Package 'biodosetools' reference manual

Title:	'shiny' Application for Biological Dosimetry
Description:	A tool to perform all different statistical tests and calculations needed by Biological dosimetry Laboratories. Detailed documentation is available in <https://biodosetools-team.github.io/documentation/>.
Authors:	Alfredo Hernández [aut] (ORCID: <https://orcid.org/0000-0002-2660-4545>), Anna Frances-Abellan [aut, cre] (ORCID: <https://orcid.org/0000-0003-4738-1712>), David Endesfelder [aut], Pere Puig [aut] (ORCID: <https://orcid.org/0000-0002-6607-9642>)
Maintainer:	Anna Frances-Abellan <[email protected]>
License:	GPL-3
Version:	3.7.2
Built:	2026-05-15 09:29:44 UTC
Source:	https://github.com/biodosetools-team/biodosetools

Calculate AIC (Akaike's 'An Information Criterion')

Description

Calculate AIC (Akaike's 'An Information Criterion')

Usage

AIC_from_data(
  general_fit_coeffs,
  data,
  dose_var = "dose",
  yield_var = "yield",
  fit_link = "identity"
)
AIC_from_data(
  general_fit_coeffs,
  data,
  dose_var = "dose",
  yield_var = "yield",
  fit_link = "identity"
)

Arguments

general_fit_coeffs

Generalised fit coefficients matrix.

data

Data (dose, yield) to calculate AIC from.

dose_var

Name of the dose variable (enquoted).

yield_var

Name of the yield variable (enquoted).

fit_link

A specification for the model link function.

Value

Numeric value of AIC.

Bar plots dosimetry

Description

Bar plots dosimetry

Usage

bar_plots(dat, curve, place)
bar_plots(dat, curve, place)

Arguments

dat

data frame of data values.

curve

manual or auto.

place

UI or save.

Value

plot

Examples

dat <- data.frame(
  "Lab" = c("A1", "A2"),
  "Module" = c("dicentrics", "dicentrics"),
  "Type" = c("manual", "manual"),
  "radiation quality" = c("Cs-137", "Co-60"),
  "calibration" = c("air kerma", "air kerma"),
  "irradiation" = c("air", "air"),
  "temperature" = c(20, 37),
  "dose rate" = c(0.446, 0.27),
  "curve origin" = c("own", "own"),
  "C" = c(0.001189589, 0.0005),
  "alpha" = c(0.01903783, 0.0142),
  "beta" = c(0.0968831, 0.0759),
  "C std.error" = c(0.0001040828, 0.0005),
  "alpha std.error" = c(0.004119324, 0.0044),
  "beta std.error" = c(0.003505209, 0.0027),
  "max curve dose" = c(6.00, 5.05),
  stringsAsFactors = FALSE,
  check.names = FALSE
)


bar_plots(
  dat = dat,
  curve = "manual",
  place = "UI"
)
dat <- data.frame(
  "Lab" = c("A1", "A2"),
  "Module" = c("dicentrics", "dicentrics"),
  "Type" = c("manual", "manual"),
  "radiation quality" = c("Cs-137", "Co-60"),
  "calibration" = c("air kerma", "air kerma"),
  "irradiation" = c("air", "air"),
  "temperature" = c(20, 37),
  "dose rate" = c(0.446, 0.27),
  "curve origin" = c("own", "own"),
  "C" = c(0.001189589, 0.0005),
  "alpha" = c(0.01903783, 0.0142),
  "beta" = c(0.0968831, 0.0759),
  "C std.error" = c(0.0001040828, 0.0005),
  "alpha std.error" = c(0.004119324, 0.0044),
  "beta std.error" = c(0.003505209, 0.0027),
  "max curve dose" = c(6.00, 5.05),
  stringsAsFactors = FALSE,
  check.names = FALSE
)


bar_plots(
  dat = dat,
  curve = "manual",
  place = "UI"
)

Calculate ZScore

Description

Calculate ZScore

Usage

calc.zValue.new(X, type, alg, c)
calc.zValue.new(X, type, alg, c)

Arguments

X

vector of estimated doses.

type

dose or freq.

alg

algorithm. Choose between algA, algB or QHampel

c

value for reference dose.

Value

Numeric value of zscore.

Examples


calc.zValue.new(X = c(3.65, 2.5, 4.85),
                type = "dose",
                alg = "algA" ,
                c = c(3.5, 3, 4.25))
calc.zValue.new(X = c(3.65, 2.5, 4.85),
                type = "dose",
                alg = "algA" ,
                c = c(3.5, 3, 4.25))

Aberration calculation functions

Description

Aberration calculation functions

Usage

calculate_aberr_power(data, aberr_prefix = "C", power = 1)

calculate_aberr_mean(X, N)

calculate_aberr_var(X, X2, N)

calculate_aberr_disp_index(mean, var)

calculate_aberr_u_value(X, N, mean, var, assessment_u = 1)

init_aberr_table(
  data,
  type = c("count", "case"),
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)
calculate_aberr_power(data, aberr_prefix = "C", power = 1)

calculate_aberr_mean(X, N)

calculate_aberr_var(X, X2, N)

calculate_aberr_disp_index(mean, var)

calculate_aberr_u_value(X, N, mean, var, assessment_u = 1)

init_aberr_table(
  data,
  type = c("count", "case"),
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)

Arguments

data

Count or case data.

aberr_prefix

Prefix of the aberrations in the data.

power

Power of aberration.

X

Sum of detected aberrations.

N

Number of cells analysed.

X2

Quadratic sum of detected aberrations.

mean

Mean.

var

Variance.

assessment_u

Expected $u$ -value of the assessment. For a Poisson distribution this should be unity.

type

Type of input data. Either "count" and "case".

aberr_module

Aberration module.

Calculate IRR (this is the same as the Odds-Ratio calculation in IAEA2011)

Description

Calculate IRR (this is the same as the Odds-Ratio calculation in IAEA2011)

Usage

calculate_aberr_IRR(case_data, fit_coeffs, badge_dose)
calculate_aberr_IRR(case_data, fit_coeffs, badge_dose)

Arguments

case_data

Case data in data frame form.

fit_coeffs

Fitting coefficients matrix.

badge_dose

Suspected (e.g. badge) dose

Value

character variable representing IRR in the for (P(X=k|D=0 Gy):P(X=k|D=badge_dose)).

Calculate aberrations table

Description

Calculate aberrations table

Usage

calculate_aberr_table(
  data,
  type = c("count", "case"),
  aberr_module = c("dicentrics", "translocations", "micronuclei"),
  assessment_u = 1
)
calculate_aberr_table(
  data,
  type = c("count", "case"),
  aberr_module = c("dicentrics", "translocations", "micronuclei"),
  assessment_u = 1
)

Arguments

data

Count or case data.

type

Type of input data. Either "count" and "case".

aberr_module

Aberration module, required for type = "case".

assessment_u

Expected $u$ -value of the assessment. For a Poisson distribution this should be unity.

Value

Data frame containing cell count ( $N$ ), aberrations ( $X$ ), and other coefficients (dispersion index, $u$ -value, ...), as well as raw count or case data.

Examples

data <- data.frame(
    ID = c("example1", "example2"),
    C0 = c(302, 160),
    C1 = c(28, 55),
    C2 = c(22, 19),
    C3 = c(8, 17),
    C4 = c(1, 9),
    C5 = c(0, 4)
)


calculate_aberr_table(data,
                      type = "case",
                      aberr_module = "dicentrics",
                      assessment_u = 1)
data <- data.frame(
    ID = c("example1", "example2"),
    C0 = c(302, 160),
    C1 = c(28, 55),
    C2 = c(22, 19),
    C3 = c(8, 17),
    C4 = c(1, 9),
    C5 = c(0, 4)
)


calculate_aberr_table(data,
                      type = "case",
                      aberr_module = "dicentrics",
                      assessment_u = 1)

Characteristic limits function———————————————–

Description

Characteristic limits function———————————————–

Usage

calculate_characteristic_limits(
  mu0 = NULL,
  n1 = NULL,
  y0 = NULL,
  n0 = NULL,
  alpha = 0.05,
  beta = 0.1,
  ymax = 100,
  type = "const"
)
calculate_characteristic_limits(
  mu0 = NULL,
  n1 = NULL,
  y0 = NULL,
  n0 = NULL,
  alpha = 0.05,
  beta = 0.1,
  ymax = 100,
  type = "const"
)

Arguments

mu0

Background rate

n1

Number of cells that will be analysed.

y0

Background number of dicentrics.

n0

Background number of cells analysed.

alpha

Type I error rate, 0.05 by default.

beta

Type II error rate, 0.1 by default.

ymax

Max dicentrics to evaluate.

type

Type.

Value

List of characteristic limits (decision_threshold, detection_limit).

Examples


control_data <- c(aberr = 4,
                  cells = 1000)

calculate_characteristic_limits(
      y0 = control_data["aberr"],
      n0 = control_data["cells"],
      n1 = 20,
      alpha = 0.05,
      beta = 0.1,
      ymax = 100,
      type = "var"
)
control_data <- c(aberr = 4,
                  cells = 1000)

calculate_characteristic_limits(
      y0 = control_data["aberr"],
      n0 = control_data["cells"],
      n1 = 20,
      alpha = 0.05,
      beta = 0.1,
      ymax = 100,
      type = "var"
)

Calculate genomic conversion factor

Description

Method based on the paper by Lucas, J. N. et al. (1992). Rapid Translocation Frequency Analysis in Humans Decades after Exposure to Ionizing Radiation. International Journal of Radiation Biology, 62(1), 53-63. <doi:10.1080/09553009214551821>.

Usage

calculate_genome_factor(dna_table, chromosomes, colors, sex)
calculate_genome_factor(dna_table, chromosomes, colors, sex)

Arguments

dna_table

DNA content fractions table. Can be dna_content_fractions_morton or dna_content_table_ihgsc.

chromosomes

Vector of stained chromosomes.

colors

Vector of colors of the stains.

sex

Sex of the individual.

Value

Numeric value of genomic conversion factor.

