Package {survlab}

Type:	Package
Title:	Survival Model-Based Imputation for Laboratory Non-Detect Data
Version:	0.2.0
Description:	Implements survival-model-based imputation for censored laboratory measurements, including Tobit-type models with several distribution options. Suitable for data with values below detection or quantification limits, the package identifies the best-fitting distribution and produces realistic imputations that respect the censoring thresholds.
License:	MIT + file LICENSE
Depends:	R (≥ 4.1.0)
Imports:	data.table (≥ 1.17.0), stats, survival (≥ 3.0.0), truncnorm (≥ 1.0.0)
Suggests:	ggplot2, knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Encoding:	UTF-8
Language:	en-US
LazyData:	true
URL:	https://lpereira-ue.github.io/survlab/, https://github.com/lpereira-ue/survlab
Config/roxygen2/version:	8.1.1
NeedsCompilation:	no
Packaged:	2026-10-22 11:25:54 UTC; lpereira
Author:	Luís Pereira [aut, cre], Paulo Infante [aut], Teresa Ferreira [ths], Paulo Quaresma [ths]
Maintainer:	Luís Pereira <d57177@alunos.uevora.pt>
Repository:	CRAN
Date/Publication:	2026-10-22 12:30:02 UTC

Impute Non-Detect Values in Laboratory Data

Description

This function imputes non-detect (censored) values in environmental laboratory analytical data using survival models with automatic distribution selection. It validates data quality requirements and fits multiple distributions to select the best model based on AIC. Each imputed value is guaranteed to be below its respective detection limit and above the specified minimum value.

Usage

impute_nondetect(
  dt,
  value_col = "value",
  cens_col = "censored",
  parameter_col = NULL,
  unit_col = NULL,
  dist = c("gaussian", "lognormal", "weibull", "exponential", "logistic", "loglogistic"),
  min_observations = 25,
  max_censored_pct = 75,
  min_value = 0,
  zero_value = 1e-05,
  control = survival::survreg.control(),
  verbose = FALSE
)

Arguments

Details

The function performs several validation checks: 1. Ensures sufficient sample size (>= min_observations) 2. Checks that censoring percentage is reasonable (<= max_censored_pct) 3. Validates that only one parameter and unit are present (if columns provided) 4. Tests multiple distributions and selects the best based on AIC among those that converged (if none converged, the best non-converged fit is used and a warning is raised) 5. Generates random imputed values below each observation's detection limit and above min_value

The imputed values are random draws from R's random number generator; call set.seed before impute_nondetect() to make them reproducible.

For non-detect observations (censored = 0), the value in value_col is treated as the detection limit for that specific analysis, allowing for different detection limits across samples or analytical methods.

IMPORTANT: This function should be applied to data containing only ONE parameter at a time. Different environmental parameters have different distributions and should not be modeled together.

When verbose = FALSE, the function operates silently except for critical errors, making it suitable for batch processing of multiple parameters.

Value

A data.table with additional columns:

[value_col]_imputed

Imputed values for non-detect observations

[value_col]_final

Final values combining original detected and imputed non-detect values

The returned object also has attributes containing model information:

best_model

The fitted survival model object

best_distribution

Name of the best-fitting distribution

detection_limits

Vector of all detection limits found in the data

max_detection_limit

The highest detection limit (for reference)

parameter

Parameter name (if parameter_col provided)

unit

Unit of measurement (if unit_col provided)

aic

AIC value of the best model

sample_size

Total number of observations

censored_pct

Percentage of censored observations

best_converged

FALSE if no distribution converged and the selected model is the best non-converged fit

fits

A data.table with one row per distribution tried: AIC, convergence, and any error or warning message

n_zero_replaced

Number of exact zeros replaced by zero_value when fitting the positive distributions

Examples

# Load example data
data(multi_censored_data)

# Basic imputation with default settings
set.seed(123)
result <- impute_nondetect(
  dt = multi_censored_data,
  value_col = "value",
  cens_col = "censored",
  verbose = FALSE
)

