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.Rhistory
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app.tab.copy[1]
app.tab.copy[1] <- 1
app.tab.copy:$1
app.tab.copy
assoc(app.tab, gp = shading_max)
assoc(app.tab.copy, gp = shading_max)
app.tab.copy[1] <- 100
assoc(app.tab.copy, gp = shading_max)
app.tab.copy[1] <- 50
app.tab.copy[1] <- 100
app.tab.copy[1] <- 44
assoc(app.tab.copy, gp = shading_max)
app.tab.copy[1] <- 1000
assoc(app.tab.copy, gp = shading_max)
app.tab.copy
app.tab.copy[1] <- 45
app.tab.copy
assoc(app.tab.copy, gp = shading_max)
app.tab.copy[1] <- 112
assoc(app.tab.copy, gp = shading_max)
app.tab.copy[1] <- 1000000
assoc(app.tab.copy, gp = shading_max)
app.tab.copy[1] <- 100000
assoc(app.tab.copy, gp = shading_max)
app.tab.copy[1] <- 10000
assoc(app.tab.copy, gp = shading_max)
app.tab.copy[1] <- 1000
assoc(app.tab.copy, gp = shading_max)
assoc(app.tab, gp = shading_max)
chisq.test(app.tab)
chisq.test(app.tab, expected)
chisq.test(app.tab)
chisq.test(app.tab)$expected
app,tab
app.tab
a = c(1.36544850498,0.989473684211,1.07692307692,1.10582010582,1.01547116737,1.63047001621,1.76397515528,0.973544973545,1.28855721393,0.956451612903,1.9287054409,1.00189753321,1.55982905983,1.59760956175,1.45336787565,1.02552719201,0.429447852761,0.943674176776,0.862745098039,0.63670411985,1.03012048193,1.1288981289,0.919472913616,1.08284023669,1.10353753236,1.54343434343,1.01467505241,1.65714285714,1.02362869198,1.44927536232,3.47663551402,1.26229508197,0.957627118644,1.44047619048,0.505,2.6323024055,1.46448087432,1.69689119171,0.959116925593,2.46581196581,0.974093264249,1.06310679612,1.04946996466,1.645,0.941818181818)
a
b = c(1.33333333333,0.394736842105,1.11688311688,1.14102564103,0.631578947368,1.42666666667,1.28,0.65,1.4358974359,1.25316455696,2.07692307692,0.608108108108,1.54237288136,0.583333333333,1.58227848101,1.10526315789,0.64,1.0,0.910256410256,1.13157894737,1.14492753623,1.19480519481,0.881578947368,1.42105263158,1.06153846154,0.666666666667,1.24675324675,1.9,1.05194805195,1.35820895522,3.796875,1.30263157895,0.721518987342,1.37878787879,0.105263157895,2.73417721519,0.186666666667,1.49350649351,0.898734177215,1.97333333333,0.947368421053,1.13698630137,1.17567567568,1.23684210526,1.03947368421)
b
t.test(a,b)
a = c(1.36544850498,0.989473684211,1.10582010582,1.01547116737,1.63047001621,1.76397515528,0.973544973545,1.28855721393,0.956451612903,1.9287054409,1.00189753321,1.55982905983,1.59760956175,1.45336787565,0.429447852761,0.63670411985,1.03012048193,1.08284023669,1.54343434343,1.65714285714,1.44927536232,3.47663551402,1.26229508197,1.44047619048,0.505,2.6323024055,1.46448087432,1.69689119171,2.46581196581,0.974093264249,1.06310679612,1.645)
a
avg(a)
AVG(a)
average(a)
mean(a)
b = c(1.33333333333,0.394736842105,1.14102564103,0.631578947368,1.42666666667,1.28,0.65,1.4358974359,1.25316455696,2.07692307692,0.608108108108,1.54237288136,0.583333333333,1.58227848101,0.64,1.13157894737,1.14492753623,1.42105263158,0.666666666667,1.9,1.35820895522,3.796875,1.30263157895,1.37878787879,0.105263157895,2.73417721519,0.186666666667,1.49350649351,1.97333333333,0.947368421053,1.13698630137,1.23684210526)
b
mean(b)
t.test(a,b)
t.test(a,b,var.equal=True)
t.test(a,b,var.equal=true)
t.test(a,b,var_equal=True)
t.test(a,b,var.equal=True)
t.test(a,b,var.equal=TRUE)
a = c(1.7,1.0,1.6,1.4,1.6,1.0,1.1,1.5,1.9,1.0,0.4,0.5,1.5,1.0,2.6,2.5,3.5)
b = c(1.9,0.6,1.2,1.4,0.6,0.4,1.4,0.2,2.1,0.7,0.6,0.1,0.7,0.9,2.7,2.0,3.8)
t.test(a,b,var.equal=TRUE)
a = c(0.5,1.5,1.0,1.6,1.0,1.0,0.4,1.0,1.5,1.0,0.6,1.1,1.1,1.0,1.6,1.0,1.8,1.3,1.4,1.4,1.4,1.1,1.6,1.3,1.7,1.6,1.5,1.7,2.5,1.9,2.6,3.5)
b = (0.1,0.2,0.4,0.6,0.6,0.6,0.6,0.7,0.7,0.9,1.1,1.1,1.1,1.1,1.2,1.3,1.3,1.3,1.3,1.4,1.4,1.4,1.4,1.4,1.5,1.5,1.6,1.9,2.0,2.1,2.7,3.8)
b = c(0.1,0.2,0.4,0.6,0.6,0.6,0.6,0.7,0.7,0.9,1.1,1.1,1.1,1.1,1.2,1.3,1.3,1.3,1.3,1.4,1.4,1.4,1.4,1.4,1.5,1.5,1.6,1.9,2.0,2.1,2.7,3.8)
mean(a)
mean(b)
t.test(a,b)
a = c(0.5,1.5,1.0,1.6,1.0,1.0,0.4,1.0,1.5,1.0,0.6,1.1,1.1,1.0,1.6,1.0,1.8,1.3,1.4,1.4,1.4,1.1,1.6,1.3,1.7,1.6,1.5,1.7,2.5,1.9,2.6,3.5,0.5,1.5,1.0,1.6,1.0,1.0,0.4,1.0,1.5,1.0,0.6,1.1,1.1,1.0,1.6,1.0,1.8,1.3,1.4,1.4,1.4,1.1,1.6,1.3,1.7,1.6,1.5,1.7,2.5,1.9,2.6,3.5,0.5,1.5,1.0,1.6,1.0,1.0,0.4,1.0,1.5,1.0,0.6,1.1,1.1,1.0,1.6,1.0,1.8,1.3,1.4,1.4,1.4,1.1,1.6,1.3,1.7,1.6,1.5,1.7,2.5,1.9,2.6,3.5,0.5,1.5,1.0,1.6,1.0,1.0,0.4,1.0,1.5,1.0,0.6,1.1,1.1,1.0,1.6,1.0,1.8,1.3,1.4,1.4,1.4,1.1,1.6,1.3,1.7,1.6,1.5,1.7,2.5,1.9,2.6,3.5,0.5,1.5,1.0,1.6,1.0,1.0,0.4,1.0,1.5,1.0,0.6,1.1,1.1,1.0,1.6,1.0,1.8,1.3,1.4,1.4,1.4,1.1,1.6,1.3,1.7,1.6,1.5,1.7,2.5,1.9,2.6,3.5)
b = c(0.1,0.2,0.4,0.6,0.6,0.6,0.6,0.7,0.7,0.9,1.1,1.1,1.1,1.1,1.2,1.3,1.3,1.3,1.3,1.4,1.4,1.4,1.4,1.4,1.5,1.5,1.6,1.9,2.0,2.1,2.7,3.8,0.1,0.2,0.4,0.6,0.6,0.6,0.6,0.7,0.7,0.9,1.1,1.1,1.1,1.1,1.2,1.3,1.3,1.3,1.3,1.4,1.4,1.4,1.4,1.4,1.5,1.5,1.6,1.9,2.0,2.1,2.7,3.8,0.1,0.2,0.4,0.6,0.6,0.6,0.6,0.7,0.7,0.9,1.1,1.1,1.1,1.1,1.2,1.3,1.3,1.3,1.3,1.4,1.4,1.4,1.4,1.4,1.5,1.5,1.6,1.9,2.0,2.1,2.7,3.8,0.1,0.2,0.4,0.6,0.6,0.6,0.6,0.7,0.7,0.9,1.1,1.1,1.1,1.1,1.2,1.3,1.3,1.3,1.3,1.4,1.4,1.4,1.4,1.4,1.5,1.5,1.6,1.9,2.0,2.1,2.7,3.8,0.1,0.2,0.4,0.6,0.6,0.6,0.6,0.7,0.7,0.9,1.1,1.1,1.1,1.1,1.2,1.3,1.3,1.3,1.3,1.4,1.4,1.4,1.4,1.4,1.5,1.5,1.6,1.9,2.0,2.1,2.7,3.8)
t.test(a,b)
source("/Users/jcgood/gitrepos/complexity/APiCSWALS.r")
options(error=utils::recover)
source("/Users/jcgood/gitrepos/complexity/APiCSWALS.r")
2
0
quit
0
exit
1
0
w
c
0
source("/Users/jcgood/gitrepos/complexity/APiCSWALS.r")
sessionInfo()
source("/Users/jcgood/gitrepos/complexity/APiCSWALS.r")
install.packages("ggplot2")
source("/Users/jcgood/gitrepos/complexity/APiCSWALS.r")
fcfit = glm(fc$Set ~ fc$Feature*fc$Complexity, family=\"binomial\")"
fcfit = glm(fc$Set ~ fc$Feature*fc$Complexity, family=binomial)"
""
fcfit = glm(fc$Set ~ fc$Feature*fc$Complexity, family=binomial)
source("/Users/jcgood/gitrepos/complexity/APiCSWALS.r")
fcplot = ggplot(fcfit)
fcplot
source("/Users/jcgood/gitrepos/complexity/APiCSWALS.r")
t.test(apicsFeatCompAvgsPar,apicsFeatCompAvgsPar)
t.test(apicsFeatCompAvgsPar,walsFeatCompAvgsPar)
t.test(apicsFeatCompAvgsPar,walsFeatCompAvgsPar,var.equal=TRUE)
plot(apicsFeatCompAvgsPar,walsFeatCompAvgsPar)
(apicsFeatCompAvgsPar,walsFeatCompAvgsPar)l,
lm(apicsFeatCompAvgsPar,walsFeatCompAvgsPar)
hist(apicsFeatCompAvgsPar)
hist(walsFeatCompAvgsPar)
hist(apicsFeatCompAvgsPar)
hist(walsFeatCompAvgsPar)
t.test(apicsFeatCompAvgsPar,walsFeatCompAvgsPar,var.equal=TRUE)
alangscompPar
alangcompPar
t.test(alangcompPar,wlangcompPar)
mean(alangcompPar)
mean(afeatcompPar)
mean(apicsFeatCompAvgsPar)
templates <- read.table("/Users/jcgood/Desktop/xxx.txt", sep="\t")#
templateframe = data.frame(templates,row.names=TRUE)#
templatedist = as.dist(templateframe)#
templatemds = cmdscale(templatedist, eig=TRUE, k=2)#
x <- templatemds$points[,1]#
y <- templatemds$points[,2]#
plot(x, y, xlab="Coordinate 1", ylab="Coordinate 2", #
main="Metric MDS", type="n")#
text(x, y, labels = row.names(templateframe), cex=.3)
source("http://bioconductor.org/biocLite.R")#
biocLite()
biocLite("GOstats")
citation("GOstats")
install.packages(MASS)
library(MASS)
isoMDS()
templates <- read.table("~/Desktop/xxx.txt", sep="\t", row.names=1)#
templateframe = data.frame(templates)#
templatedist = as.dist(templateframe)#
templatemds = cmdscale(templatedist, eig=TRUE, k=2)#
x <- templatemds$points[,1]#
y <- templatemds$points[,2]#
plot(x, y, xlab="Coordinate 1", ylab="Coordinate 2", #
main="Metric MDS", type="n")#
text(x, y, labels = row.names(templateframe), cex=.3)
ist = isoMDS(templatedist)
ist
plot(ist)
plot(ist$points[,1], ist$points[,2])
plot(ist$points[,1], ist$points[,2], type="n")
text((ist$points[,1], ist$points[,2], labels = row.names(ist), cex=.3)
text(ist$points[,1], ist$points[,2], labels = row.names(ist), cex=.3)
plot(ist$points[,1], ist$points[,2], type="n")
text(ist$points[,1], ist$points[,2], labels = row.names(templateframe), cex=.3)
plot(x, y, xlab="Coordinate 1", ylab="Coordinate 2", #
+ main="Metric MDS", type="n")#
> text(x, y, labels = row.names(templateframe), cex=.3)
plot(x, y, xlab="Coordinate 1", ylab="Coordinate 2", main="Metric MDS", type="n") text(x, y, labels = row.names(templateframe), cex=.3)
plot(x, y, xlab="Coordinate 1", ylab="Coordinate 2", main="Metric MDS", type="n")
text(x, y, labels = row.names(templateframe), cex=.3)
ist
barplot(templatemds$eig)
templatemds
template3mds
t8 = cmdscale(templatedist, eig=TRUE, k=8)
t8
t8$eig
templatesmds$eig
templatemds$eig
screeplots(t8$eig)
screeplot(t8$eig)
screeplot(t8)
t8
t8 = cmdscale(templatedist, eig=TRUE, k=100)
t8 = cmdscale(templatedist, eig=TRUE, k=21)
t8 = cmdscale(templatedist, eig=TRUE, k=20)
t8 = cmdscale(templatedist, eig=TRUE, k=19)
t8
t8 = cmdscale(templatedist, eig=TRUE, k=16)
t8
t8 = cmdscale(templatedist, eig=TRUE, k=10)
t8
eig = templatemds$eigh
eig = templatemds$eig
eig
max(eig)
eig/max(eig)
t8 = cmdscale(templatedist, eig=TRUE, k=4)
t8
t8 = cmdscale(templatedist, eig=TRUE, k=5)
t8
t8 = cmdscale(templatedist, eig=TRUE, k=6)
t8
t8 = cmdscale(templatedist, eig=TRUE, k=5)
t8
t8 = cmdscale(templatedist, eig=TRUE, k=7)
t8
barplot(eig)
library(ggplot2)#
library(plyr)#
apicsFeatCompAvgs <- c( 0.666666666667,0.131578947368,0.558441558442,0.570512820513,0.315789473684,0.713333333333,0.64,0.1625,0.626582278481,0.519230769231,0.202702702703,0.530769230769,0.194444444444,0.717948717949,0.552631578947,0.16,1.0,0.910256410256,0.565789473684,0.572463768116,0.597402597403,0.881578947368,0.473684210526,0.771186440678,0.173333333333,0.623376623377,0.633333333333,0.525974025974,0.452736318408,0.6328125,0.746753246753,0.360759493671,0.689393939394,0.791139240506,0.546835443038,0.0466666666667,0.0526315789474,0.449367088608,0.394666666667,0.236842105263,0.568493150685,0.587837837838,0.651315789474,0.412280701754,0.519736842105 )#
walsFeatCompAvgs <- c( 0.682724252492,0.331569664903,0.538091419407,0.55291005291,0.508450704225,0.816260162602,0.881619937695,0.245989304813,0.477382875606,0.482159624413,0.333333333333,0.551813471503,0.532536520584,0.6425,0.512777777778,0.10736196319,0.943625325239,0.862645348837,0.318011257036,0.515151515152,0.563541666667,0.919413919414,0.356725146199,0.781385281385,0.535641547862,0.507345225603,0.552380952381,0.511824324324,0.481300813008,0.58064516129,0.846456692913,0.478813559322,0.721556886228,0.729658792651,0.5264604811,0.368131868132,0.253768844221,0.479541734861,0.49356223176,0.244791666667,0.531553398058,0.524734982332,0.631687242798,0.549413735343,0.470873786408 )#
apicsFeatCompAvgsDF = as.data.frame(apicsFeatCompAvgs)#
walsFeatCompAvgsDF = as.data.frame(walsFeatCompAvgs)#
apicsFeatCompAvgsDF$set = "APiCS"#
walsFeatCompAvgsDF$set = "WALS"#
apicsFeatCompAvgsDF = rename(apicsFeatCompAvgsDF, c("apicsFeatCompAvgs" = "Complexity"))#
walsFeatCompAvgsDF = rename(walsFeatCompAvgsDF, c("walsFeatCompAvgs" = "Complexity"))#
awFeat = rbind(apicsFeatCompAvgsDF,walsFeatCompAvgsDF)#
distPlot = ggplot(awFeat, aes(Complexity, fill=set)) + geom_density(alpha=0.2, aes(y=..scaled..)) + theme(panel.grid=element_blank(), panel.background = element_blank())#
ggsave("/Users/jcgood/gitrepos/complexity/featDistr.pdf", plot=distPlot)#
distPlotBW = distPlot + scale_fill_grey(start = 0, end = .9)#
ggsave("/Users/jcgood/gitrepos/complexity/featDistrBW.pdf", plot=distPlotBW)#
apicsFeatCompAvgsPar <- c( 0.666666666667,0.131578947368,0.570512820513,0.315789473684,0.713333333333,0.64,0.1625,0.626582278481,0.519230769231,0.202702702703,0.194444444444,0.717948717949,0.16,0.565789473684,0.572463768116,0.473684210526,0.771186440678,0.173333333333,0.633333333333,0.452736318408,0.6328125,0.746753246753,0.689393939394,0.791139240506,0.546835443038,0.0466666666667,0.0526315789474,0.394666666667,0.236842105263,0.568493150685,0.651315789474,0.412280701754 )#
walsFeatCompAvgsPar <- c( 0.682724252492,0.331569664903,0.55291005291,0.508450704225,0.816260162602,0.881619937695,0.245989304813,0.477382875606,0.482159624413,0.333333333333,0.532536520584,0.6425,0.10736196319,0.318011257036,0.515151515152,0.356725146199,0.781385281385,0.535641547862,0.552380952381,0.481300813008,0.58064516129,0.846456692913,0.721556886228,0.729658792651,0.5264604811,0.368131868132,0.253768844221,0.49356223176,0.244791666667,0.531553398058,0.631687242798,0.549413735343 )#
apicsFeatCompAvgsParDF = as.data.frame(apicsFeatCompAvgsPar)#
walsFeatCompAvgsParDF = as.data.frame(walsFeatCompAvgsPar)#
apicsFeatCompAvgsParDF$set = "APiCS"#
walsFeatCompAvgsParDF$set = "WALS"#
apicsFeatCompAvgsParDF = rename(apicsFeatCompAvgsParDF, c("apicsFeatCompAvgsPar" = "Complexity"))#
walsFeatCompAvgsParDF = rename(walsFeatCompAvgsParDF, c("walsFeatCompAvgsPar" = "Complexity"))#
awFeatPar = rbind(apicsFeatCompAvgsParDF,walsFeatCompAvgsParDF)#
distPlotPar = ggplot(awFeatPar, aes(Complexity, fill=set)) + geom_density(alpha=0.2, aes(y=..scaled..)) + theme(panel.grid=element_blank(), panel.background = element_blank())#
ggsave("/Users/jcgood/gitrepos/complexity/featDistrPar.pdf", plot=distPlotPar)#
distPlotParBW = distPlotPar + scale_fill_grey(start = 0, end = .9)#
ggsave("/Users/jcgood/gitrepos/complexity/featDistrParBW.pdf", plot=distPlotParBW)#
alangcompPar <- c( 0.451612903226,0.378125,0.44623655914,0.462222222222,0.552083333333,0.413978494624,0.478125,0.405208333333,0.453125,0.5,0.5125,0.429032258065,0.44623655914,0.416666666667,0.440860215054,0.451111111111,0.438888888889,0.584375,0.661290322581,0.465517241379,0.446875,0.75,0.5,0.433333333333,0.478494623656,0.405376344086,0.527777777778,0.525287356322,0.433333333333,0.436458333333,0.5,0.426041666667,0.5,0.390322580645,0.536559139785,0.46875,0.534722222222,0.446428571429,0.39247311828,0.478494623656,0.576344086022,0.476041666667,0.46875,0.472916666667,0.532291666667,0.494623655914,0.5,0.833333333333,0.546875,0.53125,0.529032258065,0.567708333333,0.461111111111,0.305747126437,0.373563218391,0.369791666667,0.495833333333,0.5,0.468817204301,0.483870967742,0.5,0.483870967742,0.5,0.450537634409,0.475,0.403225806452,1.0,0.510416666667,0.382222222222,0.666666666667,0.454166666667,0.510416666667,0.466666666667,0.477419354839,0.380952380952,0.174603174603,0.529761904762,0.478494623656,0.509375,0.4625,0.3
90322580645,0.36,0.472043010753,0.291111111111,0.661290322581,0.515625,0.444444444444,0.471875,0.538095238095,0.5125 )#
wlangcompPar <- c( 0.3,0.277777777778,0.5,0.488888888889,0.382051282051,0.666666666667,0.9,0.5,1.0,0.483333333333,0.5,0.441666666667,0.6,0.644444444444,0.633333333333,0.8,0.4,0.633333333333,0.75,0.5,0.5,0.5,0.625,0.614102564103,0.375,0.666666666667,0.65,1.0,0.666666666667,0.439393939394,0.588888888889,0.391666666667,0.75,1.0,0.642857142857,0.416666666667,0.55303030303,0.8125,0.5,0.75,0.0,0.666666666667,0.375,0.5,0.5,0.5,0.766666666667,0.5,1.0,0.5,0.45,1.0,0.166666666667,0.5,0.75,0.777777777778,0.355555555556,0.75,0.666666666667,0.5,0.5,1.0,0.525,0.5,0.625,0.666666666667,0.25,1.0,0.5,0.0,0.775,0.5,0.5,0.509090909091,1.0,0.5,0.25,0.5,1.0,0.4,0.478787878788,0.512121212121,0.666666666667,0.583333333333,0.833333333333,0.333333333333,1.0,1.0,0.5,0.5,0.666666666667,0.5,0.5,0.25,1.0,0.875,0.166666666667,0.777777777778,1.0,0.683333333333,0.774074074074,1.0,0.5,0.666666666667,0.604444444444,0.625,0.5,0.5,0.25,0.5,0.0,0.75,0.666666666667,0.416666666667,0.7,0.596875,0.571428571429,0.533333333333,1.0,0.25,0.0,0.52777777
7778,0.25,0.45,0.0,0.25,0.555555555556,1.0,1.0,0.5,0.0,0.666666666667,0.555555555556,0.5,0.722222222222,0.0,0.495238095238,1.0,0.5,0.666666666667,0.5,0.307692307692,0.7,0.75,0.5,0.611111111111,0.5,0.45,0.666666666667,0.5,0.4,0.444,0.533333333333,0.125,0.0,0.416666666667,0.333333333333,0.0,0.5,0.7,0.722222222222,0.722222222222,1.0,0.5,0.576923076923,0.583333333333,1.0,0.333333333333,0.4,0.833333333333,0.5,0.5,0.0,0.5,0.833333333333,0.833333333333,0.25,0.666666666667,0.5,0.75,0.75,0.5,1.0,0.493333333333,0.666666666667,0.916666666667,0.541666666667,0.7,0.518518518519,0.0,0.625,1.0,0.548611111111,0.638888888889,1.0,0.666666666667,0.32,0.722222222222,0.611111111111,1.0,0.333333333333,0.477777777778,0.5,0.594871794872,0.52,0.375,0.588888888889,1.0,0.5,1.0,0.125,0.5,0.3,0.722222222222,0.5,0.62962962963,0.0,0.75,0.666666666667,0.833333333333,0.333333333333,0.5,0.833333333333,0.3125,0.833333333333,0.436231884058,0.571428571429,1.0,0.5,1.0,0.433333333333,0.333333333333,0.625,1.0,1.0,0.833333333333,0.5,0.666666666667,
0.479365079365,0.8,0.5,0.314814814815,0.7,0.5,0.45,0.455555555556,0.521428571429,0.833333333333,0.36,0.666666666667,0.4,0.666666666667,0.5,0.458333333333,0.533333333333,1.0,1.0,1.0,0.0,0.5,0.6,0.833333333333,0.4,1.0,0.5,0.571428571429,0.530769230769,0.6,0.5,0.544444444444,0.587037037037,0.25,0.5,0.5,0.0,0.25,0.0,0.328571428571,0.577777777778,0.441333333333,0.611111111111,0.25,0.875,0.5,0.5,0.405555555556,0.666666666667,0.456140350877,1.0,0.75,0.5,0.489583333333,0.353846153846,0.496296296296,0.75,0.583333333333,0.555555555556,0.5,0.457142857143,0.5,0.7,0.5,0.722222222222,0.5,1.0,0.666666666667,0.666666666667,0.75,0.4375,0.277777777778,0.428571428571,0.5,0.361111111111,0.75,0.666666666667,0.35,0.558333333333,0.5,0.666666666667,0.466666666667,0.785714285714,0.0,0.25,1.0,0.166666666667,1.0,0.5,0.5,0.666666666667,0.5,0.0,0.666666666667,0.6,0.5,0.55,0.5,0.5,0.5,0.666666666667,0.5,0.433333333333,0.75,0.666666666667,0.75,0.666666666667,1.0,0.625806451613,0.479310344828,0.428571428571,0.333333333333,0.166666666667,0
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185185185,0.5,0.5,0.5,0.3,1.0,0.616666666667,0.416666666667,0.452380952381,0.775,0.5,1.0,0.666666666667,0.75,0.666666666667,1.0,0.75,0.5,0.666666666667,0.5,0.5,0.541666666667,0.460606060606,0.5,0.5,0.0,0.45,0.5,0.666666666667,0.476666666667,0.5,0.583333333333,0.0,0.0,0.5,0.583333333333,0.0,0.8,0.451851851852,0.513333333333,0.166666666667,0.6,1.0,0.651515151515,0.555555555556,0.333333333333,0.349206349206,0.62,0.5,0.45,0.514583333333,0.441176470588,0.666666666667,1.0,0.666666666667,0.5,0.5,0.570833333333,0.4,0.606666666667,0.5,0.433333333333,0.35,0.505128205128,1.0,0.0,0.0,0.8,0.458333333333,0.5,0.5,0.405882352941,0.5,0.409090909091,0.433333333333,0.450574712644,0.666666666667,1.0,0.5,1.0,0.5,1.0,0.642424242424,0.5,0.625,0.5,0.5,0.75,0.627777777778,1.0,0.5 )#
alangcompSyn <- c( 0.653846153846,0.576923076923,0.730769230769,0.653846153846,0.653846153846,0.615384615385,0.538461538462,0.615384615385,0.576923076923,0.615384615385,0.692307692308,0.615384615385,0.538461538462,0.692307692308,0.5,0.615384615385,0.615384615385,0.692307692308,0.615384615385,0.615384615385,0.692307692308,0.615384615385,0.653846153846,0.653846153846,0.5,0.538461538462,0.576923076923,0.615384615385,0.653846153846,0.653846153846,0.692307692308,0.653846153846,0.576923076923,0.692307692308,0.615384615385,0.615384615385,0.615384615385,0.538461538462,0.692307692308,0.653846153846,0.653846153846,0.615384615385,0.653846153846,0.653846153846,0.576923076923,0.615384615385,0.653846153846,0.769230769231,0.538461538462,0.692307692308,0.615384615385,0.615384615385,0.692307692308,0.653846153846,0.653846153846,0.576923076923,0.615384615385,0.615384615385,0.538461538462 )#
wlangcompSyn <- c( 0.615384615385,0.615384615385,0.615384615385,0.5,0.615384615385,0.576923076923,0.615384615385,0.653846153846,0.615384615385,0.692307692308,0.615384615385,0.653846153846,0.538461538462,0.5,0.538461538462,0.538461538462,0.615384615385,0.653846153846,0.5,0.615384615385,0.538461538462,0.615384615385,0.653846153846,0.653846153846,0.653846153846,0.615384615385,0.653846153846,0.653846153846,0.615384615385,0.615384615385,0.653846153846,0.576923076923,0.576923076923,0.576923076923,0.615384615385,0.692307692308,0.653846153846,0.615384615385,0.615384615385,0.615384615385 )#
alangcompParDF = as.data.frame(alangcompPar)#
wlangcompParDF = as.data.frame(wlangcompPar)#
alangcompSynDF = as.data.frame(alangcompSyn)#
wlangcompSynDF = as.data.frame(wlangcompSyn)#
alangcompParDF$set = "APiCS"#
alangcompSynDF$set = "APiCS"#
wlangcompParDF$set = "WALS"#
wlangcompSynDF$set = "WALS"#
alangcompParDF = rename(alangcompParDF, c("alangcompPar" = "Complexity"))#
alangcompSynDF = rename(alangcompSynDF, c("alangcompSyn" = "Complexity"))#
wlangcompParDF = rename(wlangcompParDF, c("wlangcompPar" = "Complexity"))#
wlangcompSynDF = rename(wlangcompSynDF, c("wlangcompSyn" = "Complexity"))#
awPar = rbind(alangcompParDF,wlangcompParDF)#
awSyn = rbind(alangcompSynDF,wlangcompSynDF)#
parPlot = ggplot(awPar, aes(Complexity, fill=set)) + geom_density(alpha=0.2, aes(y=..scaled..)) + theme(panel.grid=element_blank(), panel.background = element_blank())#
parPlotBW = parPlot + scale_fill_grey(start = 0, end = .9)#
synPlot = ggplot(awSyn, aes(Complexity, fill=set)) + geom_density(alpha=0.2, aes(y=..scaled..)) + theme(panel.grid=element_blank(), panel.background = element_blank())#
synPlotBW = synPlot + scale_fill_grey(start = 0, end = .9)#
ggsave("/Users/jcgood/gitrepos/complexity/parDistr.pdf", plot=parPlot)#
ggsave("/Users/jcgood/gitrepos/complexity/synDistr.pdf", plot=synPlot)#
ggsave("/Users/jcgood/gitrepos/complexity/parDistrBW.pdf", plot=parPlotBW)#
ggsave("/Users/jcgood/gitrepos/complexity/synDistrBW.pdf", plot=synPlotBW)#
aParHist = ggplot(alangcompParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())#
wParHist = ggplot(wlangcompParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5, fill="#33CCCC") + geom_density(alpha=.2, fill="#33CCCC") + theme(panel.grid=element_blank(), panel.background = element_blank())#
ggsave("/Users/jcgood/gitrepos/complexity/aParHist.pdf", plot=aParHist)#
ggsave("/Users/jcgood/gitrepos/complexity/wParHist.pdf", plot=wParHist)#
fc = read.table("/Users/jcgood/gitrepos/complexity/FeatComp.txt", row.names=NULL, header=TRUE)#
fcfit = glm(fc$Set ~ fc$Feature:fc$Complexity, family="binomial")#
layout(matrix(c(1,2,3,4),2,2))#
fcplot = plot(fcfit)
mean(alangCompPar)
mean(alangcompPar)
mean(wlangcompPar)
t.test(alangCompPar,wlangCompPar)
t.test(alangcompPar,wlangcompPar)
t.test(alangcompPar,wlangcompPar)[2]
t.test(alangcompPar,wlangcompPar)[3]
library(ggplot2)#
library(plyr)#
apicsFeatCompAvgs <- c( 0.666666666667,0.131578947368,0.558441558442,0.570512820513,0.315789473684,0.713333333333,0.64,0.1625,0.626582278481,0.519230769231,0.202702702703,0.530769230769,0.194444444444,0.717948717949,0.552631578947,0.16,1.0,0.910256410256,0.565789473684,0.572463768116,0.597402597403,0.881578947368,0.473684210526,0.771186440678,0.173333333333,0.623376623377,0.633333333333,0.525974025974,0.452736318408,0.6328125,0.746753246753,0.360759493671,0.689393939394,0.791139240506,0.546835443038,0.0466666666667,0.0526315789474,0.449367088608,0.394666666667,0.236842105263,0.568493150685,0.587837837838,0.651315789474,0.412280701754,0.519736842105 )#
walsFeatCompAvgs <- c( 0.682724252492,0.331569664903,0.538091419407,0.55291005291,0.508450704225,0.816260162602,0.881619937695,0.245989304813,0.477382875606,0.482159624413,0.333333333333,0.551813471503,0.532536520584,0.6425,0.512777777778,0.10736196319,0.943625325239,0.862645348837,0.318011257036,0.515151515152,0.563541666667,0.919413919414,0.356725146199,0.781385281385,0.535641547862,0.507345225603,0.552380952381,0.511824324324,0.481300813008,0.58064516129,0.846456692913,0.478813559322,0.721556886228,0.729658792651,0.5264604811,0.368131868132,0.253768844221,0.479541734861,0.49356223176,0.244791666667,0.531553398058,0.524734982332,0.631687242798,0.549413735343,0.470873786408 )#
apicsFeatCompAvgsDF = as.data.frame(apicsFeatCompAvgs)#
walsFeatCompAvgsDF = as.data.frame(walsFeatCompAvgs)#
apicsFeatCompAvgsDF$set = "APiCS"#
walsFeatCompAvgsDF$set = "WALS"#
apicsFeatCompAvgsDF = rename(apicsFeatCompAvgsDF, c("apicsFeatCompAvgs" = "Complexity"))#
walsFeatCompAvgsDF = rename(walsFeatCompAvgsDF, c("walsFeatCompAvgs" = "Complexity"))#
awFeat = rbind(apicsFeatCompAvgsDF,walsFeatCompAvgsDF)#
distPlot = ggplot(awFeat, aes(Complexity, fill=set)) + geom_density(alpha=0.2, aes(y=..scaled..)) + theme(panel.grid=element_blank(), panel.background = element_blank())#
ggsave("/Users/jcgood/gitrepos/complexity/featDistr.pdf", plot=distPlot)#
distPlotBW = distPlot + scale_fill_grey(start = 0, end = .9)#
ggsave("/Users/jcgood/gitrepos/complexity/featDistrBW.pdf", plot=distPlotBW)#
apicsFeatCompAvgsPar <- c( 0.666666666667,0.131578947368,0.570512820513,0.315789473684,0.713333333333,0.64,0.1625,0.626582278481,0.519230769231,0.202702702703,0.194444444444,0.717948717949,0.16,0.565789473684,0.572463768116,0.473684210526,0.771186440678,0.173333333333,0.633333333333,0.452736318408,0.6328125,0.746753246753,0.689393939394,0.791139240506,0.546835443038,0.0466666666667,0.0526315789474,0.394666666667,0.236842105263,0.568493150685,0.651315789474,0.412280701754 )#
walsFeatCompAvgsPar <- c( 0.682724252492,0.331569664903,0.55291005291,0.508450704225,0.816260162602,0.881619937695,0.245989304813,0.477382875606,0.482159624413,0.333333333333,0.532536520584,0.6425,0.10736196319,0.318011257036,0.515151515152,0.356725146199,0.781385281385,0.535641547862,0.552380952381,0.481300813008,0.58064516129,0.846456692913,0.721556886228,0.729658792651,0.5264604811,0.368131868132,0.253768844221,0.49356223176,0.244791666667,0.531553398058,0.631687242798,0.549413735343 )#
apicsFeatCompAvgsParDF = as.data.frame(apicsFeatCompAvgsPar)#
walsFeatCompAvgsParDF = as.data.frame(walsFeatCompAvgsPar)#
apicsFeatCompAvgsParDF$set = "APiCS"#
walsFeatCompAvgsParDF$set = "WALS"#
apicsFeatCompAvgsParDF = rename(apicsFeatCompAvgsParDF, c("apicsFeatCompAvgsPar" = "Complexity"))#
walsFeatCompAvgsParDF = rename(walsFeatCompAvgsParDF, c("walsFeatCompAvgsPar" = "Complexity"))#
awFeatPar = rbind(apicsFeatCompAvgsParDF,walsFeatCompAvgsParDF)#
distPlotPar = ggplot(awFeatPar, aes(Complexity, fill=set)) + geom_density(alpha=0.2, aes(y=..scaled..)) + theme(panel.grid=element_blank(), panel.background = element_blank())#
ggsave("/Users/jcgood/gitrepos/complexity/featDistrPar.pdf", plot=distPlotPar)#
distPlotParBW = distPlotPar + scale_fill_grey(start = 0, end = .9)#
ggsave("/Users/jcgood/gitrepos/complexity/featDistrParBW.pdf", plot=distPlotParBW)#
alangcompPar <- c( 0.451612903226,0.378125,0.44623655914,0.462222222222,0.552083333333,0.413978494624,0.478125,0.405208333333,0.453125,0.5,0.429032258065,0.44623655914,0.416666666667,0.440860215054,0.451111111111,0.438888888889,0.584375,0.661290322581,0.465517241379,0.446875,0.433333333333,0.478494623656,0.405376344086,0.525287356322,0.433333333333,0.436458333333,0.426041666667,0.5,0.390322580645,0.536559139785,0.46875,0.446428571429,0.39247311828,0.478494623656,0.576344086022,0.476041666667,0.46875,0.472916666667,0.532291666667,0.494623655914,0.546875,0.53125,0.529032258065,0.567708333333,0.461111111111,0.305747126437,0.373563218391,0.369791666667,0.495833333333,0.468817204301,0.483870967742,0.483870967742,0.450537634409,0.475,0.403225806452,0.510416666667,0.382222222222,0.510416666667,0.466666666667,0.477419354839,0.380952380952,0.529761904762,0.478494623656,0.509375,0.4625,0.390322580645,0.36,0.472043010753,0.291111111111,0.661290322581,0.515625,0.471875,0.5125 )#
wlangcompPar <- c( 0.614102564103,0.588888888889,0.596875,0.493333333333,0.521428571429,0.5,0.489583333333,0.496296296296,0.625806451613,0.479310344828,0.549425287356,0.56,0.394871794872,0.54623655914,0.429032258065,0.432051282051,0.614444444444,0.551724137931,0.546875,0.478125,0.584946236559,0.509195402299,0.517857142857,0.529487179487,0.403846153846,0.378205128205,0.524691358025,0.454761904762,0.483950617284,0.505747126437,0.411904761905,0.587356321839,0.423076923077,0.517857142857,0.528205128205,0.445977011494,0.443333333333,0.454022988506,0.4,0.472619047619,0.595698924731,0.515555555556,0.49375,0.455555555556,0.585555555556,0.547311827957,0.445555555556,0.4625,0.512820512821,0.523958333333,0.483333333333,0.50987654321,0.559523809524,0.516129032258,0.428571428571,0.524358974359,0.502469135802,0.502380952381,0.442528735632,0.596774193548,0.533333333333,0.622222222222,0.549462365591,0.47,0.524444444444,0.582716049383,0.451851851852,0.450574712644,0.627777777778 )#
alangcompSyn <- c( 0.653846153846,0.576923076923,0.730769230769,0.653846153846,0.653846153846,0.615384615385,0.538461538462,0.615384615385,0.576923076923,0.615384615385,0.692307692308,0.615384615385,0.538461538462,0.692307692308,0.5,0.615384615385,0.615384615385,0.692307692308,0.615384615385,0.615384615385,0.692307692308,0.615384615385,0.653846153846,0.653846153846,0.5,0.538461538462,0.576923076923,0.615384615385,0.653846153846,0.653846153846,0.692307692308,0.653846153846,0.576923076923,0.692307692308,0.615384615385,0.615384615385,0.615384615385,0.538461538462,0.692307692308,0.653846153846,0.653846153846,0.615384615385,0.653846153846,0.653846153846,0.576923076923,0.615384615385,0.653846153846,0.769230769231,0.538461538462,0.692307692308,0.615384615385,0.615384615385,0.692307692308,0.653846153846,0.653846153846,0.576923076923,0.615384615385,0.615384615385,0.538461538462 )#
