grouping by ranefs
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@ -1,3 +1,182 @@
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# a) the basic things, in a table:
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# Condition Sample Size mean standard deviation standard error
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# Immediately after 2 48.705 1.534422 1.085
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# One day after 2 41.955 2.128391 1.505
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# Three days after 2 21.795 0.7707464 0.545
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# Five days after 2 12.415 1.081873 0.765
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# Seven days after 2 8.32 0.2687006 0.19
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# b) do a one way anova based on the data, like the last homework
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grp <- c(1,1,2,2,3,3,4,4,5,5)
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results <- aov(resp~factor(grp))
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anova(results)
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# c) summarize the data and the means w a plot, boxplot
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means <- c(48.705, 41.955, 21.795, 12.415, 8.32)
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# c) summarize the data and the means w a plot, boxplot
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boxplot(results)
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# c) summarize the data and the means w a plot, boxplot
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boxplot(resp)
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# c) summarize the data and the means w a plot, boxplot
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boxplot(resp)
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# c) summarize the data and the means w a plot, boxplot
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boxplot(resp~grp)
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ALevels <- c(3.36, 3.34, 3.28, 3.20, 3.26, 3.16, 3.25, 3.36, 3.01, 2.92)
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ELevels <- c(94.6, 96.0, 95.7, 93.2, 97.4, 94.3, 95.0, 97.7, 92.3, 95.1)
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Aresults <- aov(Alevels~factor(grp))
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ALevels <- c(3.36, 3.34, 3.28, 3.20, 3.26, 3.16, 3.25, 3.36, 3.01, 2.92)
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ELevels <- c(94.6, 96.0, 95.7, 93.2, 97.4, 94.3, 95.0, 97.7, 92.3, 95.1)
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Aresults <- aov(Alevels~factor(grp))
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ALevels <- c(3.36, 3.34, 3.28, 3.20, 3.26, 3.16, 3.25, 3.36, 3.01, 2.92)
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ELevels <- c(94.6, 96.0, 95.7, 93.2, 97.4, 94.3, 95.0, 97.7, 92.3, 95.1)
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Aresults <- aov(ALevels~factor(grp))
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Eresults <- aov(ELevels~factor(grp))
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# Vitamin A Anova:
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anova(Aresults)
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# Vimain E Anova:
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anova(Eresults)
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# 12.10
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# four groups, how do nemaotodes impact plant growth
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# a)
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zero_nema <- c(10.8, 9.1, 13.5, 9.2)
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thousand_name <-c(11.1, 11.1, 8.2, 11.3)
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thousand_nema <-c(11.1, 11.1, 8.2, 11.3)
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fthousand_nema <- c(5.4, 4.6, 7.4, 5.0)
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tthousand_nema <- c(5.8, 5.3, 3.2, 7.5)
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mean(zero_nema)
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sd(zero_nema)
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mean(thousand_nema)
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sd(thousand_name)
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mean(fthousand_nema)
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sd(fthousand_nema)
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mean(tthousand_nema)
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sd(tthousand_nema)
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# Table
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# Nematodes Means StdDev
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# 0 10.65 2.053452
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# 1,000 10.425 1.486327
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# 5,000 5.6 1.243651
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# 10,000 5.45 1.771064
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nema_means <- c(10.65, 10.425, 5.6, 5.45)
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barplot(nema_means)
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# c)
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groupings <- c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4)
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resp <- c(zero_nema, thousand_nema, fthousand_nema, tthousand_nema)
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results <- aov(resp~factor(groupings))
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anova(results)
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# 12.5
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# do piano lessons improve spacial temporal
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piano <- c( 2, 5, 7, -2, 2, 7, 4, 1, 0, 7, 3, 4, 3, 4, 9, 4, 5, 2, 9, 6, 0, 3, 6, -1, 3, 4, 6, 7, -2, 7, -3, 3, 4, 4)
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singing <- c(1, -1, 0, 1, -4, 0, 0, 1, 0, -1)
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computer <- c(0, 1, 1, -3, -2, 4, -1, 2, 4, 2,2, 2, -3, -3, 0, 2, 0, -1, 3, -1 )
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none <- c(5, -1, 7, 0, 4, 0, 2, 1, -6, 0, 2, -1, 0, -2)
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size(piano)
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length(piano)
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mean(piano)
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sd(piano)
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sd(piano)/sqrt(lenth(piano))
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sd(piano)/sqrt(length(piano))
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length(singing)
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mean(singing)
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sd(singing)
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sd(signing)/sqrt(length(singing))
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sd(singing)/sqrt(length(singing))
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length(computer)
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mean(computer)
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sd(computer)
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sd(computer)/sqrt(length(computer))
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length(none)
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mean(none)
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sd(none)
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sd(none)/sqrt(14)
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# a) make a table given the sample size
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# Table:
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# Lessons Size Mean Standard Dev Standard Error
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# Piano 34 3.617647 3.055196 0.5239618
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# Singing 10 -0.3 1.494434 0.4725816
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# Computer 20 0.45 2.21181 0.4945758
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# None 14 0.7857143 3.190818 0.8527819
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# b)
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# H0: The spatial-temporal reasoning test results across different lesson groups will be statistically equivalent.
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# Ha: For at least one lesson group, the results of the reasoning test will be statistically different.
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data_panel <- data.frame(
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Y=c(piano, singing, computer, none),
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Site = factor(rep(c("piano", "singing", "computer", "none"), times=c(length(piano), length(computer), length(singing), length(none))))
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)
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data_panel
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tempt <- aov(Y~Site, data=data_panel)
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anova(tempt)
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# 12.6
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TukeyHSD(tempt)
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# Summary: Looking at the TukeyHSD results, there are some interesting notes in
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# where statistically significant variance lies. If we immediately discard the
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# comparisons with large p-values, we are left with three statistically significant
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# ones. One is that students with piano lessons do better than computer lesson learners
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# by an average of 3.5 points, another is that piano outperforms no lessons by about 2.8 points
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# and lastly that singing underperforms piano by about 3.3 points. While this
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# statistical tooling is useful for proving the significance of these differences in
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# performance, we can also evaluate
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means <- c(mean(piano), mean(singing), mean(computer), mean(none))
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barplot(means)
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# (1) - Get the pilot data and clean it
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#source('~/Research/tor_wikipedia_edits/handcoded_edits/inter_coder_reliability_ns0.R')
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#source ('/data/users/mgaughan/kkex_data_110823_3')
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data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
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library(readr)
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library(ggplot2)
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# (1) - Get the pilot data and clean it
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#source('~/Research/tor_wikipedia_edits/handcoded_edits/inter_coder_reliability_ns0.R')
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#source ('/data/users/mgaughan/kkex_data_110823_3')
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data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
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data2 <- read_csv('../inst_all_packages_full_results.csv')
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# (1) - Get the pilot data and clean it
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#source('~/Research/tor_wikipedia_edits/handcoded_edits/inter_coder_reliability_ns0.R')
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#source ('/data/users/mgaughan/kkex_data_110823_3')
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data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
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library(readr)
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library(ggplot2)
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# (1) - Get the pilot data and clean it
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#source('~/Research/tor_wikipedia_edits/handcoded_edits/inter_coder_reliability_ns0.R')
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#source ('/data/users/mgaughan/kkex_data_110823_3')
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data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
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data1 <- read_csv('../expanded_data_final.csv',show_col_types = FALSE)
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# Use pilot project data to calculate power of a full study through simulation
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#
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# Parts:
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# (0) - Setup
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# (1) - Get the pilot data and clean it
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# (2) - Run the model on the pilot data and extract effects
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# (3) - Set up and run the simulation
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# ====> Set variables at the arrows <====
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#
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##############################################################################
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rm(list=ls())
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set.seed(424242)
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library(readr)
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library(ggplot2)
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data1 <- read_csv('../expanded_data_final.csv',show_col_types = FALSE)
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set.seed(424242)
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library(readr)
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library(ggplot2)
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data1 <- read_csv('../expanded_data_final.csv',show_col_types = FALSE)
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#shows the cross-age downward slopes for all underproduction averages in the face of MMT
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g3 <- ggplot(data1, aes(x=mmt, y=underproduction_mean)) +
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geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor),
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method='lm', formula= y~x) +
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xlab("MMT") +
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ylab("Underproduction Factor") +
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theme_bw()
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g3
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library(readr)
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library(ggplot2)
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data1 <- read_csv('../expanded_data_final.csv',show_col_types = FALSE)
