Chorionicity effects reupload

library(pacman)

## Warning: package 'pacman' was built under R version 4.3.1

p_load(stringr, psych, kirkegaard, psychometric)

d_table = read.csv("chorion_data.csv", row.names = 1)

v_MC = str_detect(d_table$X, pattern = "MCMZ")
v_DC = str_detect(d_table$X, pattern = "DCMZ")
v_traits = c(v_MC[-1], F)

v_MC_vals = d_table[v_MC, "correlation"]
v_DC_vals = d_table[v_DC, "correlation"]

v_MC_N = d_table[v_MC, c("N.1st.borns", "N.2nd.borns")] %>% apply(MARGIN = 1, FUN = mean)
v_DC_N = d_table[v_DC, c("N.1st.borns", "N.2nd.borns")] %>% apply(MARGIN = 1, FUN = mean)

d_data = data.frame(MC = v_MC_vals, DC = v_DC_vals, MC_N = v_MC_N, DC_N = v_DC_N)
rownames(d_data) = d_table[v_traits, "X"] %>% as.character()

d_data$delta = d_data$MC - d_data$DC #deltas
d_data$delta_abs = abs(d_data$delta) #absolute values

d_data$delta_se = apply(X = d_data, MARGIN = 1, FUN = function(row) {
  CIrdif(r1 = row["MC"], r2 = row["DC"], n1 = row["MC_N"], n2 = row["DC_N"])["SED"]
}) %>% unlist()

describe(d_data$delta)

## d_data$delta 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##       66        0       27    0.992  0.00303   0.0545  -0.0675  -0.0450 
##      .25      .50      .75      .90      .95 
##  -0.0200   0.0000   0.0200   0.0400   0.0975 
## 
## lowest : -0.16 -0.11 -0.09 -0.07 -0.06, highest: 0.06  0.11  0.13  0.17  0.24

GG_denhist(d_data, var = "delta")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggsave("chorion_effects.png")

## Saving 7 x 5 in image
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

GG_scatter(d_data, "delta_abs", "delta_se")

## `geom_smooth()` using formula = 'y ~ x'

ggsave("chorion_effects_errors.png")

## Saving 7 x 5 in image
## `geom_smooth()` using formula = 'y ~ x'