| Title: | Methods for Penetrance Estimation in Family-Based Studies |
|---|---|
| Description: | Implements statistical methods for estimating disease penetrance in family-based studies. Penetrance refers to the probability of disease§ manifestation in individuals carrying specific genetic variants. The package provides tools for age-specific penetrance estimation, handling missing data, and accounting for ascertainment bias in family studies. Cite as: Kubista, N., Braun, D. & Parmigiani, G. (2024) <doi:10.48550/arXiv.2411.18816>. |
| Authors: | Nicolas Kubista [aut, cre], BayesMendel Lab [aut] |
| Maintainer: | Nicolas Kubista <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2025-03-04 04:49:54 UTC |
| Source: | https://github.com/nicokubi/penetrance |
Function to return absolute values
absValue(x)absValue(x)
x |
Numeric, the input value. |
Numeric, the absolute value of the input.
Apply Burn-In
apply_burn_in(results, burn_in)apply_burn_in(results, burn_in)
results |
A list of MCMC chain results. |
burn_in |
The fraction roportion of results to discard as burn-in (0 to 1). The default is no burn-in, burn_in=0. |
A list of results with burn-in applied.
Apply Thinning
apply_thinning(results, thinning_factor)apply_thinning(results, thinning_factor)
results |
A list of MCMC chain results. |
thinning_factor |
The factor by which to thin the results (positive integer). The default thinning factor is 1, which implies no thinning. |
A list of results with thinning applied.
This dataset contains age-specific cancer penetrance rates for both females and males. As an example, the data is derived from the SEER program for colorectal cancer females and males.
baseline_data_default baseline_data_defaultbaseline_data_default baseline_data_default
A data frame with 94 rows and 3 variables:
Age in years
Female baseline risk
Male baseline risk
A data frame with 94 rows and 3 variables:
Age in years (1 to 94)
Penetrance rate for females
Penetrance rate for males
This function calculates the shape (alpha) and scale (beta) parameters
of a Weibull distribution given the median, first quartile, and delta values.
calculate_weibull_parameters(given_median, given_first_quartile, delta)calculate_weibull_parameters(given_median, given_first_quartile, delta)
given_median |
The median of the data. |
given_first_quartile |
The first quartile of the data. |
delta |
A constant offset value. |
A list containing the calculated Weibull parameters:
alpha |
The shape parameter of the Weibull distribution |
beta |
The scale parameter of the Weibull distribution |
# Calculate Weibull parameters params <- calculate_weibull_parameters( given_median = 50, given_first_quartile = 30, delta = 15 ) print(params)# Calculate Weibull parameters params <- calculate_weibull_parameters( given_median = 50, given_first_quartile = 30, delta = 15 ) print(params)
This function extracts the penetrance data for a specified cancer type, gene, race, and penetrance type from the provided database.
calculateBaseline(cancer_type, gene, race, type, db)calculateBaseline(cancer_type, gene, race, type, db)
cancer_type |
The type of cancer for which the risk is being calculated. |
gene |
The gene of interest for which the risk is being calculated. |
race |
The race of the individual. |
type |
The type of penetrance calculation. |
db |
The dataset used for the calculation, containing penetrance data. |
A matrix of penetrance data for the specified parameters.
Calculates empirical age density distributions for different subgroups in the data, separated by sex and genetic testing status.
calculateEmpiricalDensity( data, aff_column = "aff", age_column = "age", sex_column = "sex", geno_column = "geno", n_points = 10000, sex_specific = TRUE )calculateEmpiricalDensity( data, aff_column = "aff", age_column = "age", sex_column = "sex", geno_column = "geno", n_points = 10000, sex_specific = TRUE )
data |
A data frame containing the family data |
aff_column |
Name of the affection status column |
age_column |
Name of the age column |
sex_column |
Name of the sex column |
geno_column |
Name of the genotype column |
n_points |
Number of points to use in density estimation |
sex_specific |
Logical; whether to calculate sex-specific densities |
A list of density objects for different subgroups (tested/untested, male/female)
This function calculates the age-specific non-carrier penetrance based on SEER baseline data, penetrances for carriers, and allele frequencies. It adjusts penetrance estimates for genetic testing by incorporating the genetic risk attributable to specified alleles.
calculateNCPen(SEER_baseline, alpha, beta, delta, gamma, prev, max_age)calculateNCPen(SEER_baseline, alpha, beta, delta, gamma, prev, max_age)
SEER_baseline |
Numeric, the baseline penetrance derived from SEER data for the general population without considering genetic risk factors. |
alpha |
Numeric, shape parameter for the Weibull distribution used to model carrier risk. |
beta |
Numeric, scale parameter for the Weibull distribution used to model carrier risk. |
delta |
Numeric, location parameter for the Weibull distribution used to model carrier risk. |
gamma |
Numeric, scaling factor applied to the Weibull distribution to adjust carrier risk. |
prev |
Numeric, the prevalence of the risk allele in the population. |
max_age |
Integer, the maximum age up to which the calculations are performed. |
A list containing:
weightedCarrierRisk |
Numeric vector, the weighted risk for carriers at each age based on prevalence. |
yearlyProb |
Numeric vector, the yearly probability of not getting the disease at each age. |
cumulativeProb |
Numeric vector, the cumulative probability of not getting the disease up to each age. |
Combine Chains Function to combine the posterior samples from the multiple chains.
combine_chains(results)combine_chains(results)
results |
A list of MCMC chain results. |
A list with combined results, including median, threshold, first quartile, and asymptote values.
Combines the posterior samples from multiple MCMC chains for non-sex-specific estimations.
combine_chains_noSex(results)combine_chains_noSex(results)
results |
A list of MCMC chain results, where each element contains posterior samples of parameters. |
A list with combined results, including samples for median, threshold, first quartile, asymptote values, log-likelihoods, and log-acceptance ratios.
Default data frame structure with row names for use in the makePriors function.
distribution_data_default distribution_data_defaultdistribution_data_default distribution_data_default
A data frame for prior distribution parameters
A data frame with the following columns:
Age values (NA for default).
Proportion of people at risk (NA for default).
