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turnbull: Rename other references from s to p post change to EM-ICM

This commit is contained in:
RunasSudo 2023-10-29 12:45:59 +11:00
parent f4d436e608
commit c87f42a042
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
1 changed files with 7 additions and 7 deletions
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14
src/turnbull.rs
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@ -191,9 +191,9 @@ pub fn fit_turnbull(data_times: MatrixXx2<f64>, progress_bar: ProgressBar, max_i
(left_index, right_index) (left_index, right_index)
}).collect(); }).collect();
// Initialise s // Initialise p
// Faster to repeatedly index Vec than DVector, and we don't do any matrix arithmetic, so represent this as Vec // Faster to repeatedly index Vec than DVector, and we don't do any matrix arithmetic, so represent this as Vec
let s = vec![1.0 / intervals.len() as f64; intervals.len()]; let p = vec![1.0 / intervals.len() as f64; intervals.len()];
let mut data = TurnbullData { let mut data = TurnbullData {
data_time_interval_indexes: data_time_interval_indexes, data_time_interval_indexes: data_time_interval_indexes,
@ -208,20 +208,20 @@ pub fn fit_turnbull(data_times: MatrixXx2<f64>, progress_bar: ProgressBar, max_i
progress_bar.reset(); progress_bar.reset();
progress_bar.println("Running EM-ICM algorithm to fit Turnbull estimator"); progress_bar.println("Running EM-ICM algorithm to fit Turnbull estimator");
let (s, ll) = fit_turnbull_estimator(&mut data, progress_bar.clone(), max_iterations, ll_tolerance, s); let (p, ll) = fit_turnbull_estimator(&mut data, progress_bar.clone(), max_iterations, ll_tolerance, p);
// Get survival probabilities (1 - cumulative failure probability), excluding at t=0 (prob=1) and t=inf (prob=0) // Get survival probabilities (1 - cumulative failure probability), excluding at t=0 (prob=1) and t=inf (prob=0)
let mut survival_prob: Vec<f64> = Vec::with_capacity(data.num_intervals() - 1); let mut survival_prob: Vec<f64> = Vec::with_capacity(data.num_intervals() - 1);
let mut acc = 1.0; let mut acc = 1.0;
for j in 0..(data.num_intervals() - 1) { for j in 0..(data.num_intervals() - 1) {
acc -= s[j]; acc -= p[j];
survival_prob.push(acc); survival_prob.push(acc);
} }
// -------------------------------------------------- // --------------------------------------------------
// Compute standard errors for survival probabilities // Compute standard errors for survival probabilities
let hessian = compute_hessian(&data, &s); let hessian = compute_hessian(&data, &p);
let mut survival_prob_se: DVector<f64>; let mut survival_prob_se: DVector<f64>;
@ -233,7 +233,7 @@ pub fn fit_turnbull(data_times: MatrixXx2<f64>, progress_bar: ProgressBar, max_i
} }
SEMethod::OIMDropZeros => { SEMethod::OIMDropZeros => {
// Drop rows/columns of Hessian corresponding to intervals with zero failure probability // Drop rows/columns of Hessian corresponding to intervals with zero failure probability
let nonzero_intervals: Vec<usize> = (0..(data.num_intervals() - 1)).filter(|i| s[*i] > zero_tolerance).collect(); let nonzero_intervals: Vec<usize> = (0..(data.num_intervals() - 1)).filter(|i| p[*i] > zero_tolerance).collect();
let mut hessian_nonzero: DMatrix<f64> = DMatrix::zeros(nonzero_intervals.len(), nonzero_intervals.len()); let mut hessian_nonzero: DMatrix<f64> = DMatrix::zeros(nonzero_intervals.len(), nonzero_intervals.len());
for (nonzero_index1, orig_index1) in nonzero_intervals.iter().enumerate() { for (nonzero_index1, orig_index1) in nonzero_intervals.iter().enumerate() {
@ -262,7 +262,7 @@ pub fn fit_turnbull(data_times: MatrixXx2<f64>, progress_bar: ProgressBar, max_i
return TurnbullResult { return TurnbullResult {
failure_intervals: data.intervals, failure_intervals: data.intervals,
failure_prob: s, failure_prob: p,
survival_prob: survival_prob, survival_prob: survival_prob,
survival_prob_se: survival_prob_se.data.as_vec().clone(), survival_prob_se: survival_prob_se.data.as_vec().clone(),
ll_model: ll, ll_model: ll,