diff --git a/R/.Rhistory b/R/.Rhistory
new file mode 100644
index 0000000..2fdc808
--- /dev/null
+++ b/R/.Rhistory
@@ -0,0 +1,512 @@
+data1$new_milestones <- as.numeric(data1$milestones > 0) + 1
+# (2) - Run the model on the pilot data
+data1$formal.score <- data1$mmt / (data1$old_milestones/data1$age)
+table(data1$milestones)
+table(data1$old_milestones)
+hist(data1$old_mmt, prob=TRUE) #inequality of participation
+hist(data1$formal.score)
+data1$new_mmt <- data1$mmt - 1
+hist(data1$new_mmt, prob=TRUE)
+data1$new.age <- as.numeric(cut(data1$age/365, breaks=c(0,9,12,15,17), labels=c(1,2,3,4)))
+data1$new.formal.score <- data1$mmt / (data1$new_milestones/data1$new.age)
+hist(as.numeric(data1$new.age))
+hist(data1$formal.score)
+hist(data1$new.formal.score)
+fsmodel1 <- lm(up.fac.mean ~ formal.score, data=data1)
+summary(kmodel1)
+summary(fsmodel1)
+kmodel3 <- lm(up.fac.mean ~ formal.score, data=data1)
+# (2) - Run the model on the pilot data
+data1$formal.score <- data1$mmt / (data1$old_milestones/data1$age)
+table(data1$formal.score)
+fsmodel1 <- lm(up.fac.mean ~ is.finite(formal.score), data=data1)
+summary(fsmodel1)
+fsmodel2 <- lm(up.fac.mean ~ new.formal.score, data=data1)
+summary(kmodel2)
+summary(fsmodel2)
+mmtmodel1 <- lm(up.fac.mean ~ mmt, data=data1)
+summary(mmtmodel1)
+agemodel1 <- lm(up.fac.mean ~ new.age, data=data1)
+summary(agemodel1)
+msmodel1 <- lm(up.fac.mean ~ old_milestones, data=data1)
+summary(msmodel1)
+msmodel2 <- lm(up.fac.mean ~ new_milestones, data=data1)
+summary(msmodel2)
+texreg(list(m1,m2,m3,m4), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: no lang/network measures', 'M2: No language measures', 'M3: No network measures', 'M4: Full model'),
+custom.coef.names=c('(Intercept)', 'Package Age (years)', 'Uploader Count', 'Did maintainer change?', 'Team proportion', 'Eigenvector Centrality', 'Betweenness Centrality', 'Mean Language Age', 'Package Age : Mean Language Age'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+install.packages(textref)
+install.packages(textreg)
+library(textreg)
+install.packages("textreg_0.1.tar.gz", repos = NULL, type="source")
+install.packages("textreg_0.1.tar.gz", repos = NULL, type="source")
+install.packages("textreg_0.1.5.tar.gz", repos = NULL, type="source")
+import.packages(tm)
+import.package(tm)
+import.package("tm")
+install.package("tm")
+install.packages("tm")
+install.packages("nlp")
+yes
+install.packages("textreg_0.1.5.tar.gz", repos = NULL, type="source")
+library(textreg)
+texreg(list(m1,m2,m3,m4), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: no lang/network measures', 'M2: No language measures', 'M3: No network measures', 'M4: Full model'),
+custom.coef.names=c('(Intercept)', 'Package Age (years)', 'Uploader Count', 'Did maintainer change?', 'Team proportion', 'Eigenvector Centrality', 'Betweenness Centrality', 'Mean Language Age', 'Package Age : Mean Language Age'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+textreg(list(m1,m2,m3,m4), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: no lang/network measures', 'M2: No language measures', 'M3: No network measures', 'M4: Full model'),
+custom.coef.names=c('(Intercept)', 'Package Age (years)', 'Uploader Count', 'Did maintainer change?', 'Team proportion', 'Eigenvector Centrality', 'Betweenness Centrality', 'Mean Language Age', 'Package Age : Mean Language Age'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+textreg(list(fsmodel1,fsmodel2), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
+custom.coef.names=c('(Intercept)', 'Package Age (years)', 'Uploader Count', 'Did maintainer change?', 'Team proportion', 'Eigenvector Centrality', 'Betweenness Centrality', 'Mean Language Age', 'Package Age : Mean Language Age'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+coeff(fsmodel1)
+coef(fsmodel1)
+textreg(list(fsmodel1,fsmodel2), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
+custom.coef.names=c('(Intercept)', 'Package Age (years)'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+coef(fsmodel2)
+textreg(list(fsmodel1,fsmodel2), stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
+custom.coef.names=c('(Intercept)', 'Package Age (years)'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+textreg(list(fsmodel1,fsmodel2), stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