This function draws ages using the inverse CDF method from baseline data.
drawBaseline(baseline_data)drawBaseline(baseline_data)
baseline_data |
A data frame containing baseline data with columns 'cum_prob' and 'age'. |
A single age value drawn from the baseline data.
This function draws ages using the inverse CDF method from empirical density data, based on sex and whether the individual was tested.
drawEmpirical(empirical_density, sex, tested, sex_specific = TRUE)drawEmpirical(empirical_density, sex, tested, sex_specific = TRUE)
empirical_density |
A list of density objects containing the empirical density of ages for different groups. |
sex |
Numeric, the sex of the individual (1 for male, 2 for female). |
tested |
Logical, indicating whether the individual was tested (has a non-NA 'geno' value). |
sex_specific |
Logical, indicating whether the imputation should be sex-specific. Default is TRUE. |
A single age value drawn from the appropriate empirical density data.
Generates histograms of the posterior samples for the different parameters
generate_density_plots(data)generate_density_plots(data)
data |
A list with combined results. |
No return value, called for side effects. Creates density plots for each parameter.
# Create example data data <- list( median_male_results = rnorm(1000, 50, 5), median_female_results = rnorm(1000, 45, 5), threshold_male_results = runif(1000, 20, 30), threshold_female_results = runif(1000, 25, 35), asymptote_male_results = rbeta(1000, 2, 2), asymptote_female_results = rbeta(1000, 2, 2) ) # Generate density plots old_par <- par(no.readonly = TRUE) # Save old par settings generate_density_plots(data) par(old_par) # Restore old par settings# Create example data data <- list( median_male_results = rnorm(1000, 50, 5), median_female_results = rnorm(1000, 45, 5), threshold_male_results = runif(1000, 20, 30), threshold_female_results = runif(1000, 25, 35), asymptote_male_results = rbeta(1000, 2, 2), asymptote_female_results = rbeta(1000, 2, 2) ) # Generate density plots old_par <- par(no.readonly = TRUE) # Save old par settings generate_density_plots(data) par(old_par) # Restore old par settings
Function to generate summary statistics
generate_summary(data, verbose = FALSE)generate_summary(data, verbose = FALSE)
data |
A list with combined results. |
verbose |
Logical, whether to print summary to console. Default is FALSE. |
A data.frame containing summary statistics (min, 1st quartile, median, mean, 3rd quartile, max) for each parameter.
Generates summary statistics for the combined MCMC results for non-sex-specific estimations.
generate_summary_noSex(data, verbose = FALSE)generate_summary_noSex(data, verbose = FALSE)
data |
A list containing combined results of MCMC chains, typically the output of |
verbose |
Logical, whether to print summary to console. Default is FALSE. |
A data.frame containing summary statistics (min, 1st quartile, median, mean, 3rd quartile, max) for median, threshold, first quartile, and asymptote values.
Imputes missing ages in family-based data using a combination of Weibull distributions for affected individuals and empirical distributions for unaffected individuals. The function can perform both sex-specific and non-sex-specific imputations.
imputeAges( data, na_indices, baseline_male = NULL, baseline_female = NULL, alpha_male = NULL, beta_male = NULL, delta_male = NULL, alpha_female = NULL, beta_female = NULL, delta_female = NULL, baseline = NULL, alpha = NULL, beta = NULL, delta = NULL, max_age, sex_specific = TRUE, max_attempts = 100, geno_freq, trans, lik )imputeAges( data, na_indices, baseline_male = NULL, baseline_female = NULL, alpha_male = NULL, beta_male = NULL, delta_male = NULL, alpha_female = NULL, beta_female = NULL, delta_female = NULL, baseline = NULL, alpha = NULL, beta = NULL, delta = NULL, max_age, sex_specific = TRUE, max_attempts = 100, geno_freq, trans, lik )
data |
A data frame containing family-based data with columns: family, individual, father, mother, sex, aff, age, geno, and isProband |
na_indices |
Vector of indices where ages need to be imputed |
baseline_male, baseline_female
|
Data frames containing baseline age distributions for males/females |
alpha_male, alpha_female
|
Shape parameters for male/female Weibull distributions |
beta_male, beta_female
|
Scale parameters for male/female Weibull distributions |
delta_male, delta_female
|
Location parameters for male/female Weibull distributions |
baseline |
Data frame containing overall baseline age distribution (non-sex-specific) |
alpha, beta, delta
|
Overall Weibull parameters (non-sex-specific) |
max_age |
Maximum allowable age |
sex_specific |
Logical; whether to use sex-specific parameters |
max_attempts |
Maximum number of attempts for generating valid ages |
geno_freq |
Vector of genotype frequencies |
trans |
Transmission probabilities |
lik |
Likelihood matrix |
A data frame with the following modifications:
age |
Updated with imputed ages for previously missing values |
The rest of the data frame remains unchanged.
