在R中汇总rmse和r2

Question

Here is a sample data as data2:

lvl xy 0 20.099 21.2 100 21.133 21.4 250 20.866 21.6 500 22.679 21.8 750 22.737 22.1 0 30.396 32.0 100 31.373 32.1 250 31.303 32.2 500 33.984 32.8 750 44.563 38.0 0 22.755 18.5 100 23.194 18.8 250 23.263 20.5 500 23.061 27.9 750 25.678 36.4

I tried to get the rmse and r2 for each level (lvl) by the following lines of codes: data2 %>% group_by(lvl) %>% summarise_each(funs(rmse(data2$x~data2$y))) and summary(lm(data2$x,data2$y))$r.squared respectively, and I got the following error message when calculating rmse:

Error: argument "obs" is missing, with no default

and

# A tibble: 5 x 3 lvl xy <int> <dbl> <dbl> 1 0 0.6639888 0.6639888 2 100 0.6639888 0.6639888 3 250 0.6639888 0.6639888 4 500 0.6639888 0.6639888 5 750 0.6639888 0.6639888

when calculating r2.

I wanted to aggregate the rmse and r2 for each level. In this case I have only 5 levels.So the answer will look like 5 rows X 3 columns with column names `"lvl","rmse","r2" Thank you in advance.

Answer 1

You don't need summarise_each summarise will do what you want. If you prefer using dplyr here is a solution

data2 <-
data.frame(
  lvl = c(  0, 100, 250, 500, 750, 0, 100, 250, 500, 750, 0, 100, 250, 500, 750)
  ,x = c(
    20.099, 21.133, 20.866, 22.679, 22.737, 30.396, 31.373, 31.303, 33.984, 44.563, 22.755, 23.194, 23.263, 23.061, 25.678
  )
  ,y = c(21.2, 21.4, 21.6, 21.8, 22.1, 32.0, 32.1, 32.2, 32.8, 38.0, 18.5, 18.8, 20.5, 27.9, 36.4)
)

#install.packages("ModelMetrics")
library(ModelMetrics)

data2 %>%
  group_by(lvl) %>%
  summarise(
    RMSE = rmse(x, y)
    ,R2 = cor(x, y)^2
  )

## A tibble: 5 × 3
#    lvl     RMSE        R2
#  <dbl>    <dbl>     <dbl>
#1     0 2.701237 0.8176712
#2   100 2.575982 0.8645350
#3   250 1.729888 0.9091029
#4   500 2.920640 0.7207692
#5   750 7.267279 0.4542507

Answer 2

## split your data2 into a list by the levels of the factor and then use lapply
list_of_rsquared <- lapply(split(data2, data2$lvl), function (z) {
  summary(lm(x ~ y, data = z))$r.squared
}
)

## you will get a list of r.squared for each level . Now you can simply rbind the list of r.squared.
rsquared_vals <- do.call("rbind", list_of_rsquared)

You can use the same approach for RMSE. (I am assuming you have written a function called RMSE? because I am just using the formula you have above)

list_of_rmse <- lapply(split(data2, data2$lvl), function (z) { sqrt(mean((z$x - z$y)^2)) } )

rmse_vals <- do.call("rbind", list_of_rmse)

you can just cbind all three columns you need now:

cbind(data2$lvl, rsquared_vals, rmse_vals)