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How to create an interactive kernel density chart

2020-11-06 22:21:56 roffey

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Kernel density estimation is a useful statistical method , Used to estimate the overall shape of the distribution of random variables . let me put it another way , Nuclear density estimates ( Also known as KDE) Can help us “ smooth ” And exploration does not follow any typical probability density distribution ( For example, normal distribution , Binomial distribution, etc ) The data of .

In this tutorial , We're going to show you how to Javascript Create interactive kernel density estimation and use Highcharts Library drawing results .

Let's first explore KDE chart ; Then we're going to delve into the code .

The following demonstration shows the Gaussian kernel density estimation for random data sets :

This chart helps us estimate the probability distribution of random data sets , And we can see that the data is mainly concentrated at the beginning and end of the chart .

 

Basically , For each red data point , We draw a Gaussian kernel function in orange , Then sum all the kernel functions together , Create density estimates in blue ( Please see the demo):

By the way , There are many kernel function types , For example, Gauss , Unified ,Epanechnikov etc. . We're using a Gaussian kernel , Because it provides a smooth pattern .  
The mathematical expression of Gaussian kernel is :

 

高斯核
Now? , You have an idea of what the kernel density estimation looks like , Let's take a look at the code behind it .
There are four main steps in the code :

  1. Create a Gaussian kernel function .
  2. Processing density estimators .
  3. Dealing with kernel points .
  4. Plot the entire data point .

Gaussian kernel

 

The following code represents the Gaussian kernel function :

function GaussKDE(xi, x) {
  return (1 / Math.sqrt(2 * Math.PI)) * Math.exp(Math.pow(xi - x, 2) / -2);
}

among x Represents the main data ( Observed value ),xi Represents the range and density estimation function of the rendering kernel . In our case ,xi The scope is 88 To 107, To ensure coverage 93 To 102 The range of observational data for .

 

Density estimation point

Here's how to recycle GaussKDE() Array representation of functions and ranges to create density estimators

xiData:
//Create the density estimate
for (i = 0; i < xiData.length; i++) {
  let temp = 0;
  kernel.push([]);
  kernel[i].push(new Array(dataSource.length));
  for (j = 0; j < dataSource.length; j++) {
    temp = temp + GaussKDE(xiData[i], dataSource[j]);
    kernel[i][j] = GaussKDE(xiData[i], dataSource[j]);
  }
  data.push([xiData[i], (1 / N) * temp]);
}

Kernel point

 

Only if you want to show kernel points ( Orange chart ) You need to perform this step when . otherwise , You are already satisfied with the density estimation steps . This is the code that handles the data points of each kernel :

//Create the kernels
for (i = 0; i < dataSource.length; i++) {
  kernelChart.push([]);
  kernelChart[i].push(new Array(kernel.length));
  for (j = 0; j < kernel.length; j++) {
    kernelChart[i].push([xiData[j], (1 / N) * kernel[j][i]]);
  }
}

Basically , This loop just adds the scope xiData To kernel Each array that has been processed in the density estimation step .

 

Draw points

After processing all the data points , You can use Highcharts Rendering series . Density estimates and kernels are spline type , The observations are plotted as scatter plots :

Highcharts.chart("container", {
  chart: {
    type: "spline",
    animation: true
  },
  title: {
    text: "Gaussian Kernel Density Estimation (KDE)"
  },
  yAxis: {
    title: { text: null }
  },
  tooltip: {
    valueDecimals: 3
  },
  plotOptions: {
    series: {
      marker: {
        enabled: false
      },
      dashStyle: "shortdot",
      color: "#ff8d1e",
      pointStart: xiData[0],
      animation: {
        duration: animationTime
      }
    }
  },
  series: [
    {
      type: "scatter",
      name: "Observation",
      marker: {
        enabled: true,
        radius: 5,
        fillColor: "#ff1e1f"
      },
      data: dataPoint,
      tooltip: {
        headerFormat: "{series.name}:",
        pointFormat: "<b>{point.x}</b>"
      },
      zIndex: 9
    },
    {
      name: "KDE",
      dashStyle: "solid",
      lineWidth: 2,
      color: "#1E90FF",
      data: data
    },
    {
      name: "k(" + dataSource[0] + ")",
      data: kernelChart[0]
    },...  ]
});

Now? , You are going to explore your own data using the function of the kernel density estimation map .
Feel free to share your comments or questions in the comments section below .

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