loop lm predictions for plotting multiple lines - r

Question

I want to plot linear-model-lines for each ID.
How can I create predictions for multiple lms (or glms) using sequences of different length? I tried:
#some fake data
res<-runif(60,1,20)
var<-runif(60,10,50)
ID<-rep(c("A","B","B","C","C","C"),10)
data<- data.frame(ID,res,var)
#lm
library(data.table)
dt <- data.table(data,key="ID")
fits <- lapply(unique(data$ID),
function(z)lm(res~var, data=dt[J(z),], y=T))
#sequence for each ID of length var(ID)
mins<-matrix(with(data, tapply(var,ID,min)))
mins1<-mins[,1]
maxs<-matrix(with(data,tapply(var,ID,max)))
maxs1<-maxs[,1]
my_var<-list()
for(i in 1:3){
my_var[[i]]<- seq(from=mins1[[i]],to=maxs1[[i]],by=1)
}
# predict on sequences
predslist<- list()
predslist[[i]] <- for(i in 1:3){
dat<-fits[[i]]
predict(dat,newdata= data.frame("var"= my_var,type= "response", se=TRUE))
}
predict results error

Answer 1

Plotting lm lines only for var[i] ranges works in ggplot:
library(ggplot2)
# create ID, x, y as coded by Matt
p <- qplot(x, y)
p + geom_smooth(aes(group=ID), method="lm", size=1, se=F)

Answer 2

Is something like this what you're after?
# generating some fake data
ID <- rep(letters[1:4],each=10)
x <- rnorm(40,mean=5,sd=10)
y <- as.numeric(as.factor(ID))*x + rnorm(40)
# plotting in base R
plot(x, y, col=as.factor(ID), pch=16)
# calling lm() and adding lines
lmlist <- lapply(sort(unique(ID)), function(i) lm(y[ID==i]~x[ID==i]))
for(i in 1:length(lmlist)) abline(lmlist[[i]], col=i)
Don't know if the plotting part is where you're stuck, but the abline() function will draw a least-squares line if you pass in an object returned from lm().
If you want the least-squares lines to begin & end with the min & max x values, here's a workaround. It's not pretty, but seems to work.
plot(x, y, col=as.factor(ID), pch=16)
IDnum <- as.numeric(as.factor(ID))
for(i in 1:length(lmlist)) lines(x[IDnum==i], predict(lmlist[[i]]), col=i)