[R] why change days of the week from a factor to an ordered factor?
Richard M. Heiberger
rmh at temple.edu
Mon Dec 2 18:51:00 CET 2013
Bert,
the issue is the sort order of the levels. Time series graphs in the
alphabetical sort
order will be uninterpretable. I show the three sets of contrasts for
factors, factors
with specified levels, and ordered factors.
week <- c("Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday")
class(week)
week.f <- factor(week)
levels(week.f)
week.l <- factor(week, levels=week)
levels(week.l)
week.o <- ordered(week, levels=week)
levels(week.o)
contrasts(week.f)
contrasts(week.l)
contrasts(week.o)
Rich
On Mon, Dec 2, 2013 at 10:35 AM, Bert Gunter <gunter.berton at gene.com> wrote:
> Not true, Rich.
>
>> z <-factor(letters[1:3],lev=letters[3:1])
>> sort(z)
> [1] c b a
> Levels: c b a
>
> What you say is true only for the **default** sort order.
>
> (Although maybe the code author didn't realize this either)
>
> -- Bert
>
>
> On Mon, Dec 2, 2013 at 7:24 AM, Richard M. Heiberger <rmh at temple.edu> wrote:
>> If days of the week is not an Ordered Factor, then it will be sorted
>> alphabetically.
>> Fr Mo Sa Su Th Tu We
>>
>> Rich
>>
>> On Mon, Dec 2, 2013 at 6:24 AM, Bill <william108 at gmail.com> wrote:
>>> I am reading the code below. It acts on a csv file called dodgers.csv with
>>> the following variables.
>>>
>>>
>>>> print(str(dodgers)) # check the structure of the data frame
>>> 'data.frame': 81 obs. of 12 variables:
>>> $ month : Factor w/ 7 levels "APR","AUG","JUL",..: 1 1 1 1 1 1 1 1 1
>>> 1 ...
>>> $ day : int 10 11 12 13 14 15 23 24 25 27 ...
>>> $ attend : int 56000 29729 28328 31601 46549 38359 26376 44014 26345
>>> 44807 ...
>>> $ day_of_week: Factor w/ 7 levels "Friday","Monday",..: 6 7 5 1 3 4 2 6 7
>>> 1 ...
>>> $ opponent : Factor w/ 17 levels "Angels","Astros",..: 13 13 13 11 11 11
>>> 3 3 3 10 ...
>>> $ temp : int 67 58 57 54 57 65 60 63 64 66 ...
>>> $ skies : Factor w/ 2 levels "Clear ","Cloudy": 1 2 2 2 2 1 2 2 2 1
>>> ...
>>> $ day_night : Factor w/ 2 levels "Day","Night": 1 2 2 2 2 1 2 2 2 2 ...
>>> $ cap : Factor w/ 2 levels "NO","YES": 1 1 1 1 1 1 1 1 1 1 ...
>>> $ shirt : Factor w/ 2 levels "NO","YES": 1 1 1 1 1 1 1 1 1 1 ...
>>> $ fireworks : Factor w/ 2 levels "NO","YES": 1 1 1 2 1 1 1 1 1 2 ...
>>> $ bobblehead : Factor w/ 2 levels "NO","YES": 1 1 1 1 1 1 1 1 1 1 ...
>>> NULL
>>>>
>>>
>>> I don't understand why the author of the code decided to make the factor
>>> days_of_week into an ordered factor. Anyone know why this should be done?
>>> Thank you.