Examples

#dna_table can be: dna_content_fractions_morton OR dna_content_fractions_ihgsc

calculate_genome_factor(
  dna_table = dna_content_fractions_morton,
  chromosome = c(1, 2, 3, 4, 5, 6),
  color = c("Red", "Red", "Green", "Red", "Green", "Green"),
  sex = "male"
)
#dna_table can be: dna_content_fractions_morton OR dna_content_fractions_ihgsc

calculate_genome_factor(
  dna_table = dna_content_fractions_morton,
  chromosome = c(1, 2, 3, 4, 5, 6),
  color = c("Red", "Red", "Green", "Red", "Green", "Green"),
  sex = "male"
)

Calculate model statistics

Description

Calculate model statistics

Usage

calculate_model_stats(
  model_data,
  fit_coeffs_vec,
  glm_results = NULL,
  fit_algorithm = NULL,
  response = "yield",
  link = c("identity", "log"),
  type = c("theory", "raw"),
  Y = NULL,
  mu = NULL,
  n = NULL,
  npar = NULL,
  genome_factor = NULL,
  calc_type = c("fitting", "estimation"),
  model_family
)
calculate_model_stats(
  model_data,
  fit_coeffs_vec,
  glm_results = NULL,
  fit_algorithm = NULL,
  response = "yield",
  link = c("identity", "log"),
  type = c("theory", "raw"),
  Y = NULL,
  mu = NULL,
  n = NULL,
  npar = NULL,
  genome_factor = NULL,
  calc_type = c("fitting", "estimation"),
  model_family
)

Arguments

model_data

Data of the model.

fit_coeffs_vec

Vector of fitting coefficients.

glm_results

Results of glm.

fit_algorithm

String of the algorithm used.

response

Type of response.

link

Fit link.

type

Theoretical or raw glm model statistics.

Y

Y response (required in constraint-maxlik-optimization).

mu

mu response required in constraint-maxlik-optimization).

n

number of parameters (required in constraint-maxlik-optimization).

npar

number of parameters (required in constraint-maxlik-optimization).

genome_factor

Genomic conversion factor used in translocations.

calc_type

Calculation type, either "fitting" or "estimation".

model_family

Model family.

Value

Data frame of model statistics.

Calculate the characteristic limits table

Description

Calculate the characteristic limits table

Usage

calculate_table(input, project_yield, fit_coeffs)
calculate_table(input, project_yield, fit_coeffs)

Arguments

input

all input UI parameters

project_yield

function for calculating yield, in utils_estimation.R

fit_coeffs

curve coefficients

Value

list with all the table columns.

Calculate manual translocation rate

Description

Calculate manual translocation rate

Usage

calculate_trans_rate_manual(cells, genome_factor, expected_aberr_value)
calculate_trans_rate_manual(cells, genome_factor, expected_aberr_value)

Arguments

cells

Number of cells N.

genome_factor

Genomic conversion factor.

expected_aberr_value

Expected aberrations.

Value

Numeric value of translocation rate.

Examples

calculate_trans_rate_manual(cells = 1000,
                            genome_factor = 0.58,
                            expected_aberr_value = 0.3)
calculate_trans_rate_manual(cells = 1000,
                            genome_factor = 0.58,
                            expected_aberr_value = 0.3)

Calculate Sigurdson's translocation rate

Description

Method based on the paper by Sigurdson, A. J. et al. (2008). International study of factors affecting human chromosome translocations. Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 652(2), 112-121. <doi:10.1016/j.mrgentox.2008.01.005>.

Usage

calculate_trans_rate_sigurdson(
  cells,
  genome_factor,
  age_value,
  sex_bool = FALSE,
  sex_value = "none",
  smoker_bool = FALSE,
  ethnicity_value = "none",
  region_value = "none"
)
calculate_trans_rate_sigurdson(
  cells,
  genome_factor,
  age_value,
  sex_bool = FALSE,
  sex_value = "none",
  smoker_bool = FALSE,
  ethnicity_value = "none",
  region_value = "none"
)

Arguments

cells

Number of cells N.

genome_factor

Genomic conversion factor.

age_value

Age of the individual.

sex_bool

If TRUE, sex_value will be used.

sex_value

Sex of the individual, either "male" of "female".

smoker_bool

Whether the individual smokes or not.

ethnicity_value

Ethnicity of the individual.

region_value

Region of the individual.

Value

Numeric value of translocation rate.

Examples

calculate_trans_rate_sigurdson(
  cells = 1000,
  genome_factor = 0.58,
  age_value = 30,
  sex_bool = FALSE,
  sex_value = "none",
  smoker_bool = FALSE,
  ethnicity_value = "none",
  region_value = "none"
)
calculate_trans_rate_sigurdson(
  cells = 1000,
  genome_factor = 0.58,
  age_value = 30,
  sex_bool = FALSE,
  sex_value = "none",
  smoker_bool = FALSE,
  ethnicity_value = "none",
  region_value = "none"
)

Calculate yield from dose

Description

Calculate yield from dose

Usage

calculate_yield(
  dose,
  type = c("estimate", "lower", "upper"),
  general_fit_coeffs,
  general_fit_var_cov_mat = NULL,
  protracted_g_value = 1,
  conf_int = 0.95
)
calculate_yield(
  dose,
  type = c("estimate", "lower", "upper"),
  general_fit_coeffs,
  general_fit_var_cov_mat = NULL,
  protracted_g_value = 1,
  conf_int = 0.95
)

Arguments

dose

Numeric value of dose.

type

Type of yield calculation. Can be "estimate", "lower", or "upper".

general_fit_coeffs

Generalised fit coefficients matrix.

general_fit_var_cov_mat

Generalised variance-covariance matrix.

protracted_g_value

Protracted $G(x)$ value.

conf_int

Curve confidence interval, 95% by default.

Value

Numeric value of yield.

Calculate theoretical yield infimum

Description

Calculate theoretical yield infimum

Usage

calculate_yield_infimum(
  type = c("estimate", "lower", "upper"),
  general_fit_coeffs,
  general_fit_var_cov_mat = NULL,
  conf_int = 0.95
)
calculate_yield_infimum(
  type = c("estimate", "lower", "upper"),
  general_fit_coeffs,
  general_fit_var_cov_mat = NULL,
  conf_int = 0.95
)

Arguments

type

Type of yield calculation. Can be "estimate", "lower", or "upper".

general_fit_coeffs

Generalised fit coefficients matrix.

general_fit_var_cov_mat

Generalised variance-covariance matrix.

conf_int

Curve confidence interval, 95% by default.

Value

Numeric value of yield infimum.

Correct boundary of irradiated fractions to be bounded by 0 and 1

Description

Correct boundary of irradiated fractions to be bounded by 0 and 1

Usage

correct_boundary(x)
correct_boundary(x)

Arguments

x

Numeric value.

Value

Numeric value in [0, 1] range.

Correct yield confidence interval

Description

Correct yield confidence interval if simple method is required.

Usage

correct_conf_int(
  conf_int,
  general_fit_var_cov_mat,
  protracted_g_value = 1,
  type,
  dose = seq(0, 10, 0.2)
)
correct_conf_int(
  conf_int,
  general_fit_var_cov_mat,
  protracted_g_value = 1,
  type,
  dose = seq(0, 10, 0.2)
)

Arguments

conf_int

Confidence interval.

general_fit_var_cov_mat

Generalised variance-covariance matrix.

protracted_g_value

Protracted $G(x)$ value.

type

Type of yield calculation. Can be "estimate", "lower", or "upper".

dose

Numeric value of dose.

Value

Numeric value of corrected confidence interval.

Correct negative values

Description

Correct negative values

Usage

correct_negative_vals(x)
correct_negative_vals(x)

Arguments

x

Numeric value.

Value

Numeric value corrected to zero if negative.

Correct yields if they are below the curve

Description

Correct yields if they are below the curve

Usage

correct_yield(
  yield,
  type = "estimate",
  general_fit_coeffs,
  general_fit_var_cov_mat,
  conf_int
)
correct_yield(
  yield,
  type = "estimate",
  general_fit_coeffs,
  general_fit_var_cov_mat,
  conf_int
)

Arguments

yield

Numeric value of yield.

type

Type of yield calculation. Can be "estimate", "lower", or "upper".

general_fit_coeffs

Generalised fit coefficients matrix.

general_fit_var_cov_mat

Generalised variance-covariance matrix.

conf_int

Curve confidence interval.

Value

Numeric value of corrected yield.

Plot curves

Description

Plot curves

Usage

curves_plot(dat, curve, curve_type = "lin_quad", place)
curves_plot(dat, curve, curve_type = "lin_quad", place)

Arguments

dat

data frame of data values.

curve

manual or auto.

curve_type

lin or lin_quad.

place

UI or save.

Value

ggplot2 object.

Examples

dat <- data.frame(
  "Lab" = c("A1", "A2"),
  "Module" = c("dicentrics", "dicentrics"),
  "Type" = c("manual", "manual"),
  "radiation quality" = c("Cs-137", "Co-60"),
  "calibration" = c("air kerma", "air kerma"),
  "irradiation" = c("air", "air"),
  "temperature" = c(20, 37),
  "dose rate" = c(0.446, 0.27),
  "curve origin" = c("own", "own"),
  "C" = c(0.001189589, 0.0005),
  "alpha" = c(0.01903783, 0.0142),
  "beta" = c(0.0968831, 0.0759),
  "C std.error" = c(0.0001040828, 0.0005),
  "alpha std.error" = c(0.004119324, 0.0044),
  "beta std.error" = c(0.003505209, 0.0027),
  "max curve dose" = c(6.00, 5.05),
  stringsAsFactors = FALSE,
  check.names = FALSE
)


curves_plot(
  dat = dat,
  curve = "manual",
  curve_type = "lin_quad",
  place = "UI"
)
dat <- data.frame(
  "Lab" = c("A1", "A2"),
  "Module" = c("dicentrics", "dicentrics"),
  "Type" = c("manual", "manual"),
  "radiation quality" = c("Cs-137", "Co-60"),
  "calibration" = c("air kerma", "air kerma"),
  "irradiation" = c("air", "air"),
  "temperature" = c(20, 37),
  "dose rate" = c(0.446, 0.27),
  "curve origin" = c("own", "own"),
  "C" = c(0.001189589, 0.0005),
  "alpha" = c(0.01903783, 0.0142),
  "beta" = c(0.0968831, 0.0759),
  "C std.error" = c(0.0001040828, 0.0005),
  "alpha std.error" = c(0.004119324, 0.0044),
  "beta std.error" = c(0.003505209, 0.0027),
  "max curve dose" = c(6.00, 5.05),
  stringsAsFactors = FALSE,
  check.names = FALSE
)


curves_plot(
  dat = dat,
  curve = "manual",
  curve_type = "lin_quad",
  place = "UI"
)

Dispersion index

Description

Dispersion index

Usage

DI_plot(dat, place)
DI_plot(dat, place)

Arguments

dat

data frame of data values.

place

UI or save.

Value

plot

Examples

dat <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = c("manual", "manual", "manual", "manual", "manual", "manual"),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)

DI_plot(
  dat = dat,
  place = "UI"
)
dat <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = c("manual", "manual", "manual", "manual", "manual", "manual"),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)

DI_plot(
  dat = dat,
  place = "UI"
)

DNA Content Fractions of Human Chromosomes (IHGSC)

Description

Normalised DNA Content of Human Chromosomes from the International Human Genome Sequencing Consortium.

Usage

dna_content_fractions_ihgsc
dna_content_fractions_ihgsc

Format

A data frame with 24 rows and 3 variables:

chromosome: Chromosome.
fraction_male: Normalised content of megabases on male human DNA.
fraction_female: Normalised content of megabases on female human DNA.

Details

Last accessed in July 2020.

Source

https://www.ncbi.nlm.nih.gov/grc/human/data

DNA Content Fractions of Human Chromosomes (Morton 1991)

Description

Normalised DNA Content of Human Chromosomes from Morton, N. E. (1991). Parameters of the human genome. Proceedings of the National Academy of Sciences, 88(17), 7474-7476.

Usage

dna_content_fractions_morton
dna_content_fractions_morton

Format

A data frame with 24 rows and 3 variables:

chromosome: Chromosome.
fraction_male: Normalised content of megabases on male human DNA.
fraction_female: Normalised content of megabases on female human DNA.

Source

doi:10.1073/pnas.88.17.7474

Boxplot dose estimates

Description

Boxplot dose estimates

Usage

dose_boxplot(dat, place)
dose_boxplot(dat, place)

Arguments

dat

data frame of data values.

place

UI or save.

Value

plot

Examples

dat <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = c("manual", "manual", "manual", "manual", "manual", "manual"),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)

dose_boxplot(
  dat = dat,
  place = "UI"
)
dat <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = c("manual", "manual", "manual", "manual", "manual", "manual"),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)

dose_boxplot(
  dat = dat,
  place = "UI"
)

Prepare data to dose estimate using mixed fields manual

Description

Prepare data to dose estimate using mixed fields manual

Usage

dose_estimation_mx(input, reactive_data, data_type)
dose_estimation_mx(input, reactive_data, data_type)

Arguments

input

what you upload in the UI

reactive_data

access to the reactive data

data_type

gamma or neutron

Value

data for dose estimation using manual input.

Heterogeneous dose estimation (Mixed Poisson model)

Description

Method based on the paper by Pujol, M. et al. (2016). A New Model for Biological Dose Assessment in Cases of Heterogeneous Exposures to Ionizing Radiation. Radiation Research, 185(2), 151-162. <doi:10.1667/RR14145.1>.

Usage

estimate_hetero_mixed_poisson(
  case_data,
  fit_coeffs,
  fit_var_cov_mat,
  conf_int = 0.95,
  protracted_g_value = 1,
  gamma,
  gamma_error
)
estimate_hetero_mixed_poisson(
  case_data,
  fit_coeffs,
  fit_var_cov_mat,
  conf_int = 0.95,
  protracted_g_value = 1,
  gamma,
  gamma_error
)

Arguments

case_data

Case data in data frame form.

fit_coeffs

Fitting coefficients matrix.

fit_var_cov_mat

Fitting variance-covariance matrix.

conf_int

Confidence interval, 95% by default.

protracted_g_value

Protracted $G(x)$ value.

gamma

Survival coefficient of irradiated cells.

gamma_error

Error of the survival coefficient of irradiated cells.

Value

List containing estimated mixing proportions data frame, estimated yields data frame, estimated doses data frame, estimated fraction of irradiated blood data frame, AIC, and conf_int_* used.

Examples

#The fitting RDS result from the fitting module is needed. Alternatively, manual data
#frames that match the structure of the RDS can be used:


fit_coeffs <- data.frame(
  estimate   = c(0.001280319, 0.021038724, 0.063032534),
  std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
  statistic  = c(2.715961, 4.079156, 15.729091),
  p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


fit_var_cov_mat <- data.frame(
  coeff_C      = c(2.222231e-07, -9.949044e-07,  4.379944e-07),
  coeff_alpha  = c(-9.949044e-07, 2.660101e-05, -1.510494e-05),
  coeff_beta   = c(4.379944e-07, -1.510494e-05, 1.605914e-05),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


case_data <- data.frame(
  ID= "example1",
  N = 361,
  X = 100,
  C0 = 302,
  C1 = 28,
  C2 = 22,
  C3 = 8,
  C4 = 1,
  C5 = 0,
  y = 0.277,
  y_err = 0.0368,
  DI = 1.77,
  u = 10.4
)


#FUNCTION ESTIMATE_HETERO_MIXED_POISSON
estimate_hetero_mixed_poisson(
  case_data[1, ],
  fit_coeffs = as.matrix(fit_coeffs),
  fit_var_cov_mat = as.matrix(fit_var_cov_mat),
  conf_int = 0.95,
  protracted_g_value = 1,
  gamma = 1 / 2.7,
  gamma_error = 0
)
#The fitting RDS result from the fitting module is needed. Alternatively, manual data
#frames that match the structure of the RDS can be used:


fit_coeffs <- data.frame(
  estimate   = c(0.001280319, 0.021038724, 0.063032534),
  std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
  statistic  = c(2.715961, 4.079156, 15.729091),
  p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


fit_var_cov_mat <- data.frame(
  coeff_C      = c(2.222231e-07, -9.949044e-07,  4.379944e-07),
  coeff_alpha  = c(-9.949044e-07, 2.660101e-05, -1.510494e-05),
  coeff_beta   = c(4.379944e-07, -1.510494e-05, 1.605914e-05),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


case_data <- data.frame(
  ID= "example1",
  N = 361,
  X = 100,
  C0 = 302,
  C1 = 28,
  C2 = 22,
  C3 = 8,
  C4 = 1,
  C5 = 0,
  y = 0.277,
  y_err = 0.0368,
  DI = 1.77,
  u = 10.4
)


#FUNCTION ESTIMATE_HETERO_MIXED_POISSON
estimate_hetero_mixed_poisson(
  case_data[1, ],
  fit_coeffs = as.matrix(fit_coeffs),
  fit_var_cov_mat = as.matrix(fit_var_cov_mat),
  conf_int = 0.95,
  protracted_g_value = 1,
  gamma = 1 / 2.7,
  gamma_error = 0
)

Partial-body dose estimation (Dolphin's method)

Description

Method based on the paper by Dolphin, G. W. (1969). Biological Dosimetry with Particular Reference to Chromosome Aberration Analysis: A Review of Methods. International Atomic Energy Agency (IAEA) Retrieved from https://inis.iaea.org/search/search.aspx?orig_q=RN:45029080.

Usage

estimate_partial_body_dolphin(
  num_cases,
  case_data,
  fit_coeffs,
  fit_var_cov_mat,
  conf_int = 0.95,
  protracted_g_value = 1,
  genome_factor = 1,
  gamma,
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)
estimate_partial_body_dolphin(
  num_cases,
  case_data,
  fit_coeffs,
  fit_var_cov_mat,
  conf_int = 0.95,
  protracted_g_value = 1,
  genome_factor = 1,
  gamma,
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)

Arguments

num_cases

number of cases.

case_data

Case data in data frame form.

fit_coeffs

Fitting coefficients matrix.

fit_var_cov_mat

Fitting variance-covariance matrix.

conf_int

Confidence interval, 95% by default.

protracted_g_value

Protracted $G(x)$ value.

genome_factor

Genomic conversion factor used in translocations, else 1.

gamma

Survival coefficient of irradiated cells.

aberr_module

Aberration module.

Value

List containing estimated doses data frame, observed fraction of cells scored which were irradiated, estimated fraction of irradiated blood data frame, AIC, and conf_int_* used.

Examples

#The fitting RDS result from the fitting module is needed. Alternatively, manual data
#frames that match the structure of the RDS can be used:
fit_coeffs <- data.frame(
    estimate   = c(0.001280319, 0.021038724, 0.063032534),
    std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
    statistic  = c(2.715961, 4.079156, 15.729091),
    p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
    row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
  )


fit_var_cov_mat <- data.frame(
  coeff_C      = c(2.222231e-07, -9.949044e-07,  4.379944e-07),
  coeff_alpha  = c(-9.949044e-07, 2.660101e-05, -1.510494e-05),
  coeff_beta   = c(4.379944e-07, -1.510494e-05, 1.605914e-05),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


case_data <- data.frame(
  ID= "example1",
  N = 361,
  X = 100,
  C0 = 302,
  C1 = 28,
  C2 = 22,
  C3 = 8,
  C4 = 1,
  C5 = 0,
  y = 0.277,
  y_err = 0.0368,
  DI = 1.77,
  u = 10.4
)

#FUNCTION ESTIMATE_PARTIAL_BODY_DOLPHIN
estimate_partial_body_dolphin(
  num_cases = 1,
  case_data = case_data,
  fit_coeffs = as.matrix(fit_coeffs),
  fit_var_cov_mat = as.matrix(fit_var_cov_mat),
  conf_int = 0.95,
  gamma = 1 / 2.7,
  aberr_module = "dicentrics"
)
#The fitting RDS result from the fitting module is needed. Alternatively, manual data
#frames that match the structure of the RDS can be used:
fit_coeffs <- data.frame(
    estimate   = c(0.001280319, 0.021038724, 0.063032534),
    std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
    statistic  = c(2.715961, 4.079156, 15.729091),
    p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
    row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
  )


fit_var_cov_mat <- data.frame(
  coeff_C      = c(2.222231e-07, -9.949044e-07,  4.379944e-07),
  coeff_alpha  = c(-9.949044e-07, 2.660101e-05, -1.510494e-05),
  coeff_beta   = c(4.379944e-07, -1.510494e-05, 1.605914e-05),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


case_data <- data.frame(
  ID= "example1",
  N = 361,
  X = 100,
  C0 = 302,
  C1 = 28,
  C2 = 22,
  C3 = 8,
  C4 = 1,
  C5 = 0,
  y = 0.277,
  y_err = 0.0368,
  DI = 1.77,
  u = 10.4
)

#FUNCTION ESTIMATE_PARTIAL_BODY_DOLPHIN
estimate_partial_body_dolphin(
  num_cases = 1,
  case_data = case_data,
  fit_coeffs = as.matrix(fit_coeffs),
  fit_var_cov_mat = as.matrix(fit_var_cov_mat),
  conf_int = 0.95,
  gamma = 1 / 2.7,
  aberr_module = "dicentrics"
)

Whole-body dose estimation (delta method)

Description

Method based on 2001 manual by the International Atomic Energy Agency (IAEA). Cytogenetic Analysis for Radiation Dose Assessment, Technical Reports Series (2001). Retrieved from https://www.iaea.org/publications/6303/cytogenetic-analysis-for-radiation-dose-assessment.

Usage

estimate_whole_body_delta(
  num_cases,
  case_data,
  fit_coeffs,
  fit_var_cov_mat,
  conf_int = 0.95,
  protracted_g_value = 1,
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)
estimate_whole_body_delta(
  num_cases,
  case_data,
  fit_coeffs,
  fit_var_cov_mat,
  conf_int = 0.95,
  protracted_g_value = 1,
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)

Arguments

num_cases

number of cases.

case_data

Case data in data frame form.

fit_coeffs

Fitting coefficients matrix.

fit_var_cov_mat

Fitting variance-covariance matrix.

conf_int

Confidence interval, 95% by default.

protracted_g_value

Protracted $G(x)$ value.

aberr_module

Aberration module.

Value

List containing estimated doses data frame, AIC, and conf_int used.

Examples

#The fitting RDS result from the fitting module is needed. Alternatively, manual data
#frames that match the structure of the RDS can be used:
fit_coeffs <- data.frame(
  estimate   = c(0.001280319, 0.021038724, 0.063032534),
  std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
  statistic  = c(2.715961, 4.079156, 15.729091),
  p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


fit_var_cov_mat <- data.frame(
  coeff_C      = c(2.222231e-07, -9.949044e-07,  4.379944e-07),
  coeff_alpha  = c(-9.949044e-07, 2.660101e-05, -1.510494e-05),
  coeff_beta   = c(4.379944e-07, -1.510494e-05, 1.605914e-05),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


case_data <- data.frame(
  ID= "example1",
  N = 361,
  X = 100,
  C0 = 302,
  C1 = 28,
  C2 = 22,
  C3 = 8,
  C4 = 1,
  C5 = 0,
  y = 0.277,
  y_err = 0.0368,
  DI = 1.77,
  u = 10.4
)

#FUNCTION ESTIMATE_WHOLE_BODY_DELTA
estimate_whole_body_delta(
  num_cases = 1,
  case_data = case_data,
  fit_coeffs = as.matrix(fit_coeffs),
  fit_var_cov_mat = as.matrix(fit_var_cov_mat),
  conf_int = 0.95,
  protracted_g_value = 1,
  aberr_module = "dicentrics"
)
#The fitting RDS result from the fitting module is needed. Alternatively, manual data
#frames that match the structure of the RDS can be used:
fit_coeffs <- data.frame(
  estimate   = c(0.001280319, 0.021038724, 0.063032534),
  std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
  statistic  = c(2.715961, 4.079156, 15.729091),
  p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


fit_var_cov_mat <- data.frame(
  coeff_C      = c(2.222231e-07, -9.949044e-07,  4.379944e-07),
  coeff_alpha  = c(-9.949044e-07, 2.660101e-05, -1.510494e-05),
  coeff_beta   = c(4.379944e-07, -1.510494e-05, 1.605914e-05),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


case_data <- data.frame(
  ID= "example1",
  N = 361,
  X = 100,
  C0 = 302,
  C1 = 28,
  C2 = 22,
  C3 = 8,
  C4 = 1,
  C5 = 0,
  y = 0.277,
  y_err = 0.0368,
  DI = 1.77,
  u = 10.4
)

#FUNCTION ESTIMATE_WHOLE_BODY_DELTA
estimate_whole_body_delta(
  num_cases = 1,
  case_data = case_data,
  fit_coeffs = as.matrix(fit_coeffs),
  fit_var_cov_mat = as.matrix(fit_var_cov_mat),
  conf_int = 0.95,
  protracted_g_value = 1,
  aberr_module = "dicentrics"
)

Whole-body dose estimation (Merkle's method)

Description

Method based on the paper by Merkle, W. (1983). Statistical methods in regression and calibration analysis of chromosome aberration data. Radiation and Environmental Biophysics, 21(3), 217-233. <doi:10.1007/BF01323412>.

Usage

estimate_whole_body_merkle(
  num_cases,
  case_data,
  fit_coeffs,
  fit_var_cov_mat,
  conf_int_yield = 0.83,
  conf_int_curve = 0.83,
  protracted_g_value = 1,
  genome_factor = 1,
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)
estimate_whole_body_merkle(
  num_cases,
  case_data,
  fit_coeffs,
  fit_var_cov_mat,
  conf_int_yield = 0.83,
  conf_int_curve = 0.83,
  protracted_g_value = 1,
  genome_factor = 1,
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)

Arguments

num_cases

number of cases.

case_data

Case data in data frame form.

fit_coeffs

Fitting coefficients matrix.

fit_var_cov_mat

Fitting variance-covariance matrix.

conf_int_yield

Confidence interval of the yield, 83% by default.

conf_int_curve

Confidence interval of the curve, 83% by default.

protracted_g_value

Protracted $G(x)$ value.

genome_factor

Genomic conversion factor used in translocations, else 1.

aberr_module

Aberration module.

Value

List containing estimated doses data frame, AIC, and conf_int_* used.

Examples

#The fitting RDS result from the fitting module is needed. Alternatively, manual data
#frames that match the structure of the RDS can be used:
fit_coeffs <- data.frame(
  estimate   = c(0.001280319, 0.021038724, 0.063032534),
  std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
  statistic  = c(2.715961, 4.079156, 15.729091),
  p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


fit_var_cov_mat <- data.frame(
  coeff_C      = c(2.222231e-07, -9.949044e-07,  4.379944e-07),
  coeff_alpha  = c(-9.949044e-07, 2.660101e-05, -1.510494e-05),
  coeff_beta   = c(4.379944e-07, -1.510494e-05, 1.605914e-05),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


case_data <- data.frame(
  ID= "example1",
  N = 361,
  X = 100,
  C0 = 302,
  C1 = 28,
  C2 = 22,
  C3 = 8,
  C4 = 1,
  C5 = 0,
  y = 0.277,
  y_err = 0.0368,
  DI = 1.77,
  u = 10.4
)

#FUNCTION ESTIMATE_WHOLE_BODY_MERKLE
estimate_whole_body_merkle(
  num_cases = 1,
  case_data = case_data,
  fit_coeffs = as.matrix(fit_coeffs),
  fit_var_cov_mat = as.matrix(fit_var_cov_mat),
  conf_int_yield = 0.83,
  conf_int_curve = 0.83,
  protracted_g_value = 1,
  aberr_module = "dicentrics"
)
#The fitting RDS result from the fitting module is needed. Alternatively, manual data
#frames that match the structure of the RDS can be used:
fit_coeffs <- data.frame(
  estimate   = c(0.001280319, 0.021038724, 0.063032534),
  std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
  statistic  = c(2.715961, 4.079156, 15.729091),
  p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


fit_var_cov_mat <- data.frame(
  coeff_C      = c(2.222231e-07, -9.949044e-07,  4.379944e-07),
  coeff_alpha  = c(-9.949044e-07, 2.660101e-05, -1.510494e-05),
  coeff_beta   = c(4.379944e-07, -1.510494e-05, 1.605914e-05),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


case_data <- data.frame(
  ID= "example1",
  N = 361,
  X = 100,
  C0 = 302,
  C1 = 28,
  C2 = 22,
  C3 = 8,
  C4 = 1,
  C5 = 0,
  y = 0.277,
  y_err = 0.0368,
  DI = 1.77,
  u = 10.4
)

#FUNCTION ESTIMATE_WHOLE_BODY_MERKLE
estimate_whole_body_merkle(
  num_cases = 1,
  case_data = case_data,
  fit_coeffs = as.matrix(fit_coeffs),
  fit_var_cov_mat = as.matrix(fit_var_cov_mat),
  conf_int_yield = 0.83,
  conf_int_curve = 0.83,
  protracted_g_value = 1,
  aberr_module = "dicentrics"
)

Perform dose-effect fitting algorithm

Description

Perform dose-effect fitting. A generalized linear model (GLM) is used by default, with a maximum likelihood estimation (MLE) as a fallback method.

Usage

fit(
  count_data,
  model_formula,
  model_family,
  fit_link = "identity",
  aberr_module = c("dicentrics", "translocations", "micronuclei"),
  algorithm = c("glm", "maxlik")
)
fit(
  count_data,
  model_formula,
  model_family,
  fit_link = "identity",
  aberr_module = c("dicentrics", "translocations", "micronuclei"),
  algorithm = c("glm", "maxlik")
)

Arguments

count_data

Count data in data frame form.

model_formula

Model formula.

model_family

Model family.

fit_link

Family link.

aberr_module

Aberration module.

algorithm

Optional selection of algorithm to be used, either "glm" (for GLM) or "maxlik" (for MLE). By default, "glm" is used, with "maxlik" as a fallback method.

Details

The GLM method is based on the paper by Edwards, A. A. et al. (1979). Radiation induced chromosome aberrations and the Poisson distribution. Radiation and Environmental Biophysics, 16(2), 89-100. <doi:10.1007/BF01323216>.

The MLE method is based on the paperby Oliveira, M. et al. (2016). Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study. Biometrical Journal, 58(2), 259-279. <doi:10.1002/bimj.201400233>.

Value

List object containing fit results either using GLM or maxLik optimization.

Examples


count_data <- data.frame(
  D = c(0, 0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5),
  N = c(5000, 5002, 2008, 2002, 1832, 1168, 562, 333, 193, 103, 59),
  X = c(8, 14, 22, 55, 100, 109, 100, 103, 108, 103, 107),
  C0 = c(4992, 4988, 1987, 1947, 1736, 1064, 474, 251, 104, 35, 11),
  C1 = c(8, 14, 20, 55, 92, 99, 76, 63, 72, 41, 19),
  C2 = c(0, 0, 1, 0, 4, 5, 12, 17, 15, 21, 11),
  C3 = c(0, 0, 0, 0, 0, 0, 0, 2, 2, 4, 9),
  C4 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 6),
  C5 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3),
  mean = c(0.0016, 0.0028, 0.0110, 0.0275, 0.0546, 0.0933, 0.178, 0.309, 0.560, 1, 1.81),
  var = c(0.00160, 0.00279, 0.0118, 0.0267, 0.0560, 0.0933, 0.189, 0.353, 0.466, 0.882, 2.09),
  DI = c(0.999, 0.997, 1.08, 0.973, 1.03, 0.999, 1.06, 1.14, 0.834, 0.882, 1.15),
  u = c(-0.0748, -0.135, 2.61, -0.861, 0.790, -0.0176, 1.08, 1.82, -1.64, -0.844, 0.811)
)


fit(count_data = count_data,
    model_formula = "lin-quad",
    model_family = "automatic",
    fit_link = "identity",
    aberr_module = "dicentrics",
    algorithm = "maxlik")
count_data <- data.frame(
  D = c(0, 0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5),
  N = c(5000, 5002, 2008, 2002, 1832, 1168, 562, 333, 193, 103, 59),
  X = c(8, 14, 22, 55, 100, 109, 100, 103, 108, 103, 107),
  C0 = c(4992, 4988, 1987, 1947, 1736, 1064, 474, 251, 104, 35, 11),
  C1 = c(8, 14, 20, 55, 92, 99, 76, 63, 72, 41, 19),
  C2 = c(0, 0, 1, 0, 4, 5, 12, 17, 15, 21, 11),
  C3 = c(0, 0, 0, 0, 0, 0, 0, 2, 2, 4, 9),
  C4 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 6),
  C5 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3),
  mean = c(0.0016, 0.0028, 0.0110, 0.0275, 0.0546, 0.0933, 0.178, 0.309, 0.560, 1, 1.81),
  var = c(0.00160, 0.00279, 0.0118, 0.0267, 0.0560, 0.0933, 0.189, 0.353, 0.466, 0.882, 2.09),
  DI = c(0.999, 0.997, 1.08, 0.973, 1.03, 0.999, 1.06, 1.14, 0.834, 0.882, 1.15),
  u = c(-0.0748, -0.135, 2.61, -0.861, 0.790, -0.0176, 1.08, 1.82, -1.64, -0.844, 0.811)
)


fit(count_data = count_data,
    model_formula = "lin-quad",
    model_family = "automatic",
    fit_link = "identity",
    aberr_module = "dicentrics",
    algorithm = "maxlik")

Perform GLM (Generalised Linear Model) fitting

Description

Method based on the paper by Edwards, A. A. et al. (1979). Radiation induced chromosome aberrations and the Poisson distribution. Radiation and Environmental Biophysics, 16(2), 89-100. <doi:10.1007/BF01323216>.

Usage

fit_glm_method(
  count_data,
  model_formula,
  model_family = c("automatic", "poisson", "quasipoisson", "nb2"),
  fit_link = "identity",
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)
fit_glm_method(
  count_data,
  model_formula,
  model_family = c("automatic", "poisson", "quasipoisson", "nb2"),
  fit_link = "identity",
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)

Arguments

count_data

Count data in data frame form.

model_formula

Model formula.

model_family

Model family.

fit_link

Family link.

aberr_module

Aberration module.

Value

List object containing GLM fit results.

Perform max-likelihood optimization fitting

Description

Method based on the paper by Oliveira, M. et al. (2016). Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study. Biometrical Journal, 58(2), 259-279. <doi:10.1002/bimj.201400233>.

Usage

fit_maxlik_method(
  data,
  model_formula,
  model_family = c("automatic", "poisson", "quasipoisson", "nb2"),
  fit_link,
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)
fit_maxlik_method(
  data,
  model_formula,
  model_family = c("automatic", "poisson", "quasipoisson", "nb2"),
  fit_link,
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)

Arguments

data

Count data.

model_formula

Model formula.

model_family

Model family.

fit_link

Family link.

aberr_module

Aberration module.

Value

List object containing maxLik fit results.

Calculate curve

Description

Calculate curve

Usage

fun.curve(d, coef, curve_type)
fun.curve(d, coef, curve_type)

Arguments

d

dose

coef

curve coefficients

curve_type

type of curve: lin_quad, lin

Value

Numeric value.

Calculate absorbed dose for mixed fields exposure

Description

Calculate absorbed dose for mixed fields exposure

Usage

fun.estimate.criticality(
  num_cases,
  dics,
  cells,
  coef_gamma,
  cov_gamma,
  coef_neutron,
  cov_neutron,
  ratio,
  p
)
fun.estimate.criticality(
  num_cases,
  dics,
  cells,
  coef_gamma,
  cov_gamma,
  coef_neutron,
  cov_neutron,
  ratio,
  p
)

Arguments

num_cases

number of cases to estimate.

dics

dicentrics.

cells

total cells.

coef_gamma

Coefficients for gamma curve.

cov_gamma

Covariance matrix for gamma curve.

coef_neutron

Coefficients for neutron curve.

cov_neutron

Covariance matrix for neutron curve.

ratio

gamma-neutron ratio.

p

photon contribution in mixed curve.

Value

dose estimation

Examples

#Results from the fitting module
fit_results_gamma <- system.file("extdata",
                                 "gamma_dicentrics-fitting-results.rds",
                                 package = "biodosetools") %>%
  readRDS()

fit_results_neutrons <- system.file("extdata",
                                    "neutrons-mixed-dicentrics-fitting-results.rds",
                                    package = "biodosetools") %>%
  readRDS()

#FUNCTION TO ESTIMATE DOSE IN CRITICALITY ACCIDENTS
fun.estimate.criticality(num_cases = 2,
                         dics = c(380,456),
                         cells = c(218,567),
                         coef_gamma = fit_results_gamma[["fit_coeffs"]][,1],
                         cov_gamma = fit_results_gamma[["fit_var_cov_mat"]],
                         coef_neutron = fit_results_neutrons[["fit_coeffs"]][,1],
                         cov_neutron = fit_results_neutrons[["fit_var_cov_mat"]],
                         ratio = 1.2,
                         p = 0)
#Results from the fitting module
fit_results_gamma <- system.file("extdata",
                                 "gamma_dicentrics-fitting-results.rds",
                                 package = "biodosetools") %>%
  readRDS()

fit_results_neutrons <- system.file("extdata",
                                    "neutrons-mixed-dicentrics-fitting-results.rds",
                                    package = "biodosetools") %>%
  readRDS()

#FUNCTION TO ESTIMATE DOSE IN CRITICALITY ACCIDENTS
fun.estimate.criticality(num_cases = 2,
                         dics = c(380,456),
                         cells = c(218,567),
                         coef_gamma = fit_results_gamma[["fit_coeffs"]][,1],
                         cov_gamma = fit_results_gamma[["fit_var_cov_mat"]],
                         coef_neutron = fit_results_neutrons[["fit_coeffs"]][,1],
                         cov_neutron = fit_results_neutrons[["fit_var_cov_mat"]],
                         ratio = 1.2,
                         p = 0)

Plot at the UI and save

Description

Plot at the UI and save

Usage

generate_plot_and_download(
  data_type,
  reactive_data,
  output,
  input,
  manual,
  dose_plot,
  name_num
)
generate_plot_and_download(
  data_type,
  reactive_data,
  output,
  input,
  manual,
  dose_plot,
  name_num
)

Arguments

data_type

gamma or neutron

reactive_data

access to the reactive data

output

connection with UI

input

what you upload in the UI

manual

logical, indicates if using manual input or file data.

dose_plot

the number of the dose case to plot.

name_num

associates a number to the name.

Value

plot at UI and saving plot server logic

Get standard errors using delta method

Description

Delta method for approximating the standard error of a transformation $g(X)$ of a random variable $X = (x1, x2, ...)$ , given estimates of the mean and covariance matrix of $X$ .

Usage

get_deltamethod_std_err(
  fit_is_lq,
  variable = c("dose", "fraction_partial", "fraction_hetero"),
  mean_estimate,
  cov_estimate,
  protracted_g_value = NA,
  d0 = NA
)
get_deltamethod_std_err(
  fit_is_lq,
  variable = c("dose", "fraction_partial", "fraction_hetero"),
  mean_estimate,
  cov_estimate,
  protracted_g_value = NA,
  d0 = NA
)

Arguments

fit_is_lq

Whether the fit is linear quadratic (TRUE) or linear (FALSE).

variable

Variable resulting of the transformation $g(X)$ .

mean_estimate

The estimated mean of $X$ .

cov_estimate

The estimated covariance matrix of $X$ .

protracted_g_value

Protracted $G(x)$ value.

d0

Survival coefficient of irradiated cells.

Value

Numeric value containing the standard error of the dose estimate.

Include Markdown help

Description

Include Markdown help

Usage

include_help(...)
include_help(...)

Arguments

...

Character vector specifying directory and or file to point to inside the current package.

Load manual data

Description

Load manual data

Usage

load_manual_data(data_type, input, reactive_data, output, session)
load_manual_data(data_type, input, reactive_data, output, session)

Arguments

data_type

gamma or neutron

input

what you upload in the UI

reactive_data

access to the reactive data

output

output for the UI

session

let interaction with the shiny UI

Value

manual curve information tables.

Load and show rds files

Description

Load and show rds files

Usage

load_rds_data(input_data, data_type, reactive_data, output, session)
load_rds_data(input_data, data_type, reactive_data, output, session)

Arguments

input_data

what you upload as rds file in the UI.

data_type

gamma or neutron.

reactive_data

access to the reactive data.

output

output for the UI.

session

let interaction with the shiny UI.

Value

uploaded rds file's curve information-tables.

Load RMarkdown report

Description

Load RMarkdown report

Usage

load_rmd_report(...)
load_rmd_report(...)

Arguments

...

Character vector specifying directory and or file to point to inside the current package.

Calculate algB

Description

Calculate algB

Usage

M_estimate(x, iter_loc = 50, iter_scale = 1000)
M_estimate(x, iter_loc = 50, iter_scale = 1000)

Arguments

x

vector of n observations.

iter_loc

number of iteration steps for location estimate (default=50).

iter_scale

number of iteration steps for scale estimate (default=1000).

Value

Numeric value of zscore using algB.

Examples

X = c(3.65, 2.5, 4.85)

M_estimate(x = as.numeric(X),
           iter_loc=50,
           iter_scale=1000)
X = c(3.65, 2.5, 4.85)

M_estimate(x = as.numeric(X),
           iter_loc=50,
           iter_scale=1000)

Plot Deviaition from ref dose all blind samples

Description

Plot Deviaition from ref dose all blind samples

Usage

plot_deviation_all(zscore, select_method, place)
plot_deviation_all(zscore, select_method, place)

Arguments

zscore

zscore vector to plot for each lab.

select_method

chosen algorithm for zscore.

place

UI or save.