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mean(data1$milestone_count)
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data1$mmt <- (((data1$collaborators * 2)+ data1$contributors) / (data1$contributors + data1$collaborators)) - 1
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mean(data1$mmt)
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rm(list=ls())
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set.seed(424242)
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library(readr)
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library(ggplot2)
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data1 <- read_csv('../expanded_data_final.csv',show_col_types = FALSE)
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library(readr)
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library(ggplot2)
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data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
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data2 <- read_csv('../inst_all_packages_full_results.csv')
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@ -50,6 +229,7 @@ windowed_data$week_offset <- windowed_data$week - 27
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#separate out the cleaning d
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all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
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mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
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all_actions_data$logged_count <- log(all_actions_data$count)
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all_actions_data$log1p_count <- log1p(all_actions_data$count)
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# 3 rdd in lmer analysis
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# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
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@ -61,189 +241,157 @@ library(lattice)
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all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
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optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
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#identifying the quartiles of effect for D
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all_model_ranef <- as.data.frame(ranef(all_model, condVar=TRUE))
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View(all_model_ranef)
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d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
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#identifying the quartiles of effect for D
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all_model_ranef <- ranef(all_model, condVar=TRUE)
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d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
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dotplot(all_model_ranef)
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d_effect_ranef_all <- all_model_ranef['upstream_vcs_link']['D']
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View(all_model_ranef)
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d_effect_ranef_all <- all_model_ranef[upstream_vcs_link,2]
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d_effect_ranef_all <- all_model_ranef['upstream_vcs_link',2]
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d_effect_ranef_all <- all_model_ranef$upstream_vcs_link
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View(d_effect_ranef_all)
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dotplot(all_model_ranef)[["D"]]
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dotplot(all_model_ranef)[["upstream_vcs_link"]]
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dotplot(all_model_ranef)[["upstream_vcs_link"]["D"]]
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dotplot(all_model_ranef)$D
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View(all_model_ranef)
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for (j in 1:nschool) {
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jj <- order(all_model_ranef)[j]
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lines (x=c(j,j),y=c(ranef.lower[jj],ranef.upper[jj]))
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}
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for (j in 1:upstream_vcs_link) {
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jj <- order(all_model_ranef)[j]
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lines (x=c(j,j),y=c(ranef.lower[jj],ranef.upper[jj]))
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}
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View(all_model_ranef)
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df_ranefs <- as.data.frame(all_model_ranef)
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View(df_ranefs)
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#identifying the quartiles of effect for D
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all_model_ranef <- ranef(all_model, condVar=TRUE)$upstream_vcs_link[[2]]
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#identifying the quartiles of effect for D
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all_model_ranef <- ranef(all_model, condVar=TRUE)$upstream_vcs_link
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dotplot(all_model_ranef)
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dotplot(all_model_ranef)
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# this is the file with the lmer multi-level rddAnalysis
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library(tidyverse)
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library(plyr)
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# 0 loading the readme data in
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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# 1 preprocessing
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#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
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col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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readme_df <- readme_df[,col_order]
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readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
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readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
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readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
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readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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readme_df = readme_df[,!(names(readme_df) %in% drop)]
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# 2 some expansion needs to happens for each project
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expand_timeseries <- function(project_row) {
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longer <- project_row |>
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pivot_longer(cols = starts_with("ct"),
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names_to = "window",
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values_to = "count") |>
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unnest(count)
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longer$observation_type <- gsub("^.*_", "", longer$window)
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longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
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longer$count <- as.numeric(longer$count)
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#longer <- longer[which(longer$observation_type == "all"),]
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return(longer)
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}
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expanded_data <- expand_timeseries(readme_df[1,])
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for (i in 2:nrow(readme_df)){
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expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
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}
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#filter out the windows of time that we're looking at
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window_num <- 8
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windowed_data <- expanded_data |>
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filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 1, 0))
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#scale the age numbers
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windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
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windowed_data$week_offset <- windowed_data$week - 27
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#separate out the cleaning d
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all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
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mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
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all_actions_data$log1p_count <- log1p(all_actions_data$count)
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# 3 rdd in lmer analysis
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# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
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# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
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library(lme4)
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# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
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library(optimx)
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library(lattice)
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all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
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optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
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#identifying the quartiles of effect for D
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all_model_ranef <- ranef(all_model, condVar=TRUE)
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dotplot(all_model_ranef)$g[1]
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re <- ranef(all_model,postVar = TRUE)
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re$g$'(Intercept)' <- NULL
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re$g$'D:I(week_offset)' <- NULL
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re <- ranef(all_model, condVar=TRUE)
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re$g$'(Intercept)' <- NULL
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re$g$'D:I(week_offset)' <- NULL
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dotplot(re)
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dotplot(re)
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re <- ranef(all_model, condVar=TRUE)
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re$upstream_vcs_link$'(Intercept)' <- NULL
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re$upstream_vcs_link$'D:I(week_offset)' <- NULL
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View(re)
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re$upstream_vcs_link$'I(week_offset)' <- NULL
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dotplot(re)
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View(re)
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View(all_model_ranef)
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dotplot(all_model_ranef)
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#identifying the quartiles of effect for D
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all_model_ranef <- ranef(all_model, condVar=TRUE)
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dotplot(all_model_ranef)
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#identifying the quartiles of effect for D
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all_model_ranef <- ranef(all_model, condVar=TRUE)
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dotplot(all_model_ranef)
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re <- ranef(all_model, condVar=TRUE)
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re$upstream_vcs_link$'(Intercept)' <- NULL
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re$upstream_vcs_link$'D:I(week_offset)' <- NULL
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re$upstream_vcs_link$'I(week_offset)' <- NULL
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dotplot(re)
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dotplot(all_model_ranef)
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# this is the file with the lmer multi-level rddAnalysis
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library(tidyverse)
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library(plyr)
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# 0 loading the readme data in
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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# 1 preprocessing
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#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
|
||||
col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
|
||||
readme_df <- readme_df[,col_order]
|
||||
readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
|
||||
readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
|
||||
readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
|
||||
readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
|
||||
drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
|
||||
readme_df = readme_df[,!(names(readme_df) %in% drop)]
|
||||
# 2 some expansion needs to happens for each project
|
||||
expand_timeseries <- function(project_row) {
|
||||
longer <- project_row |>
|
||||
