nodeLabels = c("Frequency of\n attendance",
"Frequency of\n praying",
"Belief in god",
"Belief in hell",
"Exclusivity of\n religion",
"Importance of\n religion",
"Self-assessed\n religiosity",
"Importance of god",
"Membership in\n organization",
"Confidence in\n institutions",
"Belonging to\n denomination",
"Behavior", "Beliefs", "Feelings", "Communal"))
library(MplusAutomation)
runModels("/Users/alisa/Desktop/mus sep",
recursive = T)
setwd("/Users/alisa/Desktop/Research/Papers/ESS")
# Data and libraries
library(foreign)
library(car)
library(lavaan)
library(plyr)
library(dplyr)
library(readxl)
library(kableExtra)
library(MIE)
library(LittleHelpers)
library(gdata)
library(DataCombine)
library(ggplot2)
library(stringr)
library(tidyr)
library(gridExtra)
library(grid)
library(formattable)
source("ESS_functions.R")
# Downloading integrated and country specific data files, select and recode variables
## ESS 2 - country specific file for Italy
## ESS 3 - country specific file for Latvia and Romania
## ESS 4 - country specific file for Austria and Lithuania
## ESS 5 - country specific file for Austria
## ESS 7 - country specific file for Russia
## ESS 9 - country specific file for Russia
## rlgatnd - How often attend religious services apart from special occasions
## pray - How often pray apart from at religious services
## rlgdgr - How religious are you
ESS_files <- c(
"ESS1e06_6.sav", "ESS2e03_6.sav", "ESS2IT.sav", "ESS3e03_7.sav", "ESS3LV.sav",
"ESS3RO.sav", "ESS4e04_5.sav", "ESS4LT.sav", "ESS4AT.sav", "ESS5e03_4.sav",
"ESS5ATe1_1.sav", "ESS6e02_4.sav", "ESS7e02_2.sav", "Russian_social_survey_round_7.sav",
"ESS8e02_1.sav", "ESS9e03.sav", "Russian_social_survey_round_9.sav"
)
ESS_data <- lapply(ESS_files, read.spss, use.value.labels = F, to.data.frame = T)
for (i in 1:length(ESS_data)) {
ESS_data[[i]] <- select(ESS_data[[i]], c(cntry, rlgatnd, pray, rlgdgr))
colnames(ESS_data[[i]]) <- c("country", "attend", "pray", "person")
# recode
for (item in c("attend", "pray")) {
ESS_data[[i]][, item] <- Recode(
ESS_data[[i]][, item], rec = "1=7; 2=6; 3=5; 4=4; 5=3; 6=2; 7=1; else=NA"
)
}
ESS_data[[i]]$person = Recode(
ESS_data[[i]]$person, rec = "0=1; 1=2; 2=3; 3=4; 4=5; 5=6; 6=7; 7=8; 8=9; 9=10; 10=11; else=NA"
)
ESS_data[[i]]$country <- as.factor(ESS_data[[i]]$country)
}
names(ESS_data) <- c(
"ESS1", "ESS2_part", "ESS2_it", "ESS3_part", "ESS3_lv", "ESS3_ro",
"ESS4_part", "ESS4_lt", "ESS4_at", "ESS5_part", "ESS5_at", "ESS6",
"ESS7_part", "ESS7_ru", "ESS8", "ESS9_part", "ESS9_ru"
)
ESS_data$ESS2 <- rbind(ESS_data$ESS2_part, ESS_data$ESS2_it)
ESS_data$ESS3 <- rbind(ESS_data$ESS3_part, ESS_data$ESS3_lv, ESS_data$ESS3_ro)
ESS_data$ESS4 <- rbind(ESS_data$ESS4_part, ESS_data$ESS4_lt, ESS_data$ESS4_at)
ESS_data$ESS5 <- rbind(ESS_data$ESS5_part, ESS_data$ESS5_at)
ESS_data$ESS7 <- rbind(ESS_data$ESS7_part, ESS_data$ESS7_ru)