# Create sample data with the same structure as used in mhChain data <- data.frame( family = rep(1:2, each=5), individual = rep(1:5, 2), father = c(NA,1,1,1,1, NA,6,6,6,6), mother = c(NA,2,2,2,2, NA,7,7,7,7), sex = c(1,2,1,2,1, 1,2,1,2,1), aff = c(1,0,1,0,NA, 1,0,1,0,NA), age = c(45,NA,25,NA,20, 50,NA,30,NA,22), geno = c("1/2",NA,"1/2",NA,NA, "1/2",NA,"1/2",NA,NA), isProband = c(1,0,0,0,0, 1,0,0,0,0) ) # Initialize parameters na_indices <- which(is.na(data$age)) geno_freq <- c(0.999, 0.001) # Frequency of normal and risk alleles trans <- matrix(c(1,0,0.5,0.5), nrow=2) # Transmission matrix lik <- matrix(1, nrow=nrow(data), ncol=2) # Likelihood matrix # Create baseline data for both sex-specific and non-sex-specific cases age_range <- 20:94 n_ages <- length(age_range) # Sex-specific baseline data baseline_male <- data.frame( age = age_range, cum_prob = (1:n_ages)/n_ages * 0.8 # Male cumulative probabilities ) baseline_female <- data.frame( age = age_range, cum_prob = (1:n_ages)/n_ages * 0.9 # Female cumulative probabilities ) # Non-sex-specific baseline data baseline <- data.frame( age = age_range, cum_prob = (1:n_ages)/n_ages * 0.85 # Overall cumulative probabilities ) # Example with sex-specific imputation imputed_data_sex <- imputeAges( data = data, na_indices = na_indices, baseline_male = baseline_male, baseline_female = baseline_female, alpha_male = 3.5, beta_male = 20, delta_male = 20, alpha_female = 3.2, beta_female = 18, delta_female = 18, max_age = 94, sex_specific = TRUE, geno_freq = geno_freq, trans = trans, lik = lik ) # Example with non-sex-specific imputation imputed_data_nosex <- imputeAges( data = data, na_indices = na_indices, baseline = baseline, alpha = 3.3, beta = 19, delta = 19, max_age = 94, sex_specific = FALSE, geno_freq = geno_freq, trans = trans, lik = lik )# Create sample data with the same structure as used in mhChain data <- data.frame( family = rep(1:2, each=5), individual = rep(1:5, 2), father = c(NA,1,1,1,1, NA,6,6,6,6), mother = c(NA,2,2,2,2, NA,7,7,7,7), sex = c(1,2,1,2,1, 1,2,1,2,1), aff = c(1,0,1,0,NA, 1,0,1,0,NA), age = c(45,NA,25,NA,20, 50,NA,30,NA,22), geno = c("1/2",NA,"1/2",NA,NA, "1/2",NA,"1/2",NA,NA), isProband = c(1,0,0,0,0, 1,0,0,0,0) ) # Initialize parameters na_indices <- which(is.na(data$age)) geno_freq <- c(0.999, 0.001) # Frequency of normal and risk alleles trans <- matrix(c(1,0,0.5,0.5), nrow=2) # Transmission matrix lik <- matrix(1, nrow=nrow(data), ncol=2) # Likelihood matrix # Create baseline data for both sex-specific and non-sex-specific cases age_range <- 20:94 n_ages <- length(age_range) # Sex-specific baseline data baseline_male <- data.frame( age = age_range, cum_prob = (1:n_ages)/n_ages * 0.8 # Male cumulative probabilities ) baseline_female <- data.frame( age = age_range, cum_prob = (1:n_ages)/n_ages * 0.9 # Female cumulative probabilities ) # Non-sex-specific baseline data baseline <- data.frame( age = age_range, cum_prob = (1:n_ages)/n_ages * 0.85 # Overall cumulative probabilities ) # Example with sex-specific imputation imputed_data_sex <- imputeAges( data = data, na_indices = na_indices, baseline_male = baseline_male, baseline_female = baseline_female, alpha_male = 3.5, beta_male = 20, delta_male = 20, alpha_female = 3.2, beta_female = 18, delta_female = 18, max_age = 94, sex_specific = TRUE, geno_freq = geno_freq, trans = trans, lik = lik ) # Example with non-sex-specific imputation imputed_data_nosex <- imputeAges( data = data, na_indices = na_indices, baseline = baseline, alpha = 3.3, beta = 19, delta = 19, max_age = 94, sex_specific = FALSE, geno_freq = geno_freq, trans = trans, lik = lik )
Initializes the age imputation process by filling missing ages with random values between a threshold and maximum age.
imputeAgesInit(data, threshold, max_age)imputeAgesInit(data, threshold, max_age)
data |
A data frame containing family-based data |
threshold |
Minimum age value for initialization |
max_age |
Maximum age value for initialization |
A list containing:
data |
The data frame with initialized ages |
na_indices |
Indices of missing age values |
# Create sample data data <- data.frame( family = c(1, 1), individual = c(1, 2), father = c(NA, 1), mother = c(NA, NA), sex = c(1, 2), aff = c(1, 0), age = c(NA, NA), geno = c("1/2", NA), isProband = c(1, 0) ) # Initialize ages with random values between 20 and 94 result <- imputeAgesInit(data, threshold = 20, max_age = 94) # Access the results imputed_data <- result$data missing_indices <- result$na_indices# Create sample data data <- data.frame( family = c(1, 1), individual = c(1, 2), father = c(NA, 1), mother = c(NA, NA), sex = c(1, 2), aff = c(1, 0), age = c(NA, NA), geno = c("1/2", NA), isProband = c(1, 0) ) # Initialize ages with random values between 20 and 94 result <- imputeAgesInit(data, threshold = 20, max_age = 94) # Access the results imputed_data <- result$data missing_indices <- result$na_indices
This function imputes ages for unaffected individuals in a dataset based on their sex and whether they were tested, using empirical age distributions.
imputeUnaffectedAges(data, na_indices, empirical_density, max_age)imputeUnaffectedAges(data, na_indices, empirical_density, max_age)
data |
A data frame containing the individual data, including columns for age, sex, and geno. |
na_indices |
A vector of indices indicating the rows in the data where ages need to be imputed. |
empirical_density |
A list of density objects containing the empirical density of ages for different groups. |
max_age |
Integer, the maximum age considered in the analysis. |
The data frame with imputed ages for unaffected individuals.
This function calculates the likelihood for an individual based on Weibull distribution parameters without considering sex differentiation.
lik_noSex( i, data, alpha, beta, delta, gamma, max_age, baselineRisk, BaselineNC, prev )lik_noSex( i, data, alpha, beta, delta, gamma, max_age, baselineRisk, BaselineNC, prev )
i |
Integer, index of the individual in the data set. |
data |
Data frame, containing individual demographic and genetic information. Must include columns for 'age', 'aff' (affection status), and 'geno' (genotype). |
alpha |
Numeric, Weibull distribution shape parameter. |
beta |
Numeric, Weibull distribution scale parameter. |
delta |
Numeric, shift parameter for the Weibull function. |
gamma |
Numeric, asymptote parameter (only scales the entire distribution). |
max_age |
Integer, maximum age considered in the analysis. |
baselineRisk |
Numeric vector, baseline risk for each age. |
BaselineNC |
Logical, indicates if non-carrier penetrance should be based on SEER data or the calculated non-carrier penetrance. |
prev |
Numeric, prevalence of the risk allele in the population. |
Numeric vector, containing likelihood values for unaffected and affected individuals.
