On converting character variables to ordered factors, regression result
has strange names. Is it possible to obtain same variable names with
and without intercept?
Thanks,
Naresh
mydf <- data.frame(date = seq.Date(as.Date("2024-01-01"),
as.Date("2024-03-31"), by = 1))
mydf[, "wday"] <- weekdays(mydf$date, abbreviate = TRUE)
mydf.work <- subset(mydf, !(wday %in% c("Sat", "Sun")))
mydf.weekend <- subset(mydf, wday %in% c("Sat", "Sun"))
mydf.work[, "volume"] <- round(rnorm(nrow(mydf.work), mean = 20, sd
5))
mydf.weekend[, "volume"] <- round(rnorm(nrow(mydf.weekend), mean =
10,
sd = 5))
mydf <- rbind(mydf.work, mydf.weekend)
reg <- lm(volume ~ wday, data = mydf)
## Variable names as expected
coef(reg)
(Intercept) wdayMon wdaySat wdaySun wdayThu wdayTue
21.3846154 1.3076923 -12.0000000 -12.9230769 -1.9230769 -0.6923077
wdayWed
-1.6153846
reg <- lm(volume ~ wday - 1, data = mydf)
# Variable names as expected
coef(reg)
wdayFri wdayMon wdaySat wdaySun wdayThu wdayTue wdayWed
21.384615 22.692308 9.384615 8.461538 19.461538 20.692308 19.769231
# Ordered factors for weekday sequence
mydf$wday <- factor(mydf$wday, levels = c("Mon", "Tue",
"Wed", "Thu",
"Fri", "Sat", "Sun"), ordered = TRUE)
reg <- lm(volume ~ wday - 1, data = mydf)
# Variable names as expected
coef(reg)
wdayMon wdayTue wdayWed wdayThu wdayFri wdaySat wdaySun
22.692308 20.692308 19.769231 19.461538 21.384615 9.384615 8.461538
reg <- lm(volume ~ wday, data = mydf)
# Strange variable names
coef(reg)
(Intercept) wday.L wday.Q wday.C wday^4 wday^5
17.406593 -12.036715 -4.968654 -1.852819 3.291477 4.263642
wday^6
2.591317
On 09/05/2024 8:09 a.m., Naresh Gurbuxani wrote:> > On converting character variables to ordered factors, regression result > has strange names. Is it possible to obtain same variable names with > and without intercept?You are getting polynomial contrasts with the ordered factor, because you have the default setting for options("contrasts"), i.e. unordered ordered "contr.treatment" "contr.poly" If you run options(contrasts = c("contr.treatment", "contr.treatment")) you will get the same coefficient names in both cases. By the way, the coefficients have different meanings, so it makes sense they will have different names. It's perhaps a little bit more of a problem that you *don't* get different variable names when an intercept is included or not, because those coefficients also have different meanings. It may also be a little bit of a surprise that you go back to treatment contrasts when you leave out the intercept with the ordered factor, but then it almost never makes sense to leave out the intercept in a polynomial fit. Duncan Murdoch> > Thanks, > Naresh > > mydf <- data.frame(date = seq.Date(as.Date("2024-01-01"), > as.Date("2024-03-31"), by = 1)) > mydf[, "wday"] <- weekdays(mydf$date, abbreviate = TRUE) > mydf.work <- subset(mydf, !(wday %in% c("Sat", "Sun"))) > mydf.weekend <- subset(mydf, wday %in% c("Sat", "Sun")) > mydf.work[, "volume"] <- round(rnorm(nrow(mydf.work), mean = 20, sd > 5)) > mydf.weekend[, "volume"] <- round(rnorm(nrow(mydf.weekend), mean = 10, > sd = 5)) > mydf <- rbind(mydf.work, mydf.weekend) > > reg <- lm(volume ~ wday, data = mydf) > ## Variable names as expected > coef(reg) > (Intercept) wdayMon wdaySat wdaySun wdayThu wdayTue > 21.3846154 1.3076923 -12.0000000 -12.9230769 -1.9230769 -0.6923077 > wdayWed > -1.6153846 > > reg <- lm(volume ~ wday - 1, data = mydf) > # Variable names as expected > coef(reg) > wdayFri wdayMon wdaySat wdaySun wdayThu wdayTue wdayWed > 21.384615 22.692308 9.384615 8.461538 19.461538 20.692308 19.769231 > > # Ordered factors for weekday sequence > mydf$wday <- factor(mydf$wday, levels = c("Mon", "Tue", "Wed", "Thu", > "Fri", "Sat", "Sun"), ordered = TRUE) > > reg <- lm(volume ~ wday - 1, data = mydf) > # Variable names as expected > coef(reg) > wdayMon wdayTue wdayWed wdayThu wdayFri wdaySat wdaySun > 22.692308 20.692308 19.769231 19.461538 21.384615 9.384615 8.461538 > > reg <- lm(volume ~ wday, data = mydf) > # Strange variable names > coef(reg) > (Intercept) wday.L wday.Q wday.C wday^4 wday^5 > 17.406593 -12.036715 -4.968654 -1.852819 3.291477 4.263642 > wday^6 > 2.591317 > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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.
Comment in in-line below On 09/05/2024 13:09, Naresh Gurbuxani wrote:> > On converting character variables to ordered factors, regression result > has strange names. Is it possible to obtain same variable names with > and without intercept? > > Thanks, > Naresh > > mydf <- data.frame(date = seq.Date(as.Date("2024-01-01"), > as.Date("2024-03-31"), by = 1)) > mydf[, "wday"] <- weekdays(mydf$date, abbreviate = TRUE) > mydf.work <- subset(mydf, !(wday %in% c("Sat", "Sun"))) > mydf.weekend <- subset(mydf, wday %in% c("Sat", "Sun")) > mydf.work[, "volume"] <- round(rnorm(nrow(mydf.work), mean = 20, sd > 5)) > mydf.weekend[, "volume"] <- round(rnorm(nrow(mydf.weekend), mean = 10, > sd = 5)) > mydf <- rbind(mydf.work, mydf.weekend) > > reg <- lm(volume ~ wday, data = mydf) > ## Variable names as expected > coef(reg) > (Intercept) wdayMon wdaySat wdaySun wdayThu wdayTue > 21.3846154 1.3076923 -12.0000000 -12.9230769 -1.9230769 -0.6923077 > wdayWed > -1.6153846 > > reg <- lm(volume ~ wday - 1, data = mydf) > # Variable names as expected > coef(reg) > wdayFri wdayMon wdaySat wdaySun wdayThu wdayTue wdayWed > 21.384615 22.692308 9.384615 8.461538 19.461538 20.692308 19.769231 > > # Ordered factors for weekday sequence > mydf$wday <- factor(mydf$wday, levels = c("Mon", "Tue", "Wed", "Thu", > "Fri", "Sat", "Sun"), ordered = TRUE) > > reg <- lm(volume ~ wday - 1, data = mydf) > # Variable names as expected > coef(reg) > wdayMon wdayTue wdayWed wdayThu wdayFri wdaySat wdaySun > 22.692308 20.692308 19.769231 19.461538 21.384615 9.384615 8.461538 > > reg <- lm(volume ~ wday, data = mydf) > # Strange variable names > coef(reg) > (Intercept) wday.L wday.Q wday.C wday^4 wday^5 > 17.406593 -12.036715 -4.968654 -1.852819 3.291477 4.263642 > wday^6 > 2.591317Yes, that is what ordered is supposed to do, fit polynomial contrasts. When you remove the intercept that breaks it so you get a different fit, in fact the same as you got when it was not ordered. Michael> > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. >-- Michael