ESS_data$ESS9 <- rbind(ESS_data$ESS9_part, ESS_data$ESS9_ru)
ESS_data <- ESS_data[names(ESS_data) %in%
c(
"ESS2_part", "ESS2_it", "ESS3_part", "ESS3_lv", "ESS3_ro", "ESS4_part",
"ESS4_lt", "ESS4_at", "ESS5_part", "ESS5_at", "ESS7_part", "ESS7_ru",
"ESS9_part", "ESS9_ru"
) ==
FALSE]
for (i in 1:length(ESS_data)) {
ESS_data[[i]]$round <- names(ESS_data[i])
}
ESS_data <- ESS_data[order(names(ESS_data))]
# Clean
rm(ESS_files, item, i)
#-----------------------------------------------------------------------------------------------
# MGCFA
## remove Turkey from ESS 2 due to the low factor loading of frequency of religious attendance
ESS_data$ESS2 <- subset(ESS_data$ESS2, subset = !(ESS_data$ESS2$country %in% c("TR")))
ESS_data$ESS2$country <- droplevels(ESS_data$ESS2$country)
codes_list <- lapply(ESS_data, function(x) as.data.frame(levels(x[, 1])))
for (i in 1:length(codes_list)) {
codes_list[[i]] <- cbind(
1:nrow(codes_list[[i]]),
codes_list[[i]]
)
names(codes_list[i][[1]])[1] <- c("number")
names(codes_list[i][[1]])[2] <- names(codes_list[i])
}
codes_list <- lapply(codes_list, function(x) trim(x))
codes_list <- Reduce(
function(x, y) merge(x, y, by = "number", all = TRUE),
codes_list
)
codes_list <- rbind(codes_list[10:31, ], codes_list[1:9, ])
setwd("/Users/alisa/Desktop/ess/ESS1")
ESS1_align <-  extractAlignment("fixed.out", silent = T)
ESS1_se <- extractSE.ESS("fixed.out")
setwd("/Users/alisa/Desktop/ess/ESS2")
ESS2_align <-  extractAlignment("fixed.out", silent = T)
ESS2_se <- extractSE.ESS("fixed.out")
setwd("/Users/alisa/Desktop/ess/ESS3")
ESS3_align <-  extractAlignment("fixed.out", silent = T)
ESS3_se <- extractSE.ESS("fixed.out")
setwd("/Users/alisa/Desktop/ess/ESS4")
ESS4_align <-  extractAlignment("fixed.out", silent = T)
ESS4_se <- extractSE.ESS("fixed.out")
setwd("/Users/alisa/Desktop/ess/ESS5")
ESS5_align <-  extractAlignment("fixed.out", silent = T)
ESS5_se <- extractSE.ESS("fixed.out")
setwd("/Users/alisa/Desktop/ess/ESS6")
ESS6_align <-  extractAlignment("fixed.out", silent = T)
ESS6_se <- extractSE.ESS("fixed.out")
setwd("/Users/alisa/Desktop/ess/ESS7")
ESS7_align <-  extractAlignment("fixed.out", silent = T)
ESS7_se <- extractSE.ESS("fixed.out")
setwd("/Users/alisa/Desktop/ess/ESS8")
ESS8_align <-  extractAlignment("fixed.out", silent = T)
ESS8_se <- extractSE.ESS("fixed.out")
setwd("/Users/alisa/Desktop/ess/ESS9")
ESS9_align <-  extractAlignment("fixed.out", silent = T)
ESS9_se <- extractSE.ESS("fixed.out")
ESS_align <- list(
ESS1_align, ESS2_align, ESS3_align, ESS4_align, ESS5_align, ESS6_align,
ESS7_align, ESS8_align, ESS9_align
)
# Figure with factor means across rounds
ESS_means <- ESS_align
ESS_se <- list(
ESS1_se, ESS2_se, ESS3_se, ESS4_se, ESS5_se, ESS6_se, ESS7_se, ESS8_se,
ESS9_se
)
round_names <- c("ESS 1", "ESS 2", "ESS 3", "ESS 4", "ESS 5", "ESS 6", "ESS 7", "ESS 8", "ESS 9")
