Implements dynamic coefficient models for survival data where regression coefficients evolve continuously over time through adaptive updating mechanisms. This approach provides real-time modeling of changing covariate effects using Bayesian frameworks and dynamic linear models, offering sophisticated alternatives to static coefficient approaches in survival analysis.
Usage
dynamiccoeff(
data,
elapsedtime,
outcome,
covariates,
dynamic_covariates,
outcomeLevel = "1",
updating_method = "kalman",
state_dimension = 2,
process_variance = 0.1,
observation_variance = 0.1,
forgetting_factor = 0.99,
burn_in_period = 10,
confidence_level = 0.95,
smoothing_parameter = 0.2,
adaptation_rate = 0.1,
show_model_summary = TRUE,
show_coefficient_paths = TRUE,
show_state_evolution = TRUE,
show_adaptation_metrics = TRUE,
show_dynamic_plots = TRUE,
show_state_plots = TRUE,
show_diagnostic_plots = TRUE,
show_comparison_plots = TRUE,
showSummaries = FALSE,
showExplanations = FALSE
)Arguments
- data
the data as a data frame
- elapsedtime
Survival time or follow-up duration variable
- outcome
Event indicator variable (0/1, FALSE/TRUE, or factor)
- covariates
Covariate variables with constant effects over time
- dynamic_covariates
Covariate variables with dynamically updating coefficients
- outcomeLevel
Level of outcome variable indicating event occurrence
- updating_method
Method for dynamic coefficient updating
- state_dimension
Dimension of the state space for dynamic modeling
- process_variance
Variance of the process noise in state evolution
- observation_variance
Variance of the observation noise
- forgetting_factor
Forgetting factor for exponential discounting of past observations
- burn_in_period
Number of initial observations for model initialization
- confidence_level
Confidence level for dynamic coefficient intervals
- smoothing_parameter
Smoothing parameter for coefficient trajectory smoothing
- adaptation_rate
Rate of adaptation for dynamic coefficient updates
- show_model_summary
Display comprehensive dynamic model summary
- show_coefficient_paths
Display table of dynamic coefficient trajectories
- show_state_evolution
Display state space evolution results
- show_adaptation_metrics
Display adaptation and convergence metrics
- show_dynamic_plots
Display plots of dynamic coefficient evolution
- show_state_plots
Display state space visualization plots
- show_diagnostic_plots
Display model diagnostic and residual plots
- show_comparison_plots
Display comparison with static coefficient models
- showSummaries
Generate natural language summaries of the analysis results
- showExplanations
Show detailed explanations of the methodology and interpretation
Value
A results object containing:
results$todo | a html | ||||
results$modelSummary | a html | ||||
results$coefficientPaths | a table | ||||
results$stateEvolution | a table | ||||
results$adaptationMetrics | a table | ||||
results$modelComparison | a table | ||||
results$convergenceMetrics | a table | ||||
results$dynamicPlots | an image | ||||
results$statePlots | an image | ||||
results$diagnosticPlots | an image | ||||
results$comparisonPlots | an image | ||||
results$adaptationPlots | an image | ||||
results$analysisSummary | a html | ||||
results$methodExplanation | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$coefficientPaths$asDF
as.data.frame(results$coefficientPaths)