Question

尝试在R中进行估算器的自举方差并且遇到困难。基本上，我试图从一个更大的数据集中抽出50个随机行，然后，从那50行，使用20的样本大小自举1000倍特定估计器（下面的公式），然后从那里计算估计量之间的差异。我的代码如下。我很失落。

import Foundation
import AVFoundation
import UIKit

class ViewController : UIViewController {
    let captureSession = AVCaptureSession()
    let photoOutput = AVCapturePhotoOutput()
    let cameraPreview = UIView(frame: .zero)
    let progressIndicator = ProgressIndicator()

    override func viewDidLoad() {
        super.viewDidLoad()

        setupVideoPreview()

        do {
            try setupCaptureSession()
        } catch {
            let errorMessage = String(describing:error)
            print("[--ERROR--]: \(#file):\(#function):\(#line): " + errorMessage)
            alert(title: "Error", message: errorMessage)
        }
    }

    private func setupCaptureSession() throws {
        let deviceDiscovery = AVCaptureDevice.DiscoverySession(deviceTypes: [AVCaptureDevice.DeviceType.builtInWideAngleCamera], mediaType: AVMediaType.video, position: AVCaptureDevice.Position.back)
        let devices = deviceDiscovery.devices

        guard let captureDevice = devices.first else {
            let errorMessage = "No camera available"
            print("[--ERROR--]: \(#file):\(#function):\(#line): " + errorMessage)
            alert(title: "Error", message: errorMessage)
            return
        }

        let captureDeviceInput = try AVCaptureDeviceInput(device: captureDevice)
        captureSession.addInput(captureDeviceInput)
        captureSession.sessionPreset = AVCaptureSession.Preset.photo
        captureSession.startRunning()

        if captureSession.canAddOutput(photoOutput) {
            captureSession.addOutput(photoOutput)
        }
    }

    private func setupVideoPreview() {

        let previewLayer = AVCaptureVideoPreviewLayer(session: captureSession)
        previewLayer.bounds = view.bounds
        previewLayer.position = CGPoint(x:view.bounds.midX, y:view.bounds.midY)
        previewLayer.videoGravity = AVLayerVideoGravity.resizeAspectFill

        cameraPreview.layer.addSublayer(previewLayer)
        cameraPreview.addGestureRecognizer(UITapGestureRecognizer(target: self, action:#selector(capturePhoto)))

        cameraPreview.translatesAutoresizingMaskIntoConstraints = false

        view.addSubview(cameraPreview)

        let viewsDict = ["cameraPreview":cameraPreview]
        view.addConstraints(NSLayoutConstraint.constraints(withVisualFormat: "V:|-0-[cameraPreview]-0-|", options: [], metrics: nil, views: viewsDict))
        view.addConstraints(NSLayoutConstraint.constraints(withVisualFormat: "H:|-0-[cameraPreview]-0-|", options: [], metrics: nil, views: viewsDict))

    }

    @objc func capturePhoto(_ sender: UITapGestureRecognizer) {
        progressIndicator.add(toView: view)
        let photoOutputSettings = AVCapturePhotoSettings(format: [AVVideoCodecKey:AVVideoCodecType.jpeg])
        photoOutput.capturePhoto(with: photoOutputSettings, delegate: self)
    }

    func saveToPhotosAlbum(_ image: UIImage) {
        UIImageWriteToSavedPhotosAlbum(image, self, #selector(photoWasSavedToAlbum), nil)
    }

    @objc func photoWasSavedToAlbum(_ image: UIImage, _ error: Error?, _ context: Any?) {
        alert(message: "Photo saved to device photo album")
    }

    func alert(title: String?=nil, message:String?=nil) {
        let alert = UIAlertController(title: title, message: message, preferredStyle: .alert)
        alert.addAction(UIAlertAction(title: "OK", style: .default, handler: nil))
        present(alert, animated:true)
    }

}

extension ViewController : AVCapturePhotoCaptureDelegate {
    func photoOutput(_ output: AVCapturePhotoOutput, didFinishProcessingPhoto photo: AVCapturePhoto, error: Error?) {

        guard  let photoData = photo.fileDataRepresentation() else {
            let errorMessage = "Photo capture did not provide output data"
            print("[--ERROR--]: \(#file):\(#function):\(#line): " + errorMessage)
            alert(title: "Error", message: errorMessage)
            return
        }

        guard let image = UIImage(data: photoData) else {
            let errorMessage = "could not create image to save"
            print("[--ERROR--]: \(#file):\(#function):\(#line): " + errorMessage)
            alert(title: "Error", message: errorMessage)
            return
        }

        saveToPhotosAlbum(image)

        progressIndicator.hide()
    }
}

如上所述，我正在尝试计算MPG *重量之和除以重量之和^ 2。如果可以的话请帮忙。谢谢！

Answer 1

我不太确定你想要完成什么，但我试图构建一个例子。我使用了R。

附带的内置mtcars数据集

# load sample data
data(mtcars) 
df <- mtcars
# show data structure
str(df) 
'data.frame':   32 obs. of  11 variables:
$ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
$ disp: num  160 160 108 258 360 ...
$ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
$ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num  16.5 17 18.6 19.4 17 ...
$ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
$ am  : num  1 1 1 0 0 0 0 0 0 0 ...
$ gear: num  4 4 4 3 3 3 3 4 4 4 ...
$ carb: num  4 4 1 1 2 1 4 2 2 4 ...
# fix randomization seed, make sample() reproducible
set.seed(1)
# take random 10 rows from df
sampleSize <- 10
bRows <- df[sample(nrow(df), sampleSize), ]
# do 7 bootstrap replications
bSamples <- 7
# make container for results
bResults <- rep(NA, bSamples)

现在我们可以实际执行bootstrap

# loop over bootstraps
for (b in seq_len(bSamples)) { 
  # make bootstrap draw from bRows
  bData <- bRows[sample(sampleSize, size = sampleSize, replace = TRUE), ]
  # compute your statistic of interest
  bValue <- sum(bData[["mpg"]] * bData[["wt"]]) / sum((bData[["wt"]])^2)
  # store results in container
  bResults[[b]] <- bValue
}
# show what we computed
bResults
[1] 4.490459 6.297782 3.651372 3.612414 5.348291 5.149250 3.818677

这有什么帮助？

R中的Bootstrap方差？

1 个答案: