#' Calculate proportions with 95\% credible interval (confidence interval).
#' If there are replicates they will be pooled(!). It will work for both count table format and list format.
#' For it to work for table format give time vector
format_exp_data = function(data_x, time_points)
{
colnames(data_x) = paste0("T", time_points)
data_x= data_x %>% mutate(CloneSize = paste0(1:n(), ".", n())) %>% gather(Day, Counts, -CloneSize) %>%
group_by(Day) %>% mutate(TotalCounts = sum(Counts),
value = qbeta( 0.5, Counts, TotalCounts-Counts),
low_lim = qbeta(0.025, Counts, TotalCounts-Counts),
hi_lim = qbeta(0.975, Counts, TotalCounts-Counts)) %>%
mutate(Age = as.numeric(str_replace(Day, pattern = "T", replacement = ""))) %>% ungroup() %>%
select(CloneSize, Age, value, low_lim, hi_lim)
data_x
}
#' Adjust theory drift data for plotting
format_th_data = function(th_x, time_points)
{
fractions_split = nrow(th_x)
col_names = c("Age", paste0(1:fractions_split, ".", fractions_split))
## Sim process
model_preds = data.frame(Age = time_points, t(th_x))
colnames(model_preds) = col_names
model_list = model_preds %>% gather(CloneSize, value, -Age)
model_list
}
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