managing glmer
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@ -1,190 +1,8 @@
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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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theme_bw()
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g3
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wo_df_ranef |>
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library(readr)
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ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
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library(ggplot2)
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geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
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data1 <- read_csv('../expanded_data_final.csv',show_col_types = FALSE)
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theme_bw()
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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$mmt)
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rm(list=ls())
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set.seed(424242)
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library(readr)
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library(ggplot2)
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data1 <- read_csv('../expanded_data_final.csv',show_col_types = FALSE)
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library(readr)
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library(ggplot2)
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data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
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data2 <- read_csv('../inst_all_packages_full_results.csv')
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data1 <- read_csv('../kk_final_expanded_data_final.csv',show_col_types = FALSE)
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library(readr)
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library(ggplot2)
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library(tidyverse)
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data1 <- read_csv('../kk_final_expanded_data_final.csv',show_col_types = FALSE)
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# this is the file with the lmer multi-level rddAnalysis
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# this is the file with the lmer multi-level rddAnalysis
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library(tidyverse)
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library(tidyverse)
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library(plyr)
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library(plyr)
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@ -229,6 +47,8 @@ windowed_data$week_offset <- windowed_data$week - 27
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#separate out the cleaning d
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#separate out the cleaning d
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all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
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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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mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
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#find some EDA to identify which types of models might be the best for this
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hist(log(all_actions_data$count))
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all_actions_data$logged_count <- log(all_actions_data$count)
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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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all_actions_data$log1p_count <- log1p(all_actions_data$count)
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# 3 rdd in lmer analysis
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# 3 rdd in lmer analysis
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@ -240,172 +60,254 @@ library(optimx)
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library(lattice)
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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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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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optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
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summary(all_model)
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#identifying the quartiles of effect for 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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all_model_ranef <- ranef(all_model, condVar=TRUE)
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dotplot(all_model_ranef)
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dotplot(all_model_ranef)
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df_ranefs <- as.data.frame(all_model_ranef)
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df_ranefs <- as.data.frame(all_model_ranef)
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D_df_ranef <- df_ranefs[df_ranefs$term == "D"]
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D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
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D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