for (i in 1:length(ESS_means)) {
ESS_means[[i]] <- ESS_means[[i]][[4]][[1]][, 3:4] %>%
na.omit()
names(ESS_means[[i]])[names(ESS_means[[i]]) ==
"Factor.mean"] <- round_names[i]
names(ESS_means[[i]])[names(ESS_means[[i]]) ==
"Group.value"] <- "code"
code_ESS <- select(codes_list, c(number, names(ESS_data[i])))
ESS_means[[i]] <- merge(
ESS_means[[i]], code_ESS, by.x = c("code"),
by.y = c("number"),
all.x = TRUE
)
names(ESS_means[[i]])[2] <- round_names[i]
names(ESS_means[[i]])[3] <- "country"
ESS_means[[i]] <- cbind(ESS_means[[i]], ESS_se[[i]])
ESS_means[[i]] <- ESS_means[[i]][, -1]
ESS_means[[i]][, 1] <- formattable(ESS_means[[i]][, 1], digits = 2, format = "f")
ESS_means[[i]][, 3] <- as.numeric(as.character(ESS_means[[i]][, 3]))
names(ESS_means[[i]])[names(ESS_means[[i]]) ==
"ESS_se[[i]]"] <- "se"
}
View(ESS_means)
plot1 <- ggmeans(ESS_means[[1]])
plot2 <- ggmeans(ESS_means[[2]])
plot3 <- ggmeans(ESS_means[[3]])
plot4 <- ggmeans(ESS_means[[4]])
plot5 <- ggmeans(ESS_means[[5]])
plot6 <- ggmeans(ESS_means[[6]])
plot7 <- ggmeans(ESS_means[[7]])
plot8 <- ggmeans(ESS_means[[8]])
plot9 <- ggmeans(ESS_means[[9]])
## extract specific fit indices
for (i in 1:length(ESS_align)) {
ESS_align[[i]] <- ESS_align[[i]][[3]][, c(3, 6, 4, 5)]
ESS_align[[i]] <- cbind(
c(
"attend.int", "person.int", "pray.int", "attend.load", "person.load", "pray.load"
),
ESS_align[[i]]
)
colnames(ESS_align[[i]]) <- c(
"", "R-Square", "Fit Contribution", "Invariant Groups", "Non-invariant Groups"
)
name <- names(ESS_data[i])
ESS_align[[i]] <- FindReplace(
data = ESS_align[[i]], Var = "Invariant Groups", replaceData = codes_list,
from = "number", to = as.character(name),
exact = F, vector = F
)
ESS_align[[i]] <- FindReplace(
data = ESS_align[[i]], Var = "Non-invariant Groups", replaceData = codes_list,
from = "number", to = as.character(name),
exact = F, vector = F
)
}
plot9
grid.arrange(plot1, plot2, plot3, plot4, plot5, plot6, nrow = 2, ncol = 3)
grid.arrange(plot7, plot8, plot9, nrow = 1, ncol = 3,
bottom = textGrob("          AL = Albania, AT = Austria, BE = Belgium, BG = Bulgaria, HR = Croatia, CY = Cyprus, CZ = Czech Republic, DK = Denmark, EE = Estonia, FI = Finland,
FR = France, DE = Germany, GR = Greece, HU = Hungary, IS = Iceland, IE = Ireland, IL = Israel, IT = Italy, XK = Kosovo, LV = Latvia,  LT = Lithuania,
LU = Luxembourg, ME = Montenegro, NL = Netherlands, NO = Norway, PL = Poland, PT = Portugal, RO = Romania, RU = Russian Federation, RS = Serbia,
SK = Slovakia, SI = Slovenia, ES = Spain, SE = Sweden, CH = Switzerland, TR = Turkey, UA = Ukraine, GB = United Kingdom",
gp = gpar(fontface = 3, fontsize = 17.5)))
share_ni <- ESS_align
for (i in 1:length(share_ni)) {
ni_countries <- sapply(share_ni[[i]][5], function(x) strsplit(x, " ")) %>%
lapply(., length) %>%
do.call("rbind", .)
total_countries <- sapply(share_ni[[i]][4], function(x) strsplit(x, " ")) %>%
lapply(., length) %>%
do.call("rbind", .)
total_countries <- sum(ni_countries[1] + total_countries[1])
share_ni[[i]] <- cbind(share_ni[[i]][1], ni_countries)
share_ni[[i]][2] <- round(share_ni[[i]][2]/total_countries, 2)
names(share_ni[[i]])[names(share_ni[[i]]) ==
"Var.1"] <- "parameter"
names(share_ni[[i]])[names(share_ni[[i]]) ==
"ni_countries"] <- paste0("NI share_", names(ESS_data[i]))
}
share_ni <- Reduce(
function(x, y) merge(x, y, by = "parameter", all = TRUE),
share_ni
)
# R-square
rsquare <- ESS_align
for (i in 1:length(rsquare)) {
rsquare[[i]] <- rsquare[[i]][, 1:2]
names(rsquare[[i]])[1] <- "parameter"
rsquare[[i]][, 2] <- as.numeric(as.character(rsquare[[i]][, 2])) %>%
round(., 2)
names(rsquare[[i]])[names(rsquare[[i]]) ==
"R-Square"] <- paste0("R-Square_", names(ESS_data[i]))
}
rsquare <- Reduce(
function(x, y) merge(x, y, by = "parameter", all = TRUE),
rsquare
)
# Fit function contribution
fit_function <- ESS_align
for (i in 1:length(fit_function)) {
fit_function[[i]] <- fit_function[[i]][, c(1, 3)]
names(fit_function[[i]])[1] <- "parameter"
names(fit_function[[i]])[2] <- paste0("Fit-Function_", names(ESS_data[i]))
fit_function[[i]][, 2] <- as.numeric(as.character(fit_function[[i]][, 2])) %>%
round(., 2)
}
fit_function <- Reduce(
function(x, y) merge(x, y, by = "parameter", all = TRUE),
fit_function
)
# Share of countries by zones
setwd("/Users/alisa/Desktop/Research/Papers/ESS")
Country_abb <- read_excel("Country_abb.xlsx")
share_zones <- ESS_align
for (i in 1:length(share_zones)) {
share_zones[[i]] <- FindReplace(
data = share_zones[[i]], Var = "Invariant Groups", replaceData = Country_abb,
from = "abbreviation", to = "zone", exact = F, vector = F
)
share_zones[[i]] <- FindReplace(
data = share_zones[[i]], Var = "Non-invariant Groups", replaceData = Country_abb,
from = "abbreviation", to = "zone", exact = F, vector = F
)
zones <- sapply(share_zones[[i]][4], function(x) strsplit(x, " "))
Catholic_inv <- str_count(zones, "Catholic")
Protestant_inv <- str_count(zones, "Protestant")
Orthodox_inv <- str_count(zones, "Orthodox")
Islamic_inv <- str_count(zones, "Islamic")
Israel_inv <- str_count(zones, "Israel")
zones_total <- strsplit(share_zones[[i]][1, 5], " ")
Catholic_total <- Catholic_inv[1] + str_count(zones_total, "Catholic")[1]
Protestant_total <- Protestant_inv[1] + str_count(zones_total, "Protestant")[1]
Orthodox_total <- Orthodox_inv[1] + str_count(zones_total, "Orthodox")[1]
Islamic_total <- Islamic_inv[1] + str_count(zones_total, "Islamic")[1]
Israel_total <- Israel_inv[1] + str_count(zones_total, "Israel")[1]
Catholic_inv <- round(Catholic_inv/Catholic_total, 2)
Protestant_inv <- round(Protestant_inv/Protestant_total, 2)
Orthodox_inv <- round(Orthodox_inv/Orthodox_total, 2)
Islamic_inv <- round(Islamic_inv/Islamic_total, 2)
Israel_inv <- round(Israel_inv/Israel_total, 2)
share_zones[[i]] <- cbind(
share_zones[[i]][1], Catholic_inv, Protestant_inv, Orthodox_inv,
Islamic_inv, Israel_inv
)
colnames(share_zones[[i]]) <- c(
"parameter", paste0("Catholic_", names(ESS_data[i])),
paste0("Protestant_", names(ESS_data[i])),
paste0("Orthodox_", names(ESS_data[i])),
paste0("Islamic_", names(ESS_data[i])),
paste0("Israel_", names(ESS_data[i]))
)
}
share_zones <- Reduce(
function(x, y) merge(x, y, by = "parameter", all = TRUE),
share_zones
)
align_table <- Reduce(
function(x, y) merge(x, y, all = TRUE),
