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Merged
gaow merged 9 commits into from
Apr 12, 2025
Merged

Update Apr 12 #21

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95376ae
change name of alpha
xueweic Apr 10, 2025
4681650
Merge pull request #20 from StatFunGen/main
xueweic Apr 10, 2025
628d1db
update_Apr11
xueweic Apr 11, 2025
9720482
PR before change ordered indices
xueweic Apr 12, 2025
7dc74c1
change indice order
xueweic Apr 12, 2025
7c50d5f
Update documentation
xueweic Apr 12, 2025
05c7c09
Update colocboost_utils.R
xueweic Apr 12, 2025
0c3c270
Merge branch 'dev' of https://github.com/StatFunGen/colocboost into dev
xueweic Apr 12, 2025
b893699
Update documentation
xueweic Apr 12, 2025
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5 changes: 4 additions & 1 deletion NAMESPACE
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Original file line number Diff line number Diff line change
Expand Up @@ -3,10 +3,13 @@
export(colocboost)
export(colocboost_assemble)
export(colocboost_check_update_jk)
export(colocboost_get_methods)
export(colocboost_inits)
export(colocboost_plot)
export(colocboost_post_inference)
export(colocboost_update)
export(colocboost_workhorse)
export(get_cos_summary)
export(get_strong_colocalization)
importFrom(grDevices,adjustcolor)
importFrom(graphics,abline)
importFrom(graphics,axis)
Expand Down
213 changes: 104 additions & 109 deletions R/colocboost.R
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391 changes: 0 additions & 391 deletions R/colocboost_addhoc_utils.R
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61 changes: 31 additions & 30 deletions R/colocboost_assemble.R
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Original file line number Diff line number Diff line change
Expand Up @@ -7,11 +7,11 @@
#'
#' Un-colocalization signal - `colocboost_assemble_ucos` - identify the causal confidence sets for each outcome only.
#'
#' Add-hoc merging functions including
#' Add-hoc merge_cos functions including
#'
#' \itemize{
#' \item{merge_coloc_single}{merge the colocalized sets and the single causal set if pass the \code{between_purity}}
#' \item{merge_single}{merge the single causal sets for different outcomes if pass the \code{between_purity}}
#' \item{merge_coloc_single}{merge the colocalized sets and the single causal set if pass the \code{median_cos_abs_corr}}
#' \item{merge_single}{merge the single causal sets for different outcomes if pass the \code{median_cos_abs_corr}}
#' }
#'
#' Refine of the colocalization sets (TO-DO-LIST)
Expand All @@ -21,21 +21,20 @@
#' @export
colocboost_assemble <- function(cb_obj,
coverage = 0.95,
func_intw = "fun_R",
alpha = 1.5,
weight_fudge_factor = 1.5,
check_null = 0.1,
check_null_method = "profile",
check_null_max=2e-5,
dedup = TRUE,
overlap = TRUE,
n_purity = 100,
min_abs_corr = 0.5,
coverage_singlew = 0.8,
sec_coverage_thresh = 0.8,
median_abs_corr = NULL,
between_cluster = 0.8,
between_purity = 0.8,
weaker_ucos = TRUE,
merging = TRUE,
min_cluster_corr = 0.8,
median_cos_abs_corr = 0.8,
weaker_effect = TRUE,
merge_cos = TRUE,
tol = 1e-9,
output_level = 1){

Expand All @@ -47,7 +46,7 @@ colocboost_assemble <- function(cb_obj,
model_info <- get_model_info(cb_obj, outcome_names = data_info$outcome_info$outcome_names)
if (data_info$n_outcomes == 1 & output_level == 1){ output_level = 2 }
if (cb_obj$cb_model_para$num_updates == 1){
cb_output <- list("cos_summary" = NULL,
cb_output <- list("cos_summary" = NULL,
"vcp" = NULL,
"cos_details" = NULL,
"data_info" = data_info,
Expand All @@ -64,8 +63,7 @@ colocboost_assemble <- function(cb_obj,
cb_output <- cb_output[c("cos_summary", "vcp", "cos_details", "data_info", "model_info", "ucos_details", "diagnostic_details")]
}
if (data_info$n_outcome == 1){
cb_output <- list("ucos_summary" = NULL, "pip" = NULL,
"ucos_details" = NULL, "data_info" = data_info)
cb_output <- list("ucos_summary", "pip" = NULL, "ucos_details" = NULL, "data_info" = data_info)
}
}

