Package {ivdtools}

ivdtools: Statistical Tools for In Vitro Diagnostic Method Evaluation

Description

The package provides a collection of statistical workflows for evaluating in vitro diagnostic methods and reagents. Most multi-step workflows use S3 objects: construct an analysis object, add results with analysis generics, and inspect the accumulated results with summary() or plot().

Author(s)

Maintainer: hiox-tech GeorgeBinDragon@outlook.com [copyright holder]

See Also

Useful links:

• https://github.com/hiox-tech/ivdtools

• Report bugs at https://github.com/hiox-tech/ivdtools/issues

arrhenius — Arrhenius accelerated stability analysis

Description

Estimates degradation rates at multiple elevated temperatures, fits the Arrhenius equation, and extrapolates to the target storage temperature to predict shelf life.

Usage

arrhenius(
  data,
  temperature,
  time,
  value,
  target_temp,
  order = c("auto", "zero", "first"),
  direction = c("auto", "decrease", "increase"),
  limit,
  bias_type = c("relative", "absolute"),
  temp_unit = "C",
  time_unit = "d",
  conf.level = 0.95
)

Arguments

Value

An S3 object of class "arrhenius".

Examples

temp_df <- data.frame(temp = rep(c(25,30,37), each = 3),
                      time = rep(c(0,1,3), 3),
                      val = c(100,98,95, 100,96,90, 100,94,85))
arrhenius(temp_df, "temp", "time", "val", target_temp = 5, limit = 10)

Compute ROC curves and AUC for each evaluation column.

Description

Compute ROC curves and AUC for each evaluation column.

Usage

## S3 method for class 'roc'
auc(x, cols = NULL, ...)

Arguments

Value

Updated roc object with results stored in $auc_list and $auc_table

Examples

  obj <- roc(ivd_roc_example, cols = c("x1", "x2"), reference = "ref")
  obj <- auc(obj)
  obj <- auc(obj, cols = "x1")

Bias analysis (S3 method) – estimate bias at given medical decision levels

Description

Compute bias at specified medical decision levels (MDL) based on stored regression results: bias = candidate - predicted_reference = x0 - (intercept + slope * x0) = -intercept + (1 - slope) * x0

Usage

## S3 method for class 'mcr'
bias(
  x,
  mdl,
  level = 0.95,
  interval = c("", "confidence", "prediction", "both"),
  ...
)

Arguments

Details

Bias is the difference between the candidate method value and the estimated reference method value; a positive value indicates the candidate method is higher.

Value

Updated mcr object (invisible), results stored in $bias

Examples

  obj <- mcr(ivd_mcr_example, "sid", "test", "ref")
  obj <- regression(obj, method = "ols")
  bias(obj, mdl = c(200, 400, 600))
  bias(obj, mdl = c(200, 400, 600), interval = "both")

Bland-Altman analysis (S3 method)

Description

Calculate Limits of Agreement between two measurement methods, supporting three y-axis metrics: difference, ratio, and percent difference.

Usage

## S3 method for class 'mcr'
bland_altman(
  x,
  type = c("difference", "ratio", "percent"),
  x_axis = c("mean", "candidate", "reference"),
  agree.level = 0.95,
  conf.level = 0.95,
  ...
)

Arguments

Value

Updated mcr object (invisible), results stored in $bland_altman

Examples

  obj <- mcr(ivd_mcr_example, "sid", "test", "ref")
  bland_altman(obj)
  bland_altman(obj, type = "ratio")
  bland_altman(obj, type = "percent", x_axis = "reference")

bottle_anova constructor

Description

Create a bottle/batch ANOVA analysis object. Supports one-way and nested designs, with automatic assumption checks, missing value reporting, and ANOVA table output.

Usage

bottle_anova(data, formula, conf.level = 0.95)

Arguments

Value

An S3 object of class "bottle_anova" with components:

Examples

  # One-way design
  df1 <- data.frame(
    bottle = rep(c("A", "B", "C"), each = 5),
    value  = c(rnorm(5, 10, 1), rnorm(5, 12, 1), rnorm(5, 11, 1))
  )
  obj <- bottle_anova(df1, value ~ bottle)

  # Nested design
  df2 <- data.frame(
    lot   = rep(c("L1", "L2"), each = 10),
    vial  = rep(c("V1", "V2"), each = 5, times = 2),
    value = c(rnorm(5, 10, 1), rnorm(5, 10.5, 1),
              rnorm(5, 12, 1), rnorm(5, 12.5, 1))
  )
  obj2 <- bottle_anova(df2, value ~ lot/vial)

S3 confidence interval method

Description

Compute confidence intervals for each variance component (SD and %CV) based on Satterthwaite approximation. Requires variance() to be run first to populate the $results slot.

Usage

## S3 method for class 'precision'
ci(x, digits = 4, ...)

Arguments

Value

Updated precision object with results stored in $ci

Examples

  data(VCAdata1, package = "VCA")
  obj <- precision(VCAdata1, y ~ day/run, by = "sample")
  obj <- variance(obj)
  obj <- ci(obj)

Extract coefficients from an equation fit

Description

Extract coefficients from an equation fit

Usage

## S3 method for class 'fit_equation'
coef(object, ...)

Arguments

Value

A named numeric vector of model coefficients.

Examples

df <- data.frame(x = 1:5, y = c(2.1, 4.0, 6.2, 7.9, 10.1))
f <- fit_equation("E01", df, "x", "y")
coef(f)

Fit and compare multiple equations

Description

Fit and compare multiple equations

Usage

compare_equation(data, x = "x", y = "y", eqs = NULL, ...)

Arguments

Value

An object of class "compare_equation" with components:

Examples

df <- data.frame(
  x = c(0.1, 0.3, 1, 3, 10, 30, 100),
  y = c(12, 25, 48, 72, 88, 96, 99)
)
compare_equation(df, "x", "y", eqs = c("E01", "E06", "E07"))

Convert quantitative columns to binary based on cutoff values

Description

Creates new binary columns (0/1) from existing quantitative columns using specified cutoff values, appending them to the original data frame. New columns are named <col>_binary.

Usage

continuous_to_binary(data, cols, cutoff)

Arguments

Value

data frame with original columns plus new <col>_binary columns

Examples

  df <- data.frame(x = 1:10, y = rnorm(10))
  continuous_to_binary(df, "x", 5)
  continuous_to_binary(df, c("x", "y"), c(5, 0))

Correlation analysis (S3 method)

Description

Perform correlation analysis between the test method (X) and reference method (Y), supporting Pearson, Spearman, and Kendall methods.

Usage

## S3 method for class 'mcr'
correlation(x, method = c("pearson", "spearman", "kendall"), ...)

Arguments

Value

Updated mcr object (invisible), results stored in $correlation

Examples

  obj <- mcr(ivd_mcr_example, "sid", "test", "ref")
  correlation(obj)
  correlation(obj, method = "spearman")

Construct a 2x2 contingency table from TP / FP / TN / FN frequencies (silent construction)

Description

When raw data are not available and only the four diagnostic test frequencies are known, use this function to directly construct a result object with the same structure as raw_to_table(), compatible with print() for displaying the fourfold table.

Usage

counts_to_table(
  tp,
  fp,
  tn,
  fn,
  candidate = "candidate",
  reference = "reference",
  candidate_levels = c("positive", "negative"),
  reference_levels = c("positive", "negative")
)

Arguments

Value

A fourfold_table object (S3 class "fourfold_table") with the same structure as raw_to_table(): - $freq_raw Long-format frequency data.frame - $table 3x3 matrix with margins - $n Total sample size - $print Print slot - $candidate_levels Levels of the candidate method - $reference_levels Levels of the reference method Use print() directly to display the fourfold table.

Examples

  result <- counts_to_table(tp = 45, fp = 12, tn = 80, fn = 5,
                          candidate = "new method", reference = "gold standard")

Optimal Cutoff Analysis (S3 method)

Description

For each evaluation column, compute diagnostic metrics at all cutoff values, and identify the optimal cutoff (maximum Youden index, closest to (0,1) corner).

Usage

## S3 method for class 'roc'
cutoff(x, cols = NULL, ...)

Arguments

Value

Updated roc object with results stored in $cutoff_table

Examples

  obj <- roc(ivd_roc_example, cols = c("x1", "x2"), reference = "ref")
  obj <- auc(obj)
  cutoff(obj)
  cutoff(obj, cols = "x1")

Describe an analysis object

Description

Describe an analysis object

Usage

describe(x, ...)

correlation(x, ...)

regression(x, ...)

bias(x, ...)

bland_altman(x, ...)

profile(object, ...)

normal(x, ...)

ci(x, ...)

variance(x, ...)

vc(x, ...)

diagnostics(object, ...)

kappa(x, ...)

mcnemar(x, ...)

cutoff(x, ...)

mlr(x, ...)

auc(x, ...)

Arguments

Value

The value returned by the dispatched method.

Examples

obj <- mcr(ivd_mcr_example, "sid", "test", "ref")
describe(obj)

Descriptive Statistics: Raw Data Overview (S3 Method)

Description

Print variable description information for a fourfold_table object (from raw_to_table). If the object comes from counts_to_table (no raw data), indicates unavailability.

Usage

## S3 method for class 'fourfold_table'
describe(x, ...)

Arguments

Value

Updated fourfold_table object, with $describe slot filled with description results

Examples

  df <- data.frame(
    new    = c("positive","positive","negative","negative","positive","negative"),
    gold   = c("positive","negative","positive","negative","positive","positive"),
    age    = c(45, 52, 38, 61, 47, 55),
    gender = c("M","F","F","M","M","F"),
    id     = c("S001","S002","S003","S004","S005","S006")
  )
  tab <- raw_to_table(df, "new", "gold", id = "id")
  tab <- describe(tab)

Compute descriptive statistics and store results

Description

Computes data overview (rows/columns/ID duplicates), per-column summaries, and a side-by-side comparison table of candidate, reference, and Diff. Results are stored in x$describe (class mcr_describe) and displayed via print.mcr_describe() (e.g., when calling summary()).

Usage

## S3 method for class 'mcr'
describe(x, cols = NULL, digits = 4L, ...)

Arguments

Value

Updated mcr object (invisible), results stored in x$describe

Examples

  obj <- mcr(ivd_mcr_example, "sid", "test", "ref")
  describe(obj)

Descriptive Statistics (S3 method)

Description

Print distribution summaries for evaluation columns and the reference column (if numeric). When the reference is binary, evaluation column distributions are shown grouped by positive/negative.

Usage

## S3 method for class 'roc'
describe(x, cols = NULL, digits = 4L, ...)

Arguments

Value

Updated roc object with results stored in $describe

Examples

  obj <- roc(ivd_roc_example, cols = "x1", reference = "ref")
  describe(obj)

Diagnostic performance metrics: calculate sensitivity, specificity, likelihood ratios, etc. with confidence intervals for a fourfold_table

Description

Diagnostic performance metrics: calculate sensitivity, specificity, likelihood ratios, etc. with confidence intervals for a fourfold_table

Usage

## S3 method for class 'fourfold_table'
diagnostics(
  object,
  conf.level = 0.95,
  ci.method = "wilson",
  prevalence = NULL,
  ...
)

Arguments

Value

Updated fourfold_table object, with diagnostic performance metrics stored in $diagnostics: - $tp / $fp / $fn / $tn / $n Four-fold frequencies and total sample size - $sensitivity Sensitivity with CI - $specificity Specificity with CI - $ppv Positive predictive value with CI - $npv Negative predictive value with CI - $accuracy Accuracy with CI - $prevalence Prevalence with CI - $lr_positive Positive likelihood ratio with CI - $lr_negative Negative likelihood ratio with CI - $odds_ratio Odds ratio with CI - $conf_level Confidence level used - $ci_method Method name used - $prev_specified Whether user specified prevalence

Examples

  tab <- raw_to_table(ivd_qualitative_example, "new", "gold",
                      positive = "positive", id = "id")
  tab <- diagnostics(tab)
  tab <- diagnostics(tab, prevalence = 0.3)
  tab <- diagnostics(tab, conf.level = 0.90)
  tab <- diagnostics(tab, ci.method = "clopper-pearson")

General-purpose equation fitting

Description

General-purpose equation fitting

Usage

fit_equation(
  eq,
  data,
  x = "x",
  y = "y",
  start = NULL,
  df_col = NULL,
  lower = NULL,
  upper = NULL,
  constraints = NULL,
  weights = NULL,
  ...
)

Arguments

Value

an S3 object of class "fit_equation"

Examples

# ==== Response curve fitting ====

# 1) Linear
df <- data.frame(x = 1:5, y = c(2.1, 4.0, 6.2, 7.9, 10.1))
f1 <- fit_equation("E01", df, "x", "y")
print(f1)

# 2) Logarithmic
f2 <- fit_equation("E06", df, "x", "y")
print(f2)

# 3) 4PLC (dose-response, descending)
df4plc <- data.frame(
  conc = c(0.1, 0.3, 1, 3, 10, 30, 100),
  resp = c(99, 96, 88, 72, 48, 25, 12)
)
f3 <- fit_equation("E07", df4plc, "conc", "resp")
print(f3)

# 4) Custom formula
f4 <- fit_equation("a + b * log(x)", df4plc, "conc", "resp")
print(f4)

# 5) Box constraints (parameter bounds)
f5 <- fit_equation("E07", df4plc, "conc", "resp",
  lower = list(D = 0), upper = list(D = 5))
print(f5)

# 6) Inequality constraints (parameter relationship)

# require D > 1 (Hill slope cannot be too shallow)
f6 <- fit_equation("E07", df4plc, "conc", "resp",
  constraints = list(ineq = function(p) 1 - p["D"]))
print(f6)


# 7) Equality constraints (fixed parameter relationship)

# fix b = 0.5 in exponential decay y = a*exp(-b*x)
df_exp <- data.frame(x = 0:5, y = c(10, 6.1, 3.7, 2.2, 1.4, 0.8))
f7 <- fit_equation("E04", df_exp, "x", "y",
  constraints = list(eq = function(p) p["b"] - 0.5))
print(f7)


# 8) Weighted fitting via replicate_to_mean

# data with replicate measurements (8 dose levels, 3 replicates each)
df_rep <- data.frame(
  dose = rep(c(0.3, 0.6, 1.5, 4, 10, 25, 60, 150), each = 3),
  resp = c(4.1, 3.8, 3.9, 8.5, 8.9, 8.6,
           18.2, 17.9, 18.5,
           34.6, 35.1, 34.8,
           54.3, 53.9, 54.7,
           73.0, 73.6, 73.2,
           87.5, 87.1, 87.8,
           97.9, 98.3, 98.0)
)
# first summarize, generate inverse-variance weights
rep <- replicate_to_mean(df_rep, "dose", "resp", weights = "1/sd^2")

# fit 4PLC with summarized means and explicit start values
f8 <- fit_equation("E07", rep, x = "x", y = "y_mean",
  weights = rep$weight,
  start = list(A = 2, B = 100, C = 8, D = -1.5))
print(f8)


# ==== Sadler variance function fitting ====

df_var <- data.frame(
  conc = c(1, 2, 5, 10, 20, 50),
  var  = c(0.1, 0.3, 0.8, 3.2, 12.5, 78.4)
)

# 9) Single Sadler model
f9 <- fit_equation("V03", df_var, "conc", "var")
print(f9)

# 10) All 10 Sadler models
f10 <- fit_equation("sadler", df_var, "conc", "var")
print(f10)

Small deterministic one-way ANOVA example

Description

Small deterministic one-way ANOVA example

Usage

ivd_bottle_example

Format

A data frame with 12 observations from three bottles.

Small deterministic method-comparison example

Description

Small deterministic method-comparison example

Usage

ivd_mcr_example

Format

A data frame with 12 rows and columns sid, test, and ref.

Small deterministic qualitative-method example

Description

Small deterministic qualitative-method example

Usage

ivd_qualitative_example

Format

A data frame with paired binary results and sample identifiers.

Small deterministic ROC example

Description

Small deterministic ROC example

Usage

ivd_roc_example

Format

A data frame with 12 rows, two markers, and a binary reference.

Cohen's Kappa / PABAK Agreement Analysis (2x2 table only)

Description

Calculate Cohen's Kappa coefficient or PABAK (Prevalence-Adjusted Bias-Adjusted Kappa) and its confidence interval, used to evaluate agreement between two binary classification methods.

Usage

## S3 method for class 'fourfold_table'
kappa(x, prevalence = NULL, conf.level = 0.95, ...)

Arguments

Details

When data prevalence is extreme, Cohen's Kappa may be low even when observed agreement is high. In such cases, specify the prevalence parameter to compute PABAK as a correction. PABAK = 2 x p_obs - 1, which assumes expected agreement = 0.5.

Value

Updated fourfold_table object, with Kappa results stored in $kappa: - $kappa Kappa / PABAK coefficient - $se Standard error - $lower / $upper Lower/upper confidence interval bounds - $p_obs Observed agreement - $p_exp Expected agreement - $n Total sample size - $conf_level Confidence level used - $method Method name used

Examples

  tab <- counts_to_table(tp = 85, fp = 3, tn = 90, fn = 2,
                         candidate = "new method", reference = "gold standard")
  tab <- kappa(tab)                        # Cohen's Kappa
  tab <- kappa(tab, prevalence = 0.5)      # PABAK

Print the equation registry

Description

Print the equation registry

Usage

list_equation(category = NULL, engine = NULL)

Arguments

Value

invisible data.frame (matching rows), prints a formatted table to the console

Examples

# All equations
list_equation()

# Response only
list_equation(category = "Response")

# lm engine only
list_equation(engine = "lm")

List Sadler precision profile model equations

Description

List Sadler precision profile model equations

Usage

list_sadler()

Arguments

This function has no arguments.

Details

List the 10 candidate Sadler precision profile model formulas and types used in the VFP package. Includes: Constant SD, Constant CV, Linear (variance), Power model, Exponential model, etc.

Value

Invisibly returns NULL. The model table is printed to the console as a side effect.

Examples

  list_sadler()

List Westgard QC rules

Description

Prints commonly used Westgard multi-rule QC rules and their meanings. Rule names printed here can be passed to qc_chart(rules = "...").

Usage

list_westgard(brief = FALSE)

Arguments

Value

A named character vector of rule descriptions (invisibly).

Examples

  list_westgard()

Limit of blank, detection, and quantitation (LoB / LoD / LoQ)

Description

Estimates analytical sensitivity limits from a data.frame of summarized measurements (one row per sample: a sample-name column, a mean column, an SD column, and a replicate-count column). The workflow follows CLSI EP17-A2:

Usage

lob_lod_loq(
  data,
  sample_col = "x",
  mean_col = "y_mean",
  sd_col = "y_sd",
  n_col = "y_n",
  blank = NULL,
  target_cv = 0.2,
  na.rm = TRUE,
  ...
)

Arguments

Details

• LoB (Section 5.3.3.1, parametric option): LoB = M_B + cp * SD_B, pooling all blank samples, with cp = 1.645 / sqrt(1 - 1/(4*(B - K))).

• LoD (Section 5.4.3): a fixed-point iteration on the fitted precision profile, X = LoB + cp * SD_WL(X), solved for the smallest X >= LoB.

• LoQ (Appendix D1, Example 1 — functional sensitivity): the concentration X where the precision profile meets the target CV, i.e. sqrt(Var(X)) / X = target_cv.

The precision profile is fit internally with fit_equation("sadler", ...) (all 10 Sadler variance models; the best model by AIC is used for prediction).

Note that the LoQ reported here reflects precision only; bias is not included. If the precision-based LoQ is below the LoD, the LoQ is reported as max(LoQ, LoD) with a warning.

Value

An object of class "sensitivity" (printed and plottable) with components:

A warning is issued whenever a limit cannot be estimated (components set to NA).

Examples

# summarized data: two blank samples + six low levels
summ <- data.frame(
  sample = c("blank_A", "blank_B", "L1", "L2", "L3", "L4", "L5", "L6"),
  mean   = c(0, 0, 0.5, 1, 2, 5, 10, 20),
  sd     = 0.05 + 0.10 * c(0, 0, 0.5, 1, 2, 5, 10, 20),
  n      = c(15L, 15L, 5L, 5L, 5L, 5L, 5L, 5L)
)

# 1. blank + low levels: LoB / LoD / LoQ
lob_lod_loq(summ, "sample", "mean", "sd", "n",
            blank = c("blank_A", "blank_B"))

# 2. blanks only: LoB
lob_lod_loq(summ[1:2, ], "sample", "mean", "sd", "n",
            blank = c("blank_A", "blank_B"))

# 3. no blanks: LoQ only
lob_lod_loq(summ[3:8, ], "sample", "mean", "sd", "n")

McNemar Test (for fourfold_table only)

Description

Perform McNemar's test on a 2x2 paired contingency table to evaluate whether there is a systematic difference between two binary classification methods (i.e., whether marginal probabilities are equal).

Usage

## S3 method for class 'fourfold_table'
mcnemar(x, conf.level = 0.95, ...)

Arguments

Details

When discordant pairs (b + c) are few (< 25), automatically uses the exact binomial test (binom.test); otherwise uses McNemar's chi-squared test with continuity correction.

Value

Updated fourfold_table object, with McNemar results stored in $mcnemar: - $chi_sq McNemar chi-squared statistic (NA for exact test) - $df Degrees of freedom (NA for exact test) - $p_value Test p-value - $method Test method used - $b Discordant pairs (positive, negative) count - $c Discordant pairs (negative, positive) count - $n_bc Total discordant pairs - $conf.level Confidence level used

Examples

  tab <- counts_to_table(tp = 45, fp = 12, tn = 80, fn = 5,
                         candidate = "new method", reference = "gold standard")
  tab <- mcnemar(tab)

mcr constructor

Description

Create a method comparison regression (mcr) object containing sample IDs, test method, and reference method data.

Usage

mcr(data, id, candidate, reference, weights = NULL)

Arguments

Value

Returns an S3 "mcr" object containing:

and analysis result storage slots: $correlation, $regression, $outlier, $bland_altman

Examples

  df <- data.frame(sid = 1:30,
                   test = rnorm(30, 50, 10),
                   ref  = rnorm(30, 50, 10))
  obj <- mcr(df, "sid", "test", "ref")

mkt — Mean Kinetic Temperature

Description

Calculates the mean kinetic temperature from one or more temperature probes. Supports equal-interval and time-weighted trapezoidal methods.

Usage

mkt(data, temp_cols, time = NULL, temp_unit = "C", ea = 83.144)

Arguments

Value

An S3 object of class "mkt".

Examples

temp_df <- data.frame(t1 = c(25,26,27), t2 = c(24,25,26), time = 1:3)
mkt(temp_df, c("t1","t2"), "time")

Multivariate Logistic Regression (S3 method)

Description

Fit logistic regression using selected evaluation columns, compute AUC of predicted probabilities. Names must be unique; auto-generated as MLR01, MLR02 ... when not specified.

Usage

## S3 method for class 'roc'
mlr(x, cols = NULL, name = NULL, ...)

Arguments

Value

Updated roc object with results stored in $mlr_results[name]

Examples

  obj <- roc(ivd_roc_example, cols = c("x1", "x2"), reference = "ref")
  obj <- auc(obj)
  obj <- mlr(obj)                     # -> MLR01
  obj <- mlr(obj)                     # -> MLR02
  obj <- mlr(obj, name = "my_model")  # -> my_model

S3 normality test method

Description

Perform normality tests on the response values for each sample, underlying call to normal_test(). Supports automatic test selection (Shapiro-Wilk, Anderson-Darling, Lilliefors, CVM).

Usage

## S3 method for class 'precision'
normal(
  x,
  method = c("auto", "shapiro", "sw", "ad", "lillie", "cvm"),
  level = 0.95,
  ...
)

Arguments

Value

Updated precision object with results stored in $normal

Examples

  data(VCAdata1, package = "VCA")
  obj <- precision(VCAdata1, y ~ day/run, by = "sample")
  obj <- normal(obj)

Normality test

Description

Performs normality tests on a single numeric column of a data frame. Plots are generated separately via plot().

Usage

normal_test(
  data,
  col,
  method = c("auto", "shapiro", "sw", "ad", "lillie", "cvm"),
  level = 0.95
)

Arguments

Value

An S3 object of class "normal_test" containing:

call

Function call.

data_name

Data object name.

col

Column name.

method

User-specified method.

level

Confidence level.

n_total

Total number of rows.

n

Number of finite values (non-missing).

missing

Number of missing values.

test_method

Actual test method used.

statistic

Test statistic.

p_value

p-value.

y

Clean numeric vector, used by plot().

Examples

  df <- data.frame(x = rnorm(50))
  normal_test(df, "x")
  normal_test(df, "x", method = "ad")
  plot(normal_test(df, "x"))
  plot(normal_test(df, "x"), type = "his")
  plot(normal_test(df, "x"), type = c("qq", "his"))

Detect outliers in an analysis object

Description

Detect outliers in an analysis object

Usage

outlier(x, ...)

Arguments

Value

The value returned by the dispatched method.

Examples

  obj <- mcr(ivd_mcr_example, "sid", "test", "ref")
  outlier(obj, method = "grubbs")

Outlier detection (S3 method)

Description

Perform outlier detection based on the difference or percent difference between two measurements. Reuses four methods from ../precision/outliers.R.

Usage

## S3 method for class 'mcr'
outlier(
  x,
  method = c("grubbs", "esd", "dixon", "iqr"),
  type = c("difference", "percent"),
  alpha = 0.05,
  coef = 1.5,
  ...
)

Arguments

Value

Updated mcr object (invisible), results stored in $outlier

Examples

  obj <- mcr(ivd_mcr_example, "sid", "test", "ref")
  outlier(obj, method = "grubbs")
  outlier(obj, method = "iqr", type = "percent")

S3 outlier detection method

Description

Detect outliers in the response values for each sample, supporting Grubbs test and IQR method. Underlying call to outliers_test() (from outliers-and-normal.R).

Usage

## S3 method for class 'precision'
outlier(x, alpha = 0.05, method = c("grubbs", "iqr"), ...)

Arguments

Value

Updated precision object with results stored in $outlier

Examples

  data(VCAdata1, package = "VCA")
  obj <- precision(VCAdata1, y ~ day/run, by = "sample")
  obj <- outlier(obj, method = "iqr")

Outlier detection

Description

Integrates Grubbs test, generalized ESD test, Dixon Q test, and IQR method under a consistent API with an S3 print method.

Usage

outliers_test(data, col, method = c("grubbs", "esd", "dixon", "iqr"), ...)

Arguments

Value

An S3 object of class "outliers_test" containing:

method

Method name used.

data_name

Name of the input data.

col

Column name tested.

n_total

Total number of rows.

n

Number of finite observations used.

missing

Number of missing (non-finite) values.

parameters

List of method parameters.

n_outliers

Number of outliers detected.

indices

Indices of outliers in the original vector (1-based).

values

Outlier values.

details

Method-specific details (statistics, critical values, bounds, etc.).

Examples

set.seed(123)
df <- data.frame(x = c(rnorm(20), 10, -8))
outliers_test(df, "x", "grubbs")
outliers_test(df, "x", "esd", r = 3)
outliers_test(df, "x", "dixon")
outliers_test(df, "x", "iqr", coef = 2)

Plot an equation fit

Description

Plot an equation fit

Usage

## S3 method for class 'fit_equation'
plot(x, interval = "confidence", level = 0.95, frame = FALSE, ...)

Arguments

Value

x, invisibly.

Examples

df4plc <- data.frame(
  conc = c(0.1, 0.3, 1, 3, 10, 30, 100),
  resp = c(12, 25, 48, 72, 88, 96, 99)
)
f <- fit_equation("E07", df4plc, "conc", "resp")
plot(f)
plot(f, interval = "prediction", frame = TRUE)

Plot (S3 method) – unified plotting interface

Description

Draw one of four plot types for an mcr object.

Usage

## S3 method for class 'mcr'
plot(
  x,
  type = c("scatter", "regression", "bland_altman", "bias"),
  interval = c("none", "confidence", "prediction", "both"),
  conf.level = NULL,
  mdl = NULL,
  ...
)

Arguments

Value

Invisible ggplot object

Examples

  obj <- mcr(ivd_mcr_example, "sid", "test", "ref")
  plot(obj)
  plot(obj, type = "scatter")
  obj <- regression(obj)
  plot(obj, type = "regression", interval = "confidence")
  obj <- bland_altman(obj)
  plot(obj, type = "bland_altman")
  obj <- bias(obj, mdl = c(200, 400))
  plot(obj, type = "bias")

S3 plot method

Description

Draw one of five plot types for a precision object. dot (run-order scatter plot) and his (histogram + N(0,1) density) require no prior analysis; var (variance component bar chart) requires variance() to be run first; qq (normal Q-Q plot) requires normal() first; profile (precision profile) requires profile() first.

Usage

## S3 method for class 'precision'
plot(x, type = c("dot", "his", "var", "qq", "profile"), ...)

Arguments

Value

Invisible ggplot object

Examples

  data(VCAdata1, package = "VCA")
  obj <- precision(VCAdata1, y ~ day/run, by = "sample")
  plot(obj, type = "dot")
  plot(obj, type = "his")

Plot reference interval results

Description

One panel per method: jitter strip chart overlaid with reference interval limits, endpoints, and CI error bars.

Usage

## S3 method for class 'reference_interval'
plot(x, ...)

Arguments

Value

A ggplot object, invisibly. The plot is also drawn as a side effect.

Plot (S3 method) – ROC Curve

Description

Draw ROC curves for evaluation columns and/or MLR model predictions. Optionally mark optimal cutoff points (requires cutoff.roc() first).

Usage

## S3 method for class 'roc'
plot(x, cols = NULL, mlr = NULL, cutpoint = NULL, ...)

Arguments

Value

invisible ggplot object

Examples

  obj <- roc(ivd_roc_example, cols = c("x1", "x2"), reference = "ref")
  obj <- auc(obj)
  plot(obj)
  plot(obj, cols = "x1")
  obj <- auc(obj); obj <- cutoff(obj); obj <- mlr(obj)
  plot(obj, mlr = "all", cutpoint = "Youden")

Precision variance component analysis

Description

For each sample (grouped by by), estimate variance components via VCA::anovaVCA, compute SD / CV and their Satterthwaite confidence intervals.

Usage

precision(data, form, by = NULL, NegVC = FALSE, ...)

Arguments

Value

Returns an object of class precision. Use summary, profile, etc. to view results.

Examples

  data(VCAdata1, package = "VCA")
  precision(VCAdata1, y ~ day/run, by = "sample")

  # More complex: deeply nested factors with more sample groups
  data(realData, package = "VCA")
  precision(realData, y ~ lot/calibration/day/run, by = "PID")

Predict fitted values or inverse-predict (from y to x)

Description

Predict fitted values or inverse-predict (from y to x)

Usage

## S3 method for class 'fit_equation'
predict(
  object,
  newdata = NULL,
  x = NULL,
  y = NULL,
  inverse = FALSE,
  interval = NULL,
  level = 0.95,
  ...
)

Arguments

Value

data.frame with x, y, and (if requested) confidence/prediction interval columns

Examples

df4plc <- data.frame(
  conc = c(0.1, 0.3, 1, 3, 10, 30, 100),
  resp = c(12, 25, 48, 72, 88, 96, 99)
)
f <- fit_equation("E07", df4plc, "conc", "resp")

# Fitted values
predict(f)

# Confidence interval
predict(f, interval = "confidence")

# Prediction interval
predict(f, interval = "prediction")

# Inverse prediction: estimate x for y=50
predict(f, newdata = data.frame(resp = 50), inverse = TRUE)

Predict (S3 method) – predict based on regression results

Description

Predict values based on stored regression results.

Usage

## S3 method for class 'mcr'
predict(
  object,
  newdata = NULL,
  candidate = NULL,
  reference = NULL,
  inverse = FALSE,
  interval = c("none", "confidence", "prediction", "both"),
  conf.level = 0.95,
  ...
)

Arguments

Value

data.frame containing predicted values and (if requested) interval bounds

Examples

  obj <- mcr(ivd_mcr_example, "sid", "test", "ref")
  obj <- regression(obj, method = "ols")
  predict(obj, candidate = c(40, 50, 60))
  predict(obj, candidate = c(40, 50, 60), interval = "both")
  predict(obj, newdata = data.frame(xx = c(40, 50)), candidate = "xx")
  predict(obj, inverse = TRUE)  # Inverse prediction on original data

Predict (S3 method) – predict from multivariate logistic regression

Description

Predict positive-class probability for new data using a stored MLR model. Returns the input predictor columns together with predicted probability, standard error, confidence interval, and 0/1 classification.

Usage

## S3 method for class 'roc'
predict(object, mlr = NULL, newdata = NULL, column_map = NULL, ...)

Arguments

Value

data.frame containing the predictor columns used in the model, plus:

Examples

  df <- data.frame(sid = 1:100,
                   x1  = c(rnorm(50, 10, 2), rnorm(50, 12, 2)),
                   x2  = rnorm(100, 5, 1),
                   ref = rep(c(0, 1), each = 50))
  obj <- roc(df, cols = c("x1", "x2"), reference = "ref")
  obj <- auc(obj)
  obj <- mlr(obj)
  predict(obj)
  predict(obj, newdata = df[1:5, ])
  ## column_map example: rename x1 to new_x1 in newdata
  new_df <- df[1:5, c("x2", "ref")]
  new_df$new_x1 <- df$x1[1:5]
  predict(obj, newdata = new_df,
          column_map = c(new_x1 = "x1", x2 = "x2"))

S3 print method: bottle_anova

Description

Print bottle ANOVA analysis results including descriptive statistics, ANOVA table, and assumption checks.

Usage

## S3 method for class 'bottle_anova'
print(x, ...)

Arguments

Value

Invisibly returns x.

S3 print method for describe_table

Description

S3 print method for describe_table

Usage

## S3 method for class 'describe_table'
print(x, ...)

Arguments

Value

Invisible describe_table object

Examples

  df <- data.frame(
    new    = c("positive","positive","negative","negative","positive","negative"),
    gold   = c("positive","negative","positive","negative","positive","positive"),
    age    = c(45, 52, 38, 61, 47, 55),
    gender = c("M","F","F","M","M","F"),
    id     = c("S001","S002","S003","S004","S005","S006")
  )
  tab <- raw_to_table(df, "new", "gold", id = "id")
  tab <- describe(tab)

S3 print method: format diagnostics output (diagnostic performance metrics)

Description

Prints four-fold table frequencies, sensitivity, specificity, PPV, NPV, accuracy, likelihood ratios, etc.

Usage

## S3 method for class 'diagnostics'
print(x, digits = 3, ...)

Arguments

Value

Invisible diagnostics object

Examples

  df <- data.frame(
    new    = c("positive","positive","negative","negative","positive","negative"),
    gold   = c("positive","negative","positive","negative","positive","positive"),
    age    = c(45, 52, 38, 61, 47, 55),
    gender = c("M","F","F","M","M","F"),
    id     = c("S001","S002","S003","S004","S005","S006")
  )
  tab <- raw_to_table(df, "new", "gold", positive = "positive", id = "id")
  tab <- diagnostics(tab)

Print an equation fit

Description

Print an equation fit

Usage

## S3 method for class 'fit_equation'
print(x, ...)

Arguments

Value

x, invisibly.

S3 print method: format a fourfold_table object as a fourfold table

Description

Prints data source, total sample size, variable names, fourfold table (with margins), and analysis status.

Usage

## S3 method for class 'fourfold_table'
print(x, margin = TRUE, ...)

Arguments

Value

Invisible fourfold_table object

Examples

  df <- data.frame(
    new    = c("positive","positive","negative","negative","positive","negative"),
    gold   = c("positive","negative","positive","negative","positive","positive"),
    age    = c(45, 52, 38, 61, 47, 55),
    gender = c("M","F","F","M","M","F"),
    id     = c("S001","S002","S003","S004","S005","S006")
  )
  tab <- raw_to_table(df, "new", "gold", id = "id")
  print(tab)

S3 print method: format kappa_table output

Description

Prints Cohen's Kappa / PABAK coefficient, standard error, confidence interval, and agreement rates.

Usage

## S3 method for class 'kappa_table'
print(x, digits = 4, ...)

Arguments

Value

Invisible kappa_table object

Examples

  tab <- counts_to_table(tp = 85, fp = 3, tn = 90, fn = 2,
                         candidate = "new method", reference = "gold standard")
  tab <- kappa(tab)

S3 print method: format mcnemar_table output

Description

Prints McNemar test results, including discordant pair counts, test statistic, and p-value.

Usage

## S3 method for class 'mcnemar_table'
print(x, ...)

Arguments

Value

Invisible mcnemar_table object

Examples

  tab <- counts_to_table(tp = 45, fp = 12, tn = 80, fn = 5,
                         candidate = "new method", reference = "gold standard")
  tab <- mcnemar(tab)

S3 print method

Description

Print data overview and analysis slot status based on $print slot.

Usage

## S3 method for class 'mcr'
print(x, ...)

Arguments

Value

Invisible mcr object

Examples

  df <- data.frame(sid = 1:30,
                   test = rnorm(30, 50, 10),
                   ref  = rnorm(30, 50, 10))
  obj <- mcr(df, "sid", "test", "ref")
  print(obj)

Print method for bias results

Description

Print method for bias results

Usage

## S3 method for class 'mcr_bias'
print(x, ...)

Arguments

Value

The unchanged mcr_bias data frame, invisibly.

Print method for Bland-Altman results

Description

Print method for Bland-Altman results

Usage

## S3 method for class 'mcr_bland_altman'
print(x, ...)

Arguments

Value

The unchanged mcr_bland_altman object, invisibly.

Print method for correlation results

Description

Print method for correlation results

Usage

## S3 method for class 'mcr_correlation'
print(x, ...)

Arguments

Value

The unchanged mcr_correlation object, invisibly.

Print method for describe results

Description

Print method for describe results

Usage

## S3 method for class 'mcr_describe'
print(x, digits = NULL, ...)

Arguments

Value

The unchanged mcr_describe object, invisibly.

Print method for outlier results

Description

Print method for outlier results

Usage

## S3 method for class 'mcr_outlier'
print(x, ...)

Arguments

Value

The unchanged mcr_outlier object, invisibly.

Print method for regression results

Description

Print method for regression results

Usage

## S3 method for class 'mcr_regression'
print(x, ...)

Arguments

Value

The unchanged mcr_regression object, invisibly.

Print a precision object overview

Description

Print the data overview of a precision object (data class, total rows, formula, grouping variables, sample count, factor levels, balance warnings) and the analysis slot status ([x] / [ ]).

Usage

## S3 method for class 'precision'
print(x, ...)

Arguments

Value

Invisible precision object

Print confidence intervals

Description

Prints confidence interval tables for SD and \ sample, component, estimate, lower, upper.

Usage

## S3 method for class 'precision_ci'
print(x, ...)

Arguments

Value

Invisibly returns x.

Print normality test results

Description

Prints a table of Shapiro-Wilk test results per sample, including sample size, W statistic, and p-value.

Usage

## S3 method for class 'precision_normal'
print(x, ...)

Arguments

Value

Invisibly returns x.

Print outlier detection results

Description

Prints the outlier detection method, count of outliers found, and the detail table (row, sample, value, statistic, critical value).

Usage

## S3 method for class 'precision_outlier'
print(x, ...)

Arguments

Value

Invisibly returns x.

Print precision profile fits

Description

Prints the best Sadler model formula, AIC, and R-squared for each variance component.

Usage

## S3 method for class 'precision_profile'
print(x, ...)

Arguments

Value

Invisibly returns x.

Print variance component table

Description

Prints the variance component summary table with columns: sample, component, VC (variance component), \

Usage

## S3 method for class 'precision_vc'
print(x, ...)

Arguments

Value

Invisibly returns x.

Print reference interval analysis results

Description

Print reference interval analysis results

Usage

## S3 method for class 'reference_interval'
print(x, ...)

Arguments

Value

The unchanged reference_interval object, invisibly.

Print a ROC analysis object

Description

Print a ROC analysis object

Usage

## S3 method for class 'roc'
print(x, ...)

Arguments

Value

x, invisibly.

Print method for roc AUC table

Description

Print method for roc AUC table

Usage

## S3 method for class 'roc_auc_table'
print(x, ...)

Arguments

Value

The unchanged roc_auc_table data frame, invisibly.

Print method for roc cutoff table

Description

Print method for roc cutoff table

Usage

## S3 method for class 'roc_cutoff_table'
print(x, ...)

Arguments

Value

The unchanged roc_cutoff_table data frame, invisibly.

Print method for roc describe results

Description

Print method for roc describe results

Usage

## S3 method for class 'roc_describe'
print(x, digits = NULL, ...)

Arguments

Value

The unchanged roc_describe object, invisibly.

Print a multivariate ROC model

Description

Print a multivariate ROC model

Usage

## S3 method for class 'roc_mlr'
print(x, ...)

Arguments

Value

x, invisibly.

S3 print method: tukey

Description

Print Tukey HSD post-hoc test results including pairwise comparison table, significance markers, and compact letter display.

Usage

## S3 method for class 'tukey'
print(x, ...)

Arguments

Value

Invisibly returns x.

S3 precision profile method

Description

Fit precision (SD) vs. mean across samples using the Sadler model selection algorithm (10 candidate models) based on VFP::fit_vfp().

Usage

## S3 method for class 'precision'
profile(object, model.no = 1:10, ...)

Arguments

Value

Updated precision object with results stored in $profile

Examples

  data(VCAdata1, package = "VCA")
  obj <- precision(VCAdata1, y ~ day/run, by = "sample")
  obj <- variance(obj)
  obj <- profile(obj, model.no = 1)

Levey-Jennings QC chart + Westgard rule evaluation

Description

Draws a Levey-Jennings QC chart and detects out-of-control points based on selected Westgard rules. If target mean and SD are not provided, they are estimated from the data.

Usage

qc_chart(
  data,
  value,
  rules = "1-2s,1-3s,2-2s,R-4s,4-1s,10x",
  mean = NULL,
  sd = NULL,
  group = NULL,
  run = NULL
)

Arguments

Value

An object of class "qc_chart" with violation results and plot data. Use print() to show summary and plot() to draw the Levey-Jennings chart.

Examples

  set.seed(42)
  df <- data.frame(
    run   = 1:30,
    value = c(rnorm(27, 100, 5), 108, 115, 85)
  )
  res <- qc_chart(df, "value", rules = "1-2s,1-3s,10x")
  print(res)
  plot(res)

Core function: raw data -> frequency fourfold table (silent construction)

Description

Core function: raw data -> frequency fourfold table (silent construction)

Usage

raw_to_table(
  data,
  candidate,
  reference,
  id = NULL,
  na.rm = TRUE,
  positive = NULL
)

Arguments

Value

A fourfold_table object (list with S3 class "fourfold_table"), containing: - $freq_raw Raw frequencies (long format) - $table Fourfold matrix (with margins) - $n Total sample size - $print Print slot (descriptive metadata) - $candidate_levels Levels of the candidate method - $reference_levels Levels of the reference method Use print() directly to display the fourfold table.

Examples

  df <- data.frame(
    new    = c("positive","positive","negative","negative","positive","negative"),
    gold   = c("positive","negative","positive","negative","positive","positive"),
    age    = c(45, 52, 38, 61, 47, 55),
    gender = c("M","F","F","M","M","F"),
    id     = c("S001","S002","S003","S004","S005","S006")
  )
  result <- raw_to_table(df, "new", "gold", id = "id")

Reference Interval (RI) Analysis

Description

Features:

1. Reference interval calculation (CLSI EP28-A3c)

2. Three methods: percentile (non-parametric), parametric (normal), robust (Horn & Pesce)

3. Data quality report: missing values, duplicate IDs

4. Visualization: jitter strip plot + method reference intervals with CI

Usage

reference_interval(
  data,
  col,
  interval = 0.95,
  ci = 0.95,
  method = "percentile",
  id = NULL
)

Arguments

Details

Dependencies: base R + ggplot2

Value

An object of S3 class "reference_interval" with fields: $call, $col, $id_name, $n_total, $n_miss, $n_complete, $n_duplicates, $dup_rows, $interval, $ci, $methods, $percentile, $parametric, $robust, $values

Examples

  df <- data.frame(val = rnorm(200, 100, 15))
  reference_interval(df, "val")
  reference_interval(df, "val", method = "all")
  reference_interval(df, "val", method = c("percentile", "robust"))

Regression analysis (S3 method)

Description

Perform regression analysis between the test method (X) and reference method (Y). Supports five methods: Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Deming regression, Weighted Deming regression, and Passing-Bablok regression.