Calculates the penetrance for an individual based on Weibull distribution parameters. This function estimates the probability of developing cancer given the individual's genetic and demographic information.
lik.fn( i, data, alpha_male, alpha_female, beta_male, beta_female, delta_male, delta_female, gamma_male, gamma_female, max_age, baselineRisk, BaselineNC, prev )lik.fn( i, data, alpha_male, alpha_female, beta_male, beta_female, delta_male, delta_female, gamma_male, gamma_female, max_age, baselineRisk, BaselineNC, prev )
i |
Integer, index of the individual in the data set. |
data |
Data frame, containing individual demographic and genetic information. Must include columns for 'sex', 'age', 'aff' (affection status), and 'geno' (genotype). |
alpha_male |
Numeric, Weibull distribution shape parameter for males. |
alpha_female |
Numeric, Weibull distribution shape parameter for females. |
beta_male |
Numeric, Weibull distribution scale parameter for males. |
beta_female |
Numeric, Weibull distribution scale parameter for females. |
delta_male |
Numeric, shift parameter for the Weibull function for males. |
delta_female |
Numeric, shift parameter for the Weibull function for females. |
gamma_male |
Numeric, asymptote parameter for males (only scales the entire distribution). |
gamma_female |
Numeric, asymptote parameter for females (only scales the entire distribution). |
max_age |
Integer, maximum age considered in the analysis. |
baselineRisk |
Numeric matrix, baseline risk for each age by sex. Rows correspond to sex (1 for male, 2 for female) and columns to age. |
BaselineNC |
Logical, indicates if non-carrier penetrance should be based on SEER data. |
prev |
Numeric, prevalence of the risk allele in the population. |
Numeric vector, containing penetrance values for unaffected and affected individuals.
This function generates prior distributions based on user input or default parameters. It is designed to aid in the statistical analysis of risk proportions in populations, particularly in the context of cancer research. The distributions are calculated for various statistical metrics such as asymptote, threshold, median, and first quartile.
makePriors( data, sample_size, ratio, prior_params, risk_proportion, baseline_data )makePriors( data, sample_size, ratio, prior_params, risk_proportion, baseline_data )
data |
A data frame containing age and risk data. If NULL or contains NA values, default parameters are used. |
sample_size |
Numeric, the total sample size used for risk proportion calculations. |
ratio |
Numeric, the odds ratio (OR) or relative risk (RR) used in asymptote parameter calculations. |
prior_params |
List, containing prior parameters for the beta distributions. If NULL, default parameters are used. |
risk_proportion |
Data frame, with default proportions of people at risk. |
baseline_data |
Data frame with the baseline risk data. |
The function includes internal helper functions for normalizing median and first quartile values, and for computing beta distribution parameters. The function handles various settings: using default parameters, applying user inputs, and calculating parameters based on sample size and risk proportions.
If the OR/RR ratio is provided, the asymptote parameters are computed based on this ratio, overriding other inputs for the asymptote.
The function returns a list of distribution functions for the asymptote, threshold, median, and first quartile, which can be used for further statistical analysis.
A list of functions representing the prior distributions for asymptote, threshold, median, and first quartile.
Performs a single chain execution in the Metropolis-Hastings algorithm for Bayesian inference, specifically tailored for cancer risk estimation. This function can handle both sex-specific and non-sex-specific scenarios.
mhChain( seed, n_iter, burn_in, chain_id, ncores, data, twins, max_age, baseline_data, prior_distributions, prev, median_max, BaselineNC, var, age_imputation, imp_interval, remove_proband, sex_specific )mhChain( seed, n_iter, burn_in, chain_id, ncores, data, twins, max_age, baseline_data, prior_distributions, prev, median_max, BaselineNC, var, age_imputation, imp_interval, remove_proband, sex_specific )
seed |
Integer, the seed for the random number generator to ensure reproducibility. |
n_iter |
Integer, the number of iterations to perform in the Metropolis-Hastings algorithm. |
burn_in |
Integer, the number of initial iterations to discard (burn-in period). |
chain_id |
Integer, the identifier for the chain being executed. |
ncores |
Integer, the number of cores to use for parallel computation. |
data |
Data frame, containing family and genetic information used in the analysis. |
twins |
Information on monozygous twins or triplets in the pedigrees. |
max_age |
Integer, the maximum age considered in the analysis. |
baseline_data |
Numeric matrix or vector, containing baseline risk estimates for different ages and sexes. |
prior_distributions |
List, containing prior distributions for the parameters being estimated. |
prev |
Numeric, the prevalence of the risk allele in the population. |
median_max |
Logical, indicates if the maximum median age should be used for the Weibull distribution. |
BaselineNC |
Logical, indicates if non-carrier penetrance should be based on SEER data. |
var |
Numeric, the variance for the proposal distribution in the Metropolis-Hastings algorithm. |
age_imputation |
Logical, indicates if age imputation should be performed. |
imp_interval |
Integer, the interval at which age imputation should be performed when age_imputation = TRUE. |
remove_proband |
Logical, indicates if the proband should be removed from the analysis. |
sex_specific |
Logical, indicates if the analysis should differentiate by sex. |
A list containing samples, log likelihoods, log-acceptance ratio, and rejection rate for each iteration.
