Vietnam Experience Study: semen measures and intelligence
Init
options(
digits = 2,
tibble.print_max = 30
)
library(pacman)
p_load(kirkegaard,
haven,
dplyr,
readr,
rms,
GGally
)
theme_set(theme_bw())
#start parallel
doParallel::registerDoParallel(7)Functions
#convenience plot function
#remove the _ from labels on the plot automatically since otherwise the labels are too long
#add colors to the correlation matrix to make heatmap
ggpairs_easy = function(x, stars = T) {
#alternative inner function
#https://stackoverflow.com/questions/62196950/ggpairs-plot-with-heatmap-of-correlation-values-with-significance-stars-and-cust
#I removed the adaptive size and stars default since we dont want those
stars_ = stars
cor_fun <- function(data, mapping, method="pearson", use="pairwise", ndp=2, sz=5, stars=stars_, ...){
# grab data
x <- eval_data_col(data, mapping$x)
y <- eval_data_col(data, mapping$y)
# calculate correlation: for significance stars
corr <- cor.test(x, y, method=method)
est <- corr$estimate
lb.size <- sz* abs(est)
# get significance stars
if(stars){
#determine asterisks
stars <- c("***", "**", "*", "")[findInterval(corr$p.value, c(0, 0.001, 0.005, 0.01, 1))]
#force digits to be specific number
lbl <- paste0(format_digits(est, digits = ndp), stars)
}else{
#force digits to be specific number
lbl <- format_digits(est, digits = ndp)
}
# calculate correlation: for colored tiles
corr <- cor(x, y, method=method, use=use)
# calculate color based on correlation value
# corr = -1 => blue,
# corr = 0 => white,
# corr = +1 => red,
colFn <- colorRampPalette(c("blue", "white", "red"), interpolate ='spline')
fill <- colFn(100)[findInterval(corr, seq(-1, 1, length=100))]
ggplot(data = data, mapping = mapping, ...) +
theme_void() +
annotate("text",
x=mean(x, na.rm=TRUE),
y=mean(y, na.rm=TRUE),
label=lbl,
# size=lb.size,
...) +
theme(panel.background = element_rect(fill=fill, # to fill background of panel with color
colour=NA), # to remove border of panel
panel.grid.major = element_blank())
}
#do the call
GGally::ggpairs(
x,
columnLabels = colnames(x) %>% str_clean(),
upper = list(continuous = cor_fun)
# lower = list(continuous = wrap("smooth", method = "loess"))
) +
theme(
#black font
text = element_text(color = "black")
)
}Data
Main datafile.
#load VES complete data
# d = haven::read_spss("data/VESTOTAL.sav")
# d_dict = d %>% df_var_table()
#testis data
# fertility = read_excel("data/race_testis_semen_fertility_Emil.xlsx")
# fertility_dict = fertility %>% df_var_table()
#semen data
semen = read_sas("data/VEJOBIQ6_MMPI_Hormon.sas7bdat")
semen_dict = semen %>% df_var_table()
#recode
semen %<>% mutate(
#semen variables
pct_motile = v__MOTILITY,
pct_progressive = v__PROGRESSIVE,
pct_small = MS02006,
pct_normal = MS01006,
pct_large = MS03006,
pct_tapered = MS04006,
pct_amorph = MS05006,
semen_concentration = CONCENTRATION0,
semen_volume = VOL,
semen_count = COUNT,
#covars
days_abstained = DAYSAB %>% winsorise(upper = 7),
g = G_ALL,
age = AGEDYR,
race = RACE
)
#exclude those with spillage
semen2 = semen %>%
filter(
SPILL == "N",
VASCTOMY == "0"
)
#var lists
semen_vars = semen %>% select(pct_motile:semen_count) %>% names()
main_vars = c(semen_vars, "g", "age", "race", "days_abstained")
main_vars_num = main_vars %>% setdiff("race")Results
#pairs
semen2 %>% select(!!main_vars_num) %>% ggpairs_easy()## Warning: Removed 41 rows containing non-finite values (stat_density).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 45 rows containing non-finite values (stat_density).## Warning: Removed 41 rows containing missing values (geom_point).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 41 rows containing missing values (geom_point).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 41 rows containing missing values (geom_point).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 41 rows containing missing values (geom_point).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 41 rows containing missing values (geom_point).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 41 rows containing missing values (geom_point).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 33 rows containing missing values (geom_point).
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GG_save("figs/ggpairs.png")## Warning: Removed 41 rows containing non-finite values (stat_density).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 45 rows containing non-finite values (stat_density).## Warning: Removed 41 rows containing missing values (geom_point).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 41 rows containing missing values (geom_point).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 41 rows containing missing values (geom_point).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 41 rows containing missing values (geom_point).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 41 rows containing missing values (geom_point).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 41 rows containing missing values (geom_point).## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 33 rows containing missing values (geom_point).
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semen2 %>% filter(race == "White") %>% select(!!main_vars_num) %>% ggpairs_easy()## Warning: Removed 31 rows containing non-finite values (stat_density).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 34 rows containing non-finite values (stat_density).## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 24 rows containing missing values (geom_point).