pivot_longer(cols = starts_with("ct"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count)
|
||||
longer$observation_type <- gsub("^.*_", "", longer$window)
|
||||
longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
|
||||
longer$count <- as.numeric(longer$count)
|
||||
#longer <- longer[which(longer$observation_type == "all"),]
|
||||
return(longer)
|
||||
}
|
||||
expanded_data <- expand_timeseries(readme_df[1,])
|
||||
for (i in 2:nrow(readme_df)){
|
||||
expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
|
||||
}
|
||||
#filter out the windows of time that we're looking at
|
||||
window_num <- 8
|
||||
windowed_data <- expanded_data |>
|
||||
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
|
||||
mutate(D = ifelse(week > 27, 1, 0))
|
||||
#scale the age numbers
|
||||
windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
|
||||
windowed_data$week_offset <- windowed_data$week - 27
|
||||
#separate out the cleaning d
|
||||
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
|
||||
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
|
||||
all_actions_data$log1p_count <- log1p(all_actions_data$count)
|
||||
# 3 rdd in lmer analysis
|
||||
# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
||||
# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
|
||||
library(lme4)
|
||||
# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
|
||||
library(optimx)
|
||||
library(lattice)
|
||||
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
||||
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
||||
summary(all_model)
|
||||
#identifying the quartiles of effect for D
|
||||
all_model_ranef <- ranef(all_model, condVar=TRUE)
|
||||
dotplot(all_model_ranef)
|
||||
df_ranefs <- as.data.frame(all_model_ranef)
|
||||
View(df_ranefs)
|
||||
D_df_ranef <- df_ranefs[term == "D"]
|
||||
D_df_ranef <- df_ranefs[df_ranefs$term == "D"]
|
||||
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
||||
View(D_df_ranef)
|
||||
has_zero <- function(condval, condsd){
|
||||
bounds <- condsd * 1.96
|
||||
if ((condval - bounds) < 0){
|
||||
if ((condval + bounds) > 0) {
|
||||
return(1)
|
||||
} else {
|
||||
return(0)
|
||||
}
|
||||
} else {
|
||||
return(2)
|
||||
}
|
||||
}
|
||||
df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd))
|
||||
has_zero <- function(condval, condsd){
|
||||
bounds <- condsd * 1.96
|
||||
print(bounds)
|
||||
if ((condval - bounds) < 0){
|
||||
if ((condval + bounds) > 0) {
|
||||
return(1)
|
||||
} else {
|
||||
return(0)
|
||||
}
|
||||
} else {
|
||||
return(2)
|
||||
}
|
||||
}
|
||||
df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd))
|
||||
has_zero <- function(condval, condsd){
|
||||
bounds <- condsd * 1.96
|
||||
print(condval - bounds)
|
||||
if ((condval - bounds) < 0){
|
||||
if ((condval + bounds) > 0) {
|
||||
return(1)
|
||||
} else {
|
||||
return(0)
|
||||
}
|
||||
} else {
|
||||
return(2)
|
||||
}
|
||||
}
|
||||
df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd))
|
||||
has_zero <- function(condval, condsd){
|
||||
bounds <- condsd * 1.96
|
||||
return(ifelse(((condval - bounds) < 0),ifelse(((condval + bounds) > 0), 1, 0), 2))
|
||||
}
|
||||
df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd))
|
||||
df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd)) |>
|
||||
group_by(ranef_grouping) |>
|
||||
summarize(no_rows = length(ranef_grouping))
|
||||
df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd)) |>
|
||||
group_by(ranef_grouping) |>
|
||||
summarize(no_rows = length(as.factor(ranef_grouping)))
|
||||
df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd)) |>
|
||||
group_by(ranef_grouping) |>
|
||||
summarize(no_rows = length(as.factor(ranef_grouping)))
|
||||
View(df_ranefs)
|
||||
has_zero <- function(condval, condsd){
|
||||
bounds <- condsd * 1.96
|
||||
return(ifelse(((condval - bounds) < 0),ifelse(((condval + bounds) > 0), 1, 0), 2))
|
||||
}
|
||||
df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd))
|
||||
View(df_ranefs)
|
||||
df_ranefs <- df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd))
|
||||
View(df_ranefs)
|
||||
df_ranefs |>
|
||||
group_by(ranef_grouping) |>
|
||||
summarise(no_rows = length(ranef_grouping))
|
||||
df_ranefs |>
|
||||
group_by(ranef_grouping) |>
|
||||
summarise(no_rows = length(ranef_grouping))
|
||||
df_ranefs |>
|
||||
group_by(as.factor(ranef_grouping)) |>
|
||||
summarise(no_rows = length(ranef_grouping))
|
||||
hist(df_ranefs$ranef_grouping)
|
||||
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
||||
hist(D_df_ranefs$ranef_grouping)
|
||||
hist(D_df_ranef$ranef_grouping)
|
||||
#plot the ranefs
|
||||
library(ggplot2)
|
||||
D_df_ranef |>
|
||||
ggplot(aes(x=grp, y=condval))
|
||||
D_df_ranef |>
|
||||
ggplot(aes(x=grp, y=condval, col = as.factor(ranef_grouping)))
|
||||
D_df_ranef |>
|
||||
ggplot(aes(x=condsd, y=condval, col = as.factor(ranef_grouping)))
|
||||
D_df_ranef |>
|
||||
ggplot(aes(x=condval, y=condval, col = as.factor(ranef_grouping)))
|
||||
D_df_ranef |>
|
||||
ggplot(aes(x=condval, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_point()
|
||||
D_df_ranef |>
|
||||
ggplot(aes(x=grp, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_point()
|
||||
df_ranefs <- df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd))
|
||||
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
||||
hist(D_df_ranef$ranef_grouping)
|
||||
D_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_point()
|
||||
df_ranefs <- df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd))
|
||||
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
||||
df_ranefs <- df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd)) |>
|
||||
mutate(rank = rank(condval))
|
||||
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
||||
D_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_point()
|
||||
D_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd)))
|
||||
D_df_ranef |>
|
||||
ggplot(aes(x=grp, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd)))
|
||||
D_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd)))
|
||||
# mrg behavior for this
|
||||
mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
||||
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
||||
#identifying the quartiles of effect for D
|
||||
mrg_model_ranef <- ranef(mrg_model, condVar=TRUE)
|
||||
df_mrg_ranefs <- as.data.frame(mrg_model_ranef)
|
||||
#doing similar random effect analysis for this
|
||||
df_mrg_ranefs <- df_mrg_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd)) |>
|
||||
mutate(rank = rank(condval))
|
||||
D_df_mrg_ranefs <- df_mrg_ranefs[which(df_mrg_ranefs$term == "D"),]
|
||||
D_df_mrg_ranefs |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd)))
|
||||
D_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd)))
|
||||
library(tidyverse)
|
||||
library(plyr)
|
||||
#get the contrib data instead
|
||||
@ -286,8 +434,6 @@ windowed_data$week_offset <- windowed_data$week - 27
|
||||
#separate out the cleaning d
|
||||
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
|
||||
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
|
||||
#EDA?