# View imputed values for non-detects
head(result[censored == 0, .(value, value_imputed, value_final)])

# Check best distribution selected
attr(result, "best_distribution")

# Increase max iterations for difficult datasets
result <- impute_nondetect(
  dt        = multi_censored_data,
  value_col = "value",
  cens_col  = "censored",
  control   = survival::survreg.control(maxiter = 200)
)

# With parameter and unit validation
result <- impute_nondetect(
  dt = multi_censored_data,
  value_col = "value",
  cens_col = "censored",
  parameter_col = "parameter",
  unit_col = "unit"
)

# For strictly positive values (avoiding exactly zero)
result <- impute_nondetect(
  dt = multi_censored_data,
  value_col = "value",
  cens_col = "censored",
  min_value = 1e-10,
  verbose = FALSE
)

Environmental Laboratory Nitrate Data with Non-Detects

Description

A synthetic dataset containing environmental nitrate measurements with non-detect values, generated from a lognormal distribution. This dataset represents typical water quality monitoring data from an environmental laboratory, designed for demonstrating survival model-based imputation techniques.

Usage

multi_censored_data

Format

A data.table with 200 rows and 4 variables:

parameter

Character string indicating the chemical parameter ("Nitrate")

unit

Character string indicating the unit of measurement ("mg/l NO3")

value

Numeric values representing either detected measurements or detection limits for non-detect observations

censored

Integer indicator where 0 = non-detect (below detection limit), 1 = detected (above detection limit)

Details

This dataset simulates real-world environmental water quality data where nitrate measurements below certain detection limits are reported as non-detects. The data includes:

• Single parameter (Nitrate) with consistent units (mg/l NO3)

• Multiple detection limit levels reflecting different analytical conditions

• Realistic distribution of detected vs non-detect values (83.5

• Detection limits ranging from 5 to 25 mg/l NO3

• Lognormal distribution typical of environmental contaminant data

For non-detect observations (censored = 0), the 'value' column contains the detection limit for that specific analysis. For detected measurements (censored = 1), the 'value' column contains the actual measured nitrate concentration.

Source

Synthetic data generated for package demonstration, based on typical environmental water quality monitoring programs

Examples

data(multi_censored_data)

# Basic data exploration
multi_censored_data[, .(
  total_samples = .N,
  non_detects = sum(censored == 0),
  detects = sum(censored == 1)
)]

# View parameter and unit information
multi_censored_data[, .(
  parameter = unique(parameter),
  unit = unique(unit)
)]

# View detection limit levels
multi_censored_data[censored == 0, unique(value)]

# Apply survival model imputation
result <- impute_nondetect(multi_censored_data,
                          parameter_col = "parameter",
                          unit_col = "unit")
validate_imputation(result)

Validate Laboratory Non-Detect Imputation Results

Description

This function validates the quality of non-detect value imputation by checking that imputed values are below their respective limits of quantification and providing comprehensive summary statistics and model diagnostics.

Usage

validate_imputation(
  dt_imputed,
  value_col = "value",
  cens_col = "censored",
  verbose = TRUE
)

Arguments

Details

The function checks:

• All imputed values are strictly below their respective limits of quantification

• Uniqueness of imputed values

• Summary statistics by limits of quantification level

• Model fit information including parameter and unit details

• Dataset characteristics (sample size, censoring percentage)

Value

Invisibly returns the input data.table. When verbose = TRUE, prints validation results to console including:

• Whether all imputed values are below their detection limits

• Number of duplicate imputed values (if any)

• Summary statistics by detection limit level

• Model fit information

Examples

data(multi_censored_data)
result <- impute_nondetect(multi_censored_data, verbose = FALSE)
validate_imputation(result)

# Silent validation for batch processing
validate_imputation(result, verbose = FALSE)