I think you are looking for by facility in R.
fit <- with(mhw, by(mhw, Quad, function (dat) lm(grain ~ straw, data = dat)))
Since you have 4 levels in Quad, you end up with 4 linear models in fit, i.e., fit is a "by" class object (a type of "list") of length 4.
To get coefficient for each model, you can use
sapply(fit, coef)
To produce model summary, use
lapply(fit, summary)
To export ANOVA table, use
lapply(fit, anova)
As a reproducible example, I am taking the example from ?by:
tmp <- with(warpbreaks,
by(warpbreaks, tension,
function(x) lm(breaks ~ wool, data = x)))
class(tmp)
# [1] "by"
mode(tmp)
# [1] "list"
sapply(tmp, coef)
# L M H
#(Intercept) 44.55556 24.000000 24.555556
#woolB -16.33333 4.777778 -5.777778
lapply(tmp, anova)
#$L
#Analysis of Variance Table
#
#Response: breaks
# Df Sum Sq Mean Sq F value Pr(>F)
#wool 1 1200.5 1200.50 5.6531 0.03023 *
#Residuals 16 3397.8 212.36
#---
#Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
#$M
#Analysis of Variance Table
#
#Response: breaks
# Df Sum Sq Mean Sq F value Pr(>F)
#wool 1 102.72 102.722 1.2531 0.2795
#Residuals 16 1311.56 81.972
#
#$H
#Analysis of Variance Table
#
#Response: breaks
# Df Sum Sq Mean Sq F value Pr(>F)
#wool 1 150.22 150.222 2.3205 0.1472
#Residuals 16 1035.78 64.736
I was aware of this option, but not familiar with it. Thanks to @Roland for providing code for the above reproducible example:
library(nlme)
lapply(lmList(breaks ~ wool | tension, data = warpbreaks), anova)
For your data I think it would be
fit <- lmList(grain ~ straw | Quad, data = mhw)
lapply(fit, anova)
You don't need to install nlme; it comes with R as one of recommended packages.
