[英]Apply regression coefficients that have one answer per factor to many entries per factor in a dataframe in R
I have a dataframe that has a column for time, symbol, price, volatility. 我有一个数据框,其中包含时间,符号,价格,波动率的列。 I use this dataframe to run a first pass OLS regression using dummy variables for the symbol 我使用此数据框使用符号的虚拟变量运行首次通过OLS回归
fit <- lm(volatility~factor(symbol) + 0
Then I want to use the coefficients from that regression in a second pass regression, so I save the coeffiecients of the regression to reuse and then I want to use that to scale volatility 然后,我想在第二遍回归中使用该回归的系数,因此我将回归的系数保存下来以供重用,然后我要使用该系数来衡量波动率
scale <- summary(fit)$coefficients[,1]
yscale <- volatility/scale
fit2 <- lm(yscale~factor(time) + factor(symbol)*factor(time) + 0
The problem that I am having is that I want to use the factor coefficients that are applicable to each symbol. 我遇到的问题是我想使用适用于每个符号的因子系数。 So in the original dataframe I want to divide the volatility by the coeffiecient that matches its symbol. 因此,在原始数据框中,我想将波动率除以与其符号匹配的系数。 So, if I have symbols, DDX, CTY, LOL then I want to divide DDX's volatility by the coefficient with factor DDX from the regression then do the same for CTY and LOL. 因此,如果我有符号DDX,CTY,LOL,那么我想用回归中的因子DDX将DDX的波动率除以系数,然后对CTY和LOL做同样的事情。 Also, I need to figure out how to do the product in the second fit2 coefficient. 另外,我需要弄清楚如何在第二个fit2系数中进行乘积运算。
You should provide a reproducible example to get an exact answers. 您应提供一个可复制的示例以获取准确的答案。 Here some data: 这里有一些数据:
dat <- data.frame(volatility= rnorm(30),
symbol = sample(c('DDX', 'CTY', 'LOL'),30,rep=TRUE))
fit <- lm(volatility~factor(symbol) + 0,data=dat)
mm <- coef(fit)
names(mm) <- gsub('factor\\(symbol\\)','',names(mm))
I transform the names to get a pretty names that can be used later : 我将名称转换为漂亮的名称,以便以后使用:
CTY DDX LOL
-0.1991273 0.1331980 -0.1567511
Then using transform
, I divide each volatility with the corresponding coefficients: 然后使用transform
,将每个波动率除以相应的系数:
transform(dat,vol.scale = volatility/mm[symbol],coef = mm[symbol])
volatility symbol vol.scale coef
1 -0.592306253 DDX -4.44680974 0.1331980
2 1.143486046 DDX 8.58485769 0.1331980
3 -0.693694139 LOL 4.42544868 -0.1567511
4 -0.166050131 LOL 1.05932325 -0.1567511
5 1.381900588 CTY -6.93978353 -0.1991273
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