Question

是否有一个简单的命令可以使用R中的lm()函数进行一次性交叉验证？

具体来说，下面的代码有一个简单的命令吗？

x <- rnorm(1000,3,2)
y <- 2*x + rnorm(1000)

pred_error_sq <- c(0)
for(i in 1:1000) {
  x_i <- x[-i]
  y_i <- y[-i]
  mdl <- lm(y_i ~ x_i) # leave i'th observation out
  y_pred <- predict(mdl, data.frame(x_i = x[i])) # predict i'th observation
  pred_error_sq <- pred_error_sq + (y[i] - y_pred)^2 # cumulate squared prediction errors
}

y_squared <- sum((y-mean(y))^2)/100 # Variation of the data

R_squared <- 1 - (pred_error_sq/y_squared) # Measure for goodness of fit

Answer 1

另一种解决方案是使用caret

library(caret)

data <- data.frame(x = rnorm(1000, 3, 2), y = 2*x + rnorm(1000))

train(y ~ x, method = "lm", data = data, trControl = trainControl(method = "LOOCV"))

线性回归

1000个样本1个预测器

无预处理重采样：一次性交叉验证摘要   样本量：999,999,999,999,999,999 ......重新取样   结果：

RMSE Rsquared MAE
    1.050268 0.940619 0.836808

调整参数'intercept'保持不变，其值为TRUE

Answer 2

您可以使用统计技巧来使用自定义函数，避免实际计算所有N个模型：

google.charts.load('current', {
  packages: ['corechart']
}).then(function () {
  var data = new google.visualization.DataTable();
  data.addColumn('date', 'Timestamp');
  data.addColumn('number', 'a');
  data.addColumn('number', 'b');

  var options = {
    hAxis: {
      title: 'Timestamp'
    },
    vAxis: {
      title: 'something'
    },
    tooltip: { isHtml: true },
    legend: {
      position: 'none'
    }
  };

  var chart = new google.visualization.LineChart(document.getElementById('chart_div'));

  dataCall();
  $interval(dataCall, 1000);

  function dataCall() {
    $http.get({x: "XYZ"}, successCallback, failureCallback);

    function successCallback(response) {
      data.addRow([new Date(), response.a, response.b]);
      chart.draw(data, options);
    }

    function failureCallback(response) {
      console.log(response);
    }
  }
});

这在此解释：{{3}} 它仅适用于线性模型我想你可能想在公式中的平均值之后添加一个平方根。

Answer 3

您可以尝试使用DAAG包中的cv.lm：

cv.lm(data = DAAG::houseprices, form.lm = formula(sale.price ~ area),
              m = 3, dots = FALSE, seed = 29, plotit = c("Observed","Residual"),
              main="Small symbols show cross-validation predicted values",
              legend.pos="topleft", printit = TRUE)

Arguments

data        a data frame
form.lm,    a formula or lm call or lm object
m           the number of folds
dots        uses pch=16 for the plotting character
seed        random number generator seed
plotit      This can be one of the text strings "Observed", "Residual", or a logical value. The logical TRUE is equivalent to "Observed", while FALSE is equivalent to "" (no plot)
main        main title for graph
legend.pos      position of legend: one of "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right", "center".
printit     if TRUE, output is printed to the screen

Answer 4

默认情况下，https://www.rdocumentation.org/packages/boot/versions/1.3-20/topics/cv.glm中的

cv.glm执行LOOCV，仅需要数据和lm或glm函数。

Answer 5

只需编写您自己的代码，即可使用索引变量来标记一个样本外的观察值。用插入号针对最高投票者测试此方法。尽管插入符号简单易用，但我的残酷方法花费的时间更少。（代替lm，我使用LDA，但没什么大不同）

for (index in 1:dim(df)[1]){
   # here write your lm function
}

是否有一个简单的命令可以使用lm（）函数进行一次性交叉验证？

5 个答案: