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dmat_quantitative_analyses.R
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############## OBS - ONLY MEANT TO BE RUN AFTER RUNNING THE HYPERPARAMETER CELL IN THE RMD ########
geo_anova <- all_dmat %>%
# filter(region_1 != "Melanesia" & region_2 != "Melanesia") %>%
arrange(region_1, region_2, country_1, country_2, pop_1, pop_2, prs_1, prs_2) %>%
mutate(
prs_1 = factor(prs_1),
prs_2 = factor(prs_2, levels(prs_1))
) %>%
mutate(
pop_1 = ifelse(pop_1 == pop_2, pop_1, "Between") %>%
factor(c("Between", sort(unique(pop_1)))),
country_1 = ifelse(country_1 == country_2, country_1, "Between") %>%
factor(c("Between", sort(unique(country_1)))),
region_1 = ifelse(region_1 == region_2, region_1, "Between") %>%
factor(c("Between", sort(unique(region_1)))),
pop_2 = ifelse(pop_2 == pop_1, pop_2, "Between") %>%
factor(c("Between", sort(unique(pop_2)))),
country_2 = ifelse(country_2 == country_1, country_2, "Between") %>%
factor(c("Between", sort(unique(country_2)))),
region_2 = ifelse(region_2 == region_1, region_2, "Between") %>%
factor(c("Between", sort(unique(region_2))))
) %>%
filter(as.integer(prs_1) < as.integer(prs_2)) %>%
select(iou, contains("_1")) %>%
# group_by(pop_1) %>%
# filter(length(unique(prs_1)) > 1 | first(pop_1) == "Between") %>%
# group_by(country_1) %>%
# filter(length(unique(pop_1)) > 1 | first(country_1) == "Between") %>%
# group_by(region_1) %>%
# filter(length(unique(country_1)) > 1 | first(region_1) == "Between") %>%
# ungroup %>%
rename_with(function(x) str_remove(x, "_\\d$")) %>%
mutate(between = rowSums(across(c(region, country, pop), ~ .x == "Between")) %>%
factor) %>%
# mutate(iou = log(iou/(1 - iou))) %>%
lm(iou ~ I(region == "Between") + region
+ I(country == "Between") + country
+ I(pop == "Between") + pop,
data = .)
geo_anova %>%
residuals %>%
hist(1000, plot = F) %>%
unclass %>%
lapply(list) %>%
as_tibble %>%
{.[,sapply(., function(x) length(x[[1]])) == length(.$mids[[1]])]} %>%
unnest(everything()) %>%
filter(between(mids, -.1, .2)) %>%
ggplot(aes(mids, counts)) +
geom_col(color = "gray35") +
coord_cartesian(expand = F)
geo_anova %>%
anova(test = "F") %>%
broom::tidy() %>%
mutate(
pdev = scales::label_percent(.01)(sumsq / sum(sumsq)),
meansq = meansq / sum(meansq)
) %>%
mutate(
between = str_detect(term, "^I\\("),
term = ifelse(between, str_extract(term, "(?<=^I\\()[a-zA-Z]+"), term),
between = ifelse(between, "between", "within"),
pdev = set_names(pdev, term)
) %>%
select(between, term, pdev) %>%
complete(term, between) %>%
pivot_wider(
id_cols = term, names_from = between, values_from = pdev
) %>%
mutate(term = c("region" = "Region",
"country" = "Country",
"pop" = "Population",
"Residuals" = "Residuals")[term],
term = factor(term, c("Region", "Country", "Population", "Residuals"))) %>%
arrange(term) %>%
kable(
"latex",
booktabs = T,
col.names = c("", "Between", "Within"),
escape = F
) %>%
row_spec(0, bold = T) %>%
add_header_above(c("", "Proportion of $d_\\mathrm{IoU}$ explained by" = 2), escape = F, bold = T) %>%
write_lines(paste0("shared_ancestry_explained_geography", file_suffix, ".txt"))
# Average IoU for individuals both with total segment lengths within a bin
all_dmat %>%
# filter(!epas1 | (prs_1 %in% epas_set & prs_2 %in% epas_set)) %>%
mutate(
len_1 = arch_person_length[prs_1] * downscale,
len_2 = arch_person_length[prs_2] * downscale
) %>%
mutate(region_1 = if (!epas1) region_1 else ifelse(country_1 == "Tibet", "Tibet", region_1),
region_2 = if (!epas1) region_2 else ifelse(country_2 == "Tibet", "Tibet", region_2)) %>%
filter(region_1 == region_2) %>%
arrange(region_1, region_2, country_1, country_2, pop_1, pop_2, prs_1, prs_2) %>%
mutate(prs_1 = factor(prs_1, unique(prs_1)),
prs_2 = factor(prs_2, levels(prs_1))) %>%
filter(as.numeric(prs_1) > as.numeric(prs_2)) %>%
mutate(
len = len_1,
# len_1 = cut(len_1, breaks = scales::breaks_pretty(n = 30)(range(len)), dig.lab = 25),
# len_2 = cut(len_2, breaks = scales::breaks_pretty(n = 30)(range(len)), dig.lab = 25)
across(c(len_1, len_2), function(x) {
