# a) the basic things, in a table:
#   Condition           Sample Size       mean    standard deviation  standard error
# Immediately after       2               48.705      1.534422          1.085
# One day after           2               41.955      2.128391          1.505
# Three days after        2               21.795      0.7707464         0.545
# Five days after         2               12.415      1.081873          0.765
# Seven days after        2               8.32        0.2687006         0.19
# b) do a one way anova based on the data, like the last homework
grp <- c(1,1,2,2,3,3,4,4,5,5)
results <- aov(resp~factor(grp))
anova(results)
# c) summarize the data and the means w a plot, boxplot
means <- c(48.705, 41.955, 21.795, 12.415, 8.32)
# c) summarize the data and the means w a plot, boxplot
boxplot(results)
# c) summarize the data and the means w a plot, boxplot
boxplot(resp)
# c) summarize the data and the means w a plot, boxplot
boxplot(resp)
# c) summarize the data and the means w a plot, boxplot
boxplot(resp~grp)
ALevels <- c(3.36, 3.34, 3.28, 3.20, 3.26, 3.16, 3.25, 3.36, 3.01, 2.92)
ELevels <- c(94.6, 96.0, 95.7, 93.2, 97.4, 94.3, 95.0, 97.7, 92.3, 95.1)
Aresults <- aov(Alevels~factor(grp))
ALevels <- c(3.36, 3.34, 3.28, 3.20, 3.26, 3.16, 3.25, 3.36, 3.01, 2.92)
ELevels <- c(94.6, 96.0, 95.7, 93.2, 97.4, 94.3, 95.0, 97.7, 92.3, 95.1)
Aresults <- aov(Alevels~factor(grp))
ALevels <- c(3.36, 3.34, 3.28, 3.20, 3.26, 3.16, 3.25, 3.36, 3.01, 2.92)
ELevels <- c(94.6, 96.0, 95.7, 93.2, 97.4, 94.3, 95.0, 97.7, 92.3, 95.1)
Aresults <- aov(ALevels~factor(grp))
Eresults <- aov(ELevels~factor(grp))
# Vitamin A Anova:
anova(Aresults)
# Vimain E Anova:
anova(Eresults)
# 12.10
# four groups, how do nemaotodes impact plant growth
# a)
zero_nema <- c(10.8, 9.1, 13.5, 9.2)
thousand_name <-c(11.1, 11.1, 8.2, 11.3)
thousand_nema <-c(11.1, 11.1, 8.2, 11.3)
fthousand_nema <- c(5.4, 4.6, 7.4, 5.0)
tthousand_nema <- c(5.8, 5.3, 3.2, 7.5)
mean(zero_nema)
sd(zero_nema)
mean(thousand_nema)
sd(thousand_name)
mean(fthousand_nema)
sd(fthousand_nema)
mean(tthousand_nema)
sd(tthousand_nema)
# Table
# Nematodes       Means       StdDev
#   0             10.65         2.053452
# 1,000           10.425        1.486327
# 5,000           5.6           1.243651
# 10,000          5.45          1.771064
nema_means <- c(10.65, 10.425, 5.6, 5.45)
barplot(nema_means)
# c)
groupings <- c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4)
resp <- c(zero_nema, thousand_nema, fthousand_nema, tthousand_nema)
results <- aov(resp~factor(groupings))
anova(results)
# 12.5
# do piano lessons improve spacial temporal
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)
singing <- c(1, -1, 0, 1, -4, 0, 0, 1, 0, -1)
computer <- c(0, 1, 1, -3, -2, 4, -1, 2, 4, 2,2, 2, -3, -3, 0, 2, 0, -1, 3, -1 )
none <- c(5, -1, 7, 0, 4, 0, 2, 1, -6, 0, 2, -1, 0, -2)
size(piano)
length(piano)
mean(piano)
sd(piano)
sd(piano)/sqrt(lenth(piano))
sd(piano)/sqrt(length(piano))
length(singing)
mean(singing)
sd(singing)
sd(signing)/sqrt(length(singing))
sd(singing)/sqrt(length(singing))
length(computer)
mean(computer)
sd(computer)
sd(computer)/sqrt(length(computer))
length(none)
mean(none)
sd(none)
sd(none)/sqrt(14)
