#---------------- # SSANOVA-R-Code-NWAVdemo-rev.txt #---------------- # author: Alicia Wassink (wassink@uw.edu) # credits: This script cribs heavily from Josef Fruehwald's excellent tutorial (downloaded 3/22/2010 # entitled, "SS ANOVA.pdf") # relevant publication: LVC-2013 #---------------- ##This demo code was prepared for NWAV-2013 vowel trajectory workshop ##It will compare (ae) BAG vowel in different following contexts, to test the ##hypothesis that following velars, particularly (ŋ) condition raising of the vowel. ##First, we clear memory and read in a couple of necessary libraries. rm(list=ls()) install.packages(ggplot2) # package for plotting install.packages(gss) # containing the SSANOVA algorithm to be used library(ggplot2) library(gss) ##Now, we read in one gender x generation datafile (choose the one called PNWE-SSANOVA-spreadsheet.txt) ##Formant data were extracted from each vowel at 20-50-80%. This enables presentation of common proportions ##through each vowel. aedata.all <- read.delim(file=file.choose(), header=T, fileEncoding="UTF-16", dec=".") ##choose one of the below comparisons by removing the # at the start of the line aedata<-subset(aedata.all, Vowel %in% c("æ")) #aedata<-subset(aedata.all, Vowel %in% c("ɛ","æ")) #aedata<-subset(aedata.all, Vowel %in% c("æ", "ej")) #aedata<-subset(aedata.all, Vowel %in% c("ɛ", "ej")) # For simplicity, the comparison you chose will be always be called "aedata" below. attach(aedata) ##create a "layered" plot using our measured interval data, partitioned by FollowingPhone. formants <- ggplot(aedata, aes(x = Interval, group = Group, colour = Following.Phone)) formants <- formants + geom_line(aes(y = F.1), alpha = 0.8) formants <- formants + geom_line(aes(y = F.2), alpha = 0.8) formants <- formants + ylab("Hz") formants ##fit a model of each formant to the data using the SS-ANOVA outcomes f1.model <- ssanova(F1 ~ Following.Phone + Interval + Following.Phone:Interval, data = aedata) f2.model <- ssanova(F2 ~ Following.Phone + Interval + Following.Phone:Interval, data = aedata) ##create a new array of dummy data with interval values from 0 to 1 (100 rows per group) #note: +/- 5% bound on seq(). Going outside generates 'inputs are out of bounds' error grid <- expand.grid(Interval = seq(min(Interval), max(Interval), length = 100), Following.Phone = c("o","ŋ")) ##use SS-ANOVA model to predict formant values and standard errors for the new array grid\$F1.Fit <- predict(f1.model, newdata = grid, se = T)\$fit grid\$F1.SE <- predict(f1.model, newdata = grid, se = T)\$se.fit grid\$F2.Fit <- predict(f2.model, newdata = grid, se = T)\$fit grid\$F2.SE <- predict(f2.model, newdata = grid, se = T)\$se.fit ##plot SS-ANOVA curves, with 95% confidence intervals formant.comparison <- ggplot(grid, aes(x = Interval, colour = Following.Phone)) formant.comparison<-formant.comparison + geom_line(aes(y = F1.Fit),lwd = 2,alpha = 0.8) formant.comparison<-formant.comparison + geom_line(aes(y = F2.Fit),lwd = 2,alpha = 0.8) formant.comparison<-formant.comparison + geom_line(aes(y = F1.Fit+(1.96*F1.SE)),lty = 2) formant.comparison<-formant.comparison + geom_line(aes(y = F1.Fit-(1.96*F1.SE)),lty = 2) formant.comparison<-formant.comparison + geom_line(aes(y = F2.Fit+(1.96*F2.SE)),lty = 2) formant.comparison<-formant.comparison + geom_line(aes(y = F2.Fit-(1.96*F2.SE)),lty = 2) formant.comparison<-formant.comparison + ylab("Hz") print(formant.comparison) #print R^2 for tests of significance summary(f1.model) summary(f2.model)