+custom.coef.names=c('(Intercept)', 'Package Age (years)'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel1,fsmodel2), stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
+custom.coef.names=c('(Intercept)', 'Package Age (years)'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+install.packages(texreg)
+install.packages("texreg_1.39.3.tar.gz", repos = NULL, type="source")
+library(texreg)
+texreg(list(fsmodel1,fsmodel2), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
+custom.coef.names=c('(Intercept)', 'Package Age (years)'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel1,fsmodel2), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
+custom.coef.names=c('(Intercept)', 'Package Age (years)', 'test'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, msmodel2, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score', 'M3: original milestones', 'M4: binomial milestones', 'M5: age (grouped)'  ),
+custom.coef.names=c('(Intercept)', 'Package Age (years)', 'test'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, msmodel2, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score', 'M3: original milestones', 'M4: binomial milestones', 'M5: age (grouped)'  ),
+custom.coef.names=c('(Intercept)', 'Package Age (years)', 'test', 'test', 'test', 'test', 'test', ),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, msmodel2, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score', 'M3: original milestones', 'M4: binomial milestones', 'M5: age (grouped)'  ),
+custom.coef.names=c('(Intercept)', 'Package Age (years)', 'test', 'test', 'test', 'test', 'test' ),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, msmodel2, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score','M3: MMT', 'M4: original milestones', 'M5: binomial milestones', 'M6: age (grouped)'  ),
+custom.coef.names=c('(Intercept)', 'Package Age (years)', 'test', 'test', 'test', 'test', 'test' ),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, msmodel2, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score','M3: MMT', 'M4: original milestones', 'M5: binomial milestones', 'M6: age (grouped)'  ),
+custom.coef.names=c('(Intercept)', 'Package Age (years)', 'Relationship to Underproduction', 'test', 'test', 'test', 'test' ),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score','M3: MMT', 'M4: original milestones', 'M6: age (grouped)'  ),
+custom.coef.names=c('Original formality relationship to underproduction', 'Augmented formality relationship to Underproduction', 'MMT relationship to underproduction', 'milestone usage relationship to underproduction', 'age group relationship to underproduction' ),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score','M3: MMT', 'M4: original milestones', 'M6: age (grouped)'  ),
+custom.coef.names=c('Original formality relationship to underproduction', 'Augmented formality relationship to Underproduction', 'MMT relationship to underproduction', 'milestone usage relationship to underproduction', 'age group relationship to underproduction', 'test' ),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score','M3: MMT', 'M4: original milestones', 'M6: age (grouped)'  ),
+custom.coef.names=c('(Intercept)', 'Original formality relationship to underproduction', 'Augmented formality relationship to Underproduction', 'MMT relationship to underproduction', 'Milestone usage relationship to underproduction', 'Age group relationship to underproduction'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+data5 <- subset(data1, is.finite(data1$formal.score))
+fsmodel1 <- lm(up.fac.mean ~ formal.score, data=data5)
+summary(fsmodel1)
+texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: original formality score', 'M2: augmented formality score','M3: MMT', 'M4: original milestones', 'M6: age (grouped)'  ),
+custom.coef.names=c('(Intercept)', 'Original formality relationship to underproduction', 'Augmented formality relationship to Underproduction', 'MMT relationship to underproduction', 'Milestone usage relationship to underproduction', 'Age group relationship to underproduction'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: orig. formality', 'M2: augm. formality','M3: MMT', 'M4: milestones', 'M6: age (grouped)'  ),