# Create sample data in PanelPRO format data <- data.frame( ID = 1:10, PedigreeID = rep(1, 10), Sex = c(0, 1, 0, 1, 0, 1, 0, 1, 0, 1), # 0=female, 1=male MotherID = c(NA, NA, 1, 1, 3, 3, 5, 5, 7, 7), FatherID = c(NA, NA, 2, 2, 4, 4, 6, 6, 8, 8), isProband = c(1, rep(0, 9)), CurAge = c(45, 35, 55, 40, 50, 45, 60, 38, 52, 42), isAff = c(1, 0, 1, 0, 1, 0, 1, 0, 1, 0), Age = c(40, NA, 50, NA, 45, NA, 55, NA, 48, NA), Geno = c(1, NA, 1, 0, 1, 0, NA, NA, 1, NA) ) # Transform data into required format data <- transformDF(data) # Set parameters for the chain seed <- 123 n_iter <- 10 burn_in <- 0.1 # 10% burn-in chain_id <- 1 ncores <- 1 max_age <- 100 # Create baseline data (simplified example) baseline_data <- matrix( c(rep(0.005, max_age), rep(0.008, max_age)), # Increased baseline risks ncol = 2, dimnames = list(NULL, c("Male", "Female")) ) # Set prior distributions with carefully chosen bounds prior_distributions <- list( prior_params = list( asymptote = list(g1 = 2, g2 = 3), # Mode around 0.4 threshold = list(min = 20, max = 30), # Narrower range for threshold median = list(m1 = 3, m2 = 2), # Mode around 0.6 first_quartile = list(q1 = 2, q2 = 3) # Mode around 0.4 ) ) # Create variance vector for all 8 parameters in sex-specific case # Using very small variances for initial stability var <- c(0.005, 0.005, # asymptotes (smaller variance since between 0-1) 1, 1, # thresholds 1, 1, # medians 1, 1) # first quartiles # Run the chain results <- mhChain( seed = seed, n_iter = n_iter, burn_in = burn_in, chain_id = chain_id, ncores = ncores, data = data, twins = NULL, max_age = max_age, baseline_data = baseline_data, prior_distributions = prior_distributions, prev = 0.05, # Increased prevalence median_max = FALSE, # Changed to FALSE for simpler median constraints BaselineNC = TRUE, var = var, age_imputation = FALSE, imp_interval = 10, remove_proband = TRUE, sex_specific = TRUE )# Create sample data in PanelPRO format data <- data.frame( ID = 1:10, PedigreeID = rep(1, 10), Sex = c(0, 1, 0, 1, 0, 1, 0, 1, 0, 1), # 0=female, 1=male MotherID = c(NA, NA, 1, 1, 3, 3, 5, 5, 7, 7), FatherID = c(NA, NA, 2, 2, 4, 4, 6, 6, 8, 8), isProband = c(1, rep(0, 9)), CurAge = c(45, 35, 55, 40, 50, 45, 60, 38, 52, 42), isAff = c(1, 0, 1, 0, 1, 0, 1, 0, 1, 0), Age = c(40, NA, 50, NA, 45, NA, 55, NA, 48, NA), Geno = c(1, NA, 1, 0, 1, 0, NA, NA, 1, NA) ) # Transform data into required format data <- transformDF(data) # Set parameters for the chain seed <- 123 n_iter <- 10 burn_in <- 0.1 # 10% burn-in chain_id <- 1 ncores <- 1 max_age <- 100 # Create baseline data (simplified example) baseline_data <- matrix( c(rep(0.005, max_age), rep(0.008, max_age)), # Increased baseline risks ncol = 2, dimnames = list(NULL, c("Male", "Female")) ) # Set prior distributions with carefully chosen bounds prior_distributions <- list( prior_params = list( asymptote = list(g1 = 2, g2 = 3), # Mode around 0.4 threshold = list(min = 20, max = 30), # Narrower range for threshold median = list(m1 = 3, m2 = 2), # Mode around 0.6 first_quartile = list(q1 = 2, q2 = 3) # Mode around 0.4 ) ) # Create variance vector for all 8 parameters in sex-specific case # Using very small variances for initial stability var <- c(0.005, 0.005, # asymptotes (smaller variance since between 0-1) 1, 1, # thresholds 1, 1, # medians 1, 1) # first quartiles # Run the chain results <- mhChain( seed = seed, n_iter = n_iter, burn_in = burn_in, chain_id = chain_id, ncores = ncores, data = data, twins = NULL, max_age = max_age, baseline_data = baseline_data, prior_distributions = prior_distributions, prev = 0.05, # Increased prevalence median_max = FALSE, # Changed to FALSE for simpler median constraints BaselineNC = TRUE, var = var, age_imputation = FALSE, imp_interval = 10, remove_proband = TRUE, sex_specific = TRUE )
Calculate Log Likelihood using clipp Package
mhLogLikelihood_clipp( paras, families, twins, max_age, baseline_data, prev, geno_freq, trans, BaselineNC, ncores )mhLogLikelihood_clipp( paras, families, twins, max_age, baseline_data, prev, geno_freq, trans, BaselineNC, ncores )
paras |
Numeric vector of parameters |
families |
Data frame of pedigree information |
twins |
Information on monozygous twins |
max_age |
Integer, maximum age |
baseline_data |
Numeric matrix of baseline risk data |
prev |
Numeric, prevalence |
geno_freq |
Numeric vector of frequencies |
trans |
Numeric matrix of transmission probabilities |
BaselineNC |
Logical for baseline choice |
ncores |
Integer for parallel computation |
Numeric value representing the calculated log likelihood.