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View(D_df_ranef)
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#below this groups the ranefs
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has_zero <- function(condval, condsd){
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bounds <- condsd * 1.96
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if ((condval - bounds) < 0){
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if ((condval + bounds) > 0) {
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return(1)
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} else {
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return(0)
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}
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} else {
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return(2)
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}
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}
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df_ranefs |>
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mutate(ranef_grouping = has_zero(condval, condsd))
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has_zero <- function(condval, condsd){
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bounds <- condsd * 1.96
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print(bounds)
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if ((condval - bounds) < 0){
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if ((condval + bounds) > 0) {
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return(1)
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} else {
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return(0)
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}
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} else {
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return(2)
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}
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}
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df_ranefs |>
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mutate(ranef_grouping = has_zero(condval, condsd))
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has_zero <- function(condval, condsd){
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bounds <- condsd * 1.96
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print(condval - bounds)
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if ((condval - bounds) < 0){
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if ((condval + bounds) > 0) {
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return(1)
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} else {
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return(0)
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}
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} else {
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return(2)
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}
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}
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df_ranefs |>
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mutate(ranef_grouping = has_zero(condval, condsd))
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has_zero <- function(condval, condsd){
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has_zero <- function(condval, condsd){
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bounds <- condsd * 1.96
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bounds <- condsd * 1.96
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return(ifelse(((condval - bounds) < 0),ifelse(((condval + bounds) > 0), 1, 0), 2))
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return(ifelse(((condval - bounds) < 0),ifelse(((condval + bounds) > 0), 1, 0), 2))
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}
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}
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df_ranefs |>
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mutate(ranef_grouping = has_zero(condval, condsd))
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df_ranefs |>
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mutate(ranef_grouping = has_zero(condval, condsd)) |>
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group_by(ranef_grouping) |>
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summarize(no_rows = length(ranef_grouping))
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df_ranefs |>
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mutate(ranef_grouping = has_zero(condval, condsd)) |>
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group_by(ranef_grouping) |>
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summarize(no_rows = length(as.factor(ranef_grouping)))
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df_ranefs |>
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mutate(ranef_grouping = has_zero(condval, condsd)) |>
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group_by(ranef_grouping) |>
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summarize(no_rows = length(as.factor(ranef_grouping)))
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View(df_ranefs)
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has_zero <- function(condval, condsd){
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bounds <- condsd * 1.96
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return(ifelse(((condval - bounds) < 0),ifelse(((condval + bounds) > 0), 1, 0), 2))