|
||||
hist(log(all_actions_data$count))
|
||||
all_actions_data$logged_count <- log(all_actions_data$count)
|
||||
all_actions_data$log1p_count <- log1p(all_actions_data$count)
|
||||
# now for merge
|
||||
@ -303,210 +449,64 @@ all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_o
|
||||
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
||||
summary(all_model)
|
||||
#identifying the quartiles of effect for D
|
||||
all_model_ranef <- as.data.frame(ranef(all_model))
|
||||
View(all_model_ranef)
|
||||
# mrg behavior for this
|
||||
mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset | upstream_vcs_link), data=mrg_actions_data, REML=FALSE, control = lmerControl(
|
||||
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
||||
summary(mrg_model)
|
||||
#identifying the quartiles of effect for D
|
||||
mrg_model_ranef <- ranef(mrg_model)
|
||||
View(mrg_model_ranef)
|
||||
dotplot(mrg_model_ranef)
|
||||
#load in data
|
||||
contrib_df <- read_csv("../final_data/deb_contrib_pop_change.csv")
|
||||
readme_df <- read_csv("../final_data/deb_readme_pop_change.csv")
|
||||
View(readme_df)
|
||||
#some expansion needs to happens for each project
|
||||
expand_timeseries <- function(project_row) {
|
||||
longer <- project_row |>
|
||||
pivot_longer(cols = ends_with("new"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count)
|
||||
#longer$observation_type <- gsub("^.*_", "", longer$window)
|
||||
#longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
|
||||
#longer$count <- as.numeric(longer$count)
|
||||
#longer <- longer[which(longer$observation_type == "all"),]
|
||||
return(longer)
|
||||
all_model_ranef <- ranef(all_model)
|
||||
#d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
|
||||
#d_effect_ranef_all$quartile <- ntile(d_effect_ranef_all$condval, 4)
|
||||
df_ranefs <- as.data.frame(all_model_ranef)
|
||||
has_zero <- function(condval, condsd){
|
||||
bounds <- condsd * 1.96
|
||||
return(ifelse(((condval - bounds) < 0),ifelse(((condval + bounds) > 0), 1, 0), 2))
|
||||
}
|
||||
expanded_data <- expand_timeseries(readme_df[1,])
|
||||
View(expand_timeseries)
|
||||
View(expanded_data)
|
||||
longer <- project_row |>
|
||||
pivot_longer(cols = ends_with("new"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count) |>
|
||||
mutate(after_doc = str_detect(window, "after"))
|
||||
#some expansion needs to happens for each project
|
||||
expand_timeseries <- function(project_row) {
|
||||
longer <- project_row |>
|
||||
pivot_longer(cols = ends_with("new"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count) |>
|
||||
mutate(after_doc = str_detect(window, "after"))
|
||||
return(longer)
|
||||
}
|
||||
expanded_data <- expand_timeseries(readme_df[1,])
|
||||
longer <- project_row |>
|
||||
pivot_longer(cols = ends_with("new"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count) |>
|
||||
mutate(after_doc = as.numeric(str_detect(window, "after")))
|
||||
return(longer)
|
||||
#some expansion needs to happens for each project
|
||||
expand_timeseries <- function(project_row) {
|
||||
longer <- project_row |>
|
||||
pivot_longer(cols = ends_with("new"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count) |>
|
||||
mutate(after_doc = as.numeric(str_detect(window, "after")))
|
||||
return(longer)
|
||||
}
|
||||
expanded_data <- expand_timeseries(readme_df[1,])
|
||||
#some expansion needs to happens for each project
|
||||
expand_timeseries <- function(project_row) {
|
||||
longer <- project_row |>
|
||||
pivot_longer(cols = ends_with("new"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count) |>
|
||||
mutate(after_doc = as.numeric(str_detect(window, "after"))) |>
|
||||
mutate(is_collab = as.numeric(str_detect(window, "collab")))
|
||||
return(longer)
|
||||
}
|
||||
expanded_data <- expand_timeseries(readme_df[1,])
|
||||
for (i in 2:nrow(readme_df)){
|
||||
expanded_readme_data <- rbind(expanded_readme_data, expand_timeseries(readme_df[i,]))
|
||||
}
|
||||
expanded_readme_data <- expand_timeseries(readme_df[1,])
|
||||
for (i in 2:nrow(readme_df)){
|
||||
expanded_readme_data <- rbind(expanded_readme_data, expand_timeseries(readme_df[i,]))
|
||||
}
|
||||
expanded_contrib_data <- expand_timeseries(contrib_df[1,])
|
||||
for (i in 2:nrow(contrib_df)){
|
||||
expanded_contrib_data <- rbind(expanded_contrib_data, expand_timeseries(contrib_df[i,]))
|
||||
}
|
||||
View(expanded_contrib_data)
|
||||
readme_model <- lmer(count ~ after_doc + (1| upstream_vcs_link), data=expanded_readme_data, REML=FALSE)
|
||||
summary(readme_model)
|
||||
readme_model <- lmer(count ~ after_doc + ( after_doc | upstream_vcs_link), data=expanded_readme_data, REML=FALSE)
|
||||
summary(readme_model)
|
||||
contrib_model <- lmer(count ~ after_doc + ( after_doc | upstream_vcs_link), data=expanded_contrib_data, REML=FALSE)
|
||||
summary(contrib_model)
|
||||
collab_readme_model <- lmer(count ~ after_doc + ( after_doc | upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
||||
#breaking out the types of population counts
|
||||
collab_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 1),]
|
||||
contrib_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 0),]