if (!epas1) {
cut_width(len_1, 10000 * downscale, boundary = 1, dig.lab = 25) %>%
fct_na_value_to_level("[0,0]") %>%
fct_relabel(~str_replace_all(.x, "(?<=0)1", "0"))
}
else {
print("----------------")
print(sum(is.na(x)))
x[is.na(x)] <- 0
print(sum(is.na(x)))
print("----------------")
cut(x, c(0, 1, c(50, 100, 150, 200, 300, 400, 500, 600, 700, 800) * 10^3), dig.lab = 25, right = F) %>%
fct_relabel(~ifelse(.x == "[0,1)", "[0,0]", .x))
}
})
) %>%
filter(len_1 == len_2) %>%
group_by(len_1, region_1) %>%
summarize(
iou = median(iou),
n = n(),
.groups = "drop"
) %>%
mutate(len_1 = droplevels(len_1)) %>%
complete(len_1, region_1, fill = list(n = 0)) %>%
mutate(
iou = ifelse(is.na(iou), "-", scales::label_percent(.1)(iou)),
iou_n = paste0(iou, " (\\textit{", n, "})"),
region_1 = region_1 %>%
factor(c("West-Eurasia",
"South-Asia",
"East-Asia",
"Tibet",
"Central-Asia-Siberia",
"Melanesia")),
region_1 = droplevels(region_1)
) %>%
arrange(region_1) %>%
select(!c(iou,n)) %>%
mutate(
iou_n = str_replace(iou_n, "%", "\\\\%"),
len_1 = if (!epas1) str_replace_all(len_1, "0{6}(?!0)", "M") else str_replace_all(len_1, "0{3}(?!0)", "K"),
len_1 = len_1 %>% # This looks very cursed, but it just left & right aligns the intervals
str_replace("(?<=(\\(|\\[)\\d{2}[MK],)(?=\\d{3}[MK])", "\\\\hspace{0.25cm}") %>%
str_replace("(?<=(\\(|\\[)\\d{2}[MK],)(?=\\d{2}[MK])", "\\\\hspace{0.5cm}") %>%
str_replace("(?<=(\\(|\\[)[01],)(?=\\d{2}[MK])", "\\\\hspace{0.75cm}") %>%
str_replace("(?<=(\\(|\\[)[01],)(?=0\\])", "\\\\hspace{1.25cm}") %>%
str_replace("(?<=(\\(|\\[)\\d{3}[MK],)", " "),
) %>%
mutate(
region_1 = paste0("\\textbf{", region_1, "}")
) %>%
pivot_wider(
id_cols = len_1,
names_from = region_1,
values_from = iou_n
) %>%
rename("\\makecell{\\textbf{Total Archaic}\\\\\\textbf{Segment Length}}" = 1) %>%
kable("latex",
booktabs = T,
escape = F,
align = "c") %>%
add_header_above(c("", "$Q_{\\\\%50}(d_\\\\mathrm{IoU})$ (\\\\textit{n})" = if (!epas1) 5 else 6), escape = F) %>%
as.character %>%
str_split("\\n") %>%
unlist %>%
extract(which(nchar(.) != 0)) %>%
paste0(collapse = "\n\t") %>%
{paste0("\t", .)} %>%
write_lines(paste0("segment_length_cor", file_suffix,".txt"))
# all_coef <- all_dmat %>%
# filter(!epas1 | (prs_1 %in% epas_set & prs_2 %in% epas_set)) %>%
# arrange(region_1, region_2, country_1, country_2, pop_1, pop_2, prs_1, prs_2) %>%
# mutate(prs_1 = factor(prs_1),
# prs_2 = factor(prs_2, levels(prs_1)),) %>%
# mutate(
# len_1 = arch_person_length[prs_1] * 1000,
# len_2 = arch_person_length[prs_2] * 1000
# ) %>%
# nest(dat = !contains("_1")) %>%
# mutate(
# mod1 = map(dat, function(x) lmer(iou ~ len_2 + (1 | region_2) + (1 | country_2) + (1 | pop_2), data = x))
# ) %>%
# mutate(
# coef = map(mod1, function(x) as_tibble(set_colnames(t(coefficients(summary(x))[1:2,1]), c("Intercept", "Slope"))))
# ) %>%
# unnest(coef) %>%
# select(!c(dat, mod1))
#
# tibble(
# coef = list(all_coef %>%
# lmer(Slope ~ len_1 + (1 | region_1 / country_1 / pop_1), data = .) %>%
# summary %>%
# coefficients() %>%
# as.data.frame %>%
# rownames_to_column("second_term") %>%
# as_tibble,
# all_coef %>%
# lmer(Intercept ~ len_1 + (1 | region_1 / country_1 / pop_1), data = .) %>%
# summary %>%
# coefficients() %>%
# as.data.frame %>%
# rownames_to_column("second_term") %>%
# as_tibble
# ),
# term = c("Intercept", "Slope")
# ) %>%
# unnest(coef) %>%
# relocate(term, .before = second_term) %>%
# mutate(second_term = c("(Intercept)" = "Intercept",
# "len_1" = "Slope")[second_term]) %>%
# mutate(across(where(is.numeric), label_prettify_scientific(F,3)))
#
#
# all_coef %>%
# pivot_longer(!contains("_1"), names_to = "parm", values_to = "est") %>%
# ggplot(aes(len_1, est, color = region_1)) +
# geom_point() +
# geom_smooth(method = "lm") +
# scale_y_continuous(trans = abslog(),
# labels = prettify_scientific) +
# scale_x_continuous(labels = prettify_scientific) +
# facet_wrap(~parm, scales = "free_y")