>>>
>>> Here is the code:
>>>
>>> # Predictive Model for Los Angeles Dodgers Promotion and Attendance
>>>
>>> library(car) # special functions for linear regression
>>> library(lattice) # graphics package
>>>
>>> # read in data and create a data frame called dodgers
>>> dodgers <- read.csv("dodgers.csv")
>>> print(str(dodgers)) # check the structure of the data frame
>>>
>>> # define an ordered day-of-week variable
>>> # for plots and data summaries
>>> dodgers$ordered_day_of_week <- with(data=dodgers,
>>> ifelse ((day_of_week == "Monday"),1,
>>> ifelse ((day_of_week == "Tuesday"),2,
>>> ifelse ((day_of_week == "Wednesday"),3,
>>> ifelse ((day_of_week == "Thursday"),4,
>>> ifelse ((day_of_week == "Friday"),5,
>>> ifelse ((day_of_week == "Saturday"),6,7)))))))
>>> dodgers$ordered_day_of_week <- factor(dodgers$ordered_day_of_week,
>>> levels=1:7,
>>> labels=c("Mon", "Tue", "Wed", "Thur", "Fri", "Sat", "Sun"))
>>>
>>> # exploratory data analysis with standard graphics: attendance by day of
>>> week
>>> with(data=dodgers,plot(ordered_day_of_week, attend/1000,
>>> xlab = "Day of Week", ylab = "Attendance (thousands)",
>>> col = "violet", las = 1))
>>>
>>> # when do the Dodgers use bobblehead promotions
>>> with(dodgers, table(bobblehead,ordered_day_of_week)) # bobbleheads on
>>> Tuesday
>>>
>>> # define an ordered month variable
>>> # for plots and data summaries
>>> dodgers$ordered_month <- with(data=dodgers,
>>> ifelse ((month == "APR"),4,
>>> ifelse ((month == "MAY"),5,
>>> ifelse ((month == "JUN"),6,
>>> ifelse ((month == "JUL"),7,
>>> ifelse ((month == "AUG"),8,
>>> ifelse ((month == "SEP"),9,10)))))))
>>> dodgers$ordered_month <- factor(dodgers$ordered_month, levels=4:10,
>>> labels = c("April", "May", "June", "July", "Aug", "Sept", "Oct"))
>>>
>>> # exploratory data analysis with standard R graphics: attendance by month
>>> with(data=dodgers,plot(ordered_month,attend/1000, xlab = "Month",
>>> ylab = "Attendance (thousands)", col = "light blue", las = 1))
>>>
>>> # exploratory data analysis displaying many variables
>>> # looking at attendance and conditioning on day/night
>>> # the skies and whether or not fireworks are displayed
>>> library(lattice) # used for plotting
>>> # let us prepare a graphical summary of the dodgers data
>>> group.labels <- c("No Fireworks","Fireworks")
>>> group.symbols <- c(21,24)
>>> group.colors <- c("black","black")
>>> group.fill <- c("black","red")
>>> xyplot(attend/1000 ~ temp | skies + day_night,
>>> data = dodgers, groups = fireworks, pch = group.symbols,
>>> aspect = 1, cex = 1.5, col = group.colors, fill = group.fill,
>>> layout = c(2, 2), type = c("p","g"),
>>> strip=strip.custom(strip.levels=TRUE,strip.names=FALSE, style=1),
>>> xlab = "Temperature (Degrees Fahrenheit)",
>>> ylab = "Attendance (thousands)",
>>> key = list(space = "top",
>>> text = list(rev(group.labels),col = rev(group.colors)),
>>> points = list(pch = rev(group.symbols), col = rev(group.colors),
>>> fill = rev(group.fill))))
>>>
>>> # attendance by opponent and day/night game
>>> group.labels <- c("Day","Night")
>>> group.symbols <- c(1,20)
>>> group.symbols.size <- c(2,2.75)
>>> bwplot(opponent ~ attend/1000, data = dodgers, groups = day_night,
>>> xlab = "Attendance (thousands)",
>>> panel = function(x, y, groups, subscripts, ...)