+custom.coef.names=c('(Intercept)', 'Original formality/underproduction', 'Augmented formality/Underproduction', 'MMT/underproduction', 'Milestones / underproduction', 'Age/underproduction'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c('M1: orig. formality', 'M2: augm. formality','M3: MMT', 'M4: milestones', 'M5: age (grouped)'  ),
+custom.coef.names=c('(Intercept)', 'Original formality', 'Augmented formality', 'MMT', 'Milestones', 'Age'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones', 'M4: age (grouped)'  ),
+custom.coef.names=c('(Intercept)', 'Original formality', 'Augmented formality', 'MMT', 'Milestones', 'Age'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones', 'M4: age (grouped)'  ),
+custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones', 'Age'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+#Sample values
+powerCheck(300, 1000)
+source('powerAnalysis.R') #my little "lib"
+#Sample values
+powerCheck(300, 1000)
+powerCheck(200, 1000)
+powerCheck(250, 1000)
+powerCheck(275, 5000)
+powerCheck(275, 1000)
+source('powerAnalysis.R') #my little "lib"
+#Sample values
+powerCheck(300, 1000)
+powerCheck(275, 1000)
+powerCheck(500, 1000)
+powerCheck(700, 1000)
+powerCheck(7000, 1000)
+fsmodel2 <- lm(up.fac.mean ~ new.formal.score + as.factor(new.age), data=data1)
+summary(fsmodel2)
+mmtmodel1 <- lm(up.fac.mean ~ mmt + as.factor(new.age), data=data1)
+summary(mmtmodel1)
+mmtmodel1 <- lm(up.fac.mean ~ mmt + as.factor(age), data=data1)
+summary(mmtmodel1)
+mmtmodel1 <- lm(up.fac.mean ~ mmt + as.factor(new.age), data=data1)
+summary(mmtmodel1)
+msmodel1 <- lm(up.fac.mean ~ old_milestones + as.factor(new.age), data=data1)
+summary(msmodel1)
+fsmodel2 <- lm(up.fac.mean ~ new.formal.score + as.factor(new.age), data=data1)
+summary(fsmodel2)
+texreg(list(fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
+custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
+custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones', 'test'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel2, mmtmodel1, msmodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
+custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones', 'test'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel2, mmtmodel1, msmodel1), omit.coef = 'factor', stars=NULL, digits=2,
+custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
+custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+source('powerAnalysis.R') #my little "lib"
+powerCheck(250, 1000)
+powerCheck(275, 1000)
+#Sample values
+powerCheck(300, 1000)
+summary(mmtmodel1)
+texreg(list(fsmodel2, mmtmodel1, msmodel1), stars=NULL, digits=2,
+custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
+custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel2, mmtmodel1, msmodel1), stars=NULL, digits=2,
+custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
+custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones', 'age 2', 'age 3', 'age 4'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+mmtmodel1 <- lm(up.fac.mean ~ mmt + as.factor(new.age), data=data1)
+msmodel1 <- lm(up.fac.mean ~ old_milestones + as.factor(new.age), data=data1)
+fsmodel2 <- lm(up.fac.mean ~ new.formal.score, data=data1)
+texreg(list(fsmodel2, mmtmodel1, msmodel1), stars=NULL, digits=2,
+custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
+custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones', 'age 2', 'age 3', 'age 4'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+texreg(list(fsmodel2, mmtmodel1, msmodel1), stars=NULL, digits=2,
+custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
+custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'age 2', 'age 3', 'age 4', 'Milestones'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+summary(msmodel1)
+texreg(list(fsmodel2, mmtmodel1, msmodel1), stars=NULL, digits=2,
+custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
+custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Age-2', 'Age-3', 'Age-4', 'Milestones'),
+use.packages=FALSE, table=FALSE, ci.force = TRUE)
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g
+library(ggplot2)
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g
+msmodel1 <- lm(up.fac.mean ~ old_milestones + as.factor(new.age), data=data1)
+summary(msmodel1)
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()+
+scale_color_brewer(palette="Dark2")
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_color_brewer(palette="Dark2")
