But I think Bill continues to confuse the sort order of factor levels with the order of an ordered factor.
?ordered ## and some time with an R tutorial might help! Note: > f1 <- factor(1:5, lev = 5:1) > f2 <- factor(1:5,lev=5:1,ordered=TRUE) > identical(f1,f2) [1] FALSE > ## They are different classes!! > f1 [1] 1 2 3 4 5 Levels: 5 4 3 2 1 > f2 [1] 1 2 3 4 5 Levels: 5 < 4 < 3 < 2 < 1 > sort(f1) [1] 5 4 3 2 1 Levels: 5 4 3 2 1 > sort(f2) [1] 5 4 3 2 1 Levels: 5 < 4 < 3 < 2 < 1 Cheers, Bert On Tue, Dec 3, 2013 at 3:20 PM, David Winsemius <dwinsem...@comcast.net> wrote: > > On Dec 2, 2013, at 6:58 PM, Bill wrote: > >> Duncan, >> Thanks. Why doesn't >> coloursf2 <- factor(1:8, levels = 8:1) >> >> give an ordering when you do str(coloursf2) like >> "8"<"7"<"6" ... > > Because the default for 'ordered' in factor is FALSE: > >> coloursf2 <- factor(1:8, levels = 8:1, ordered=TRUE) >> coloursf2 > [1] 1 2 3 4 5 6 7 8 > Levels: 8 < 7 < 6 < 5 < 4 < 3 < 2 < 1 > >> >> Bill >> >> >> On Mon, Dec 2, 2013 at 6:29 PM, Duncan Mackay <dulca...@bigpond.com> wrote: >> >>> Hi Bill >>> >>> eg >>> >>>> colours = 1:8 >>>> coloursf = factor(1:8) >>>> colourso = ordered(1:8) >>>> str(coloursf) >>> Factor w/ 8 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 >>>> str(colourso) >>> Ord.factor w/ 8 levels "1"<"2"<"3"<"4"<..: 1 2 3 4 5 6 7 8 >>> >>> coloursf2 <- factor(1:8, levels = 8:1) >>> str(coloursf2) >>> >>> Duncan >>> >>> Duncan >>> Duncan Mackay >>> Department of Agronomy and Soil Science >>> University of New England >>> Armidale NSW 2351 >>> Email: home: mac...@northnet.com.au >>> >>> >>> ordered used in >>> used in MASS::polr and GEE for polytomous logistic regression >>> >>> -----Original Message----- >>> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] >>> On >>> Behalf Of Bill >>> Sent: Monday, 2 December 2013 21:24 >>> To: r-help@r-project.org >>> Subject: [R] why change days of the week from a factor to an ordered >>> factor? >>> >>> 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@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. >>> >>> >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> 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. > > David Winsemius > Alameda, CA, USA > > ______________________________________________ > 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. -- Bert Gunter Genentech Nonclinical Biostatistics (650) 467-7374 ______________________________________________ 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.