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}
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df_ranefs |>
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mutate(ranef_grouping = has_zero(condval, condsd))
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View(df_ranefs)
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df_ranefs <- df_ranefs |>
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df_ranefs <- df_ranefs |>
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mutate(ranef_grouping = has_zero(condval, condsd))
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mutate(ranef_grouping = has_zero(condval, condsd)) |>
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View(df_ranefs)
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mutate(rank = rank(condval))
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df_ranefs |>
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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$term == "D"),]
|
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
||||||
hist(D_df_ranefs$ranef_grouping)
|
|
||||||
hist(D_df_ranef$ranef_grouping)
|
hist(D_df_ranef$ranef_grouping)
|
||||||
|
D_df_ranef |>
|
||||||
|
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||||
|
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||||
|
geom_bw()
|
||||||
#plot the ranefs
|
#plot the ranefs
|
||||||
library(ggplot2)
|
library(ggplot2)
|
||||||
D_df_ranef |>
|
D_df_ranef |>
|
||||||
ggplot(aes(x=grp, y=condval))
|
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||||
D_df_ranef |>
|
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||||
ggplot(aes(x=grp, y=condval, col = as.factor(ranef_grouping)))
|
geom_bw()
|
||||||
D_df_ranef |>
|
|
||||||
ggplot(aes(x=condsd, y=condval, col = as.factor(ranef_grouping)))
|
|
||||||
D_df_ranef |>
|
|
||||||
ggplot(aes(x=condval, y=condval, col = as.factor(ranef_grouping)))
|
|
||||||
D_df_ranef |>
|
|
||||||
ggplot(aes(x=condval, y=condval, col = as.factor(ranef_grouping))) +
|
|
||||||
geom_point()
|
|
||||||
D_df_ranef |>
|
|
||||||
ggplot(aes(x=grp, y=condval, col = as.factor(ranef_grouping))) +
|
|
||||||
geom_point()
|
|
||||||
df_ranefs <- df_ranefs |>
|
|
||||||
mutate(ranef_grouping = has_zero(condval, condsd))
|
|
||||||
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
|
||||||
hist(D_df_ranef$ranef_grouping)
|
|
||||||
D_df_ranef |>
|
D_df_ranef |>
|
||||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||||
geom_point()
|
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||||
df_ranefs <- df_ranefs |>
|
theme_bw()
|
||||||
mutate(ranef_grouping = has_zero(condval, condsd))
|
#identifying the quartiles of effect for D
|
||||||
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
all_model_ranef <- ranef(all_model, condVar=TRUE)
|
||||||
|
dotplot(all_model_ranef)
|
||||||
|
df_ranefs <- as.data.frame(all_model_ranef)
|
||||||
|
#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 |>
|
df_ranefs <- df_ranefs |>
|
||||||
mutate(ranef_grouping = has_zero(condval, condsd)) |>
|
mutate(ranef_grouping = has_zero(condval, condsd)) |>
|
||||||
mutate(rank = rank(condval))
|
mutate(rank = rank(condval))
|
||||||
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
||||||
D_df_ranef |>
|
D_df_ranef |>
|
||||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
||||||
geom_point()
|
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||||
|
theme_bw()
|
||||||
|
D_df_ranefs <- D_df_ranefs |>
|
||||||
|
mutate(rank = rank(condval))
|
||||||
|
D_df_ranef <- D_df_ranef |>
|
||||||
|
mutate(rank = rank(condval))
|
||||||
D_df_ranef |>
|
D_df_ranef |>
|
||||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
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_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
||||||
D_df_ranef |>
|
theme_bw()
|
||||||
ggplot(aes(x=grp, y=condval, col = as.factor(ranef_grouping))) +
|
#identifying the quartiles of effect for D
|
||||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd)))
|
all_model_blup <- blup(all_model)
|
||||||
D_df_ranef |>
|
all_model_ranef <- ranef(all_model)
|
||||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
View(all_model_ranef)
|
||||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd)))
|
df_ranefs <- as.data.frame(all_model_ranef)
|
||||||
# mrg behavior for this
|
dotplot(all_model_ranef)
|
||||||
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(
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_coef <- coef(all_model)
|
||||||
|
View(all_model_coef)
|
||||||
|
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
||||||
|
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
||||||
|
View(D_df_ranef)
|
||||||
|
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')))
|
||||||
|
all_model_ranef <- ranef(all_model)
|
||||||
|
df_ranefs <- as.data.frame(all_model_ranef)
|
||||||
|
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
||||||
|
View(D_df_ranef)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_variances <- postVar(all_model)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_variances <- vcov(all_model, condVar=TRUE)
|
||||||
|
View(all_model_variances)
|
||||||
|
print(all_model_variances)
|
||||||
|
View(all_model_variances)
|
||||||
|
conditional_variances_random <- lapply(all_model_variances, diag)
|
||||||
|
dotplot(conditional_variances_random)
|
||||||
|
dotplot(conditional_variances_random,
|
||||||
|
col = "blue",
|
||||||
|
pch = 19,
|
||||||
|
main = "Conditional Variances of Random Effects",
|
||||||
|
xlab = "Conditional Variance",
|
||||||
|