+g + scale_color_viridis_b()
+g + scale_color_viridis_d()
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_color_viridis_b()
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_color_viridis_b()
+g + scale_color_viridis_b() scale_fill_continuous(name = "New Legend Title")
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_color_viridis_b() scale_fill_continuous(name = "New Legend Title")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_color_viridis_b() + scale_fill_continuous(name = "New Legend Title")
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_color_viridis_b() + scale_fill_continuous(name = "New Legend Title")
+g + scale_color_viridis_b() + scale_color_continuous(name = "New Legend Title")
+g + scale_color_viridis_b() + scale_color_continuous(name = "Age Group")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_color_viridis_b() + scale_color_continuous(name = "Age Group")
+g
+g + scale_color_viridis_b(name = "Age Group")
+g + scale_color_viridis_b(name = "Age Group", labels = c('1', '2', '3', '4'))
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_color_viridis_b(name = "Age Group", labels = c('1', '2', '3', '4'))
+g
+g + scale_fill_viridis_b(name = "Age Group", labels = c('1', '2', '3', '4'))
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_fill_viridis_b(name = "Age Group", labels = c('1', '2', '3', '4'))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_fill_viridis_b(name = "Age Group")
+g
+g + scale_fill_viridis_b(name = "Age Group")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_colour_viridis_b(name = "Age Group")
+g + scale_fill_continuous(name = "Age Group")
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_fill_continuous(name = "Age Group")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor")
+g + scale_fill_continuous(name = "Age Group")
+g + scale_fill_continuous(name = "Age Group", labels=c("Woman", "Man"))
+g + scale_color_continuous(name = "Age Group", labels=c("Woman", "Man"))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_color_continuous(name = "Age Group", labels=c("Woman", "Man"))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g + scale_color_continuous(type = "viridis", name = "Age Group", labels=c("Woman", "Man"))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw() +
+scale_color_continuous(type = "viridis", name = "Age Group")
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw() +
+scale_color_continuous(type = "viridis", name = "Age Group", labels="0-9y", "9-12y", "12-15y", "15-17y")
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw() +
+scale_color_continuous(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y"))
+g
++ theme(legend.position = c(0.8, 0.2))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw() +
+scale_color_continuous(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
+theme(legend.position = c(0.8, 0.2))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw() +
+scale_color_continuous(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
+theme(legend.position = c(0.1, 0.1))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw() +
+scale_color_continuous(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
+theme(legend.position = c(0.1, 0.2))
+g
+summary(mmtmodel1)
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=as.factor(new.age))+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw() +
+scale_color_continuous(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
+theme(legend.position = c(0.1, 0.2))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=as.factor(new.age))+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw() +
+scale_color_discrete(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
+theme(legend.position = c(0.1, 0.2))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw() +
+scale_color_identity(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
+theme(legend.position = c(0.1, 0.2))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=new.age)+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw() +
+scale_color_identity(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
+theme(legend.position = c(0.1, 0.2))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+aes(x=mmt, y=up.fac.mean, color=round(new.age))+
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw() +
