[英]How can I predict a single value from a linear regression in R?
I've made a linear regression of dollar prices to GDPPC like so: 我将美元价格与GDPPC进行了线性回归,如下所示:
r = lm(dollar_value ~ GDPPC, prices_gdp)
( prices_gdp
is a data.table
, if that matters). (如果重要,
prices_gdp
是一个data.table
)。
I can now easily generate a bunch of values based on a data.table
using predict
. 现在,我可以使用
predict
轻松地基于data.table
生成一堆值。 But what I want to do (in order to plot a geom_abline
on a chart) is calculate the dollar value when GDPPC is zero, and get that back as a number—something like 但是我想要做的(为了在图表上绘制
geom_abline
)是在geom_abline
为零时计算美元值,然后将其取回为数字, 就像
predict(r, 0)
This gives me an error: Error in eval(predvars, data, env): object 'GDPPC' not found
. 这给了我一个错误:
Error in eval(predvars, data, env): object 'GDPPC' not found
的错误: Error in eval(predvars, data, env): object 'GDPPC' not found
。 Is there any way of doing this short of creating a new dummy data.table
with GDPPC=0 as its only row, feeding it in, and then pulling the number out? 除了创建一个新的虚拟数据表(
data.table
= 0作为唯一行),将其输入然后将其取出来之外,还有什么办法可以做到?
You can just create the same data table and put the regressor GDPPC to zero. 您可以只创建相同的数据表,并将回归器GDPPC设置为零。 Try:
尝试:
predict(r, data.frame(GDPPC = 0))
You could create a function which extracts the name of the term in the model and makes the call to predict for you. 您可以创建一个函数,该函数提取模型中术语的名称并进行调用以为您预测。
preds <- function(o, vals){
#' Make prediction from simple linear regression
#'
#' Makes a prediction from a simple linear regression without
#' needing to manually create a data.frame. This will fail
#' on models with more than one predictor.
#' @param o The lm object to use to make predictions
#' @param vals The values to make predictions for.
dat <- setNames(data.frame(vals), as.character(formula(o)[[3]]))
predict(o, newdata = dat)
}
and using it... 并使用它...
> o <- lm(mpg ~ wt, data = mtcars)
> preds(o, 1:3)
1 2 3
31.94065 26.59618 21.25171
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