Usage

## S3 method for class 'mcr'
regression(
  x,
  method = c("ols", "wls", "deming", "wdeming", "pb"),
  conf.level = 0.95,
  lambda = 1,
  weights = NULL,
  ...
)

Arguments

Value

Updated mcr object (invisible), results stored in $regression

Examples

  obj <- mcr(ivd_mcr_example, "sid", "test", "ref")
  regression(obj, method = "ols")
  regression(obj, method = "deming")
  regression(obj, method = "pb")

Summarize replicate measurements

Description

Groups data by x, computes mean, SD, and sample size for each group. When weights is specified, returns an additional weight column. Summary metadata (call, weighting method, column names) is stored as attributes for downstream reference.

Usage

replicate_to_mean(data, x, y, weights = NULL, na.rm = TRUE)

Arguments

Value

A data.frame with columns:

Attributes: call, weights (method name), x_col, y_col

Examples

# Basic summary
df <- data.frame(
  conc = rep(c(1, 2, 5, 10), each = 3),
  resp = c(3.1, 3.2, 2.9, 5.8, 6.1, 5.9,
           14.2, 14.5, 14.0, 28.1, 27.9, 28.3)
)
rep <- replicate_to_mean(df, "conc", "resp")
rep

# Inverse-variance weighting
rep_w <- replicate_to_mean(df, "conc", "resp", weights = "1/sd^2")
rep_w

Extract residuals from an equation fit

Description

Extract residuals from an equation fit

Usage

## S3 method for class 'fit_equation'
residuals(object, ...)

Arguments

Value

A numeric vector of residuals.

Examples

df <- data.frame(x = 1:5, y = c(2.1, 4.0, 6.2, 7.9, 10.1))
f <- fit_equation("E01", df, "x", "y")
residuals(f)

roc constructor

Description

Create an ROC analysis object.

Usage

roc(data, cols, reference, id = NULL, positive = NULL)

Arguments

Details

Supports multiple evaluation columns (quantitative) and a single reference column (binary 0/1, factor, or character with two levels). Automatically detects direction, reports missing values and ID duplicates.

Value

Returns an S3 object of class "roc"

Examples

  df <- data.frame(
    sid = 1:100,
    x1  = c(rnorm(50, 10, 2), rnorm(50, 12, 2)),
    ref = rep(c(0, 1), each = 50),
    age = 1:100,
    sex = rep(c("F", "M"), each = 50)
  )
  obj <- roc(df, cols = "x1", reference = "ref", id = "sid")
  print(obj)

Sample size and power for Bland-Altman agreement assessment

Description

Given the clinical limit delta and either n (sample size) or power (target power), calculates the other using the method of Lu et al. (2016).

Usage

sample_size_bland_altman(
  n = NULL,
  power = NULL,
  mu,
  sd,
  delta,
  conf.level = 0.95,
  agree.level = 0.95,
  n.min = 3L,
  n.max = 1e+05
)

Arguments

Value

An object of class sample_size_bland_altman: a numeric power value when n is supplied, or an integer sample size when power is supplied. Analysis inputs are retained as attributes.

References

Lu MJ, Zhong WH, Liu YX, Miao HZ, Li YC, Ji MH (2016) Sample size for assessing agreement between two methods of measurement by Bland-Altman method. International Journal of Biostatistics, 12(2). doi:10.1515/ijb-2015-0039

Examples

# Mode 1: compute power given sample size
sample_size_bland_altman(n = 203, mu = 0.2, sd = 1, delta = 2.5)

# Mode 2: compute sample size given target power
sample_size_bland_altman(power = 0.80, mu = 0.2, sd = 1, delta = 2.5)

Single-arm target value test – sample size / significance level / power

Description

Given any two of {n, alpha, power}, compute the third using a confidence-interval inversion approach for a one-sample proportion test against a known target value p0.

Usage

sample_size_proportion(
  p0 = 0.2,
  p1 = 0.35,
  n = NULL,
  alpha = NULL,
  power = NULL,
  alternative = c("greater", "less", "two.sided"),
  method = c("wilson", "wald", "wald-cc", "agresti-coull", "jeffreys", "wilson-cc",
    "clopper-pearson"),
  n.max = 1e+06,
  tol = 1e-08
)

Arguments

Value

An object of class sample_size_proportion.

• If computing n: returns an integer sample size.

• If computing power: returns a numeric power in [0, 1].

• If computing alpha: returns a numeric significance level in (0, 1).

Metadata is stored in attributes (method, p0, p1, alpha, power, x_crit, alternative, type). Use print for full context.

Examples

# Mode 1 -- compute n (given alpha and power)
sample_size_proportion(p0 = 0.2, p1 = 0.35,
                       alpha = 0.025, power = 0.8, method = "wilson")

# Mode 2 -- compute power (given n and alpha)
sample_size_proportion(p0 = 0.2, p1 = 0.35,
                       n = 60, alpha = 0.025, method = "wald-cc")

# Mode 3 -- compute alpha (given n and power)
sample_size_proportion(p0 = 0.2, p1 = 0.35,
                       n = 60, power = 0.8, method = "clopper-pearson")

Sample size or half-width for a single proportion confidence interval

Description

Given a target proportion p and either d (half-width) or n (sample size), compute the unknown quantity using one of seven confidence interval methods.

Usage

sample_size_proportion_ci(
  p,
  n = NULL,
  d = NULL,
  conf.level = 0.95,
  method = c("wilson", "wald", "wald-cc", "agresti-coull", "jeffreys", "wilson-cc",
    "clopper-pearson"),
  n.max = 1e+06,
  tol = 1e-06
)

Arguments

Value

An object of class sample_size_proportion_ci.

• If d is given: returns an integer sample size n with metadata stored in attributes (method, p, d, conf.level, type).

• If n is given: returns a numeric half-width d with metadata stored in attributes (method, p, n, conf.level, type).

Use print to see full context.

Examples

# Mode 1: compute n (given p and d)
sample_size_proportion_ci(p = 0.3, d = 0.05)

# Mode 2: compute d (given p and n)
sample_size_proportion_ci(p = 0.3, n = 320, conf.level = 0.99)

stability_bias — Node-wise bias analysis across storage conditions

Description

Each storage condition is baseline-corrected to its own first time point. Absolute and relative biases are computed and compared across conditions at matched time points.

Usage

stability_bias(
  data,
  condition,
  time,
  value,
  reference_condition = NULL,
  limit = NULL,
  bias_type = c("relative", "absolute"),
  time_unit = "d"
)

Arguments

Value

An S3 object of class "stability_bias".

Examples

set.seed(42)
df <- data.frame(cond = rep(c("2-8C","25C"), each = 8),
                 t = rep(c(0,1,3,7), 4),
                 val = c(rnorm(4,100,2), rnorm(4,98,2),
                         rnorm(4,100,2), rnorm(4,92,3)))
stability_bias(df, "cond", "t", "val", limit = 5)

stability_plan — CLSI EP25 Appendix A time-point planning

Description

Based on the ratio of expected drift to allowable drift and the reproducibility variability, looks up the minimum number of time points required per regression series in the EP25 Appendix A tables.