list(rsquare, fit_function, share_ni, share_zones)
)
align_table <- align_table %>%
gather(variable, value, -parameter) %>%
mutate(
location = sub(
".*(ESS1|ESS2|ESS3|ESS4|ESS5|ESS6|ESS7|ESS8|ESS9).*", "\\1",
variable
),
variable = sub("_?(ESS1|ESS2|ESS3|ESS4|ESS5|ESS6|ESS7|ESS8|ESS9)_?", "", variable)
) %>%
spread(variable, value)
align_table <- cbind(
align_table[, 1:2], align_table[, 10], align_table[, 4], align_table[, 7],
align_table[, 3], align_table[, 8:9], align_table[, 5:6]
)
align_table$parameter[duplicated(align_table$parameter)] <- NA
align_table[, 2:10][is.na(align_table[, 2:10])] <- "no"
align_table[, 6:10][align_table[, 6:10] == 0] <- "--"
colnames(align_table) <- c(
"Parameter", "Round", "R-square", "Fit function", "NI %", "Catholic",
"Orthodox", "Protestant", "Islamic", "Israel"
)
align_table$`Fit function` <- round(align_table$`Fit function`, 1)
kable(align_table) %>%
add_header_above(
c(
` ` = 1, ` ` = 1, `Fit indices` = 2, ` ` = 1, `Share of invariant countries, by zone` = 5
)
) %>%
group_rows("Frequency of religious attendance", 1, 18) %>%
group_rows("Self-assessed religiosity", 19, 36) %>%
group_rows("Frequency of praying", 37, 54) %>%
footnote(
general = "Fit Function = Fit Function Contribution,
NI % = Share of non-invariant parameters,
-- = no invariant countries, no = countries did not participate in survey.
For the ESS rounds abbreviations, see the note to Table 1.
Baseline group and N groups:
ESS 1 - Czech Republic, 22; ESS 2 - Czech Republic, 25;
ESS 3 - Germany, 25; ESS 4 - Cyprus, 31; ESS 5 - Estonia, 28;
ESS 6 - Czech Republic, 29; ESS 7 -  Czech Republic, 22;
ESS 8 -  Czech Republic, 23; ESS 9 -  Czech Republic, 30"
)
## mean for share of NI countries
share_ni$Share <- rowMeans(share_ni[, 2:10])
## mean for R-square
rsquare$Rsquare <- rowMeans(rsquare[, 2:10])
align_table_sum <- as.data.frame(cbind(share_ni$Share, rsquare$Rsquare))
align_table_sum[nrow(align_table_sum) + 1, ] <- NA
align_table_sum[7, 1:2] <- colMeans(align_table_sum[1:6, 1:2])
align_table_sum <- round(align_table_sum, 2)
## mean for Fit function contribution
fit_function[nrow(fit_function) + 1, ] <- NA
fit_function[7, 2:10] <- colMeans(fit_function[1:6, 2:10])
fit_function[7, 1] <- "Mean"
## replace the differences for the fit function contribution with the model specific mean
fit_function[1:6, 2] <- fit_function[7, 2] - fit_function[1:6, 2]
fit_function[1:6, 3] <- fit_function[7, 3] - fit_function[1:6, 3]
fit_function[1:6, 4] <- fit_function[7, 4] - fit_function[1:6, 4]
fit_function[1:6, 5] <- fit_function[7, 5] - fit_function[1:6, 5]
fit_function[1:6, 6] <- fit_function[7, 6] - fit_function[1:6, 6]
fit_function[1:6, 7] <- fit_function[7, 7] - fit_function[1:6, 7]
fit_function[1:6, 8] <- fit_function[7, 8] - fit_function[1:6, 8]
fit_function[1:6, 9] <- fit_function[7, 9] - fit_function[1:6, 9]
fit_function[1:6, 10] <- fit_function[7, 10] - fit_function[1:6, 10]
fit_function[, 2:10] <- round(fit_function[, 2:10], 1)
align_table_sum <- as.data.frame(
cbind(fit_function[, 1], align_table_sum, fit_function[, 2:10])
)
colnames(align_table_sum) <- c("", "", "", round_names)
kable(align_table_sum) %>%
add_header_above(
c(
`Parameter` = 1, `NI %` = 1, `R-square` = 1, `Fit Function Contribution` = 9
)
) %>%
group_rows("Frequency of religious attendance", 1, 2) %>%
group_rows("Self-assessed religiosity", 3, 4) %>%
group_rows("Frequency of praying", 5, 6) %>%
group_rows("Mean", 7, 7) %>%
footnote(
general = "NI % = Share of non-invariant parameters.