Value

ggplot2 object.

Examples


data_frame_zscore <- data.frame(
  Lab = factor(c("A1", "A2", "A1", "A2", "A1", "A2")),
  Sample = factor(c(1, 1, 2, 2, 3, 3)),
  Type = rep("manual", 6),
  Reference = c(2.56, 3.41, 4.54, 2.56, 3.41, 4.54),
  Dose = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  Deviation = c(0.02922998, -0.79902999, -1.39394510, 0.45868228, 0.84939953, -0.55077813),
  Zscore = c(1.677656, 2.925426, -2.585664, -3.833434, -1.295906, -2.543676),
  stringsAsFactors = FALSE
)


plot_deviation_all(
  zscore = data_frame_zscore,
  select_method = "algA",
  place = "UI"
)
data_frame_zscore <- data.frame(
  Lab = factor(c("A1", "A2", "A1", "A2", "A1", "A2")),
  Sample = factor(c(1, 1, 2, 2, 3, 3)),
  Type = rep("manual", 6),
  Reference = c(2.56, 3.41, 4.54, 2.56, 3.41, 4.54),
  Dose = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  Deviation = c(0.02922998, -0.79902999, -1.39394510, 0.45868228, 0.84939953, -0.55077813),
  Zscore = c(1.677656, 2.925426, -2.585664, -3.833434, -1.295906, -2.543676),
  stringsAsFactors = FALSE
)


plot_deviation_all(
  zscore = data_frame_zscore,
  select_method = "algA",
  place = "UI"
)

Plot dose estimation curve

Description

Plot dose estimation curve

Usage

plot_estimated_dose_curve(
  est_doses,
  fit_coeffs,
  fit_var_cov_mat,
  protracted_g_value = 1,
  conf_int_curve,
  aberr_name,
  place
)
plot_estimated_dose_curve(
  est_doses,
  fit_coeffs,
  fit_var_cov_mat,
  protracted_g_value = 1,
  conf_int_curve,
  aberr_name,
  place
)

Arguments

est_doses

List of dose estimations results from estimate_*() family of functions.

fit_coeffs

Fitting coefficients matrix.

fit_var_cov_mat

Fitting variance-covariance matrix.

protracted_g_value

Protracted $G(x)$ value.

conf_int_curve

Confidence interval of the curve.

aberr_name

Name of the aberration to use in the y-axis.

place

UI or save.

Value

ggplot2 object.

Examples

#The fitting RDS result from the fitting module is needed. Alternatively, manual data
#frames that match the structure of the RDS can be used:
fit_coeffs <- data.frame(
  estimate   = c(0.001280319, 0.021038724, 0.063032534),
  std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
  statistic  = c(2.715961, 4.079156, 15.729091),
  p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)

fit_var_cov_mat <- data.frame(
  coeff_C      = c(2.222231e-07, -9.949044e-07,  4.379944e-07),
  coeff_alpha  = c(-9.949044e-07, 2.660101e-05, -1.510494e-05),
  coeff_beta   = c(4.379944e-07, -1.510494e-05, 1.605914e-05),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)

results_whole_merkle <- list(
  list(
    est_doses = data.frame(
      lower = 1.541315,
      estimate = 1.931213,
      upper = 2.420912
    ),
    est_yield = data.frame(
      lower = 0.1980553,
      estimate = 0.277,
      upper = 0.3841655
    ),
    AIC = 7.057229,
    conf_int = c(yield_curve = 0.83)
  )
)


#FUNCTION PLOT_ESTIMATED_DOSE_CURVE
plot_estimated_dose_curve(
  est_doses = list(whole = results_whole_merkle),
  fit_coeffs = as.matrix(fit_coeffs),
  fit_var_cov_mat = as.matrix(fit_var_cov_mat),
  protracted_g_value = 1,
  conf_int_curve = 0.95,
  aberr_name = "Micronuclei",
  place = "UI"
)
#The fitting RDS result from the fitting module is needed. Alternatively, manual data
#frames that match the structure of the RDS can be used:
fit_coeffs <- data.frame(
  estimate   = c(0.001280319, 0.021038724, 0.063032534),
  std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
  statistic  = c(2.715961, 4.079156, 15.729091),
  p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)

fit_var_cov_mat <- data.frame(
  coeff_C      = c(2.222231e-07, -9.949044e-07,  4.379944e-07),
  coeff_alpha  = c(-9.949044e-07, 2.660101e-05, -1.510494e-05),
  coeff_beta   = c(4.379944e-07, -1.510494e-05, 1.605914e-05),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)

results_whole_merkle <- list(
  list(
    est_doses = data.frame(
      lower = 1.541315,
      estimate = 1.931213,
      upper = 2.420912
    ),
    est_yield = data.frame(
      lower = 0.1980553,
      estimate = 0.277,
      upper = 0.3841655
    ),
    AIC = 7.057229,
    conf_int = c(yield_curve = 0.83)
  )
)


#FUNCTION PLOT_ESTIMATED_DOSE_CURVE
plot_estimated_dose_curve(
  est_doses = list(whole = results_whole_merkle),
  fit_coeffs = as.matrix(fit_coeffs),
  fit_var_cov_mat = as.matrix(fit_var_cov_mat),
  protracted_g_value = 1,
  conf_int_curve = 0.95,
  aberr_name = "Micronuclei",
  place = "UI"
)

Criticality Plot

Description

Criticality Plot

Usage

plot_estimated_dose_curve_mx(
  name,
  est_doses,
  fit_coeffs,
  fit_var_cov_mat,
  curve_type,
  protracted_g_value = 1,
  conf_int_curve = 0.95,
  place
)
plot_estimated_dose_curve_mx(
  name,
  est_doses,
  fit_coeffs,
  fit_var_cov_mat,
  curve_type,
  protracted_g_value = 1,
  conf_int_curve = 0.95,
  place
)

Arguments

name

the dose to plot.

est_doses

List of dose estimations results.

fit_coeffs

Fitting coefficients matrix.

fit_var_cov_mat

Fitting variance-covariance matrix.

curve_type

gamma or neutron.

protracted_g_value

Protracted $G(x)$ value.

conf_int_curve

Confidence interval of the curve.

place

UI, report or save. Where the plot will be displayed.

Value

ggcurve

Examples

#The fitting RDS result from the fitting module is needed. Alternatively, manual data
#frames that match the structure of the RDS can be used:
fit_coeffs <- data.frame(
  estimate   = c(0.001280319, 0.021038724, 0.063032534),
  std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
  statistic  = c(2.715961, 4.079156, 15.729091),
  p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


fit_var_cov_mat <- data.frame(
  coeff_C      = c(2.222231e-07, -9.949044e-07,  4.379944e-07),
  coeff_alpha  = c(-9.949044e-07, 2.660101e-05, -1.510494e-05),
  coeff_beta   = c(4.379944e-07, -1.510494e-05, 1.605914e-05),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


est_doses <- list(
  list(
    gamma = c(est = 2.173586, lwr = 1.784314, upr = 2.562857),
    neutron = c(est = 1.811322, lwr = 1.486929, upr = 2.135715),
    total = c(est = 3.984907, lwr = 3.271243, upr = 4.698572)
  )
  )

#FUNCTION PLOT_ESTIMATED_DOSE_CURVE_MX
plot_estimated_dose_curve_mx(
  name = "Sample1",
  est_doses = est_doses[[1]],
  fit_coeffs = as.matrix(fit_coeffs)[,1],
  fit_var_cov_mat = as.matrix(fit_var_cov_mat),
  curve_type = "gamma",
  protracted_g_value = 1,
  conf_int_curve = 0.95,
  place = "UI")
#The fitting RDS result from the fitting module is needed. Alternatively, manual data
#frames that match the structure of the RDS can be used:
fit_coeffs <- data.frame(
  estimate   = c(0.001280319, 0.021038724, 0.063032534),
  std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
  statistic  = c(2.715961, 4.079156, 15.729091),
  p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


fit_var_cov_mat <- data.frame(
  coeff_C      = c(2.222231e-07, -9.949044e-07,  4.379944e-07),
  coeff_alpha  = c(-9.949044e-07, 2.660101e-05, -1.510494e-05),
  coeff_beta   = c(4.379944e-07, -1.510494e-05, 1.605914e-05),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)


est_doses <- list(
  list(
    gamma = c(est = 2.173586, lwr = 1.784314, upr = 2.562857),
    neutron = c(est = 1.811322, lwr = 1.486929, upr = 2.135715),
    total = c(est = 3.984907, lwr = 3.271243, upr = 4.698572)
  )
  )

#FUNCTION PLOT_ESTIMATED_DOSE_CURVE_MX
plot_estimated_dose_curve_mx(
  name = "Sample1",
  est_doses = est_doses[[1]],
  fit_coeffs = as.matrix(fit_coeffs)[,1],
  fit_var_cov_mat = as.matrix(fit_var_cov_mat),
  curve_type = "gamma",
  protracted_g_value = 1,
  conf_int_curve = 0.95,
  place = "UI")

Plot fit dose curve

Description

Plot fit dose curve

Usage

plot_fit_dose_curve(fit_results_list, aberr_name, place)
plot_fit_dose_curve(fit_results_list, aberr_name, place)

Arguments

fit_results_list

List of fit results.

aberr_name

Name of the aberration to use in the y-axis.

place

Where the plot will be displayed.

Value

ggplot2 object.

