Package 'ValCurvaR'

Fit an analytical calibration curve

Description

Fit an analytical calibration curve

Usage

ajustar_curva(
  dados,
  metodo = c("auto", "ols", "wls"),
  pesos = c("variancia_nivel", "1/x", "1/x2", "1/y", "1/y2")
)

Arguments

dados	Validated data from validar_curva() or a data frame with internal .x and .y columns.
metodo	One of "auto", "ols" or "wls".
pesos	WLS strategy. "variancia_nivel" uses the inverse experimental variance at each concentration; other choices are empirical alternatives.

Value

A valcurva_fit object.

---

Evaluate the impact of suspicious observations without deleting them

Description

Fits leave-one-out models solely for technical review. It never changes the original data or approves exclusion of a measurement.

Usage

analisar_sensibilidade(ajuste, observacoes = NULL)

Arguments

ajuste	A valcurva_fit object.
observacoes	Optional row numbers. By default, all observations flagged by Cook's distance, leverage, DFFITS, DFBETAS or studentized residuals are used.

Value

A data frame comparing each leave-one-out model with the original fit.

---

Evaluate calibration-curve adequacy against explicit criteria

Description

No universal acceptance limits are imposed. Supply only the limits applicable to the analytical procedure, matrix, and intended range. A missing criterion is reported as not assessed rather than silently approved.

Usage

avaliar_adequacao(ajuste, criterios = list())

Arguments

ajuste	A valcurva_fit object.
criterios	A named list. Supported names are p_falta_ajuste_min, p_mandel_min, cv_max_percentual, and erro_relativo_retrocalculo_max_percentual.

Value

An object of class valcurva_adequacao with a criterion table, back-calculation table, and conclusion.

---

Compare OLS and WLS calibration models

Description

Compare OLS and WLS calibration models

Usage

comparar_modelos(ols, wls)

Arguments

ols	An OLS valcurva_fit object.
wls	A WLS valcurva_fit object.

Value

A data frame intended as decision support, not an automatic approval.

---

Eurachem A5.2 calibration data

Description

Fifteen individual absorbance measurements at five cadmium calibration levels. Values are transcribed from Table A5.2 of the Eurachem/CITAC uncertainty guide and are supplied in long format to preserve replicates.

Usage

dados_eurachem_a52()

Value

A data frame with concentracao_mg_L, replica, and absorbancia.

Examples

dados <- dados_eurachem_a52()
curva <- validar_curva(dados, concentracao_mg_L, absorbancia, replica)
ajuste <- ajustar_curva(curva, metodo = "ols")
grafico_calibracao(ajuste)

---

Calculate calibration-curve diagnostics

Description

Calculate calibration-curve diagnostics

Usage

diagnosticar_curva(ajuste)

Arguments

ajuste

An object returned by ajustar_curva().

Value

A named list of diagnostic results.

---

Generate a complete calibration-validation report in PDF

Description

Generate a complete calibration-validation report in PDF

Usage

gerar_relatorio_pdf(
  ajuste,
  arquivo = file.path(tempdir(), "relatorio-calibracao.pdf"),
  titulo = "Relatorio de validacao da curva de calibracao",
  criterios = list(),
  quiet = TRUE
)

Arguments

ajuste	A valcurva_fit object.
arquivo	Output PDF path. By default, a temporary file is used so the function does not write to the user's working directory.
titulo	Title shown in the report.
criterios	Optional acceptance criteria forwarded to avaliar_adequacao().
quiet	Logical; suppress R Markdown progress messages.

Value

Invisibly, the normalized path to the generated PDF.

---

Plot an analytical calibration diagram

Description

Draws individual data, centroids, fitted line and 95% prediction limits in the visual language of the metrology article supplied for this project.

Usage

grafico_calibracao(ajuste, nivel = 0.95, ...)

Arguments

ajuste	A valcurva_fit object.
nivel	Confidence level for prediction limits.
...	Graphical parameters passed to graphics::plot().