Expand All @@ -79,23 +77,22 @@ colocboost_assemble <- function(cb_obj,
# --------- about colocalized confidence sets ---------------------------------
out_cos <- colocboost_assemble_cos(cb_obj,
coverage = coverage,
func_intw = func_intw,
alpha = alpha,
weight_fudge_factor = weight_fudge_factor,
check_null = check_null,
check_null_method = check_null_method,
dedup = dedup,
overlap = overlap,
n_purity = n_purity,
min_abs_corr = min_abs_corr,
coverage_singlew = coverage_singlew,
sec_coverage_thresh = sec_coverage_thresh,
median_abs_corr = median_abs_corr,
between_cluster = between_cluster,
between_purity = between_purity,
min_cluster_corr = min_cluster_corr,
median_cos_abs_corr = median_cos_abs_corr,
tol = tol)

# --------- about non-colocalized confidence sets ---------------------------------
L <- cb_obj$cb_model_para$L
if (L == 1){ weaker_ucos = FALSE }
if (L == 1){ weaker_effect = FALSE }
update <- cb_obj$cb_model_para$update_status
ucos_each <- list()
change_obj_each <- purity_each <- vector(mode='list', length=L)
Expand Down Expand Up @@ -135,9 +132,9 @@ colocboost_assemble <- function(cb_obj,
n_purity = n_purity,
min_abs_corr = min_abs_corr,
median_abs_corr = median_abs_corr,
between_cluster = between_cluster,
between_purity = between_purity,
weaker_ucos = weaker_ucos,
min_cluster_corr = min_cluster_corr,
median_cos_abs_corr = median_cos_abs_corr,
weaker_effect = weaker_effect,
tol = tol)
aaa <- out_ucos_each$ucos$ucos
if (length(aaa) != 0){
Expand Down Expand Up @@ -168,7 +165,7 @@ colocboost_assemble <- function(cb_obj,
if ( length(out_cos$cos$cos)!=0 & length(out_ucos$ucos_each)!=0){
past_out <- merge_cos_ucos(cb_obj, out_cos, out_ucos, coverage = coverage,
min_abs_corr = min_abs_corr,tol = tol,
between_purity = between_purity)
median_cos_abs_corr = median_cos_abs_corr)
} else if (length(out_cos$cos$cos)!=0 & length(out_ucos$ucos_each)==0) {
past_out <- list("ucos" = NULL, "cos" = out_cos)
} else if (length(out_cos$cos$cos)==0 & length(out_ucos$ucos_each)!=0){
Expand All @@ -178,29 +175,34 @@ colocboost_assemble <- function(cb_obj,
}
# ----- Merge two ucos sets
if (length(past_out$ucos$ucos_each) > 1){
if (merging) {
if (merge_cos) {
past_out <- merge_ucos(cb_obj, past_out,
min_abs_corr = min_abs_corr,
median_abs_corr = median_abs_corr,
n_purity = n_purity,
between_purity = between_purity,
median_cos_abs_corr = median_cos_abs_corr,
tol = tol)
}
}

############# - extract colocboost output - ####################
# - colocalization results
cb_obj$cb_model_para$alpha <- alpha
cb_obj$cb_model_para$weight_fudge_factor <- weight_fudge_factor
cb_obj$cb_model_para$coverage <- coverage
if (cb_obj$cb_model_para$M==1){
# fixme for single iteration model
cb_obj <- get_max_profile(cb_obj, check_null_max=0.01, check_null_method = "profile")
}
cos_results <- get_cos_details(cb_obj, coloc_out = past_out$cos$cos, data_info = data_info)
cb_output <- list("vcp" = cos_results$vcp,
"cos_details" = cos_results$cos_results,
"data_info" = data_info,
"model_info" = model_info)
class(cb_output) <- "colocboost"