+scale_color_identity(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
+theme(legend.position = c(0.1, 0.2))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_smooth() +
+xlab("MMT") +
+ylab("Underproduction Factor") +
+theme_bw()
+g
diff --git a/R/EDA.R b/R/EDA.R
index 289ab84..5acc661 100644
--- a/R/EDA.R
+++ b/R/EDA.R
@@ -1,7 +1,7 @@
 
 library(dplyr)
-df<-read.csv('~/Research/kkex_repo/power_data_111023_mmt.csv')
-df1 <- read.csv('~/Research/kkex_repo/inst_all_packages_full_results.csv')
+df<-read.csv('../power_data_111023_mmt.csv')
+df1 <- read.csv('../inst_all_packages_full_results.csv')
 
 hist(df$age/365) #there's a big bump at 9 years, why?
 hist(df$contributors) #skewed
diff --git a/R/calculatePower.R b/R/calculatePower.R
index c69389a..e7dca57 100644
--- a/R/calculatePower.R
+++ b/R/calculatePower.R
@@ -20,6 +20,7 @@ library(ggplot2)
 # (1) - Get the pilot data and clean it
 #source('~/Research/tor_wikipedia_edits/handcoded_edits/inter_coder_reliability_ns0.R')
 #source ('/data/users/mgaughan/kkex_data_110823_3')
+
 data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
 data2 <- read_csv('../inst_all_packages_full_results.csv')
 #d$nd <- to_logical(d$not.damaging, custom_true=c("Y")) 
@@ -27,45 +28,62 @@ data2 <- read_csv('../inst_all_packages_full_results.csv')
 python_labeled <- as.numeric(data2$up.fac.mean[match(paste('python',tolower(data1$pkg), sep = "-"), data2$pkg)])
 same_labeled <- as.numeric(data2$up.fac.mean[match(tolower(data1$pkg), data2$pkg)])
 data1$up.fac.mean <- pmin(python_labeled, same_labeled, na.rm=TRUE)
-data1$milestones <- as.numeric(data1$milestones > 0) + 1
+data1$old_milestones <- data1$milestones
+data1$new_milestones <- as.numeric(data1$milestones > 0) + 1
 # (2) - Run the model on the pilot data
-data1$formal.score <- data1$mmt / (data1$milestones/data1$age)
-table(data1$milestones)
+data1$formal.score <- data1$mmt / (data1$old_milestones/data1$age)
+table(data1$formal.score)
 hist(data1$old_mmt, prob=TRUE) #inequality of participation
 hist(data1$formal.score)
 hist(data1$age/365)
 data1$new_mmt <- data1$mmt - 1
 hist(data1$new_mmt, prob=TRUE)
 
+data3 <- subset(data1, data1$old_milestones > 0 )
+data3$formal.score <- data3$mmt / (data3$old_milestones/data3$age)
+
 data1$new.age <- as.numeric(cut(data1$age/365, breaks=c(0,9,12,15,17), labels=c(1,2,3,4)))
-data1$formal.score <- data1$mmt / (data1$milestones/data1$new.age)
+data1$new.formal.score <- data1$mmt / (data1$new_milestones/data1$new.age)
 hist(as.numeric(data1$new.age))
-table(data1$formal.score)
-hist(data1$formal.score)
-kmodel1 <- lm(up.fac.mean ~ mmt, data=data1)
-summary(kmodel1)
-kmodel1 <- lm(up.fac.mean ~ new_mmt, data=data1)
-summary(kmodel1)
-kmodel1 <- lm(up.fac.mean ~ new.age, data=data1)
-summary(kmodel1)
-kmodel1 <- lm(up.fac.mean ~ milestones, data=data1)
-summary(kmodel1)
+table(data1$new.age)
+hist(data1$new.formal.score)
+data5 <- subset(data1, is.finite(data1$formal.score))
+mmtmodel1 <- lm(up.fac.mean ~ mmt + as.factor(new.age), data=data1)
+summary(mmtmodel1)
+agemodel1 <- lm(up.fac.mean ~ new.age, data=data1)
+summary(agemodel1)
+msmodel1 <- lm(up.fac.mean ~ old_milestones + as.factor(new.age), data=data1)
+summary(msmodel1)
+msmodel2 <- lm(up.fac.mean ~ new_milestones, data=data1)
+summary(msmodel2)
+fsmodel1 <- lm(up.fac.mean ~ formal.score, data=data5)
+summary(fsmodel1)
+t.test(data3$formal.score)
+fsmodel2 <- lm(up.fac.mean ~ new.formal.score, data=data1)
+summary(fsmodel2)
 hist(data1$formal.score)
 cor.test(data1$formal.score, data1$up.fac.mean)
 cor.test(data1$mmt, data1$up.fac.mean)
 cor.test(data1$milestones, data1$up.fac.mean)
 cor.test(data1$age, data1$up.fac.mean)
 
-g <- ggplot(data1, aes(x=new_mmt, y=up.fac.mean)) +
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
   geom_point() +
-  geom_smooth() 
+  geom_smooth() +
+  xlab("MMT") +
+  ylab("Underproduction Factor") +
+  theme_bw() 
+g
 g
 
 data2 <- subset(data1, (data1$age / 365) < 14 )
 hist(floor(data2$age))
 g <- ggplot(data2, aes(x=mmt, y=up.fac.mean)) +
   geom_point() +
-  geom_smooth() 
+  geom_smooth() +
+  xlab("MMT") +
+  ylab("Underproduction Factor") +
+  theme_bw()
 g
 
 data2$yearsOld <- floor(data2$age / 365)
@@ -105,16 +123,22 @@ n <- 100 #a guess for necessary sample size (per group)
 #makeData(10) #DEBUGGING CODE -- you can uncomment this if you want to see it work
 #<====
 