Examples

#In order to plot the fitted curve we need the output of the fit() function:
count_data <- data.frame(
  D = c(0, 0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5),
  N = c(5000, 5002, 2008, 2002, 1832, 1168, 562, 333, 193, 103, 59),
  X = c(8, 14, 22, 55, 100, 109, 100, 103, 108, 103, 107),
  C0 = c(4992, 4988, 1987, 1947, 1736, 1064, 474, 251, 104, 35, 11),
  C1 = c(8, 14, 20, 55, 92, 99, 76, 63, 72, 41, 19),
  C2 = c(0, 0, 1, 0, 4, 5, 12, 17, 15, 21, 11),
  C3 = c(0, 0, 0, 0, 0, 0, 0, 2, 2, 4, 9),
  C4 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 6),
  C5 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3),
  mean = c(0.0016, 0.0028, 0.0110, 0.0275, 0.0546, 0.0933, 0.178, 0.309, 0.560, 1, 1.81),
  var = c(0.00160, 0.00279, 0.0118, 0.0267, 0.0560, 0.0933, 0.189, 0.353, 0.466, 0.882, 2.09),
  DI = c(0.999, 0.997, 1.08, 0.973, 1.03, 0.999, 1.06, 1.14, 0.834, 0.882, 1.15),
  u = c(-0.0748, -0.135, 2.61, -0.861, 0.790, -0.0176, 1.08, 1.82, -1.64, -0.844, 0.811)
)


fit_results <- fit(count_data = count_data,
                   model_formula = "lin-quad",
                   model_family = "automatic",
                   fit_link = "identity",
                   aberr_module = "dicentrics",
                   algorithm = "maxlik")

#FUNTION PLOT_FIT_DOSE_CURVE()
plot_fit_dose_curve(
  fit_results,
  aberr_name = "Dicentrics",
  place = "UI"
)
#In order to plot the fitted curve we need the output of the fit() function:
count_data <- data.frame(
  D = c(0, 0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5),
  N = c(5000, 5002, 2008, 2002, 1832, 1168, 562, 333, 193, 103, 59),
  X = c(8, 14, 22, 55, 100, 109, 100, 103, 108, 103, 107),
  C0 = c(4992, 4988, 1987, 1947, 1736, 1064, 474, 251, 104, 35, 11),
  C1 = c(8, 14, 20, 55, 92, 99, 76, 63, 72, 41, 19),
  C2 = c(0, 0, 1, 0, 4, 5, 12, 17, 15, 21, 11),
  C3 = c(0, 0, 0, 0, 0, 0, 0, 2, 2, 4, 9),
  C4 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 6),
  C5 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3),
  mean = c(0.0016, 0.0028, 0.0110, 0.0275, 0.0546, 0.0933, 0.178, 0.309, 0.560, 1, 1.81),
  var = c(0.00160, 0.00279, 0.0118, 0.0267, 0.0560, 0.0933, 0.189, 0.353, 0.466, 0.882, 2.09),
  DI = c(0.999, 0.997, 1.08, 0.973, 1.03, 0.999, 1.06, 1.14, 0.834, 0.882, 1.15),
  u = c(-0.0748, -0.135, 2.61, -0.861, 0.790, -0.0176, 1.08, 1.82, -1.64, -0.844, 0.811)
)


fit_results <- fit(count_data = count_data,
                   model_formula = "lin-quad",
                   model_family = "automatic",
                   fit_link = "identity",
                   aberr_module = "dicentrics",
                   algorithm = "maxlik")

#FUNTION PLOT_FIT_DOSE_CURVE()
plot_fit_dose_curve(
  fit_results,
  aberr_name = "Dicentrics",
  place = "UI"
)

Plot deviation

Description

Plot deviation

Usage

plot_interlab_deviation(zscore, sum_table, place)
plot_interlab_deviation(zscore, sum_table, place)

Arguments

zscore

data frame with Lab, Sample, Type, Reference, Dose, Deviation and Zscore.

sum_table

summary table.

place

UI or save.

Value

ggplot2 object.

Examples

data_frame_zscore <- data.frame(
  Lab = factor(c("A1", "A2", "A1", "A2", "A1", "A2")),
  Sample = factor(c(1, 1, 2, 2, 3, 3)),
  Type = rep("manual", 6),
  Reference = c(2.56, 3.41, 4.54, 2.56, 3.41, 4.54),
  Dose = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  Deviation = c(0.02922998, -0.79902999, -1.39394510, 0.45868228, 0.84939953, -0.55077813),
  Zscore = c(1.677656, 2.925426, -2.585664, -3.833434, -1.295906, -2.543676),
  stringsAsFactors = FALSE
)

sum_table <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = rep("manual", 6),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)


#FUNCTION PLOT_INTERLAB_DEVIATION:
plot_interlab_deviation(
  zscore = data_frame_zscore,
  sum_table = sum_table,
  place = "UI"
)
data_frame_zscore <- data.frame(
  Lab = factor(c("A1", "A2", "A1", "A2", "A1", "A2")),
  Sample = factor(c(1, 1, 2, 2, 3, 3)),
  Type = rep("manual", 6),
  Reference = c(2.56, 3.41, 4.54, 2.56, 3.41, 4.54),
  Dose = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  Deviation = c(0.02922998, -0.79902999, -1.39394510, 0.45868228, 0.84939953, -0.55077813),
  Zscore = c(1.677656, 2.925426, -2.585664, -3.833434, -1.295906, -2.543676),
  stringsAsFactors = FALSE
)

sum_table <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = rep("manual", 6),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)


#FUNCTION PLOT_INTERLAB_DEVIATION:
plot_interlab_deviation(
  zscore = data_frame_zscore,
  sum_table = sum_table,
  place = "UI"
)

Plot zscore v2

Description

Plot zscore v2

Usage

plot_interlab_v2(zscore, select_method, sum_table, place)
plot_interlab_v2(zscore, select_method, sum_table, place)

Arguments

zscore

data frame with Lab, Sample, Type, Reference, Dose, Deviation and Zscore.

select_method

Zscore algorithm.

sum_table

summary table.

place

UI or save.

Value

ggplot2 object.

Examples

data_frame_zscore <- data.frame(
  Lab = factor(c("A1", "A2", "A1", "A2", "A1", "A2")),
  Sample = factor(c(1, 1, 2, 2, 3, 3)),
  Type = rep("manual", 6),
  Reference = c(2.56, 3.41, 4.54, 2.56, 3.41, 4.54),
  Dose = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  Deviation = c(0.02922998, -0.79902999, -1.39394510, 0.45868228, 0.84939953, -0.55077813),
  Zscore = c(1.677656, 2.925426, -2.585664, -3.833434, -1.295906, -2.543676),
  stringsAsFactors = FALSE
)


sum_table <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = rep("manual", 6),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)


#FUNCTION PLOT_INTERLAB_V2
plot_interlab_v2(zscore = data_frame_zscore,
                 select_method = "algA",
                 sum_table = sum_table,
                 place = "UI"
                 )
data_frame_zscore <- data.frame(
  Lab = factor(c("A1", "A2", "A1", "A2", "A1", "A2")),
  Sample = factor(c(1, 1, 2, 2, 3, 3)),
  Type = rep("manual", 6),
  Reference = c(2.56, 3.41, 4.54, 2.56, 3.41, 4.54),
  Dose = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  Deviation = c(0.02922998, -0.79902999, -1.39394510, 0.45868228, 0.84939953, -0.55077813),
  Zscore = c(1.677656, 2.925426, -2.585664, -3.833434, -1.295906, -2.543676),
  stringsAsFactors = FALSE
)


sum_table <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = rep("manual", 6),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)


#FUNCTION PLOT_INTERLAB_V2
plot_interlab_v2(zscore = data_frame_zscore,
                 select_method = "algA",
                 sum_table = sum_table,
                 place = "UI"
                 )

Plot triage

Description

Plot triage

Usage

plot_triage(num_cases, est_doses_whole, est_doses_partial, assessment, place)
plot_triage(num_cases, est_doses_whole, est_doses_partial, assessment, place)

Arguments

num_cases

number of cases.

est_doses_whole

dose estimation list of cases.

est_doses_partial

dose estimation list of cases.

assessment

partial or whole.

place

UI or save.

Value

ggplot2 object.

Examples

est_doses_whole <- data.frame(
    ID = c("example1", "example2"),
    lower = c(1.407921, 2.604542),
    estimate = c(1.931245, 3.301014),
    upper = c(2.632199, 4.221749)
  )


plot_triage(
  num_cases = 2,
  est_doses_whole ,
  est_doses_partial = NULL,
  assessment = "whole",
  place = "UI"
)

est_doses_whole <- data.frame(
    ID = c("example1", "example2"),
    lower = c(1.407921, 2.604542),
    estimate = c(1.931245, 3.301014),
    upper = c(2.632199, 4.221749)
  )


plot_triage(
  num_cases = 2,
  est_doses_whole ,
  est_doses_partial = NULL,
  assessment = "whole",
  place = "UI"
)

Plot triage interlab

Description

Plot triage interlab

Usage

plot_triage_interlab(line_triage, sum_table, place)
plot_triage_interlab(line_triage, sum_table, place)

Arguments

line_triage

reference value for selected sample.

sum_table

summary table.

place

UI or save.

Value

ggplot2 object.

Examples

line_triage <- list(`1` = 2.56, `2` = 3.41, `3` = 4.54)

sum_table <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = c("manual", "manual", "manual", "manual", "manual", "manual"),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)

plot_triage_interlab(
  line_triage = line_triage,
  sum_table = sum_table,
  place = "UI"
)
line_triage <- list(`1` = 2.56, `2` = 3.41, `3` = 4.54)

sum_table <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = c("manual", "manual", "manual", "manual", "manual", "manual"),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)

plot_triage_interlab(
  line_triage = line_triage,
  sum_table = sum_table,
  place = "UI"
)

Plot Z-scores all blind samples

Description

Plot Z-scores all blind samples

Usage

plot_zscore_all(zscore, select_method, place)
plot_zscore_all(zscore, select_method, place)

Arguments

zscore

zscore vector to plot for each lab.

select_method

chosen algorithm for zscore.

place

UI or save.

Value

ggplot2 object.

Examples


data_frame_zscore <- data.frame(
  Lab = factor(c("A1", "A2", "A1", "A2", "A1", "A2")),
  Sample = factor(c(1, 1, 2, 2, 3, 3)),
  Type = rep("manual", 6),
  Reference = c(2.56, 3.41, 4.54, 2.56, 3.41, 4.54),
  Dose = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  Deviation = c(0.02922998, -0.79902999, -1.39394510, 0.45868228, 0.84939953, -0.55077813),
  Zscore = c(1.677656, 2.925426, -2.585664, -3.833434, -1.295906, -2.543676),
  stringsAsFactors = FALSE
)


plot_zscore_all(
  zscore = data_frame_zscore,
  select_method = "algA",
  place = "UI"
)
data_frame_zscore <- data.frame(
  Lab = factor(c("A1", "A2", "A1", "A2", "A1", "A2")),
  Sample = factor(c(1, 1, 2, 2, 3, 3)),
  Type = rep("manual", 6),
  Reference = c(2.56, 3.41, 4.54, 2.56, 3.41, 4.54),
  Dose = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  Deviation = c(0.02922998, -0.79902999, -1.39394510, 0.45868228, 0.84939953, -0.55077813),
  Zscore = c(1.677656, 2.925426, -2.585664, -3.833434, -1.295906, -2.543676),
  stringsAsFactors = FALSE
)


plot_zscore_all(
  zscore = data_frame_zscore,
  select_method = "algA",
  place = "UI"
)

Prepare count data for max-likelihood optimization fitting

Description

Prepare count data for max-likelihood optimization fitting

Usage

prepare_maxlik_count_data(
  count_data,
  model_formula,
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)
prepare_maxlik_count_data(
  count_data,
  model_formula,
  aberr_module = c("dicentrics", "translocations", "micronuclei")
)

Arguments

count_data

Count data in data frame form.

model_formula

Model formula.

aberr_module

Aberration module.