Usage

stability_plan(
  allowable_drift,
  variability,
  replicates = 1L,
  expected_drift = NULL,
  power = c(0.8, 0.9),
  mode = c("simple", "components"),
  bias_type = c("relative", "absolute")
)

Arguments

Value

An S3 object of class "stability_plan".

Examples

stability_plan(4, 1.5, replicates = c(1,2,3))

comp <- data.frame(source = c("repeatability","lot","operator"),
                   variability = c(1.5,1.0,0.8),
                   levels = c(1,3,2))
stability_plan(4, comp, replicates = c(1,2,3), mode = "components")

stability_regression — CLSI EP25 regression-based stability assessment

Description

Performs simple linear regression on a single condition's measurements (self mode) or on paired biases between test and reference conditions (compare mode), with one-sided confidence limits to evaluate whether drift remains within allowable limits.

Usage

stability_regression(
  data,
  time,
  value,
  condition = NULL,
  mode = c("self", "compare"),
  test_condition = NULL,
  reference_condition = NULL,
  bias_type = c("relative", "absolute"),
  direction = c("auto", "decrease", "increase"),
  conf.level = 0.95,
  limit = NULL,
  time_unit = "d"
)

Arguments

Value

An S3 object of class "stability_regression".

Examples

set.seed(42)
df <- data.frame(cond = rep(c("2-8C","25C"), each = 8),
                 t = rep(c(0,1,3,7), 4),
                 val = c(rnorm(4,100,2), rnorm(4,98,2),
                         rnorm(4,100,2), rnorm(4,92,3)))
stability_regression(df, "t", "val", "cond", test_condition = "25C", limit = 5)

set.seed(42)
df <- data.frame(cond = rep(c("Ref","Test"), each = 8),
                 t = rep(c(0,1,3,7), 4),
                 val = c(rnorm(4,100,2), rnorm(4,99,2),
                         rnorm(4,100,2), rnorm(4,94,3)))
stability_regression(df, "t", "val", "cond", mode = "compare",
                     test_condition = "Test", reference_condition = "Ref", limit = 5)

stability_time — Predict time-to-limit from a stability regression

Description

Given a fitted regression model, computes the time at which the point estimate or one-sided confidence limit first reaches the allowable bias. May extrapolate beyond the observed time range.

Usage

stability_time(object, limit = NULL, max_time = NULL)

Arguments

Value

An S3 object of class "stability_time".

Examples

set.seed(42)
df <- data.frame(cond = rep(c("2-8C","25C"), each = 8),
                 t = rep(c(0,1,3,7), 4),
                 val = c(rnorm(4,100,2), rnorm(4,98,2),
                         rnorm(4,100,2), rnorm(4,92,3)))
r <- stability_regression(df, "t", "val", "cond", test_condition = "25C", limit = 5)
stability_time(r)

set.seed(42)
df <- data.frame(cond = rep(c("Ref","Test"), each = 8),
                 t = rep(c(0,1,3,7), 4),
                 val = c(rnorm(4,100,2), rnorm(4,99,2),
                         rnorm(4,100,2), rnorm(4,94,3)))
r2 <- stability_regression(df, "t", "val", "cond", mode = "compare",
                           test_condition = "Test", reference_condition = "Ref", limit = 5)
stability_time(r2, limit = 5)

S3 summary method

Description

Summarizes all key analysis information already executed in a fourfold_table object, formatted following summary.mcr() pattern (mcr.R).

Usage

## S3 method for class 'fourfold_table'
summary(object, digits = 3, ...)

Arguments

Value

Invisible fourfold_table object

Examples

  tab <- counts_to_table(tp = 85, fp = 3, tn = 90, fn = 2,
                         candidate = "new method", reference = "gold standard")
  summary(tab)
  tab <- diagnostics(tab); tab <- kappa(tab); tab <- mcnemar(tab)
  summary(tab)

S3 summary method

Description

Summarize key information from all executed analyses in the mcr object (descriptive statistics, correlation analysis, regression analysis, outlier detection, Bland-Altman analysis, and bias analysis).

Usage

## S3 method for class 'mcr'
summary(object, ...)

Arguments

Value

Invisible mcr object

Examples

  df <- data.frame(sid = 1:30,
                   test = rnorm(30, 50, 10),
                   ref  = rnorm(30, 50, 10))
  obj <- mcr(df, "sid", "test", "ref")
  summary(obj)
  obj <- correlation(obj)
  obj <- regression(obj, method = "ols")
  obj <- bland_altman(obj)
  summary(obj)

S3 summary method

Description

Summarize all executed analyses in a precision object, including data description, analysis status checklist, outlier detection, normality tests, variance components, confidence intervals, and precision profile.

Usage

## S3 method for class 'precision'
summary(object, ...)

Arguments

Value

Invisible precision object

Examples

  data(VCAdata1, package = "VCA")
  obj <- precision(VCAdata1, y ~ day/run, by = "sample")
  summary(obj)

Summary (S3 method)

Description

Output key information from all analyses performed on the roc object.

Usage

## S3 method for class 'roc'
summary(object, ...)

Arguments

Value

invisible roc object

Examples

  obj <- roc(ivd_roc_example, cols = c("x1", "x2"), reference = "ref")
  summary(obj)
  obj <- auc(obj)
  obj <- cutoff(obj)
  obj <- mlr(obj)
  summary(obj)

Tukey HSD post-hoc test

Description

Tukey HSD post-hoc test

Usage

tukey(x, ...)

Arguments

Value

The value returned by the dispatched method.

Examples

  obj <- bottle_anova(ivd_bottle_example, value ~ bottle)
  res <- tukey(obj)

Tukey HSD post-hoc test (S3 method)

Description

Perform Tukey HSD pairwise comparisons on an ANOVA result, with compact letter display.

Usage

## S3 method for class 'bottle_anova'
tukey(x, term = NULL, conf.level = NULL, ...)

Arguments

Value

An S3 object of class "tukey" with components:

Note: The original bottle_anova object's $tukey_results is also updated.

Examples

  obj <- bottle_anova(ivd_bottle_example, value ~ bottle)
  res <- tukey(obj)
  res

S3 variance component estimation method

Description

For each sample, run VCA::anovaVCA() to estimate variance components, compute standard deviation (SD), coefficient of variation (CV%), and their percentage of total variance. Results are stored in the $results and $vc slots.

Usage

## S3 method for class 'precision'
variance(x, digits = 4, ...)

Arguments

Value

Updated precision object, $results contains per-sample VCA results, $vc is the summary variance component table

Examples

  data(VCAdata1, package = "VCA")
  obj <- precision(VCAdata1, y ~ day/run, by = "sample")
  obj <- variance(obj)

Youden plot

Description

Draws a Youden plot comparing two measurement systems. If target means and SDs are not provided, they are estimated from the data.

Usage

youden_plot(
  data,
  sample1,
  sample2,
  mean1 = NULL,
  mean2 = NULL,
  sd1 = NULL,
  sd2 = NULL
)

Arguments

Value

An object of class "youden_plot" with summary statistics and plot data. Use print() to show summary and plot() to draw the Youden plot.

Examples

  set.seed(42)
  df <- data.frame(
    m1 = rnorm(30, 100, 5),
    m2 = rnorm(30,  98, 5)
  )
  res <- youden_plot(df, "m1", "m2", mean1 = 100, mean2 = 100, sd1 = 5, sd2 = 5)
  print(res)
  plot(res)