R2 and the share of noninvariant countries are the averages across nine survey rounds.
The fit contribution is presented separately for each survey because it is not standardized;
thus, its averaging across surveys would be biased.
The positive values indicate the contribution higher than its average value in a survey,
while the negative values indicate the contribution lower than its average value in a survey.
The means at the bottom of the table are the raw means for each survey.
For the ESS rounds abbreviations, see the note to Table 1.
Baseline group and N groups:
ESS 1 - Czech Republic, 22; ESS 2 - Czech Republic, 25;
ESS 3 - Germany, 25; ESS 4 - Cyprus, 31; ESS 5 - Estonia, 28;
ESS 6 - Czech Republic, 29; ESS 7 -  Czech Republic, 22;
ESS 8 -  Czech Republic, 23; ESS 9 -  Czech Republic, 30"
)
share_ni
colMeans(share_ni[,2:10])
round(colMeans(share_ni[,2:10])*100, 1)
round(colMeans(share_ni[,2:10])*100, 0)
round(colMeans(rsquare[,2:10]), 3)
ESS_means
ESS_means1 <- Reduce(function(...) merge(..., all=T), ESS_means)
View(ESS_means1)
ESS_means1 <- ESS_means %>% reduce(left_join, by = "country")
ESS_means1 <- ESS_means %>% purrr::reduce(left_join, by = "country")
View(ESS_means1)
ESS_means1 <- select(ESS_means1, c("country", "ESS 1""ESS 2", "ESS 3", "ESS 4", "ESS 5", "ESS 6", "ESS 7", "ESS 8", "ESS 9"))
?select
ESS_means1 <- dplyr::select(ESS_means1,
c("country", "ESS 1""ESS 2", "ESS 3", "ESS 4", "ESS 5", "ESS 6", "ESS 7", "ESS 8", "ESS 9"))
ESS_means1 <- ESS_means %>% purrr::reduce(left_join, by = "country")
ESS_means1 <- dplyr::select(ESS_means1,
c("country", "ESS 1", "ESS 2", "ESS 3", "ESS 4", "ESS 5", "ESS 6", "ESS 7", "ESS 8", "ESS 9"))
ESS_means1
?cor
cor(ESS_means1[, 2:10], use = pairwise.complete.obs")
?cor
cor(ESS_means1[, 2:10], use = "pairwise.complete.obs"")
?cor
cor(ESS_means1[, 2:10], use = "pairwise.complete.obs"")
cor(ESS_means1[, 2:10], use = "pairwise.complete.obs")
cor(ESS_means1[, 2:10], use = "pairwise.complete.obs")
round(cor(ESS_means1[, 2:10], use = "pairwise.complete.obs"), 2)
ESS_means1 <- ESS_means %>% purrr::reduce(left_join, by = "country")
View(ESS_means1)
?reduce
ESS_means1 <- do.call("cbind", ESS_means)
# correlations across rounds
ESS.means1 <- Reduce(function(x,y) merge(x, y, by = "Country", all = TRUE),
ESS_means)
ESS_means
# correlations across rounds
ESS.means1 <- Reduce(function(x,y) merge(x, y, by = "country", all = TRUE),
ESS_means)
cor(ESS.means1[,2:10], use="pairwise.complete.obs")