### - extract summary table
target_idx <- cb_obj$cb_model_para$target_idx
summary_table <- get_cos_summary(cb_output, target_outcome = data_info$outcome_info$outcome_names[target_idx])
target_outcome_idx <- cb_obj$cb_model_para$target_outcome_idx
summary_table <- get_cos_summary(cb_output)
cb_output <- c(cb_output, list(cos_summary = summary_table))
cb_output <- cb_output[c("cos_summary", "vcp", "cos_details", "data_info", "model_info")]

Expand All @@ -223,7 +225,7 @@ colocboost_assemble <- function(cb_obj,
if (!is.null(cb_output$ucos_details$ucos)){
cb_output$pip <- apply(do.call(cbind,cb_output$ucos_details$ucos_weight), 1, function(w0) 1-prod(1-w0))
names(cb_output$pip) <- data_info$variables
cb_output$ucos_summary <- get_summary_table_fm(cb_output)
cb_output$ucos_summary <- get_ucos_summary(cb_output)
} else {
tmp <- list("pip" = NULL, "ucos_summary" = NULL)
cb_output <- c(cb_output, tmp)
Expand All @@ -232,7 +234,6 @@ colocboost_assemble <- function(cb_obj,
}
}
}

return(cb_output)

}
33 changes: 16 additions & 17 deletions R/colocboost_assemble_cos.R
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Original file line number Diff line number Diff line change
@@ -1,18 +1,17 @@
#' @importFrom stats as.dist cutree hclust
colocboost_assemble_cos <- function(cb_obj,
coverage = 0.95,
func_intw = "fun_R",
alpha = 1.5,
weight_fudge_factor = 1.5,
check_null = 0.1,
check_null_method = "profile",
dedup = TRUE,
overlap = TRUE,
n_purity = 100,
min_abs_corr = 0.5,
coverage_singlew = 0.8,
sec_coverage_thresh = 0.8,
median_abs_corr = NULL,
between_cluster = 0.8,
between_purity = 0.8,
min_cluster_corr = 0.8,
median_cos_abs_corr = 0.8,
tol = 1e-9){

if (!inherits(cb_obj, "colocboost")){
Expand Down Expand Up @@ -43,7 +42,7 @@ colocboost_assemble_cos <- function(cb_obj,
X_dict <- cb_data$dict[coloc_outcomes[iiii]]
tmp <- w_purity(avWeight[,iiii,drop=FALSE],
X=cb_data$data[[X_dict]]$X,Xcorr=cb_data$data[[X_dict]]$XtX,
N=cb_data$data[[coloc_outcomes[iiii]]]$N, n=n_purity, coverage = coverage_singlew,
N=cb_data$data[[coloc_outcomes[iiii]]]$N, n=n_purity, coverage = sec_coverage_thresh,
min_abs_corr = min_abs_corr, median_abs_corr = median_abs_corr,
miss_idx = cb_data$data[[coloc_outcomes[iiii]]]$variable_miss)
check_purity[iiii] <- length(tmp) == 1
Expand All @@ -54,8 +53,8 @@ colocboost_assemble_cos <- function(cb_obj,
pos_purity <- which(check_purity)
avWeight <- avWeight[,pos_purity,drop=FALSE]
coloc_outcomes <- coloc_outcomes[pos_purity]
weights <- get_integrated_weight(avWeight, func_intw = func_intw, alpha = alpha)
coloc_cos <- get_in_csets(weights, coverage = coverage)
weights <- get_integrated_weight(avWeight, weight_fudge_factor = weight_fudge_factor)
coloc_cos <- get_in_cos(weights, coverage = coverage)
evidence_strength <- sum(weights[coloc_cos[[1]]])