ylab = "Random Effect",
|
||||||
|
scales = list(x = list(log = TRUE)),
|
||||||
|
auto.key = list(space = "right"))
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_variances <- vcov(all_model, full=TRUE, condVar=TRUE)
|
||||||
|
View(all_model_variances)
|
||||||
|
summary(all_model)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_variances <- vcov(all_model, full=TRUE, condVar=TRUE)
|
||||||
|
View(all_model_variances)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_variances <- varCorr(all_model)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_variances <- VarCorr(all_model)
|
||||||
|
View(all_model_variances)
|
||||||
|
View(conditional_variances_random)
|
||||||
|
View(all_model_variances)
|
||||||
|
attr(VarCorr(all_model)$upstream_vcs_link, "stddevs")^2
|
||||||
|
values <- attr(VarCorr(all_model)$upstream_vcs_link, "stddevs")^2
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_variances <- vcov(all_model)
|
||||||
|
View(all_model_variances)
|
||||||
|
print(all_model_variances)
|
||||||
|
all_model_ranef <- ranef(all_model)$upstream_vcs_link
|
||||||
|
View(all_model_ranef)
|
||||||
|
all_model_ranef <- cov(ranef(all_model))
|
||||||
|
random_effects <- ranef(all_model)
|
||||||
|
random_effects_variances <- lapply(random_effects$upstream_vcs_link, function(x) {
|
||||||
|
variances <- var(x$D:I(week_offset))
|
||||||
|
return(variances)
|
||||||
|
})
|
||||||
|
variances <- var(x$D)
|
||||||
|
summary_of_all <- summary(all_model)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
variance_components <- summary_of_all$varcor
|
||||||
|
View(variance_components)
|
||||||
|
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')))
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
||||||
#identifying the quartiles of effect for D
|
#identifying the quartiles of effect for D
|
||||||
mrg_model_ranef <- ranef(mrg_model, condVar=TRUE)
|
varcorr_of_all <- VarCorr(all_model)
|
||||||
df_mrg_ranefs <- as.data.frame(mrg_model_ranef)
|
View(varcorr_of_all)
|
||||||
#doing similar random effect analysis for this
|
print(varcorr_of_all)
|
||||||
df_mrg_ranefs <- df_mrg_ranefs |>
|
all_coefficients <- coef(all_model)
|
||||||
mutate(ranef_grouping = has_zero(condval, condsd)) |>
|
all_standard_errors <- sqrt(diag(vcov(all_model)))
|
||||||
mutate(rank = rank(condval))
|
all_conf_intervals <- cbind(coefficients - 1.96 * standard_errors,
|
||||||
D_df_mrg_ranefs <- df_mrg_ranefs[which(df_mrg_ranefs$term == "D"),]
|
coefficients + 1.96 * standard_errors)
|
||||||
D_df_mrg_ranefs |>
|
all_conf_intervals <- cbind(all_coefficients - 1.96 * all_standard_errors,
|
||||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
all_coefficients + 1.96 * all_standard_errors)
|
||||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd)))
|
View(all_coefficients)
|
||||||
D_df_ranef |>
|
View(conditional_variances_random)
|
||||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
View(all_coefficients)
|
||||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd)))
|
#identifying the quartiles of effect for D
|
||||||
|
confint(all_model)
|
||||||
|
all_coefficients <- coef(all_model)
|
||||||
|
all_standard_errors <- sqrt(diag(vcov(all_model)))[3]
|
||||||
|
all_standard_errors <- sqrt(diag(vcov(all_model)))
|
||||||
|
all_standard_errors <- sqrt(diag(vcov(all_model)))[4]
|
||||||
|
all_standard_errors <- sqrt(diag(vcov(all_model)))[5]
|
||||||
|
all_standard_errors <- sqrt(diag(vcov(all_model)))[6]
|
||||||
|
all_standard_errors <- sqrt(diag(vcov(all_model)))[1]
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_ranef <- ranef(all_model, condVar=TRUE)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_ranef_condvar <- ranef(all_model, condVar = TRUE)
|
||||||
|
all_model_ranef <- ranef(all_model, condVar = FALSE)
|
||||||
|
View(all_model_ranef)
|
||||||
|
View(all_model_ranef_condvar)
|
||||||
|
dotplot(all_model_ranef)
|
||||||
|
dotplot(all_model_ranef_condvar)
|
||||||
|
View(all_model_ranef_condvar)
|
||||||
|
all_model_ranef_condvar[["upstream_vcs_link"]][["D"]]
|
||||||
|
View(all_model_ranef)
|
||||||
|
all_model_ranef_condvar$upstream_vcs_link
|
||||||
|
all_model_ranef_condvar$upstream_vcs_link$D
|
||||||
|
conditional_variances <- diag(vcov(model)$upstream_vcs_link$D)
|
||||||
|
conditional_variances <- diag(vcov(all_model)$upstream_vcs_link$D)
|
||||||
|
conditional_variances <- diag(vcov(all_model))
|
||||||
|
conditional_variances <- vcov(all_model)
|
||||||
|
View(conditional_variances)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_ranef_condvar <- var(ranef(all_model, condVar = TRUE))
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_ranef_condvar <- var(ranef(all_model, condVar = TRUE)$upstream_vcs_link$D)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_ranef_condvar <- ranef(all_model, condVar = TRUE)$upstream_vcs_link$D
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_ranef_condvar <- ranef(all_model, condVar = TRUE)
|
||||||
|
View(all_model_ranef_condvar)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_ranef_condvar <- ranef(all_model, condVar = TRUE)
|
||||||
|
View(all_model_ranef_condvar)
|
||||||
|
attr(all_model_ranef_condvar$upstream_vcs_link$D, "condVar")
|
||||||
|
attr(all_model_ranef_condvar$upstream_vcs_link, "condVar")
|
||||||
|
df_ranefs <- as.data.frame(all_model_ranef_condvar)
|
||||||
|
View(df_ranefs)
|
||||||
|
View(all_model_ranef_condvar)
|
||||||
|
#all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
||||||
|
# optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
||||||