# a) make a table given the sample size
# Table:
#   Lessons     Size    Mean    Standard Dev    Standard Error
#   Piano       34      3.617647  3.055196        0.5239618
#   Singing     10      -0.3      1.494434        0.4725816
#   Computer    20      0.45      2.21181         0.4945758
#   None        14      0.7857143 3.190818        0.8527819
# b)
# H0: The spatial-temporal reasoning test results across different lesson groups will be statistically equivalent.
# Ha: For at least one lesson group, the results of the reasoning test will be statistically different.
data_panel <- data.frame(
Y=c(piano, singing, computer, none),
Site = factor(rep(c("piano", "singing", "computer", "none"), times=c(length(piano), length(computer), length(singing), length(none))))
)
data_panel
tempt <- aov(Y~Site, data=data_panel)
anova(tempt)
# 12.6
TukeyHSD(tempt)
# Summary: Looking at the TukeyHSD results, there are some interesting notes in
# where statistically significant variance lies. If we immediately discard the
# comparisons with large p-values, we are left with three statistically significant
# ones. One is that students with piano lessons do better than computer lesson learners
# by an average of 3.5 points, another is that piano outperforms no lessons by about 2.8 points
# and lastly that singing underperforms piano by about 3.3 points. While this
# statistical tooling is useful for proving the significance of these differences in
# performance, we can also evaluate
means <- c(mean(piano), mean(singing), mean(computer), mean(none))
barplot(means)
# (1) - Get the pilot data and clean it
#source('~/Research/tor_wikipedia_edits/handcoded_edits/inter_coder_reliability_ns0.R')
#source ('/data/users/mgaughan/kkex_data_110823_3')
data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
library(readr)
library(ggplot2)
# (1) - Get the pilot data and clean it
#source('~/Research/tor_wikipedia_edits/handcoded_edits/inter_coder_reliability_ns0.R')
#source ('/data/users/mgaughan/kkex_data_110823_3')
data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
data2 <- read_csv('../inst_all_packages_full_results.csv')
# (1) - Get the pilot data and clean it
#source('~/Research/tor_wikipedia_edits/handcoded_edits/inter_coder_reliability_ns0.R')
#source ('/data/users/mgaughan/kkex_data_110823_3')
data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
library(readr)
library(ggplot2)
# (1) - Get the pilot data and clean it
#source('~/Research/tor_wikipedia_edits/handcoded_edits/inter_coder_reliability_ns0.R')
#source ('/data/users/mgaughan/kkex_data_110823_3')
data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
data1 <- read_csv('../expanded_data_final.csv',show_col_types = FALSE)
# Use pilot project data to calculate power of a full study through simulation
#
# Parts:
# (0) - Setup
# (1) - Get the pilot data and clean it
# (2) - Run the model on the pilot data and extract effects
# (3) - Set up and run the simulation
# ====> Set variables at the arrows <====
#
##############################################################################
rm(list=ls())
set.seed(424242)
library(readr)
library(ggplot2)
data1 <- read_csv('../expanded_data_final.csv',show_col_types = FALSE)
set.seed(424242)
library(readr)
library(ggplot2)
data1 <- read_csv('../expanded_data_final.csv',show_col_types = FALSE)
#shows the cross-age downward slopes for all underproduction averages in the face of MMT
g3 <- ggplot(data1, aes(x=mmt, y=underproduction_mean)) +
geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor),
method='lm', formula= y~x) +
xlab("MMT") +
ylab("Underproduction Factor") +
theme_bw()
g3
library(readr)
library(ggplot2)
data1 <- read_csv('../expanded_data_final.csv',show_col_types = FALSE)
mean(data1$milestone_count)
data1$mmt <- (((data1$collaborators * 2)+ data1$contributors) / (data1$contributors + data1$collaborators)) - 1
mean(data1$mmt)
rm(list=ls())
set.seed(424242)
library(readr)
library(ggplot2)
data1 <- read_csv('../expanded_data_final.csv',show_col_types = FALSE)
library(readr)
library(ggplot2)
data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
data2 <- read_csv('../inst_all_packages_full_results.csv')
data1 <- read_csv('../kk_final_expanded_data_final.csv',show_col_types = FALSE)
library(readr)
library(ggplot2)
library(tidyverse)
data1 <- read_csv('../kk_final_expanded_data_final.csv',show_col_types = FALSE)
# this is the file with the lmer multi-level rddAnalysis
library(tidyverse)
library(plyr)
# 0 loading the readme data in
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
readme_df <- read_csv("../final_data/deb_readme_did.csv")
# 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")
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$observation_type == "mrg"),]
all_actions_data$logged_count <- log(all_actions_data$count)
all_actions_data$log1p_count <- log1p(all_actions_data$count)
# 3 rdd in lmer analysis
# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
library(lme4)
# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
library(optimx)
library(lattice)
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
#identifying the quartiles of effect for D
all_model_ranef <- ranef(all_model, condVar=TRUE)
dotplot(all_model_ranef)
df_ranefs <- as.data.frame(all_model_ranef)
D_df_ranef <- df_ranefs[df_ranefs$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
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
contrib_df <- read_csv("../final_data/deb_contrib_did.csv")
#some preprocessing and expansion
col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
contrib_df <- contrib_df[,col_order]
contrib_df$ct_before_all <- str_split(gsub("[][]","", contrib_df$before_all_ct), ", ")
contrib_df$ct_after_all <- str_split(gsub("[][]","", contrib_df$after_all_ct), ", ")
contrib_df$ct_before_mrg <- str_split(gsub("[][]","", contrib_df$before_mrg_ct), ", ")
contrib_df$ct_after_mrg <- str_split(gsub("[][]","", contrib_df$after_mrg_ct), ", ")
drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
contrib_df = contrib_df[,!(names(contrib_df) %in% drop)]
# 2 some expansion needs to happens for each project
expand_timeseries <- function(project_row) {
longer <- project_row |>
pivot_longer(cols = starts_with("ct"),
names_to = "window",
values_to = "count") |>
unnest(count)
longer$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(contrib_df[1,])
for (i in 2:nrow(contrib_df)){
expanded_data <- rbind(expanded_data, expand_timeseries(contrib_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$logged_count <- log(all_actions_data$count)
all_actions_data$log1p_count <- log1p(all_actions_data$count)
# now for merge
mrg_actions_data$logged_count <- log(mrg_actions_data$count)
mrg_actions_data$log1p_count <- log1p(mrg_actions_data$count)
#TKTK ---------------------
#imports for models
library(lme4)
library(optimx)
library(lattice)
#models -- TKTK need to be fixed
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')))
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$condval, 4)
df_ranefs <- as.data.frame(all_model_ranef)
has_zero <- function(condval, condsd){
bounds <- condsd * 1.96
return(ifelse(((condval - bounds) < 0),ifelse(((condval + bounds) > 0), 1, 0), 2))
}
df_ranefs <- df_ranefs |>
mutate(ranef_grouping = has_zero(condval, condsd)) |>
mutate(rank = rank(condval))
wo_df_ranef <- df_ranefs[which(df_ranefs$term == "week_offset"),]
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)))
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()