# ----- null filtering
Expand Down Expand Up @@ -144,7 +143,7 @@ colocboost_assemble_cos <- function(cb_obj,
X_dict <- cb_data$dict[coloc_outcomes[iiii]]
tmp <- w_purity(avWeight[,iiii,drop=FALSE],
X=cb_data$data[[X_dict]]$X,Xcorr=cb_data$data[[X_dict]]$XtX,
N=cb_data$data[[coloc_outcomes[iiii]]]$N, n=n_purity, coverage = coverage_singlew,
N=cb_data$data[[coloc_outcomes[iiii]]]$N, n=n_purity, coverage = sec_coverage_thresh,
min_abs_corr = min_abs_corr, median_abs_corr = median_abs_corr,
miss_idx = cb_data$data[[coloc_outcomes[iiii]]]$variable_miss)
check_purity[iiii] <- length(tmp) == 1
Expand All @@ -155,8 +154,8 @@ colocboost_assemble_cos <- function(cb_obj,
pos_purity <- which(check_purity)
avWeight <- avWeight[,pos_purity,drop=FALSE]
coloc_outcomes <- coloc_outcomes[pos_purity]
weights <- get_integrated_weight(avWeight, func_intw = func_intw, alpha = alpha)
coloc_cos <- get_in_csets(weights, coverage = coverage)
weights <- get_integrated_weight(avWeight, weight_fudge_factor = weight_fudge_factor)
coloc_cos <- get_in_cos(weights, coverage = coverage)
evidence_strength <- sum(weights[coloc_cos[[1]]])

# ----- null filtering
Expand Down Expand Up @@ -189,7 +188,7 @@ colocboost_assemble_cos <- function(cb_obj,
# Hierachical Clustering iteration based on sequenced weights
cormat = get_cormat(t(weight_coloc))
hc = hclust(as.dist(1-cormat))
n_cluster = get_n_cluster(hc, cormat, between_cluster = between_cluster)$n_cluster
n_cluster = get_n_cluster(hc, cormat, min_cluster_corr = min_cluster_corr)$n_cluster
index = cutree(hc,n_cluster)
B = sapply(1:n_cluster, function(t) as.numeric(index==t))
B <- as.matrix(B)
Expand All @@ -205,7 +204,7 @@ colocboost_assemble_cos <- function(cb_obj,
X_dict <- cb_data$dict[coloc_outcomes[iiii]]
tmp <- w_purity(weight_cluster,
X=cb_data$data[[X_dict]]$X,Xcorr=cb_data$data[[X_dict]]$XtX,
N=cb_data$data[[coloc_outcomes[iiii]]]$N, n=n_purity, coverage = coverage_singlew,
N=cb_data$data[[coloc_outcomes[iiii]]]$N, n=n_purity, coverage = sec_coverage_thresh,
min_abs_corr = min_abs_corr, median_abs_corr = median_abs_corr,
miss_idx = cb_data$data[[coloc_outcomes[iiii]]]$variable_miss)
check_purity[[iiii]] <- tmp
Expand Down Expand Up @@ -241,8 +240,8 @@ colocboost_assemble_cos <- function(cb_obj,
for (i.w in 1:length(avWeight_coloc)){
w <- avWeight_coloc[[i.w]]
if (sum(w) != 0){
weights <- get_integrated_weight(w, func_intw = func_intw, alpha = alpha)
csets <- get_in_csets(weights, coverage = coverage)
weights <- get_integrated_weight(w, weight_fudge_factor = weight_fudge_factor)
csets <- get_in_cos(weights, coverage = coverage)
coloc_cos[[fl]] <- unlist(csets)
evidence_strength[[fl]] <- sum(weights[coloc_cos[[fl]]])
avWeight_csets[[fl]] <- w
Expand Down Expand Up @@ -325,7 +324,7 @@ colocboost_assemble_cos <- function(cb_obj,



# --- filter 2*: remove overlap confidence sets based on between_purity
# --- filter 2*: remove overlap confidence sets based on median_cos_abs_corr
if (length(coloc_sets) >= 2){
if (overlap){

Expand All @@ -351,7 +350,7 @@ colocboost_assemble_cos <- function(cb_obj,
ave_between[i.between, j.between] <- ave_between[j.between, i.between] <- res[3]
}
}
is.between <- (min_between>min_abs_corr) * (abs(max_between-1)<tol) * (ave_between>between_purity)
is.between <- (min_between>min_abs_corr) * (abs(max_between-1)<tol) * (ave_between>median_cos_abs_corr)
if (sum(is.between) != 0){
temp <- sapply(1:nrow(is.between), function(x){
tt <- c(x, which(is.between[x,] != 0))
Expand Down
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