Last updated: 2020-02-01
Checks: 7 0
Knit directory: HHVtransmission/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 93301d6 | Bryan | 2020-02-01 | wflow_publish(c("analysis/_site.yml“,”analysis/about.Rmd“,”analysis/index.Rmd", |
| html | a1d2e7b | Bryan | 2020-01-24 | update with documentation |
| Rmd | af2a4c1 | Bryan | 2019-12-30 | all updates after co-author review; edits to tables/figs; ID75 |
| html | af2a4c1 | Bryan | 2019-12-30 | all updates after co-author review; edits to tables/figs; ID75 |
processing_functions.R)These functions were used for data manipulation generally related to presentation in tables and figures.
round_away_0 = function (x, digits = 0, trailing_zeros = FALSE)
{
rounded_vals <- sign(x) * round(abs(x) + 1e-15, digits)
if (trailing_zeros) {
rounded_vals[!is.na(rounded_vals)] <- formatC(rounded_vals[!is.na(rounded_vals)],
digits, format = "f")
neg_to_change <- paste0("-0.", paste0(rep(0, digits),
collapse = ""))
if (any(rounded_vals == neg_to_change, na.rm = TRUE))
rounded_vals[rounded_vals == neg_to_change] <- substr(neg_to_change,
2, nchar(neg_to_change))
}
rounded_vals
}
round_if_numeric = function (x, digits = 0, trailing_zeros = FALSE)
{
if (is.numeric(x))
round_away_0(x, digits = digits, trailing_zeros = trailing_zeros)
else x
}
stat_paste = function (stat1, stat2 = NULL, stat3 = NULL, digits = 0, trailing_zeros = TRUE,
bound_char = c("(", "[", "{", "|"), sep = ", ", na_str_out = "---")
{
bound_char <- match.arg(bound_char)
end_bound_char <- switch(bound_char, `(` = ")", `[` = "]",
`{` = "}", `|` = "|")
stat1_pasted_obj <- ifelse(is.na(stat1), na_str_out, as.character(round_if_numeric(stat1,
digits = digits, trailing_zeros = trailing_zeros)))
if (is.null(stat2)) {
pasted_output <- stat1_pasted_obj
}
else {
stat2_pasted_obj <- ifelse(is.na(stat2), na_str_out,
as.character(round_if_numeric(stat2, digits = digits,
trailing_zeros = trailing_zeros)))
if (is.null(stat3)) {
pasted_output <- ifelse(is.na(stat1) & is.na(stat2),
na_str_out, paste0(stat1_pasted_obj, " ", bound_char,
stat2_pasted_obj, end_bound_char))
}
else {
stat3_pasted_obj <- ifelse(is.na(stat3), na_str_out,
as.character(round_if_numeric(stat3, digits = digits,
trailing_zeros = trailing_zeros)))
pasted_output <- ifelse(is.na(stat1) & is.na(stat2) &
is.na(stat3), na_str_out, paste0(stat1_pasted_obj,
" ", bound_char, stat2_pasted_obj, sep, stat3_pasted_obj,
end_bound_char))
}
}
pasted_output
}
clean_pvalues = function (pvalues, digits = 3, bold = FALSE, italic = FALSE,
background = NULL, sig_alpha = 0.05, missing_char = "---", trailing_zeros = TRUE)
{
lower_cutoff = 10^(-digits)
missing_p = which(is.na(pvalues))
below_cutoff_p = which(pvalues < lower_cutoff)
sig_p = which(pvalues < sig_alpha)
if (trailing_zeros)
pvalues_new = round_away_0(pvalues, trailing_zeros = TRUE,
digits = digits) else pvalues_new =
as.character(round_away_0(pvalues, trailing_zeros = FALSE, digits))
pvalues_new[missing_p] = missing_char
pvalues_new[below_cutoff_p] = paste0("<", lower_cutoff)
pvalues_new[sig_p] = kableExtra::cell_spec(pvalues_new[sig_p],
bold = bold, italic = italic,
background = background, escape = FALSE)
pvalues_new
}Labels for exposure analysis plots:
infection_labels = tibble(
labels = c("No Transmission", "Transmission"),
colours = c("#4393C3", "#D6604D"),
breaks = c(0, 1)
)risk_fit_functions.R)These are functions that prepare the data for model fitting.
# Generate sufficient statistics for use in likelihood function ----
# create_likdat for marginal models, create_likdat_combined for two exposure model
# dat: data for optimization formatted per this analysis,
## requires infant_wks, infectious_1wk, exposure (10^count), FamilyID
## output is input data for two components of survival likelihood
### (infectious_1wk = 0: survival input; infectious_1wk = 1: infection interval input)
create_likdat = function(dat){
dat %>%
group_by(FamilyID, infectious_1wk) %>%
summarize(
total_exposure = sum(exposure), #should be 1 input for infectious_1wk == 1
total_weeks = max(infant_wks)
) %>%
ungroup()
}
# two exposure sources (S and M)
create_likdat_combined = function(dat){
dat %>%
group_by(FamilyID, infectious_1wk) %>%
summarize(
total_exposureM = sum(10^M), #should be 1 input for infectious_1wk == 1
total_exposureS = sum(10^S), #should be 1 input for infectious_1wk == 1
total_weeks = max(infant_wks)
) %>%
ungroup()
}
# Survival log-likelihood functions ----
# surv_logLik for marginal/single exposure source, combined for two exposure sources
# log_betas: input vector of logged betas (log beta0, log betaE) for fitting
# dat: lik data for optimization formatted per this analysis,
## requires infectious_1wk (surv_indicator), total_exposure, total_weeks, FamilyID
surv_logLik = function(log_betas, likdat){
beta0 = exp(log_betas)[1]
betaE = exp(log_betas)[2]
logsurv = likdat %>% subset(infectious_1wk == 0) %>%
mutate(calc = -total_exposure * betaE - total_weeks * beta0)
loginf = likdat %>% subset(infectious_1wk == 1) %>%
mutate(calc = log(1 - exp(-beta0 - betaE * total_exposure)))
-(sum(logsurv$calc) + sum(loginf$calc))
}
surv_logLik_combined = function(log_betas, likdat){
beta0 = exp(log_betas)[1]
betaM = exp(log_betas)[2]
betaS = exp(log_betas)[3]
logsurv = likdat %>% subset(infectious_1wk == 0) %>%
mutate(calc = -total_exposureM * betaM - total_exposureS * betaS - total_weeks * beta0)
loginf = likdat %>% subset(infectious_1wk == 1) %>%
mutate(calc = log(1 - exp(-beta0 - total_exposureM * betaM - total_exposureS * betaS)))
-(sum(logsurv$calc) + sum(loginf$calc))
}optim_functions.R)These are wrappers around the optim function to fit the model parameters. The tidy functions process the model output into a tidy results format.
# Wrappers for the optim functions ----
# the fitters are similar except marginal expects initials to be a n X 2 matrix and combined expects initials to be a n X 3 matrix. Initials are randomly generated initial values
# likdat is processed data from create_likdat (risk_fit_functions.R)
# fitfun is the appropriate survival likelihood function (risk_fit_functions.R)
# get_best_fit() is below
marginal_fitter = function(likdat, initials, fitfun = surv_logLik){
if(!is.matrix(initials)) initials = matrix(initials, ncol = 2)
fits_mods = map2(initials[,1], initials[,2],
~optim(c(.x, .y), fitfun, likdat = likdat))
fit_modsBFGS = map2(initials[1], initials[2],
~optim(c(.x, .y), fitfun, likdat = likdat, method = "BFGS"))
get_best_fit(fits_mods, fit_modsBFGS)
}
combined_fitter = function(likdat, initials, fitfun = surv_logLik_combined){
fits_mods = pmap(as.data.frame(initials),
~optim(c(..1, ..2, ..3), fitfun, likdat = likdat))
fit_modsBFGS = pmap(as.data.frame(initials),
~optim(c(..1, ..2, ..3), fitfun, likdat = likdat,
method = "BFGS"))
get_best_fit(fits_mods, fit_modsBFGS)
}
# get_best_fit traverses the model results to find the smallest log likelihood
# which.min chooses the first minimum (effectively random as initials were generated randomly)
# BFGS is chosen of NM for ties because BFGS will find boundary (zero) fits
get_best_fit = function(fits_mods, fit_modsBFGS){
loglik = map(fits_mods, ~(.x$value)) %>% flatten_dbl()
loglikBFGS = map(fit_modsBFGS, ~(.x$value)) %>% flatten_dbl()
best_fitNM = which.min(loglik)
best_fitBFGS = which.min(loglikBFGS)
if(loglikBFGS[best_fitBFGS] <= loglik[best_fitNM]){
final_model = fit_modsBFGS[[best_fitBFGS]]
final_model$method = "BFGS"
} else {
final_model = fits_mods[[best_fitNM]]
final_model$method = "NM"
}
final_model
}
## Tidy processing functions ----
# Wrappers to convert optim mode output into tidy data
tidy_fits = function(lik_res){
tibble(
beta0 = exp(lik_res$par[1]),
betaE = exp(lik_res$par[2]),
loglik = lik_res$value,
method = lik_res$method
)
}
tidy_fits_combined = function(lik_res){
tibble(
beta0 = exp(lik_res$par[1]),
betaM = exp(lik_res$par[2]),
betaS = exp(lik_res$par[3]),
loglik = lik_res$value,
method = lik_res$method
)
}build_all.R)Builds the project website by compiling all of the Rmarkdown files using the workflowr API.
# builds full pipeline
wflow_build(c("analysis/about.Rmd", "analysis/index.Rmd",
"analysis/project-functions.Rmd", "analysis/setup-exposure-data.Rmd",
"analysis/general-statistics.Rmd", "analysis/setup-model-data.Rmd",
"analysis/transmission-risk-sensitivity.Rmd", "analysis/transmission-risk.Rmd"))build_all.R)Publishes the project website by compiling all of the Rmarkdown files using the workflowr API.
# publishes full pipeline
wflow_publish(c("analysis/_site.yml", "analysis/about.Rmd", "analysis/index.Rmd",
"analysis/project-functions.Rmd", "analysis/setup-exposure-data.Rmd",
"analysis/general-statistics.Rmd", "analysis/setup-model-data.Rmd",
"analysis/transmission-risk-sensitivity.Rmd", "analysis/transmission-risk.Rmd"))
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.2
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] workflowr_1.4.0 Rcpp_1.0.3 digest_0.6.23 rprojroot_1.3-2
[5] backports_1.1.5 git2r_0.26.1 magrittr_1.5 evaluate_0.14
[9] rlang_0.4.2 stringi_1.4.3 fs_1.3.1 whisker_0.4
[13] rmarkdown_1.17 tools_3.6.1 stringr_1.4.0 glue_1.3.1
[17] xfun_0.10 yaml_2.2.0 compiler_3.6.1 htmltools_0.4.0
[21] knitr_1.25