round(cor(ESS.means1[,2:10], use="pairwise.complete.obs"), 2)
View(ESS_means1)
round(cor(ESS.means1[,c("ESS 1", "ESS 2", "ESS 3", "ESS 4", "ESS 5", "ESS 6", "ESS 7", "ESS 8", "ESS 9")],
use="pairwise.complete.obs"), 2)
View(ESS_means1)
ESS_means <- lapply(ESS_means, function(x) trim(x))
ESS.means1 <- Reduce(function(x,y) merge(x, y, by = "country", all = TRUE),
ESS_means)
round(cor(ESS.means1[,c("ESS 1", "ESS 2", "ESS 3", "ESS 4", "ESS 5", "ESS 6", "ESS 7", "ESS 8", "ESS 9")],
use="pairwise.complete.obs"), 2)
View(ESS1_align)
View(ESS.means1)
min(round(cor(ESS.means1[,c("ESS 1", "ESS 2", "ESS 3", "ESS 4", "ESS 5", "ESS 6", "ESS 7", "ESS 8", "ESS 9")],
use="pairwise.complete.obs"), 2))
setwd("/Users/alisa/Desktop/ess/ESS1")
ESS1_sim <-  extractAlignmentSim(
c("sim100.out", "sim500.out", "sim1000.out", "sim1500.out", "sim2000.out"),
silent = T
)
setwd("/Users/alisa/Desktop/ess/ESS2")
ESS2_sim <-  extractAlignmentSim(
c("sim100.out", "sim500.out", "sim1000.out", "sim1500.out", "sim2000.out"),
silent = T
)
setwd("/Users/alisa/Desktop/ess/ESS3")
ESS3_sim <-  extractAlignmentSim(
c("sim100.out", "sim500.out", "sim1000.out", "sim1500.out", "sim2000.out"),
silent = T
)
setwd("/Users/alisa/Desktop/ess/ESS4")
ESS4_sim <-  extractAlignmentSim(
c("sim100.out", "sim500.out", "sim1000.out", "sim1500.out", "sim2000.out"),
silent = T
)
setwd("/Users/alisa/Desktop/ess/ESS5")
ESS5_sim <-  extractAlignmentSim(
c("sim100.out", "sim500.out", "sim1000.out", "sim1500.out", "sim2000.out"),
silent = T
)
setwd("/Users/alisa/Desktop/ess/ESS6")
ESS6_sim <-  extractAlignmentSim(
c("sim100.out", "sim500.out", "sim1000.out", "sim1500.out", "sim2000.out"),
silent = T
)
setwd("/Users/alisa/Desktop/ess/ESS7")
ESS7_sim <-  extractAlignmentSim(
c("sim100.out", "sim500.out", "sim1000.out", "sim1500.out", "sim2000.out"),
silent = T
)
setwd("/Users/alisa/Desktop/ess/ESS8")
ESS8_sim <-  extractAlignmentSim(
c("sim100.out", "sim500.out", "sim1000.out", "sim1500.out", "sim2000.out"),
silent = T
)
setwd("/Users/alisa/Desktop/ess/ESS9")
ESS9_sim <-  extractAlignmentSim(
c("sim100.out", "sim500.out", "sim1000.out", "sim1500.out", "sim2000.out"),
silent = T
)
setwd("/Users/alisa/Desktop/ess/ESS3")
ESS3_sim <-  extractAlignmentSim(
c("sim100.out", "sim500.out", "sim1000.out", "sim1500.out", "sim2000.out"),
silent = T
)
setwd("/Users/alisa/Desktop/ess/ESS3")
ESS3_sim <-  extractAlignmentSim(
c("sim100.out", "sim500.out", "sim1000.out", "sim1500.out", "sim2000.out"),
silent = T
)