|
||||
collab_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 1),]
|
||||
contrib_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 0),]
|
||||
collab_readme_model <- lmer(count ~ after_doc + ( after_doc | upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
||||
collab_readme_model <- lmer(count ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
||||
summary(collab_readme_model)
|
||||
contrib_readme_model <- lmer(count ~ after_doc + ( 1| upstream_vcs_link), data=contrib_pop_readme, REML=FALSE)
|
||||
summary(contrib_readme_model)
|
||||
collab_readme_model <- lmer(count ~ after_doc + (after_doc| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
||||
collab_readme_model <- lmer(count ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
||||
summary(collab_readme_model)
|
||||
contrib_readme_model <- lmer(count ~ after_doc + ( 1| upstream_vcs_link), data=contrib_pop_readme, REML=FALSE)
|
||||
summary(contrib_readme_model)
|
||||
collab_contrib_model <- lmer(count ~ after_doc + ( 1 | upstream_vcs_link), data=collab_pop_contrib, REML=FALSE)
|
||||
summary(collab_contrib_model)
|
||||
contrib_contrib_model <- lmer(count ~ after_doc + ( 1 | upstream_vcs_link), data=contrib_pop_contrib, REML=FALSE)
|
||||
summary(contrib_contrib_model)
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = after_doc, y = count, col = is_collab)) +
|
||||
geom_point()
|
||||
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab)) +
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
|
||||
geom_point()
|
||||
expanded_readme_data |>
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
|
||||
geom_point()
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = as.factor(after_doc), y = count, col = as.factor(is_collab))) +
|
||||
geom_point()
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = as.factor(after_doc), y = scale(count), col = as.factor(is_collab))) +
|
||||
geom_point()
|
||||
expanded_readme_data$log1pcount <- log1p(expanded_readme_data$count)
|
||||
expanded_contrib_data$log1pcount <- log1p(expanded_contrib_data$count)
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = as.factor(after_doc), y = log1pcount, col = as.factor(is_collab))) +
|
||||
geom_point()
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = as.factor(after_doc), y = log1pcount, col = as.factor(is_collab))) +
|
||||
geom_point() +
|
||||
geom_smooth(method = 'lm', se = F)
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
|
||||
geom_point() +
|
||||
geom_smooth(method = 'lm', se = F)
|
||||
expanded_contrib_data |>
|
||||
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
|
||||
geom_point() +
|
||||
geom_smooth(method = 'lm', se = F)
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
|
||||
geom_point() +
|
||||
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
|
||||
geom_point() +
|
||||
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
||||
expanded_readme_data$logcount <- log(expanded_readme_data$count)
|
||||
expanded_contrib_data$logcount <- log(expanded_contrib_data$count)
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = after_doc, y = logcount, col = as.factor(is_collab))) +
|
||||
geom_point() +
|
||||
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
||||
expanded_contrib_data |>
|
||||
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
|
||||
geom_point() +
|
||||
geom_smooth(method = 'lm', se = F)
|
||||
expanded_contrib_data |>
|
||||
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
|
||||
geom_point() +
|
||||
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
||||
expanded_contrib_data |>
|
||||
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
|
||||
geom_point() +
|
||||
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = after_doc, y = logcount, col = as.factor(is_collab))) +
|
||||
geom_point() +
|
||||
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
||||
collab_readme_model <- lmer(logcount ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
||||
#breaking out the types of population counts
|
||||
collab_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 1),]
|
||||
contrib_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 0),]
|
||||
collab_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 1),]
|
||||
contrib_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 0),]
|
||||
collab_readme_model <- lmer(logcount ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
||||
collab_readme_model <- lmer(log1pcount ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
||||
summary(collab_readme_model)
|
||||
contrib_readme_model <- lmer(log1pcount ~ after_doc + ( 1| upstream_vcs_link), data=contrib_pop_readme, REML=FALSE)
|
||||
summary(contrib_readme_model)
|
||||
collab_contrib_model <- lmer(log1pcount ~ after_doc + ( 1 | upstream_vcs_link), data=collab_pop_contrib, REML=FALSE)
|
||||
summary(collab_contrib_model)
|
||||
contrib_contrib_model <- lmer(log1pcount ~ after_doc + ( 1 | upstream_vcs_link), data=contrib_pop_contrib, REML=FALSE)
|
||||
summary(contrib_contrib_model)
|
||||
df_ranefs <- df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd)) |>
|
||||
