Hi all,
I have a pretty basic question about categorical variables but I can't
seem to be able to find answer so I am hoping someone here can help. I
found that if the factor names are all in numbers, fitting the model
in lm would return labels that are not very recognizable.
# Example: let's just assume that we want to fit this model
fit <- lm(height ~ age + Seed, data=Loblolly)
# See the category names are all mangled up here
fit
Call:
lm(formula = height ~ age + Seed, data = Loblolly)
Coefficients:
(Intercept) age Seed.L Seed.Q Seed.C
Seed^4
-1.31240 2.59052 4.86941 0.87307 0.37894
-0.46853
Seed^5 Seed^6 Seed^7 Seed^8 Seed^9
Seed^10
0.55237 0.39659 -0.06507 0.35074 -0.83442
0.42085
Seed^11 Seed^12 Seed^13
0.53906 -0.29803 -0.77254
One possible solution I found is to rename the categorical variables
seed.str <- paste("S", Loblolly$Seed, sep="")
seed.str <- factor(seed.str)
fit <- lm(height ~ age + seed.str, data=Loblolly)
fit
Call:
lm(formula = height ~ age + seed.str, data = Loblolly)
Coefficients:
(Intercept) age seed.strS303 seed.strS305 seed.strS307
-0.4301 2.5905 0.8600 1.8683 -1.9183
seed.strS309 seed.strS311 seed.strS315 seed.strS319 seed.strS321
0.5350 -1.5933 -0.8867 -0.3650 -2.0350
seed.strS323 seed.strS325 seed.strS327 seed.strS329 seed.strS331
0.3067 -1.3233 -2.6400 -2.9333 -2.2267
Now it is actually possible to see which one is which, but is kind of
lame. Can someone point me to a more elegant solution? Thank you so
much.
Saiwing Yeung
Dear Saiwing Yeung, You appear to be using orthogonal-polynomial contrasts (generated by contr.poly) for Seed, which suggests that Seed is either an ordered factor or that you've assigned these contrasts to it. Because Seed has 14 levels, you end up fitting an degree-13 polynomial. If Seed is indeed an ordered factor and you want to use contr.treatment instead then you could, e.g., set Loblolly$Seed <- as.factor(Loblolly$Seed). (If I'm right about Seed being an ordered factor, your solution worked because it changed Seed to a factor, not because it used non-numeric level names.) I hope this helps, John> -----Original Message----- > From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]On> Behalf Of Saiwing Yeung > Sent: March-21-09 5:02 PM > To: r-help at r-project.org > Subject: [R] factor with numeric names > > Hi all, > > I have a pretty basic question about categorical variables but I can't > seem to be able to find answer so I am hoping someone here can help. I > found that if the factor names are all in numbers, fitting the model > in lm would return labels that are not very recognizable. > > # Example: let's just assume that we want to fit this model > fit <- lm(height ~ age + Seed, data=Loblolly) > > # See the category names are all mangled up here > fit > > > Call: > lm(formula = height ~ age + Seed, data = Loblolly) > > Coefficients: > (Intercept) age Seed.L Seed.Q Seed.C > Seed^4 > -1.31240 2.59052 4.86941 0.87307 0.37894 > -0.46853 > Seed^5 Seed^6 Seed^7 Seed^8 Seed^9 > Seed^10 > 0.55237 0.39659 -0.06507 0.35074 -0.83442 > 0.42085 > Seed^11 Seed^12 Seed^13 > 0.53906 -0.29803 -0.77254 > > > > One possible solution I found is to rename the categorical variables > > seed.str <- paste("S", Loblolly$Seed, sep="") > seed.str <- factor(seed.str) > fit <- lm(height ~ age + seed.str, data=Loblolly) > fit > > > > Call: > lm(formula = height ~ age + seed.str, data = Loblolly) > > Coefficients: > (Intercept) age seed.strS303 seed.strS305 seed.strS307 > -0.4301 2.5905 0.8600 1.8683 -1.9183 > seed.strS309 seed.strS311 seed.strS315 seed.strS319 seed.strS321 > 0.5350 -1.5933 -0.8867 -0.3650 -2.0350 > seed.strS323 seed.strS325 seed.strS327 seed.strS329 seed.strS331 > 0.3067 -1.3233 -2.6400 -2.9333 -2.2267 > > > Now it is actually possible to see which one is which, but is kind of > lame. Can someone point me to a more elegant solution? Thank you so > much. > > Saiwing Yeung > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guidehttp://www.R-project.org/posting-guide.html> and provide commented, minimal, self-contained, reproducible code.
Hi Saiwing, If all you are asking is how to rename a factor vector, the easiest way would be to use: levels(Loblolly$Seed) <- c( a vector of level names you would like to use for the factor - separated by commas) If you are asking how to make your output look better, I am not sure I have an idea (except for using "summary(fit)" - but I guess that is not what you mean) Best, Tal On Sat, Mar 21, 2009 at 11:02 PM, Saiwing Yeung <saiwing@berkeley.edu>wrote:> Hi all, > > I have a pretty basic question about categorical variables but I can't seem > to be able to find answer so I am hoping someone here can help. I found that > if the factor names are all in numbers, fitting the model in lm would return > labels that are not very recognizable. > > # Example: let's just assume that we want to fit this model > fit <- lm(height ~ age + Seed, data=Loblolly) > > # See the category names are all mangled up here > fit > > > Call: > lm(formula = height ~ age + Seed, data = Loblolly) > > Coefficients: > (Intercept) age Seed.L Seed.Q Seed.C > Seed^4 > -1.31240 2.59052 4.86941 0.87307 0.37894 -0.46853 > Seed^5 Seed^6 Seed^7 Seed^8 Seed^9 Seed^10 > 0.55237 0.39659 -0.06507 0.35074 -0.83442 0.42085 > Seed^11 Seed^12 Seed^13 > 0.53906 -0.29803 -0.77254 > > > > One possible solution I found is to rename the categorical variables > > seed.str <- paste("S", Loblolly$Seed, sep="") > seed.str <- factor(seed.str) > fit <- lm(height ~ age + seed.str, data=Loblolly) > fit > > > > Call: > lm(formula = height ~ age + seed.str, data = Loblolly) > > Coefficients: > (Intercept) age seed.strS303 seed.strS305 seed.strS307 > -0.4301 2.5905 0.8600 1.8683 -1.9183 > seed.strS309 seed.strS311 seed.strS315 seed.strS319 seed.strS321 > 0.5350 -1.5933 -0.8867 -0.3650 -2.0350 > seed.strS323 seed.strS325 seed.strS327 seed.strS329 seed.strS331 > 0.3067 -1.3233 -2.6400 -2.9333 -2.2267 > > > Now it is actually possible to see which one is which, but is kind of lame. > Can someone point me to a more elegant solution? Thank you so much. > > Saiwing Yeung > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >-- ---------------------------------------------- My contact information: Tal Galili Phone number: 972-50-3373767 FaceBook: Tal Galili My Blogs: http://www.r-statistics.com/ http://www.talgalili.com http://www.biostatistics.co.il [[alternative HTML version deleted]]