+texreg(list(fsmodel2, mmtmodel1, msmodel1), stars=NULL, digits=2,
+       custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ), 
+       custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Age-2', 'Age-3', 'Age-4', 'Milestones'), 
+       use.packages=FALSE, table=FALSE, ci.force = TRUE)
+
+coef(fsmodel1)
 #print("Levels are:")
 #print(levels(d$source))
 powerCheck(n, nSims)
 #powerCheck2(n, nSims) like doesn't really work
 
 #Sample values
-powerCheck(100, 1000) 
-powerCheck(200, 1000) 
-powerCheck(300, 1000)
+powerCheck(300, 1000) 
+powerCheck(275, 1000) 
+powerCheck(7000, 1000)
 
 powerCheck2(50, 1000) 
-powerCheck2(200, 1000) 
-powerCheck2(500, 1000)
+powerCheck2(75, 1000) 
+powerCheck2(900, 1000)
diff --git a/R/formal-underprod-ggplot.png b/R/formal-underprod-ggplot.png
new file mode 100644
index 0000000..e348326
Binary files /dev/null and b/R/formal-underprod-ggplot.png differ
diff --git a/R/mess-mmt-ggplot.png b/R/mess-mmt-ggplot.png
new file mode 100644
index 0000000..0f8170f
Binary files /dev/null and b/R/mess-mmt-ggplot.png differ
diff --git a/R/mmt-underprod-ggplot.png b/R/mmt-underprod-ggplot.png
new file mode 100644
index 0000000..ad7cde1
Binary files /dev/null and b/R/mmt-underprod-ggplot.png differ
diff --git a/R/newmmt-underprod-plot.png b/R/newmmt-underprod-plot.png
new file mode 100644
index 0000000..84e9010
Binary files /dev/null and b/R/newmmt-underprod-plot.png differ
diff --git a/R/powerAnalysis.R b/R/powerAnalysis.R
index 2c6f0ce..9c6138f 100644
--- a/R/powerAnalysis.R
+++ b/R/powerAnalysis.R
@@ -37,7 +37,8 @@ makeDataNew2 <- function(n) {
   tDF <- data.frame(
     ## don't sim the outcome
     #up.fac.mean=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean
-    formal.score=rlnorm(n=n, mean=6.220282, sd = 2.544058) # formal.score
+    #formal.score=rlnorm(n=n, mean=6.220282, sd = 2.544058) # formal.score
+    formal.score=rbeta(n=n, 1, 3) * 10000
   )
   tDF[is.na(tDF) | tDF=="Inf"] = NA
   #sDF <- melt(tDF, id.vars = 0) #AKA the index is the unique id, as far as that goes
@@ -58,7 +59,7 @@ powerCheck <- function(n, nSims) { #run a power calculation on the dataset given
     ## outcome goes here --v
     # e.g. simData$up.fac.mean <- (usefuleffsizeA * mmt) + (usefuleffsizeB * milestones) + rnorm(n=1, mean=0, sd=1) ##plus some noise
     #simData$up.fac.mean <- (-2.075 * simData$mmt) + (0.4284  * simData$milestones) + rnorm(n=1, mean=0, sd=1)
-    simData$up.fac.mean <- (-2.0745 * simData$new_mmt) + (0.4284  * simData$milestones) + rnorm(n=n, mean=0, sd=1)
+    simData$up.fac.mean <- (-1.38 * simData$new_mmt) + (0.40  * simData$milestones)  + rnorm(n=n, mean=0, sd=1)
     #have updated for kkex through here, now need to look at the underproduction work
     #m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData)
     ## could leave age out for now?
@@ -88,7 +89,7 @@ powerCheck2 <- function(n, nSims) { #run a power calculation on the dataset give
     #have updated for kkex through here, now need to look at the underproduction work
     #m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData)
     ## outcome goes here --v
-    simData$up.fac.mean <- (0.5 * simData$formal.score) + rnorm(1, mean=0, sd=1) ##plus some noise
+    simData$up.fac.mean <- (0.00017 * simData$formal.score) + rnorm(n, mean=0, sd=1) ##plus some noise
     m1.sim <- lm(up.fac.mean ~ formal.score, data=simData)
     p0 <- coef(summary(m1.sim))[1,4] 
     p1 <- coef(summary(m1.sim))[2,4]
diff --git a/R/texreg_1.39.3.tar.gz b/R/texreg_1.39.3.tar.gz
new file mode 100644
index 0000000..b8a5548
Binary files /dev/null and b/R/texreg_1.39.3.tar.gz differ
diff --git a/R/textreg_0.1.5.tar.gz b/R/textreg_0.1.5.tar.gz
new file mode 100644
index 0000000..25aa698
Binary files /dev/null and b/R/textreg_0.1.5.tar.gz differ
diff --git a/kkex-github-api-key.rtf b/kkex-github-api-key.rtf
new file mode 100644
index 0000000..699d182
--- /dev/null
+++ b/kkex-github-api-key.rtf
@@ -0,0 +1,8 @@
+{\rtf1\ansi\ansicpg1252\cocoartf2708
+\cocoatextscaling0\cocoaplatform0{\fonttbl\f0\fswiss\fcharset0 Helvetica;}
+{\colortbl;\red255\green255\blue255;}
+{\*\expandedcolortbl;;}
+\margl1440\margr1440\vieww11520\viewh8400\viewkind0
+\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0
+
+\f0\fs24 \cf0 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}
\ No newline at end of file