mutate(rank = rank(condval))
|
||||
wo_df_ranef <- df_ranefs[which(df_ranefs$term == "week_offset"),]
|
||||
library(ggplot2)
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = after_doc, y = logcount, col = as.factor(is_collab))) +
|
||||
geom_point() +
|
||||
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
||||
expanded_readme_data |>
|
||||
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
|
||||
geom_point() +
|
||||
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
||||
expanded_contrib_data |>
|
||||
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
|
||||
geom_point() +
|
||||
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
||||
wo_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd)))
|
||||
wo_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||
geom_bw()
|
||||
wo_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||
theme_bw()
|
||||
wo_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_pointrange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||
theme_bw()
|
||||
wo_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_crossbar(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd)), width=0.2) +
|
||||
theme_bw()
|
||||
wo_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||
theme_bw()
|
||||
wo_df_ranef |>
|
||||
ggplot(aes(x=grp, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||
theme_bw()
|
||||
wo_df_ranef <- wo_df_ranef |>
|
||||
arrange(condval)
|
||||
wo_df_ranef |>
|
||||
ggplot(aes(x=grp, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||
theme_bw()
|
||||
View(wo_df_ranef)
|
||||
df_ranefs <- df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd))
|
||||
wo_df_ranef <- df_ranefs[which(df_ranefs$term == "week_offset"),]
|
||||
wo_df_ranef <- wo_df_ranef |>
|
||||
mutate(rank = rank(condval))
|
||||
library(ggplot2)
|
||||
wo_df_ranef |>
|
||||
ggplot(aes(x=grp, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||
theme_bw()
|
||||
wo_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||
theme_bw()
|
||||
|
||||
@ -58,8 +58,24 @@ all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_o
|
||||
summary(all_model)
|
||||
#identifying the quartiles of effect for D
|
||||
all_model_ranef <- ranef(all_model)
|
||||
d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
|
||||
d_effect_ranef_all$quartile <- ntile(d_effect_ranef_all$condval, 4)
|
||||
#d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
|
||||
#d_effect_ranef_all$quartile <- ntile(d_effect_ranef_all$condval, 4)
|
||||
df_ranefs <- as.data.frame(all_model_ranef)
|
||||
has_zero <- function(condval, condsd){
|
||||
bounds <- condsd * 1.96
|
||||
return(ifelse(((condval - bounds) < 0),ifelse(((condval + bounds) > 0), 1, 0), 2))
|
||||
}
|
||||
df_ranefs <- df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd))
|
||||
wo_df_ranef <- df_ranefs[which(df_ranefs$term == "week_offset"),]
|
||||
wo_df_ranef <- wo_df_ranef |>
|
||||
mutate(rank = rank(condval))
|
||||
library(ggplot2)
|
||||
wo_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||
theme_bw()
|
||||
#plotting ranefs
|
||||
#model residuals
|
||||
all_residuals <- residuals(all_model)
|
||||
qqnorm(all_residuals)
|
||||
|
||||
@ -66,7 +66,23 @@ summary(all_model)
|
||||
all_model_ranef <- ranef(all_model, condVar=TRUE)
|
||||
dotplot(all_model_ranef)
|
||||
df_ranefs <- as.data.frame(all_model_ranef)
|
||||
#D_df_ranef <- df_ranefs[df_ranefs$term == "D"]
|
||||
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
||||
#below this groups the ranefs
|
||||
has_zero <- function(condval, condsd){
|
||||
bounds <- condsd * 1.96
|
||||
return(ifelse(((condval - bounds) < 0),ifelse(((condval + bounds) > 0), 1, 0), 2))
|
||||
}
|
||||
df_ranefs <- df_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd)) |>
|
||||
mutate(rank = rank(condval))
|
||||
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
||||
hist(D_df_ranef$ranef_grouping)
|
||||
#plot the ranefs
|
||||
library(ggplot2)
|
||||
D_df_ranef |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||
geom_bw()
|
||||
#d_effect_ranef_all <- all_model_ranef$upstream_vcs_link
|
||||
#d_effect_ranef_all$quartile <- ntile(d_effect_ranef_all$condval, 4)
|
||||
#model residuals
|
||||
@ -78,9 +94,18 @@ mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(
|
||||
summary(mrg_model)
|
||||
#identifying the quartiles of effect for D
|
||||
mrg_model_ranef <- ranef(mrg_model, condVar=TRUE)
|
||||
df_mrg_ranefs <- as.data.frame(mrg_model_ranef)
|
||||
dotplot(mrg_model_ranef)
|
||||
d_effect_ranef_mrg <- mrg_model_ranef[mrg_model_ranef$term=="D",]
|
||||
d_effect_ranef_mrg$quartile <- ntile(d_effect_ranef_mrg$condval, 4)
|
||||
#doing similar random effect analysis for this
|
||||
df_mrg_ranefs <- df_mrg_ranefs |>
|
||||
mutate(ranef_grouping = has_zero(condval, condsd)) |>
|
||||
mutate(rank = rank(condval))
|
||||
D_df_mrg_ranefs <- df_mrg_ranefs[which(df_mrg_ranefs$term == "D"),]
|
||||
D_df_mrg_ranefs |>
|
||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd)))
|
||||
#merge model residuals
|
||||
mrg_residuals <- residuals(mrg_model)
|
||||
qqnorm(mrg_residuals)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user