# Create example parameters and data paras <- c(0.8, 0.7, 20, 25, 50, 45, 30, 35) # Example parameters # Create sample data in PanelPRO format families <- data.frame( ID = 1:10, PedigreeID = rep(1, 10), Sex = c(0, 1, 0, 1, 0, 1, 0, 1, 0, 1), # 0=female, 1=male MotherID = c(NA, NA, 1, 1, 3, 3, 5, 5, 7, 7), FatherID = c(NA, NA, 2, 2, 4, 4, 6, 6, 8, 8), isProband = c(1, rep(0, 9)), CurAge = c(45, 35, 55, 40, 50, 45, 60, 38, 52, 42), isAff = c(1, 0, 1, 0, 1, 0, 1, 0, 1, 0), Age = c(40, NA, 50, NA, 45, NA, 55, NA, 48, NA), Geno = c(1, NA, 1, 0, 1, 0, NA, NA, 1, NA) ) # Transform data into required format families <- transformDF(families) trans <- matrix( c( 1, 0, # both parents are wild type 0.5, 0.5, # mother is wildtype and father is a heterozygous carrier 0.5, 0.5, # father is wildtype and mother is a heterozygous carrier 1 / 3, 2 / 3 # both parents are heterozygous carriers ), nrow = 4, ncol = 2, byrow = TRUE ) # Calculate log likelihood loglik <- mhLogLikelihood_clipp( paras = paras, families = families, twins = NULL, max_age = 94, baseline_data = baseline_data_default, prev = 0.001, geno_freq = c(0.999, 0.001), trans = trans, BaselineNC = TRUE, ncores = 1 )# Create example parameters and data paras <- c(0.8, 0.7, 20, 25, 50, 45, 30, 35) # Example parameters # Create sample data in PanelPRO format families <- data.frame( ID = 1:10, PedigreeID = rep(1, 10), Sex = c(0, 1, 0, 1, 0, 1, 0, 1, 0, 1), # 0=female, 1=male MotherID = c(NA, NA, 1, 1, 3, 3, 5, 5, 7, 7), FatherID = c(NA, NA, 2, 2, 4, 4, 6, 6, 8, 8), isProband = c(1, rep(0, 9)), CurAge = c(45, 35, 55, 40, 50, 45, 60, 38, 52, 42), isAff = c(1, 0, 1, 0, 1, 0, 1, 0, 1, 0), Age = c(40, NA, 50, NA, 45, NA, 55, NA, 48, NA), Geno = c(1, NA, 1, 0, 1, 0, NA, NA, 1, NA) ) # Transform data into required format families <- transformDF(families) trans <- matrix( c( 1, 0, # both parents are wild type 0.5, 0.5, # mother is wildtype and father is a heterozygous carrier 0.5, 0.5, # father is wildtype and mother is a heterozygous carrier 1 / 3, 2 / 3 # both parents are heterozygous carriers ), nrow = 4, ncol = 2, byrow = TRUE ) # Calculate log likelihood loglik <- mhLogLikelihood_clipp( paras = paras, families = families, twins = NULL, max_age = 94, baseline_data = baseline_data_default, prev = 0.001, geno_freq = c(0.999, 0.001), trans = trans, BaselineNC = TRUE, ncores = 1 )
This function calculates the log likelihood for a set of parameters and data without considering sex differentiation using the clipp package.
mhLogLikelihood_clipp_noSex( paras, families, twins, max_age, baseline_data, prev, geno_freq, trans, BaselineNC, ncores )mhLogLikelihood_clipp_noSex( paras, families, twins, max_age, baseline_data, prev, geno_freq, trans, BaselineNC, ncores )
paras |
Numeric vector, the parameters for the Weibull distribution and scaling factors. Should contain in order: gamma, delta, given_median, given_first_quartile. |
families |
Data frame, containing pedigree information with columns for 'age', 'aff' (affection status), and 'geno' (genotype). |
twins |
Information on monozygous twins or triplets in the pedigrees. |
max_age |
Integer, maximum age considered in the analysis. |
baseline_data |
Numeric vector, baseline risk data for each age. |
prev |
Numeric, prevalence of the risk allele in the population. |
geno_freq |
Numeric vector, represents the frequency of the risk type and its complement in the population. |
trans |
Numeric matrix, transition matrix that defines the probabilities of allele transmission from parents to offspring. |
BaselineNC |
Logical, indicates if non-carrier penetrance should be based on the baseline data or the calculated non-carrier penetrance. |
ncores |
Integer, number of cores to use for parallel computation. |
Numeric, the calculated log likelihood.
Details about the clipp package and methods can be found in the package documentation.
This dataset contains the simulated output data for the penetrance package.
data(out_sim)data(out_sim)
A list with the following components:
A data frame with 18000 observations of 8 variables:
numeric, Median value for males
numeric, Median value for females
numeric, Threshold value for males
numeric, Threshold value for females
numeric, First quartile value for males
numeric, First quartile value for females
numeric, Asymptote value for males
numeric, Asymptote value for females
A list of 1 element, mfrow: integer vector of length 2
A list of 1 element, mfrow: integer vector of length 2
A list of 2 elements: rect and text
A list of 2 elements: rect and text
A list of 19 numeric vectors with 18000 elements each
A list of 1 element which is a list of 24 elements, each with 18000 elements
A data frame with 4727 observations of 9 variables:
integer, Individual ID
numeric, Indicator if the individual is a proband
integer, Family ID
numeric, Mother's ID
numeric, Father's ID
numeric, Affected status
numeric, Sex of the individual
numeric, Age of the individual
character, Genotype
data(out_sim) head(out_sim$summary_stats)data(out_sim) head(out_sim$summary_stats)
A comprehensive package for penetrance estimation in family-based studies. This package implements Bayesian methods using Metropolis-Hastings algorithm for estimating age-specific penetrance of genetic variants. It supports both sex-specific and non-sex-specific analyses, and provides various visualization tools for examining MCMC results.
This function implements the Independent Metropolis-Hastings algorithm for Bayesian penetrance estimation of cancer risk. It utilizes parallel computing to run multiple chains and provides various options for analyzing and visualizing the results.