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_ranef_condvar <- ranef(all_model, condVar = TRUE)
|
||||||
|
attr(all_model_ranef_condvar$upstream_vcs_link, "condVar")
|
||||||
|
#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')))
|
||||||
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=TRUE)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_ranef_condvar <- ranef(all_model, condVar = TRUE)
|
||||||
|
attr(all_model_ranef_condvar$upstream_vcs_link, "condVar")
|
||||||
|
df_ranefs <- as.data.frame(all_model_ranef_condvar)
|
||||||
|
View(df_ranefs)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_ranef_condvar <- ranef(all_model, condVar = TRUE)
|
||||||
|
View(all_model_ranef_condvar)
|
||||||
|
all_model_ranef <- ranef(all_model, condVar = FALSE)
|
||||||
|
View(all_model_ranef_condvar)
|
||||||
|
View(all_model_ranef_condvar[["upstream_vcs_link"]])
|
||||||
|
all_model_ranef_condvar[["upstream_vcs_link"]][["D"]]
|
||||||
|
View(all_model_ranef)
|
||||||
|
df_rn_no_cv <- as.data.frame(all_model_ranef)
|
||||||
|
View(df_rn_no_cv)
|
||||||
|
View(df_ranefs)
|
||||||
|
attr(all_model_ranef_condvar$upstream_vcs_link, "postVar")
|
||||||
|
attr(all_model_ranef_condvar$upstream_vcs_link$D, "postVar")
|
||||||
|
attr(all_model_ranef_condvar$upstream_vcs_link, "postVar")
|
||||||
|
attr(all_model_ranef_condvar$upstream_vcs_link, "postVar")[[4]]
|
||||||
|
attr(all_model_ranef_condvar$upstream_vcs_link, "postVar")[[3]]
|
||||||
|
attr(all_model_ranef_condvar$upstream_vcs_link, "postVar")[[2]]
|
||||||
|
attr(all_model_ranef_condvar$upstream_vcs_link, "postVar")[4]
|
||||||
|
attr(all_model_ranef_condvar$upstream_vcs_link, "postVar")
|
||||||
|
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')))
|
||||||
|
isSingular(all_model)
|
||||||
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
||||||
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
||||||
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset| upstream_vcs_link), data=all_actions_data, REML=FALSE)
|
||||||
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (I:(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
|
||||||
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
|
||||||
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D| upstream_vcs_link), data=all_actions_data, REML=FALSE)
|
||||||
|
summary_of_all <- summary(all_model)
|
||||||
|
summary(all_model)
|
||||||
|
#identifying the quartiles of effect for D
|
||||||
|
all_model_ranef_condvar <- ranef(all_model, condVar = TRUE)
|
||||||
|
attr(all_model_ranef_condvar$upstream_vcs_link, "postVar")
|
||||||
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
||||||
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
||||||
|
# this is the file with the lmer multi-level rddAnalysis
|
||||||
library(tidyverse)
|
library(tidyverse)
|
||||||
library(plyr)
|
library(plyr)
|
||||||
#get the contrib data instead
|
# 0 loading the readme data in
|
||||||
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
|
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
|
||||||
contrib_df <- read_csv("../final_data/deb_contrib_did.csv")
|
readme_df <- read_csv("../final_data/deb_readme_did.csv")
|
||||||
#some preprocessing and expansion
|
# 1 preprocessing
|
||||||
|
#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
|
||||||
col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
|
col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
|
||||||
contrib_df <- contrib_df[,col_order]
|
readme_df <- readme_df[,col_order]
|
||||||
contrib_df$ct_before_all <- str_split(gsub("[][]","", contrib_df$before_all_ct), ", ")
|
readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
|
||||||
contrib_df$ct_after_all <- str_split(gsub("[][]","", contrib_df$after_all_ct), ", ")
|
readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
|
||||||
contrib_df$ct_before_mrg <- str_split(gsub("[][]","", contrib_df$before_mrg_ct), ", ")
|
readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
|
||||||
contrib_df$ct_after_mrg <- str_split(gsub("[][]","", contrib_df$after_mrg_ct), ", ")
|
readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
|
||||||
drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
|
drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
|
||||||
contrib_df = contrib_df[,!(names(contrib_df) %in% drop)]
|
readme_df = readme_df[,!(names(readme_df) %in% drop)]
|
||||||
# 2 some expansion needs to happens for each project
|
# 2 some expansion needs to happens for each project
|
||||||
expand_timeseries <- function(project_row) {
|
expand_timeseries <- function(project_row) {
|
||||||
longer <- project_row |>
|
longer <- project_row |>
|
||||||
@ -419,9 +321,9 @@ longer$count <- as.numeric(longer$count)
|
|||||||
#longer <- longer[which(longer$observation_type == "all"),]
|
#longer <- longer[which(longer$observation_type == "all"),]
|
||||||
return(longer)
|
return(longer)
|
||||||
}
|
}
|
||||||
expanded_data <- expand_timeseries(contrib_df[1,])
|
expanded_data <- expand_timeseries(readme_df[1,])
|
||||||
for (i in 2:nrow(contrib_df)){
|
for (i in 2:nrow(readme_df)){
|
||||||
expanded_data <- rbind(expanded_data, expand_timeseries(contrib_df[i,]))
|
expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
|
||||||
}
|
}
|
||||||
#filter out the windows of time that we're looking at
|
#filter out the windows of time that we're looking at
|
||||||
window_num <- 8
|
window_num <- 8
|
||||||
@ -434,79 +336,177 @@ windowed_data$week_offset <- windowed_data$week - 27
|
|||||||
#separate out the cleaning d
|
#separate out the cleaning d
|