Value

Invisibly, a data frame used to draw the prediction curves.

---

Plot inverse prediction uncertainty over the calibration range

Description

Plot inverse prediction uncertainty over the calibration range

Usage

grafico_incerteza_predicao(ajuste, k = c(1, 2, 3, 4, 9, 16))

Arguments

ajuste	A valcurva_fit object.
k	Numeric vector of numbers of replicate measurements.

Value

Invisibly, plotted uncertainty data.

---

Plot Cook's distance and leverage against their screening limits

Description

Plot Cook's distance and leverage against their screening limits

Usage

grafico_influencia(ajuste)

Arguments

ajuste

A valcurva_fit object.

Value

Invisibly, the influence diagnostics.

---

Plot a normal Q-Q graph with a simulation envelope for residuals

Description

Plot a normal Q-Q graph with a simulation envelope for residuals

Usage

grafico_qq(ajuste, nivel = 0.95, simulacoes = 999L, semente = NULL)

Arguments

ajuste	A valcurva_fit object.
nivel	Envelope coverage level.
simulacoes	Number of normal samples used to build the envelope.
semente	Optional seed for a reproducible envelope.

Value

Invisibly, data used to construct the graph.

---

Plot standardized residuals against concentration

Description

Plot standardized residuals against concentration

Usage

grafico_residuos(ajuste)

Arguments

ajuste

A valcurva_fit object.

Value

Invisibly, residual diagnostic data.

---

Plot instrumental-response variability by concentration

Description

Plot instrumental-response variability by concentration

Usage

grafico_variancia(ajuste)

Arguments

ajuste

A valcurva_fit object.

Value

Invisibly, the level summary.

---

Create the four-panel calibration figure

Description

Create the four-panel calibration figure

Usage

painel_calibracao(ajuste, k = c(1, 2, 3, 4, 9, 16))

Arguments

ajuste	A valcurva_fit object.
k	Replicate counts for the uncertainty panel.

Value

Invisibly, ajuste.

---

Build an auditable calibration summary

Description

Build an auditable calibration summary

Usage

relatorio_auditoria(ajuste, criterios = list())

Arguments

ajuste	A valcurva_fit object.
criterios	Optional criteria forwarded to avaliar_adequacao().

Value

A named list with model, diagnostics, adequacy assessment, and data.

---

Back-calculate concentration with GUM uncertainty propagation

Description

The measurement model is x = (y - a) / b. The covariance of the fitted intercept (a) and slope (b) is retained explicitly in the combined uncertainty. It must not be discarded or treated as two independent inputs.

Usage

retrocalcular_concentracao(
  ajuste,
  sinal,
  k = 1L,
  u_sinal = NULL,
  nivel = 0.95,
  fator_cobertura = NULL
)

Arguments

ajuste	A valcurva_fit object.
sinal	Numeric vector of instrumental responses.
k	Number of replicate signals averaged for each result.
u_sinal	Standard uncertainty of each reported signal. If NULL, the residual standard deviation of the fit divided by sqrt(k) is used as a data-derived estimate.
nivel	Confidence level.
fator_cobertura	Optional coverage factor. If NULL, a two-sided Student t factor using the regression degrees of freedom is used. This keeps the correlated regression coefficients as one uncertainty block.

Value

A data frame with concentration, combined and expanded GUM uncertainty, and the individual variance contributions.

---

Validate analytical calibration data

Description

Keeps the original measurements in long format. A horizontal table is also accepted: omit sinal and every numeric column other than concentracao is converted internally to a replicate column. Replicates must not be averaged before validation because they are needed for variance and lack-of-fit diagnostics.

Usage

validar_curva(dados, concentracao, sinal = NULL, replica = NULL)

Arguments

dados	A data frame.
concentracao	Concentration column, supplied bare or as a string.
sinal	Instrumental response column, supplied bare or as a string. Omit it for horizontal data with one numeric response column per replicate.
replica	Optional replicate identifier column.

Value

A data.frame with standard internal columns .x, .y and .replica.

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