R中值变化的线性拟合预测？

Question

I've got some golf data that looks like this:

ID          round   GIR         score
Tiger Woods 3       0.666666667 68
Tiger Woods 2       0.611111111 75
Tiger Woods 1       0.666666667 71
Adam Scott  3       0.611111111 68
Adam Scott  2       0.888888889 68
Adam Scott  1       0.666666667 66

And I'm trying to make a linear model that says "based on greens in regulation, my round four score is going to be this." Here's my script so far.

#load in data
gir2 <- read.csv("girforscore.csv")

#establish linear model
fit <- lm(score ~ GIR * ID, data = gir2)

#apply linear model
lmresultsGIR <- setNames(predict(fit, newdata = data.frame(ID = unique(gir2$ID), GIR = .6111111)), 
                      unique(gir2$ID))
#show model
head(lmresultsGIR, n=10)

My question is, assuming I have round 4 GIR data:

ID           round  GIR        
Tiger Woods  4      0.666666667 
Tiger Woods  4      0.611111111 

how do I update my script to pick round 4 GIR data by ID, instead of just hardcoding a magic value of .6111111 like I have now?

Answer 1

Try giving this a shot.

#load in data
gir2 <- read.csv("girforscore.csv")

#establish linear model
model <- na.omit(gir2)
fit <- lm(score ~ ID + GIR, data = model)

#subset data for round 4
round4 <- subset(gir2, round == 4)

#apply linear model
predict <- predict(fit, newdata = round4, se.fit = TRUE)

#easier than setNames for this particular example
round4$score <- predict$fit

#view round 4 predicted scores
round4

ID round       GIR    score
7 Tiger Woods     4 0.6666667 71.29545
8 Tiger Woods     4 0.6111111 71.40909