||||||
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
|
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
|
||||||
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
|
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
|
||||||
all_actions_data$logged_count <- log(all_actions_data$count)
|
|
||||||
all_actions_data$log1p_count <- log1p(all_actions_data$count)
|
all_actions_data$log1p_count <- log1p(all_actions_data$count)
|
||||||
# now for merge
|
# 3 rdd in lmer analysis
|
||||||
mrg_actions_data$logged_count <- log(mrg_actions_data$count)
|
# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
||||||
mrg_actions_data$log1p_count <- log1p(mrg_actions_data$count)
|
# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
|
||||||
#TKTK ---------------------
|
|
||||||
#imports for models
|
|
||||||
library(lme4)
|
library(lme4)
|
||||||
|
# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
|
||||||
library(optimx)
|
library(optimx)
|
||||||
library(lattice)
|
library(lattice)
|
||||||
#models -- TKTK need to be fixed
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
||||||
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
|
||||||
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
||||||
summary(all_model)
|
|
||||||
#identifying the quartiles of effect for D
|
#identifying the quartiles of effect for D
|
||||||
all_model_ranef <- ranef(all_model)
|
mmcm = coef(all_model)$upstream_vcs_link[, 1]
|
||||||
#d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
|
vcov.vals = as.data.frame(VarCorr(all_model))
|
||||||
#d_effect_ranef_all$quartile <- ntile(d_effect_ranef_all$condval, 4)
|
View(vcov.vals)
|
||||||
df_ranefs <- as.data.frame(all_model_ranef)
|
#identifying the quartiles of effect for D
|
||||||
has_zero <- function(condval, condsd){
|
mmcm = coef(all_model)$upstream_vcs_link
|
||||||
bounds <- condsd * 1.96
|
View(mmcm)
|
||||||
return(ifelse(((condval - bounds) < 0),ifelse(((condval + bounds) > 0), 1, 0), 2))
|
summary(all_model)$coef[,2]
|
||||||
|
View(mmcm)
|
||||||
|
variance_components <- VarCorr(all_model)
|
||||||
|
group_variance <- attr(variance_components$upstream_vcs_link, "stddev")^2
|
||||||
|
View(mmcm)
|
||||||
|
fixef(all())
|
||||||
|
fixef(all_model
|
||||||
|
summary(all_model)$coef[,2]
|
||||||
|
fixef(all_model)
|
||||||
|
fixed_impacts = fixef(all_model)
|
||||||
|
dotplot(all_model_ranef_condvar)
|
||||||
|
all_model_ranef_condvar <- ranef(all_model, condVar = TRUE)
|
||||||
|
dotplot(all_model_ranef_condvar)
|
||||||
|
broom.mixed::tidy(all_model, effects = "ran_vals", conf.int = TRUE)
|
||||||
|
test <- broom.mixed::tidy(all_model, effects = "ran_vals", conf.int = TRUE)
|
||||||
|
View(test)
|
||||||
|
all_gmodel <- glmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, family = Gamma)
|
||||||
|
all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, family = Gamma)
|
||||||
|
all_gmodel <- glmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, family=poisson)
|
||||||
|
all_gmodel <- glmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D | upstream_vcs_link), data=all_actions_data, family=poisson)
|
||||||
|
all_gmodel <- glmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D | upstream_vcs_link), data=all_actions_data, family=binomial)
|
||||||
|
all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D | upstream_vcs_link), data=all_actions_data, family=binomial)
|
||||||
|
df_ranefs <- as.data.frame(all_model_ranef_condvar)
|
||||||
|
all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D | upstream_vcs_link), data=all_actions_data, family=binomial)
|
||||||
|
all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (1 | upstream_vcs_link), data=all_actions_data, family=poisson)
|
||||||
|
all_model_ranef_condvar <- ranef(all_gmodel, condVar = TRUE)
|
||||||
|
all_model_ranef_condvar <- ranef(all_model, condVar = TRUE)
|
||||||
|
all_gmodel_ranef_condvar <- ranef(all_gmodel, condVar = TRUE)
|
||||||
|
View(all_gmodel_ranef_condvar)
|
||||||
|
test <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
|
||||||
|
View(test)
|
||||||
|
all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)+ scaled_project_age | upstream_vcs_link), data=all_actions_data)
|
||||||
|
test <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
|
||||||
|
View(test)
|
||||||
|
summary(all_gmodel)
|
||||||
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=TRUE, control = lmerControl(
|
||||||
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
||||||
|
test <- broom.mixed::tidy(all_model, effects = "ran_vals", conf.int = TRUE)
|
||||||
|
View(test)
|
||||||
|
test_condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
|
||||||
|
View(test_condvals)
|
||||||
|
test_glmer_ranef_D <- test_condvals [which(test_condvals $term == "D"),]
|
||||||
|
View(test_glmer_ranef_D)
|
||||||
|
test_glmer_ranef_D <- test_condvals [which(test_condvals $term == "D"),]
|
||||||
|
has_zero <- function(estimate, low, high){
|
||||||
|
return(ifelse((low < 0),ifelse((high > 0), 1, 0), 2))
|
||||||
}
|
}
|
||||||
df_ranefs <- df_ranefs |>
|
test_glmer_ranef_D <- test_glmer_ranef_D |>
|
||||||
mutate(ranef_grouping = has_zero(condval, condsd)) |>
|
mutate(ranef_grouping = has_zero(estimate, conf.low, conf.high)) |>
|
||||||
mutate(rank = rank(condval))
|
mutate(rank = rank(estimate))
|
||||||
wo_df_ranef <- df_ranefs[which(df_ranefs$term == "week_offset"),]
|
test_glmer_ranef_D |>
|
||||||
library(ggplot2)
|
|
||||||
wo_df_ranef |>
|
|
||||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
