Revision 2868f59b32d05a61091e70962e6e6a16463c6a64 authored by Susi Zajitschek on 29 October 2020, 00:41:32 UTC, committed by GitHub on 29 October 2020, 00:41:32 UTC
1 parent b9802e9
calc_pop_stats.R
calculate_population_stats <- function(mydata, min_individuals = 5) {
mydata %>%
group_by(population, strain_name, production_center, sex) %>%
summarise(
trait = parameter_name[1],
x_bar = mean(data_point),
x_sd = sd(data_point),
n_ind = n()
) %>%
ungroup() %>%
filter(n_ind > min_individuals) %>%
# Check both sexes present & filter those missing
group_by(population) %>%
mutate(
n_sex = n_distinct(sex)
) %>%
ungroup() %>%
filter(n_sex ==2) %>%
select(-n_sex) %>%
arrange(production_center, strain_name, population, sex)
}
create_meta_analysis_effect_sizes <- function(mydata) {
i <- seq(1, nrow(mydata), by = 2)
input <- data.frame(
n1i = mydata$n_ind[i],
n2i = mydata$n_ind[i + 1],
x1i = mydata$x_bar[i],
x2i = mydata$x_bar[i + 1],
sd1i = mydata$x_sd[i],
sd2i = mydata$x_sd[i + 1]
)
mydata[i,] %>%
select(strain_name, production_center) %>%
mutate(
effect_size_CVR = Calc.lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
sample_variance_CVR = Calc.var.lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
effect_size_VR = Calc.lnVR(CSD = input$sd1i, CN = input$n1i, ESD = input$sd2i, EN = input$n2i),
sample_variance_VR = Calc.var.lnVR(CN = input$n1i, EN = input$n2i),
effect_size_RR = Calc.lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
sample_variance_RR = Calc.var.lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
err = as.factor(seq_len(n()))
)
}
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