将回归摘要写入R中的csv文件 [英] Write Regression summary to the csv file in R

问题描述

我有一家公司通过销售各种产品(csv文件)获得的收入数据，其中之一如下:

I have data on revenue of a company from sales of various products (csv files), one of which looks like the following:

> abc
   Order.Week..BV. Product.Number Quantity Net.ASP Net.Price
1         2013-W44        ABCDEF       92  823.66       749
2         2013-W44        ABCDEF       24  898.89       749
3         2013-W44        ABCDEF      243  892.00       749
4         2013-W45        ABCDEF       88  796.84       699
5         2013-W45        ABCDEF       18  744.80       699

现在，我正在拟合一个多元回归模型，其中Net.Price为Y和Quantity，Net.ASP为x1和x2.此类文件有100多个，我正在尝试使用以下代码进行操作:

Now, I'm fitting a multiple regression model with Net.Price as Y and Quantity, Net.ASP as x1 and x2. There are more than 100 such files and I'm trying to do it using the following code:

fileNames <- Sys.glob("*.csv")

for (fileName in fileNames) {      

abc <- read.csv(fileName, header = TRUE, sep = ",")

fit <- lm(Net.Price ~ Quantity + Net.ASP, data = abc)

x <- data.frame (abc, summary(fit))

write.csv (x, file = fileName)

}

现在，我知道x <- data.frame (abc, summary(fit))行是错误的，因为它说的是Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : cannot coerce class ""summary.lm"" to a data.frame，但是我想将每个csv文件的回归模型摘要写入文件本身.请帮忙.

Now, I understand the line x <- data.frame (abc, summary(fit)) is wrong, as it's saying Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : cannot coerce class ""summary.lm"" to a data.frame ,but I want to write the summary of regression model for each csv file to the file itself. Please help.

推荐答案

提供了数据集和注释，我会做类似的事情

Provided your data set and your comments, I would do something like

abc <- read.table(text = "
Order.Week..BV.    Product.Number    Quantity    Net.ASP    Net.Price
1         2013-W44        ABCDEF       92  823.66       749
2         2013-W44        ABCDEF       24  898.89       749
3         2013-W44        ABCDEF      243  892.00       749
4         2013-W45        ABCDEF       88  796.84       699
5         2013-W45        ABCDEF       18  744.80       699", header = T) # Yor data

fit <- lm(Net.Price ~ Quantity + Net.ASP, data = abc)
x <- cbind(abc, t(as.numeric(coefficients(fit))), t(as.numeric(summary(fit)$coefficients[, 4])), summary(fit)$r.squared)
names(x)[(length(x) - 6):length(x)] <- c(paste("coeff", names(coefficients(fit))), paste("P-value", names(summary(fit)$coefficients[, 4])), "R-squared")

哪个会回来

  Order.Week..BV. Product.Number Quantity Net.ASP Net.Price coeff (Intercept) coeff Quantity coeff Net.ASP P-value (Intercept) P-value Quantity
1        2013-W44         ABCDEF       92  823.66       749          434.0829    0.001853692     0.3545852          0.09474093        0.9898202
2        2013-W44         ABCDEF       24  898.89       749          434.0829    0.001853692     0.3545852          0.09474093        0.9898202
3        2013-W44         ABCDEF      243  892.00       749          434.0829    0.001853692     0.3545852          0.09474093        0.9898202
4        2013-W45         ABCDEF       88  796.84       699          434.0829    0.001853692     0.3545852          0.09474093        0.9898202
5        2013-W45         ABCDEF       18  744.80       699          434.0829    0.001853692     0.3545852          0.09474093        0.9898202
  P-value Net.ASP R-squared
1       0.1865054 0.7165826
2       0.1865054 0.7165826
3       0.1865054 0.7165826
4       0.1865054 0.7165826
5       0.1865054 0.7165826

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