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GG_save("figs/ggpairs_white.png")## Warning: Removed 31 rows containing non-finite values (stat_density).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 34 rows containing non-finite values (stat_density).## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 34 rows containing missing values (geom_point).## Warning: Removed 24 rows containing missing values (geom_point).
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GG_scatter(semen2, "g", "pct_motile") +
geom_smooth() +
scale_x_continuous("g factor") +
scale_y_continuous("Percent motile cells")## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#progressive cells
GG_scatter(semen2, "g", "pct_progressive") +
geom_smooth() +
scale_x_continuous("g factor") +
scale_y_continuous("Percent progressive cells")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#progressive cells
GG_scatter(semen2, "g", "pct_small") +
geom_smooth() +
scale_x_continuous("g factor") +
scale_y_continuous("Percent small cells")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#progressive cells
GG_scatter(semen2, "g", "pct_normal") +
geom_smooth() +
scale_x_continuous("g factor") +
scale_y_continuous("Percent normal cells")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#progressive cells
GG_scatter(semen2, "g", "pct_large") +
geom_smooth() +
scale_x_continuous("g factor") +
scale_y_continuous("Percent large cells")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#progressive cells
GG_scatter(semen2, "g", "pct_tapered") +
geom_smooth() +
scale_x_continuous("g factor") +
scale_y_continuous("Percent tapered cells")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#semen concentration
GG_scatter(semen2, "g", "pct_amorph") +
geom_smooth() +
scale_x_continuous("g factor") +
scale_y_continuous("Semen amorph (M/l)")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#semen concentration
GG_scatter(semen2, "g", "semen_concentration") +
geom_smooth() +
scale_x_continuous("g factor") +
scale_y_continuous("Semen concentration (M/l)")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#semen volume
GG_scatter(semen2, "g", "semen_volume") +
geom_smooth() +
scale_x_continuous("g factor") +
scale_y_continuous("Semen volume (ml)")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#semen count
GG_scatter(semen2, "g", "semen_count") +
geom_smooth() +
scale_x_continuous("g factor") +
scale_y_continuous("Semen count")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#standardized data for models
semen2_std = semen2 %>% df_standardize(messages = F)
semen2_std_whites = semen2_std %>% filter(race == "White")
#all races
list(
pct_motile = ols(pct_motile ~ g + age + days_abstained + race, data = semen2_std),
pct_progressive = ols(pct_progressive ~ g + age + days_abstained + race, data = semen2_std),
pct_small = ols(pct_small ~ g + age + days_abstained + race, data = semen2_std),
pct_normal = ols(pct_normal ~ g + age + days_abstained + race, data = semen2_std),
pct_large = ols(pct_large ~ g + age + days_abstained + race, data = semen2_std),
pct_tapered = ols(pct_tapered ~ g + age + days_abstained + race, data = semen2_std),
pct_amorph = ols(pct_amorph ~ g + age + days_abstained + race, data = semen2_std),
semen_concentration = ols(semen_concentration ~ g + age + days_abstained + race, data = semen2_std),
semen_volume = ols(semen_volume ~ g + age + days_abstained + race, data = semen2_std),
semen_count = ols(semen_count ~ g + age + days_abstained + race, data = semen2_std)
) %>%
summarize_models()#whites only
list(
pct_motile = ols(pct_motile ~ g + age + days_abstained, data = semen2_std_whites),
pct_progressive = ols(pct_progressive ~ g + age + days_abstained, data = semen2_std_whites),
pct_small = ols(pct_small ~ g + age + days_abstained, data = semen2_std_whites),
pct_normal = ols(pct_normal ~ g + age + days_abstained, data = semen2_std_whites),
pct_large = ols(pct_large ~ g + age + days_abstained, data = semen2_std_whites),
pct_tapered = ols(pct_tapered ~ g + age + days_abstained, data = semen2_std_whites),
pct_amorph = ols(pct_amorph ~ g + age + days_abstained, data = semen2_std_whites),
semen_concentration = ols(semen_concentration ~ g + age + days_abstained, data = semen2_std_whites),
semen_volume = ols(semen_volume ~ g + age + days_abstained, data = semen2_std_whites),
semen_count = ols(semen_count ~ g + age + days_abstained, data = semen2_std_whites)
) %>%
summarize_models()#race alone
list(
pct_motile = ols(pct_motile ~ race, data = semen2_std),
pct_progressive = ols(pct_progressive ~ race, data = semen2_std),
pct_small = ols(pct_small ~ race, data = semen2_std),
pct_normal = ols(pct_normal ~ race, data = semen2_std),
pct_large = ols(pct_large ~ race, data = semen2_std),
pct_tapered = ols(pct_tapered ~ race, data = semen2_std),
pct_amorph = ols(pct_amorph ~ race, data = semen2_std),
semen_concentration = ols(semen_concentration ~ race, data = semen2_std),
semen_volume = ols(semen_volume ~ race, data = semen2_std),
semen_count = ols(semen_count ~ race, data = semen2_std)
) %>%
summarize_models()