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_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||||
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()
|
theme_bw()
|
||||||
wo_df_ranef |>
|
test_glmer_ranef_D |>
|
||||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||||
geom_pointrange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||||
theme_bw()
|
theme_bw()
|
||||||
wo_df_ranef |>
|
summary(all_gmodel)
|
||||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data)
|
||||||
geom_crossbar(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd)), width=0.2) +
|
summary(all_gmodel)
|
||||||
|
test_condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
|
||||||
|
test_glmer_ranef_D <- test_condvals [which(test_condvals $term == "D"),]
|
||||||
|
has_zero <- function(estimate, low, high){
|
||||||
|
return(ifelse((low < 0),ifelse((high > 0), 1, 0), 2))
|
||||||
|
}
|
||||||
|
test_glmer_ranef_D <- test_glmer_ranef_D |>
|
||||||
|
mutate(ranef_grouping = has_zero(estimate, conf.low, conf.high)) |>
|
||||||
|
mutate(rank = rank(estimate))
|
||||||
|
test_glmer_ranef_D |>
|
||||||
|
ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||||
|
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||||
theme_bw()
|
theme_bw()
|
||||||
wo_df_ranef |>
|
View(test_glmer_ranef_D)
|
||||||
ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
View(test_condvals)
|
||||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data)
|
||||||
|
summary(all_gmodel)
|
||||||
|
test_condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
|
||||||
|
View(test_condvals)
|
||||||
|
all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, family = Poisson)
|
||||||
|
all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, family = poisson)
|
||||||
|
summary(all_gmodel)
|
||||||
|
all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D | upstream_vcs_link), data=all_actions_data, family = poisson)
|
||||||
|
summary(all_gmodel)
|
||||||
|
test_condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
|
||||||
|
test_glmer_ranef_D <- test_condvals [which(test_condvals $term == "D"),]
|
||||||
|
has_zero <- function(estimate, low, high){
|
||||||
|
return(ifelse((low < 0),ifelse((high > 0), 1, 0), 2))
|
||||||
|
}
|
||||||
|
test_glmer_ranef_D <- test_glmer_ranef_D |>
|
||||||
|
mutate(ranef_grouping = has_zero(estimate, conf.low, conf.high)) |>
|
||||||
|
mutate(rank = rank(estimate))
|
||||||
|
test_glmer_ranef_D |>
|
||||||
|
ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||||
|
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||||
theme_bw()
|
theme_bw()
|
||||||
wo_df_ranef |>
|
all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, family = poisson)
|
||||||
ggplot(aes(x=grp, y=condval, col = as.factor(ranef_grouping))) +
|
all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, nAGQ=0, family = poisson)
|
||||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
summary(all_gmodel)
|
||||||
|
test_condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
|
||||||
|
test_glmer_ranef_D <- test_condvals [which(test_condvals $term == "D"),]
|
||||||
|
has_zero <- function(estimate, low, high){
|
||||||
|
return(ifelse((low < 0),ifelse((high > 0), 1, 0), 2))
|
||||||
|
}
|
||||||
|
test_glmer_ranef_D <- test_glmer_ranef_D |>
|
||||||
|
mutate(ranef_grouping = has_zero(estimate, conf.low, conf.high)) |>
|
||||||
|
mutate(rank = rank(estimate))
|
||||||
|
test_glmer_ranef_D |>
|
||||||
|
ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||||
|
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||||
theme_bw()
|
theme_bw()
|
||||||
wo_df_ranef <- wo_df_ranef |>
|
variance(all_actions_data$log1p_count)
|
||||||
arrange(condval)
|
var(all_actions_data$log1p_count)
|
||||||
wo_df_ranef |>
|
mean (all_actions_data$log1p_count)
|
||||||
ggplot(aes(x=grp, y=condval, col = as.factor(ranef_grouping))) +
|
#all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, nAGQ=0, family = poisson)
|
||||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link),data=all_actions_data)
|
||||||
theme_bw()
|
#all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, nAGQ=0, family = poisson)
|
||||||
View(wo_df_ranef)
|
all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link),
|
||||||
df_ranefs <- df_ranefs |>
|
control=glmerControl(optimizer="bobyqa",
|
||||||
mutate(ranef_grouping = has_zero(condval, condsd))
|
optCtrl=list(maxfun=2e5)), data=all_actions_data)
|
||||||
wo_df_ranef <- df_ranefs[which(df_ranefs$term == "week_offset"),]
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#all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, nAGQ=0, family = poisson)
|
||||||
wo_df_ranef <- wo_df_ranef |>
|
all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D | upstream_vcs_link),
|
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mutate(rank = rank(condval))
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control=glmerControl(optimizer="bobyqa",
|
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library(ggplot2)
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optCtrl=list(maxfun=2e5)), data=all_actions_data)
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wo_df_ranef |>
|
summary(all_gmodel)
|
||||||
ggplot(aes(x=grp, y=condval, col = as.factor(ranef_grouping))) +
|
test_condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
|
||||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
test_glmer_ranef_D <- test_condvals [which(test_condvals $term == "D"),]
|
||||||
theme_bw()
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has_zero <- function(estimate, low, high){
