I am looking for some advice.

在Vega Lite（Deneb）中，如何将一个回归类型参数列表应用于计算的回归线？

In the below code, I have tried replacing:
"method": "poly",

with

"method": {"expr": "regressionline"}

where "regressionline" is the name of my parameter list, but I dont think it is correct.

I was wondering if it is actually possible, and if there is another method.

在Vega Lite（Deneb）中，如何将一个回归类型参数列表应用于计算的回归线？

Many thanks

  1. {
  2.   "data": {"name": "dataset"},
  3.   "params": [
  4.     {
  5.       "name": "regressionline",
  6.       "value": "linear",
  7.       "bind": {
  8.         "input": "radio",
  9.         "options": [
  10.           "linear",
  11.           "log",
  12.           "exp",
  13.           "pow",
  14.           "quad",
  15.           "poly"
  16.         ]
  17.       }
  18.     }
  19.   ],
  20.   "layer": [
  21.     {
  22.       "mark": "rect",
  23.       "encoding": {
  24.         "x": {
  25.           "bin": {"maxbins": 20},
  26.           "field": "Total Ref Estimate - RF",
  27.           "title": "Total Estimate"
  28.         },
  29.         "y": {
  30.           "bin": {"maxbins": 10},
  31.           "field": "Premarket % Costs",
  32.           "title": "Premarket % Costs"
  33.         },
  34.         "color": {
  35.           "aggregate": "count",
  36.           "scale": {
  37.             "scheme": "goldgreen"
  38.           },
  39.           "legend": {
  40.             "direction": "horizontal",
  41.             "gradientLength": 120,
  42.             "orient": "none",
  43.             "legendX": 500,
  44.             "legendY": 10
  45.           }
  46.         }
  47.       }
  48.     },
  49.     {
  50.       "mark": {
  51.         "type": "point",
  52.         "tooltip": true,
  53.         "color": "white",
  54.         "stroke": "black",
  55.         "strokeWidth": 0.5,
  56.         "size": 25
  57.       },
  58.       "encoding": {
  59.         "x": {
  60.           "field": "Total Ref Estimate - RF",
  61.           "type": "quantitative",
  62.           "axis": {
  63.             "format": "$.1s",
  64.             "labelFontSize": 10
  65.           },
  66.           "scale": {
  67.             "domain": [0, 70000000]
  68.           }
  69.         },
  70.         "y": {
  71.           "field": "Premarket % Costs",
  72.           "type": "quantitative",
  73.           "scale": {"domain": [0, 1]},
  74.           "axis": {
  75.             "format": ".0%",
  76.             "labelFontSize": 10
  77.           }
  78.         },
  79.         "xOffset": {
  80.           "field": "External ID"
  81.         }
  82.       }
  83.     },
  84.     {
  85.       "mark": {
  86.         "type": "line",
  87.         "color": "firebrick",
  88.         "strokeWidth": 2.5,
  89.         "strokeDash": [1, 4]
  90.       },
  91.       "transform": [
  92.         {
  93.           "regression": "Premarket % Costs",
  94.           "on": "Total Ref Estimate - RF",
  95.           "method": "poly",
  96.           "extent": [1000000, 30000000]
  97.         }
  98.       ],
  99.       "encoding": {
  100.         "x": {
  101.           "field": "Total Ref Estimate - RF",
  102.           "type": "quantitative"
  103.         },
  104.         "y": {
  105.           "field": "Premarket % Costs",
  106.           "type": "quantitative"
  107.         }
  108.       }
  109.     },
  110.     {
  111.       "transform": [
  112.         {
  113.           "regression": "Premarket % Costs",
  114.           "on": "Total Ref Estimate - RF",
  115.           "method": "poly",
  116.           "extent": [1000000, 30000000],
  117.           "params": true
  118.         },
  119.         {
  120.           "calculate": "'R²: '+format(datum.rSquared, '.2f')",
  121.           "as": "R2"
  122.         }
  123.       ],
  124.       "mark": {
  125.         "type": "text",
  126.         "color": "firebrick",
  127.         "x": "width",
  128.         "align": "right",
  129.         "y": -5
  130.       },
  131.       "encoding": {
  132.         "text": {
  133.           "type": "nominal",
  134.           "field": "R2"
  135.         }
  136.       }
  137.     }
  138.   ]
  139. }

答案1

得分: 1

以下对我有效。

"method": {"signal": "regressionline"},

模式引发验证错误（因为VL不明确支持信号），但这确实有效，我可以成功地在线性、二次和多项式之间切换。对于其他方法的有效性我不太确定，因为我已经有一段时间没有研究线性回归了。

英文:

The following works for me.

"method": {"signal": "regressionline"},

The schema throws a validation error (as VL doesn't support signals explicitly) but this does work and I can successfully switch between linear, quad and poly. Not sure about the validity of the other methods as I haven't looked at linear regressions in a while.

huangapple
  • 本文由 发表于 2023年7月18日 11:57:32
  • 转载请务必保留本文链接：https://go.coder-hub.com/76709441.html
  • deneb
  • regression
  • vega
  • vega-lite
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