wlangcompSyn <- c( 0.615384615385,0.615384615385,0.615384615385,0.5,0.615384615385,0.576923076923,0.615384615385,0.653846153846,0.615384615385,0.692307692308,0.615384615385,0.653846153846,0.538461538462,0.5,0.538461538462,0.538461538462,0.615384615385,0.653846153846,0.5,0.615384615385,0.538461538462,0.615384615385,0.653846153846,0.653846153846,0.653846153846,0.615384615385,0.653846153846,0.653846153846,0.615384615385,0.615384615385,0.653846153846,0.576923076923,0.576923076923,0.576923076923,0.615384615385,0.692307692308,0.653846153846,0.615384615385,0.615384615385,0.615384615385 )#
alangcompParDF = as.data.frame(alangcompPar)#
wlangcompParDF = as.data.frame(wlangcompPar)#
alangcompSynDF = as.data.frame(alangcompSyn)#
wlangcompSynDF = as.data.frame(wlangcompSyn)#
alangcompParDF$set = "APiCS"#
alangcompSynDF$set = "APiCS"#
wlangcompParDF$set = "WALS"#
wlangcompSynDF$set = "WALS"#
alangcompParDF = rename(alangcompParDF, c("alangcompPar" = "Complexity"))#
alangcompSynDF = rename(alangcompSynDF, c("alangcompSyn" = "Complexity"))#
wlangcompParDF = rename(wlangcompParDF, c("wlangcompPar" = "Complexity"))#
wlangcompSynDF = rename(wlangcompSynDF, c("wlangcompSyn" = "Complexity"))#
awPar = rbind(alangcompParDF,wlangcompParDF)#
awSyn = rbind(alangcompSynDF,wlangcompSynDF)#
parPlot = ggplot(awPar, aes(Complexity, fill=set)) + geom_density(alpha=0.2, aes(y=..scaled..)) + theme(panel.grid=element_blank(), panel.background = element_blank())#
parPlotBW = parPlot + scale_fill_grey(start = 0, end = .9)#
synPlot = ggplot(awSyn, aes(Complexity, fill=set)) + geom_density(alpha=0.2, aes(y=..scaled..)) + theme(panel.grid=element_blank(), panel.background = element_blank())#
synPlotBW = synPlot + scale_fill_grey(start = 0, end = .9)#
ggsave("/Users/jcgood/gitrepos/complexity/parDistr.pdf", plot=parPlot)#
ggsave("/Users/jcgood/gitrepos/complexity/synDistr.pdf", plot=synPlot)#
ggsave("/Users/jcgood/gitrepos/complexity/parDistrBW.pdf", plot=parPlotBW)#
ggsave("/Users/jcgood/gitrepos/complexity/synDistrBW.pdf", plot=synPlotBW)#
aParHist = ggplot(alangcompParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())#
wParHist = ggplot(wlangcompParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5, fill="#33CCCC") + geom_density(alpha=.2, fill="#33CCCC") + theme(panel.grid=element_blank(), panel.background = element_blank())#
ggsave("/Users/jcgood/gitrepos/complexity/aParHist.pdf", plot=aParHist)#
ggsave("/Users/jcgood/gitrepos/complexity/wParHist.pdf", plot=wParHist)#
fc = read.table("/Users/jcgood/gitrepos/complexity/FeatComp.txt", row.names=NULL, header=TRUE)#
fcfit = glm(fc$Set ~ fc$Feature:fc$Complexity, family="binomial")#
layout(matrix(c(1,2,3,4),2,2))#
fcplot = plot(fcfit)
t.test(alangcompPar,wlangcompPar)[3]
st.dec(alangcompPar)
st.dev(alangcompPar)
stdev(alangcompPar)
mean(alangcompPar)
mean(alangcompPar)[2]
mean(alangcompPar)[1]
(alangcompsd
mean(alangcompPar)[1])
sd(alangcompPar)[1])
sd(alangcompPar)
sd(wlangcompPar)
souce(APiCSWALS.r)
source(APiCSWALS.r)
source(APiCSWALS.r)
source('APiCSWALS.r')
source('APiCSWALS.r')
source('APiCSWALS.r')
mean(apicsFeatCompAvgsPar)
source('APiCSWALS.r')
mean(apicsFeatCompAvgsPar)
source('APiCSWALS.r')
mean(apicsFeatCompAvgsPar)
t.test(apicsFeatCompAvgsPar,walsFeatCompAvgsPar)[2]
t.test(apicsFeatCompAvgsPar,walsFeatCompAvgsPar)[3]
mean(walsFeatCompAvgsPar)
ggplot(alangcompParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.05) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.05) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(walsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())
ggplot(apicsFeatCompAvgsParDF,aes(Complexity, fill=set)) + geom_histogram(alpha=0.5) + geom_density(alpha=.2) + theme(panel.grid=element_blank(), panel.background = element_blank())