Value

Data frame of parsed count data.

Project yield into dose-effect fitting curve

Description

Project yield into dose-effect fitting curve

Usage

project_yield(
  yield,
  type = "estimate",
  general_fit_coeffs,
  general_fit_var_cov_mat = NULL,
  protracted_g_value = 1,
  conf_int = 0.95
)
project_yield(
  yield,
  type = "estimate",
  general_fit_coeffs,
  general_fit_var_cov_mat = NULL,
  protracted_g_value = 1,
  conf_int = 0.95
)

Arguments

yield

Yield to be projected.

type

Type of yield calculation. Can be "estimate", "lower", or "upper".

general_fit_coeffs

Generalised fit coefficients matrix.

general_fit_var_cov_mat

Generalised variance-covariance matrix.

protracted_g_value

Protracted $G(x)$ value.

conf_int

Curve confidence interval, 95% by default.

Value

Numeric value of projected dose.

Examples

fit_coeffs <- data.frame(
  estimate   = c(0.001280319, 0.021038724, 0.063032534),
  std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
  statistic  = c(2.715961, 4.079156, 15.729091),
  p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)



project_yield(yield = 0.67,
              type = "estimate",
              general_fit_coeffs = fit_coeffs[, "estimate"],
              general_fit_var_cov_mat = NULL,
              protracted_g_value = 1,
              conf_int = 0.95)
fit_coeffs <- data.frame(
  estimate   = c(0.001280319, 0.021038724, 0.063032534),
  std.error  = c(0.0004714055, 0.0051576170, 0.0040073856),
  statistic  = c(2.715961, 4.079156, 15.729091),
  p.value    = c(6.608367e-03, 4.519949e-05, 9.557291e-56),
  row.names =  c("coeff_C", "coeff_alpha", "coeff_beta")
)



project_yield(yield = 0.67,
              type = "estimate",
              general_fit_coeffs = fit_coeffs[, "estimate"],
              general_fit_var_cov_mat = NULL,
              protracted_g_value = 1,
              conf_int = 0.95)

Calculate protracted function $G(x)$

Description

Calculation based on the paper by Lea, D. E. & Catcheside, D. G. (1942). The mechanism of the induction by radiation of chromosome aberrations inTradescantia. Journal of Genetics, 44(2-3), 216-245. <doi:10.1007/BF02982830>.

Usage

protracted_g_function(time, time_0 = 2)
protracted_g_function(time, time_0 = 2)

Arguments

time

Time over which the irradiation occurred.

time_0

The mean lifetime of the breaks, which has been shown to be on the order of ~ 2 hours (default value).

Value

Numeric value of $G(x)$ .

Examples

protracted_g_function(time = 3,
                      time_0 = 2)
protracted_g_function(time = 3,
                      time_0 = 2)

Calculate QHampel

Description

Calculate QHampel

Usage

QHampel(y, lab, tol.G1 = 1e-06)
QHampel(y, lab, tol.G1 = 1e-06)

Arguments

y

vector of data values.

lab

corresponding lab numbers.

tol.G1

decimal place accuracy of the numerator of s.star.

Value

Numeric value of zscore using QHampel algorithm.

Examples

X = c(3.65, 2.5, 4.85)

QHampel(y = as.numeric(X),
        lab = 1:length(X),
        tol.G1=0.000001)

X = c(3.65, 2.5, 4.85)

QHampel(y = as.numeric(X),
        lab = 1:length(X),
        tol.G1=0.000001)

Calculate R regression confidence factor

Description

Calculate R regression confidence factor depending on selected confidence interval and type of fit.

Usage

R_factor(general_fit_coeffs, conf_int = 0.95)
R_factor(general_fit_coeffs, conf_int = 0.95)

Arguments

general_fit_coeffs

Generalised fit coefficients matrix.

conf_int

Confidence interval, 95% by default.

Value

Numeric value of R regression confidence factor.

Run the Shiny Application

Description

Run the Shiny Application

Usage

run_app(...)
run_app(...)

Arguments

...

A series of options to be used inside the app.

Value

Used for side-effect.

Summary and curve tables

Description

Summary and curve tables

Usage

summary_curve_tables(num_labs, list_lab_names, all_rds)
summary_curve_tables(num_labs, list_lab_names, all_rds)

Arguments

num_labs

number of laboratories participating.

list_lab_names

list with the laboratory names.

all_rds

list with the rds files.

Value

list(sorted_table_ilc, sorted_table_curve)

Examples

#fit_results_X is the output from the Estimation module

fit_results_A1 <- system.file("extdata", "A1_Estimation_results.rds", package = "biodosetools") %>%
  readRDS()

fit_results_A2 <- system.file("extdata", "A2_Estimation_results.rds", package = "biodosetools") %>%
  readRDS()



summary_curve_tables(num_labs = 2,
                     list_lab_names = c("A", "B"),
                     all_rds = list(fit_results_A1, fit_results_A2)
                     )
#fit_results_X is the output from the Estimation module

fit_results_A1 <- system.file("extdata", "A1_Estimation_results.rds", package = "biodosetools") %>%
  readRDS()

fit_results_A2 <- system.file("extdata", "A2_Estimation_results.rds", package = "biodosetools") %>%
  readRDS()



summary_curve_tables(num_labs = 2,
                     list_lab_names = c("A", "B"),
                     all_rds = list(fit_results_A1, fit_results_A2)
                     )

U-test

Description

U-test

Usage

u_test_plot(dat, place)
u_test_plot(dat, place)

Arguments

dat

data frame of data values.

place

UI or save.

Value

plot

Examples

dat <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = c("manual", "manual", "manual", "manual", "manual", "manual"),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)

u_test_plot(
  dat = dat,
  place = "UI"
)
dat <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = c("manual", "manual", "manual", "manual", "manual", "manual"),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)

u_test_plot(
  dat = dat,
  place = "UI"
)

Update dose estimation box with yield values

Description

Update dose estimation box with yield values

Usage

update_outputs(num_cases, reactive_data, output, yield_fun, manual, table)
update_outputs(num_cases, reactive_data, output, yield_fun, manual, table)

Arguments

num_cases

number of cases to estimate.

reactive_data

to access reactive data.

output

output for the UI.

yield_fun

function for calculating yield, in utils_estimation.R

manual

logical, indicates if using manual input or file data.

table

the data input table.

Value

yield per dose. Numeric

Boxplot yield

Description

Boxplot yield

Usage

yield_boxplot(dat, place)
yield_boxplot(dat, place)

Arguments

dat

data frame of data values.

place

UI or save.

Value

plot

Examples

dat <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = c("manual", "manual", "manual", "manual", "manual", "manual"),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)


yield_boxplot(
  dat =  dat,
  place = "UI"
)
dat <- data.frame(
  Lab = c("A1", "A2", "A1", "A2", "A1", "A2"),
  Module = c("dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics", "dicentrics"),
  Type = c("manual", "manual", "manual", "manual", "manual", "manual"),
  Sample = c(1, 1, 2, 2, 3, 3),
  N = c(200, 200, 200, 200, 200, 200),
  X = c(140, 111, 204, 147, 368, 253),
  estimate = c(2.589230, 2.610970, 3.146055, 3.018682, 4.259400, 3.989222),
  lower = c(2.083403, 2.231150, 2.614931, 2.622748, 3.882349, 3.552963),
  upper = c(3.208857, 3.045616, 3.785879, 3.471269, 4.688910, 4.487336),
  y = c(0.700, 0.555, 1.020, 0.735, 1.840, 1.265),
  y.err = c(0.0610, 0.0495, 0.0733, 0.0556, 0.0923, 0.0785),
  DI = c(1.0625, 0.8818, 1.0539, 0.8406, 0.9255, 0.9731),
  u = c(0.6252, -1.1840, 0.5389, -1.5951, -0.7442, -0.2692),
  stringsAsFactors = FALSE
)


yield_boxplot(
  dat =  dat,
  place = "UI"
)

Calculate yield error

Description

Calculate yield error using Merkle's method

Usage

yield_error_fun(dose, general_fit_var_cov_mat = NULL, protracted_g_value = 1)
yield_error_fun(dose, general_fit_var_cov_mat = NULL, protracted_g_value = 1)

Arguments

dose

Numeric value of dose.

general_fit_var_cov_mat

Generalised variance-covariance matrix.

protracted_g_value

Protracted $G(x)$ value.

Value

Numeric value of yield error.

Calculate yield

Description

Calculate yield

Usage

yield_fun(dose, general_fit_coeffs, protracted_g_value = 1)
yield_fun(dose, general_fit_coeffs, protracted_g_value = 1)

Arguments

dose

Numeric value of dose.

general_fit_coeffs

Generalised fit coefficients matrix.

protracted_g_value

Protracted $G(x)$ value.

Value

Numeric value of yield.

Package 'biodosetools'

Help Index

Calculate AIC (Akaike's 'An Information Criterion')

Description

Usage

Arguments

Value

Bar plots dosimetry

Description

Usage

Arguments

Value

Examples

Calculate ZScore

Description

Usage

Arguments

Value

Examples

Aberration calculation functions

Description

Usage

Arguments

Calculate IRR (this is the same as the Odds-Ratio calculation in IAEA2011)

Description

Usage

Arguments

Value

Calculate aberrations table

Description

Usage

Arguments

Value

Examples

Characteristic limits function———————————————–

Description

Usage

Arguments

Value

Examples

Calculate genomic conversion factor

Description

Usage

Arguments

Value

Examples

Calculate model statistics

Description

Usage

Arguments

Value

Calculate the characteristic limits table

Description

Usage

Arguments

Value

Calculate manual translocation rate

Description

Usage

Arguments

Value

Examples

Calculate Sigurdson's translocation rate

Description

Usage

Arguments

Value

Examples

Calculate yield from dose

Description

Usage

Arguments

Value

Calculate theoretical yield infimum

Description

Usage

Arguments

Value

Correct boundary of irradiated fractions to be bounded by 0 and 1

Description