>>> {panel.grid(h = (length(levels(dodgers$opponent)) - 1), v = -1)
>>> panel.stripplot(x, y, groups = groups, subscripts = subscripts,
>>> cex = group.symbols.size, pch = group.symbols, col = "darkblue")
>>> },
>>> key = list(space = "top",
>>> text = list(group.labels,col = "black"),
>>> points = list(pch = group.symbols, cex = group.symbols.size,
>>> col = "darkblue")))
>>>
>>> # specify a simple model with bobblehead entered last
>>> my.model <- {attend ~ ordered_month + ordered_day_of_week + bobblehead}
>>>
>>> # employ a training-and-test regimen
>>> set.seed(1234) # set seed for repeatability of training-and-test split
>>> training_test <- c(rep(1,length=trunc((2/3)*nrow(dodgers))),
>>> rep(2,length=(nrow(dodgers) - trunc((2/3)*nrow(dodgers)))))
>>> dodgers$training_test <- sample(training_test) # random permutation
>>> dodgers$training_test <- factor(dodgers$training_test,
>>> levels=c(1,2), labels=c("TRAIN","TEST"))
>>> dodgers.train <- subset(dodgers, training_test == "TRAIN")
>>> print(str(dodgers.train)) # check training data frame
>>> dodgers.test <- subset(dodgers, training_test == "TEST")
>>> print(str(dodgers.test)) # check test data frame
>>>
>>> # fit the model to the training set
>>> train.model.fit <- lm(my.model, data = dodgers.train)
>>> # obtain predictions from the training set
>>> dodgers.train$predict_attend <- predict(train.model.fit)
>>>
>>> # evaluate the fitted model on the test set
>>> dodgers.test$predict_attend <- predict(train.model.fit,
>>> newdata = dodgers.test)
>>>
>>> # compute the proportion of response variance
>>> # accounted for when predicting out-of-sample
>>> cat("\n","Proportion of Test Set Variance Accounted for: ",
>>> round((with(dodgers.test,cor(attend,predict_attend)^2)),
>>> digits=3),"\n",sep="")
>>>
>>> # merge the training and test sets for plotting
>>> dodgers.plotting.frame <- rbind(dodgers.train,dodgers.test)
>>>
>>> # generate predictive modeling visual for management
>>> group.labels <- c("No Bobbleheads","Bobbleheads")
>>> group.symbols <- c(21,24)
>>> group.colors <- c("black","black")
>>> group.fill <- c("black","red")
>>> xyplot(predict_attend/1000 ~ attend/1000 | training_test,
>>> data = dodgers.plotting.frame, groups = bobblehead, cex = 2,
>>> pch = group.symbols, col = group.colors, fill = group.fill,
>>> layout = c(2, 1), xlim = c(20,65), ylim = c(20,65),
>>> aspect=1, type = c("p","g"),
>>> panel=function(x,y, ...)
>>> {panel.xyplot(x,y,...)
>>> panel.segments(25,25,60,60,col="black",cex=2)
>>> },
>>> strip=function(...) strip.default(..., style=1),
>>> xlab = "Actual Attendance (thousands)",
>>> ylab = "Predicted Attendance (thousands)",
>>> key = list(space = "top",
>>> text = list(rev(group.labels),col = rev(group.colors)),
>>> points = list(pch = rev(group.symbols),
>>> col = rev(group.colors),
>>> fill = rev(group.fill))))
>>>
>>> # use the full data set to obtain an estimate of the increase in
>>> # attendance due to bobbleheads, controlling for other factors
>>> my.model.fit <- lm(my.model, data = dodgers) # use all available data
>>> print(summary(my.model.fit))
>>> # tests statistical significance of the bobblehead promotion
>>> # type I anova computes sums of squares for sequential tests
>>> print(anova(my.model.fit))
>>>
>>> cat("\n","Estimated Effect of Bobblehead Promotion on Attendance: ",
>>> round(my.model.fit$coefficients[length(my.model.fit$coefficients)],
>>> digits = 0),"\n",sep="")
>>>
>>> # standard graphics provide diagnostic plots
>>> plot(my.model.fit)
>>>
>>> # additional model diagnostics drawn from the car package
>>> library(car)
>>> residualPlots(my.model.fit)
>>> marginalModelPlots(my.model.fit)
>>> print(outlierTest(my.model.fit))
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
>>> R-help at 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.
>>
>> ______________________________________________
>> R-help at 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.
>
>
>
> --
>
> Bert Gunter
> Genentech Nonclinical Biostatistics
>
> (650) 467-7374
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