Cumulative Linear Regression
Sorry I'm new to R, but I have a dataframe with gamelogs for multiple players. I am trying to get the slope factor for each player point over all of their games. I've seen that I aggregate
can use operators like sum
and average
, and getting the coefficients from linear regression is pretty easy. How to combine them?
a <- c("player1","player1","player1","player2","player2","player2")
b <- c(1,2,3,4,5,6)
c <- c(15,12,13,4,15,9)
gamelogs <- data.frame(name=a, game=b, pts=c)
I want this to become:
name pts slope
player1 -.4286
player2 .08242
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You can also do magic with base lm
to do everything at once:
coef(lm(game ~ pts*name - pts, data=gamelogs))[3:4]
coef(lm(game ~ pts:name + name, data=gamelogs))[3:4]
#pts:nameplayer1 pts:nameplayer2
# -0.42857143 0.08241758
How data.frame
:
data.frame(slope=coef(lm(game ~ pts*name - pts, data=gamelogs))[3:4])
# slope
#pts:nameplayer1 -0.42857143
#pts:nameplayer2 0.08241758
See here for further explanation of in-call modeling lm
:
https://stat.ethz.ch/R-manual/R-devel/library/stats/html/formula.html
http://faculty.chicagobooth.edu/richard.hahn/teaching/FormulaNotation.pdf#2
In this case, it pts*name
expands to pts + name + pts:name
, which when removed - pts
means it's equivalent topts:name + name
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You could do
s <- split(gamelogs, gamelogs$name)
vapply(s, function(x) lm(game ~ pts, x)[[1]][2], 1)
# player1 player2
# -0.42857143 0.08241758
or
do.call(rbind, lapply(s, function(x) coef(lm(game ~ pts, x))[2]))
# pts
# player1 -0.42857143
# player2 0.08241758
Or, if you want to use dplyr
, you can do
library(dplyr)
models <- group_by(gamelogs, name) %>%
do(mod = lm(game ~ pts, data = .))
cbind(
name = models$name,
do(models, data.frame(slope = coef(.$mod)[2]))
)
# name slope
# 1 player1 -0.42857143
# 2 player2 0.08241758
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