Laboratory assay optimization and experimental design for clinical research. Includes power calculations, sample size determination, design optimization, and quality control for laboratory assays.
Usage
assayoptimization(
response_var,
factors,
blocking_vars = NULL,
covariates = NULL,
design_type = "factorial",
optimization_goal = "maximize_response",
target_value = 1,
power_analysis = TRUE,
alpha_level = 0.05,
power_level = 0.8,
effect_size = 0.5,
replicates = 3,
randomization = "complete",
quality_control = TRUE,
control_charts = "xbar_r",
robust_methods = FALSE,
response_surface = TRUE,
contour_plots = TRUE,
optimization_plots = TRUE,
export_design = FALSE,
method_validation = FALSE,
confidence_level = 0.95
)Arguments
- response_var
Primary assay response variable for optimization
- factors
List of experimental factors for optimization
- blocking_vars
Variables for experimental blocking
- covariates
Covariates to include in the analysis
- design_type
Experimental design approach for factor optimization
- optimization_goal
Goal for assay optimization
- target_value
Target response value for optimization
- power_analysis
Whether to perform power analysis
- alpha_level
Significance level for hypothesis testing
- power_level
Target power for the experimental design
- effect_size
Expected effect size for power calculations
- replicates
Number of replicates per experimental condition
- randomization
Randomization strategy for the experiment
- quality_control
Whether to include QC analysis
- control_charts
Control chart methodology for QC
- robust_methods
Whether to use robust statistical approaches
- response_surface
Whether to perform response surface methodology
- contour_plots
Whether to generate contour plots
- optimization_plots
Whether to generate optimization plots
- export_design
Whether to export the design matrix
- method_validation
Whether to include validation analysis
- confidence_level
Confidence level for statistical intervals
Value
A results object containing:
results$instructions | a html | ||||
results$design_summary | a html | ||||
results$power_analysis | a html | ||||
results$optimization_results | a html | ||||
results$factor_effects | a html | ||||
results$response_surface_summary | a html | ||||
results$quality_control_summary | a html | ||||
results$validation_metrics | a html | ||||
results$design_plot | an image | ||||
results$effects_plot | an image | ||||
results$response_surface_plot | an image | ||||
results$optimization_plot | an image | ||||
results$control_chart | an image | ||||
results$diagnostic_plots | an image | ||||
results$power_plot | an image |
Examples
# Example: Optimize PCR assay design
assayoptimization(
data = pcr_data,
response_var = concentration,
factors = c("primer_conc", "temperature", "cycles"),
optimization_goal = "maximize_efficiency",
design_type = "factorial"
)