penetrance( pedigree, twins = NULL, n_chains = 1, n_iter_per_chain = 10000, ncores = 6, max_age = 94, baseline_data = baseline_data_default, remove_proband = FALSE, age_imputation = FALSE, median_max = TRUE, BaselineNC = TRUE, var = c(0.1, 0.1, 2, 2, 5, 5, 5, 5), burn_in = 0, thinning_factor = 1, imp_interval = 100, distribution_data = distribution_data_default, prev = 1e-04, sample_size = NULL, ratio = NULL, prior_params = prior_params_default, risk_proportion = risk_proportion_default, summary_stats = TRUE, rejection_rates = TRUE, density_plots = TRUE, plot_trace = TRUE, penetrance_plot = TRUE, penetrance_plot_pdf = TRUE, plot_loglikelihood = TRUE, plot_acf = TRUE, probCI = 0.95, sex_specific = TRUE )penetrance( pedigree, twins = NULL, n_chains = 1, n_iter_per_chain = 10000, ncores = 6, max_age = 94, baseline_data = baseline_data_default, remove_proband = FALSE, age_imputation = FALSE, median_max = TRUE, BaselineNC = TRUE, var = c(0.1, 0.1, 2, 2, 5, 5, 5, 5), burn_in = 0, thinning_factor = 1, imp_interval = 100, distribution_data = distribution_data_default, prev = 1e-04, sample_size = NULL, ratio = NULL, prior_params = prior_params_default, risk_proportion = risk_proportion_default, summary_stats = TRUE, rejection_rates = TRUE, density_plots = TRUE, plot_trace = TRUE, penetrance_plot = TRUE, penetrance_plot_pdf = TRUE, plot_loglikelihood = TRUE, plot_acf = TRUE, probCI = 0.95, sex_specific = TRUE )
pedigree |
A data frame containing the pedigree data in the required format. It should include the following columns:
|
twins |
A list specifying identical twins or triplets in the family. For example, to indicate that "ora024" and "ora027" are identical twins, and "aey063" and "aey064" are identical twins, use the following format: |
n_chains |
Integer, the number of chains for parallel computation. Default is 1. |
n_iter_per_chain |
Integer, the number of iterations for each chain. Default is 10000. |
ncores |
Integer, the number of cores for parallel computation. Default is 6. |
max_age |
Integer, the maximum age considered for analysis. Default is 94. |
baseline_data |
Data for the baseline risk estimates (probability of developing cancer), such as population-level risk from a cancer registry. Default data, for exemplary purposes, is for Colorectal cancer from the SEER database. |
remove_proband |
Logical, indicating whether to remove probands from the analysis. Default is FALSE. |
age_imputation |
Logical, indicating whether to perform age imputation. Default is FALSE. |
median_max |
Logical, indicating whether to use the baseline median age or |
BaselineNC |
Logical, indicating that the non-carrier penetrance is assumed to be the baseline penetrance. Default is TRUE. |
var |
Numeric vector, variances for the proposal distribution in the Metropolis-Hastings algorithm. Default is |
burn_in |
Numeric, the fraction of results to discard as burn-in (0 to 1). Default is 0 (no burn-in). |
thinning_factor |
Integer, the factor by which to thin the results. Default is 1 (no thinning). |
imp_interval |
Integer, the interval at which age imputation should be performed when age_imputation = TRUE. |
distribution_data |
Data for generating prior distributions. |
prev |
Numeric, prevalence of the carrier status. Default is 0.0001. |
sample_size |
Optional numeric, sample size for distribution generation. |
ratio |
Optional numeric, ratio parameter for distribution generation. |
prior_params |
List, parameters for prior distributions. |
risk_proportion |
Numeric, proportion of risk for distribution generation. |
summary_stats |
Logical, indicating whether to include summary statistics in the output. Default is TRUE. |
rejection_rates |
Logical, indicating whether to include rejection rates in the output. Default is TRUE. |
density_plots |
Logical, indicating whether to include density plots in the output. Default is TRUE. |
plot_trace |
Logical, indicating whether to include trace plots in the output. Default is TRUE. |
penetrance_plot |
Logical, indicating whether to include penetrance plots in the output. Default is TRUE. |
penetrance_plot_pdf |
Logical, indicating whether to include PDF plots in the output. Default is TRUE. |
plot_loglikelihood |
Logical, indicating whether to include log-likelihood plots in the output. Default is TRUE. |
plot_acf |
Logical, indicating whether to include autocorrelation function (ACF) plots for posterior samples. Default is TRUE. |
probCI |
Numeric, probability level for credible intervals in penetrance plots. Must be between 0 and 1. Default is 0.95. |
sex_specific |
Logical, indicating whether to use sex-specific parameters in the analysis. Default is TRUE. |
Key features:
Bayesian estimation of penetrance using family-based data
Support for sex-specific and non-sex-specific analyses
Age imputation for missing data
Visualization tools for MCMC diagnostics
Integration with the clipp package for likelihood calculations
A list containing combined results from all chains, including optional statistics and plots.
Maintainer: Nicolas Kubista [email protected]
Authors:
BayesMendel Lab
Useful links:
# Create example baseline data (simplified for demonstration) baseline_data_default <- data.frame( Age = 1:94, Female = rep(0.01, 94), Male = rep(0.01, 94) ) # Create example distribution data distribution_data_default <- data.frame( Age = 1:94, Risk = rep(0.01, 94) ) # Create example prior parameters prior_params_default <- list( shape = 2, scale = 50 ) # Create example risk proportion risk_proportion_default <- 0.5 # Create a simple example pedigree example_pedigree <- data.frame( PedigreeID = rep(1, 4), ID = 1:4, Sex = c(1, 0, 1, 0), # 1 for male, 0 for female MotherID = c(NA, NA, 2, 2), FatherID = c(NA, NA, 1, 1), isProband = c(0, 0, 1, 0), CurAge = c(70, 68, 45, 42), isAff = c(0, 0, 1, 0), Age = c(NA, NA, 40, NA), geno = c(NA, NA, 1, NA) ) # Basic usage with minimal iterations result <- penetrance( pedigree = list(example_pedigree), n_chains = 1, n_iter_per_chain = 10, # Very small number for example ncores = 1, # Single core for example summary_stats = TRUE, plot_trace = FALSE, # Disable plots for quick example density_plots = FALSE, penetrance_plot = FALSE, penetrance_plot_pdf = FALSE, plot_loglikelihood = FALSE, plot_acf = FALSE ) # View basic results head(result$summary_stats)# Create example baseline data (simplified for demonstration) baseline_data_default <- data.frame( Age = 1:94, Female = rep(0.01, 94), Male = rep(0.01, 94) ) # Create example distribution data distribution_data_default <- data.frame( Age = 1:94, Risk = rep(0.01, 94) ) # Create example prior parameters prior_params_default <- list( shape = 2, scale = 50 ) # Create example risk proportion risk_proportion_default <- 0.5 # Create a simple example pedigree example_pedigree <- data.frame( PedigreeID = rep(1, 4), ID = 1:4, Sex = c(1, 0, 1, 0), # 1 for male, 0 for female MotherID = c(NA, NA, 2, 2), FatherID = c(NA, NA, 1, 1), isProband = c(0, 0, 1, 0), CurAge = c(70, 68, 45, 42), isAff = c(0, 0, 1, 0), Age = c(NA, NA, 40, NA), geno = c(NA, NA, 1, NA) ) # Basic usage with minimal iterations result <- penetrance( pedigree = list(example_pedigree), n_chains = 1, n_iter_per_chain = 10, # Very small number for example ncores = 1, # Single core for example summary_stats = TRUE, plot_trace = FALSE, # Disable plots for quick example density_plots = FALSE, penetrance_plot = FALSE, penetrance_plot_pdf = FALSE, plot_loglikelihood = FALSE, plot_acf = FALSE ) # View basic results head(result$summary_stats)
This function plots the autocorrelation for sex-specific or non-sex-specific posterior samples across multiple MCMC chains.