|
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wo_df_ranef |>
|
return(ifelse((low < 0),ifelse((high > 0), 1, 0), 2))
|
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ggplot(aes(x=rank, y=condval, col = as.factor(ranef_grouping))) +
|
}
|
||||||
geom_linerange(aes(ymin= condval - (1.96 * condsd), ymax= condval + (1.96 * condsd))) +
|
test_glmer_ranef_D <- test_glmer_ranef_D |>
|
||||||
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mutate(ranef_grouping = has_zero(estimate, conf.low, conf.high)) |>
|
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mutate(rank = rank(estimate))
|
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test_glmer_ranef_D |>
|
||||||
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ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||||
|
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||||
theme_bw()
|
theme_bw()
|
||||||
|
#all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, nAGQ=0, family = poisson)
|
||||||
|
#all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset) | upstream_vcs_link),
|
||||||
|
# control=glmerControl(optimizer="bobyqa",
|
||||||
|
# optCtrl=list(maxfun=2e5)), data=all_actions_data)
|
||||||
|
all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset) | upstream_vcs_link), data=all_actions_data)
|
||||||
|
#all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, nAGQ=0, family = poisson)
|
||||||
|
#all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset) | upstream_vcs_link),
|
||||||
|
# control=glmerControl(optimizer="bobyqa",
|
||||||
|
# optCtrl=list(maxfun=2e5)), data=all_actions_data)
|
||||||
|
all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset) | upstream_vcs_link), data=all_actions_data, verbose=TRUE)
|
||||||
|
#all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, nAGQ=0, family = poisson)
|
||||||
|
#all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset) | upstream_vcs_link),
|
||||||
|
# control=glmerControl(optimizer="bobyqa",
|
||||||
|
# optCtrl=list(maxfun=2e5)), data=all_actions_data)
|
||||||
|
all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset) | upstream_vcs_link), data=all_actions_data)
|
||||||
|
#all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, nAGQ=0, family = poisson)
|
||||||
|
#all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset) | upstream_vcs_link),
|
||||||
|
# control=glmerControl(optimizer="bobyqa",
|
||||||
|
# optCtrl=list(maxfun=2e5)), data=all_actions_data)
|
||||||
|
all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D | upstream_vcs_link), data=all_actions_data)
|
||||||
|
@ -63,18 +63,39 @@ all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(
|
|||||||
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
||||||
summary_of_all <- summary(all_model)
|
summary_of_all <- summary(all_model)
|
||||||
#identifying the quartiles of effect for D
|
#identifying the quartiles of effect for D
|
||||||
|
mmcm = coef(all_model)$upstream_vcs_link
|
||||||
|
fixed_impacts = fixef(all_model)
|
||||||
|
summary(all_model)$coef[,2]
|
||||||
|
variance_components <- VarCorr(all_model)
|
||||||
all_model_ranef_condvar <- ranef(all_model, condVar = TRUE)
|
all_model_ranef_condvar <- ranef(all_model, condVar = TRUE)
|
||||||
all_model_ranef <- ranef(all_model, condVar = FALSE)
|
dotplot(all_model_ranef_condvar)
|
||||||
|
test <- broom.mixed::tidy(all_model, effects = "ran_vals", conf.int = TRUE)
|
||||||
attr(all_model_ranef_condvar$upstream_vcs_link, "postVar")
|
attr(all_model_ranef_condvar$upstream_vcs_link, "postVar")
|
||||||
|
|
||||||
all_coefficients <- coef(all_model)
|
all_coefficients <- coef(all_model)
|
||||||
all_standard_errors <- sqrt(diag(vcov(all_model)))[1]
|
all_standard_errors <- sqrt(diag(vcov(all_model)))[1]
|
||||||
#all_conf_intervals <- cbind(all_coefficients - 1.96 * all_standard_errors,
|
|
||||||
# all_coefficients + 1.96 * all_standard_errors)
|
|
||||||
|
|
||||||
df_ranefs <- as.data.frame(all_model_ranef_condvar)
|
var(all_actions_data$log1p_count) # 1.125429
|
||||||
df_rn_no_cv <- as.data.frame(all_model_ranef)
|
mean (all_actions_data$log1p_count) # 0.6426873
|
||||||
D_df_ranef <- df_ranefs[which(df_ranefs$term == "D"),]
|
|
||||||
|
#all_gmodel <- glmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, nAGQ=0, family = poisson)
|
||||||
|
#all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset) | upstream_vcs_link),
|
||||||
|
# control=glmerControl(optimizer="bobyqa",
|
||||||
|
# optCtrl=list(maxfun=2e5)), data=all_actions_data)
|
||||||
|
all_gmodel <- glmer.nb(count ~ D * I(week_offset)+ scaled_project_age + (D | upstream_vcs_link), data=all_actions_data)
|
||||||
|
summary(all_gmodel)
|
||||||
|
test_condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
|
||||||
|
test_glmer_ranef_D <- test_condvals [which(test_condvals $term == "D"),]
|
||||||
|
has_zero <- function(estimate, low, high){
|
||||||
|
return(ifelse((low < 0),ifelse((high > 0), 1, 0), 2))
|
||||||
|
}
|
||||||
|
test_glmer_ranef_D <- test_glmer_ranef_D |>
|
||||||
|
mutate(ranef_grouping = has_zero(estimate, conf.low, conf.high)) |>
|
||||||
|
mutate(rank = rank(estimate))
|
||||||
|
test_glmer_ranef_D |>
|
||||||
|
ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||||
|
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||||
|
theme_bw()
|
||||||
#below this groups the ranefs
|
#below this groups the ranefs
|
||||||
"""
|
"""
|
||||||
has_zero <- function(condval, condsd){
|
has_zero <- function(condval, condsd){
|
||||||
|
Loading…
Reference in New Issue
Block a user