It defaults to key parameters like asymptote_male_samples, asymptote_female_samples, etc.
plot_acf(results, n_chains, max_lag = 50)plot_acf(results, n_chains, max_lag = 50)
results |
A list of MCMC chain results. |
n_chains |
The number of chains. |
max_lag |
Integer, the maximum lag to be considered for the autocorrelation plot. Default is 50. |
A series of autocorrelation plots for each chain.
This function plots the log-likelihood values across iterations for multiple MCMC chains. It helps visualize the convergence of the chains based on the log-likelihood values.
plot_loglikelihood(results, n_chains)plot_loglikelihood(results, n_chains)
results |
A list of MCMC chain results, each containing the |
n_chains |
The number of chains. |
A series of log-likelihood plots for each chain.
This function plots the Weibull PDF with credible intervals for the given MCMC results. It allows for visualization of density curves based on the posterior samples.
plot_pdf(combined_chains, prob, max_age, sex = "NA")plot_pdf(combined_chains, prob, max_age, sex = "NA")
combined_chains |
List of combined MCMC chain results containing posterior samples for penetrance parameters. |
prob |
Numeric, probability level for confidence intervals (between 0 and 1). |
max_age |
Integer, maximum age to plot. |
sex |
Character, one of "Male", "Female", or "NA" for non-sex-specific. Default is "NA". |
A plot showing the Weibull PDF with credible intervals.
This function plots the Weibull distribution with credible intervals for the given MCMC results. It allows for visualization of penetrance curves based on the posterior samples.
plot_penetrance(combined_chains, prob, max_age, sex = "NA")plot_penetrance(combined_chains, prob, max_age, sex = "NA")
combined_chains |
List of combined MCMC chain results containing posterior samples for penetrance parameters. |
prob |
Numeric, probability level for confidence intervals (between 0 and 1). |
max_age |
Integer, maximum age to plot. |
sex |
Character, one of "Male", "Female", or "NA" for non-sex-specific. Default is "NA". |
A plot showing the Weibull distribution with credible intervals.
Plot MCMC Trace Plots
plot_trace(results, n_chains, verbose = FALSE)plot_trace(results, n_chains, verbose = FALSE)
results |
A list of MCMC chain results. |
n_chains |
Integer, the number of chains. |
verbose |
Logical, whether to print progress messages. Default is FALSE. |
No return value, called for side effects. Creates trace plots for each parameter.
# Create example results list results <- list( list( median_samples = rnorm(100), threshold_samples = runif(100), first_quartile_samples = rgamma(100, 2, 2), asymptote_samples = rbeta(100, 2, 2) ) ) # Generate trace plots plot_trace(results, n_chains = 1)# Create example results list results <- list( list( median_samples = rnorm(100), threshold_samples = runif(100), first_quartile_samples = rgamma(100, 2, 2), asymptote_samples = rbeta(100, 2, 2) ) ) # Generate trace plots plot_trace(results, n_chains = 1)
Print MCMC Rejection Rates
printRejectionRates(results, verbose = TRUE)printRejectionRates(results, verbose = TRUE)
results |
A list of MCMC chain results. |
verbose |
Logical, whether to print rates to console. Default is TRUE. |
Extracts and prints the rejection rates from MCMC chain results.
A named numeric vector containing the rejection rate (between 0 and 1) for each MCMC chain. Names are of the form "Chain X" where X is the chain number.
# Create example results list with two chains results <- list( list(rejection_rate = 0.3), list(rejection_rate = 0.4) ) # Get rejection rates without printing rates <- printRejectionRates(results, verbose = FALSE) # Print rejection rates rates <- printRejectionRates(results)# Create example results list with two chains results <- list( list(rejection_rate = 0.3), list(rejection_rate = 0.4) ) # Get rejection rates without printing rates <- printRejectionRates(results, verbose = FALSE) # Print rejection rates rates <- printRejectionRates(results)
Default parameters for the prior distributions used in the makePriors function.
prior_params_default prior_params_defaultprior_params_default prior_params_default
A list of prior distribution parameters
A list with the following components:
A list with components g1 and g2, default values for the asymptote parameters.
A list with components min and max, default values for the threshold parameters.
A list with components m1 and m2, default values for the median parameters.
A list with components q1 and q2, default values for the first quartile parameters.
Default proportions of people at risk used in the makePriors function.
risk_proportion_default risk_proportion_defaultrisk_proportion_default risk_proportion_default
A data frame of risk proportions
A data frame with the following columns:
Proportion of people at risk at the median age.
Proportion of people at risk at the first quartile age.
Proportion of people at risk at the maximum age.
A dataset containing processed information about the first simulated 130 families.
These families are referenced in the vigniette simulation_study.Rmd and using_penetrance.Rmd
The user must specify the pedigree argument as a data frame which contains the
family data (see test_fam). The family data must be in the correct format with the following columns:
simulated_familiessimulated_families
A list of processed family data.
IDA numeric value representing the unique identifier for each individual. There should be no duplicated entries.
SexA numeric value where 0 indicates female and 1 indicates male. Missing entries are not currently supported.
MotherIDA numeric value representing the unique identifier for an individual's mother.
FatherIDA numeric value representing the unique identifier for an individual's father.
isProbandA numeric value where 1 indicates the individual is a proband and 0 otherwise.
CurAgeA numeric value indicating the age of censoring (current age if the person is alive or age at death if the person is deceased). Allowed ages range from 1 to 94.
isAffA numeric value indicating the affection status of cancer, with 1 for diagnosed individuals and 0 otherwise. Missing entries are not supported.
AgeA numeric value indicating the age of cancer diagnosis, encoded as NA if the individual was not diagnosed. Allowed ages range from 1 to 94.
GenoA column for germline testing or tumor marker testing results. Positive results should be coded as 1, negative results as 0, and unknown results as NA or left empty.
Generated for package example
A dataset containing processed information about the first simulated 130 families.
These families are referenced in the vigniette simulation_study_real.Rmd
The user must specify the pedigree argument as a data frame which contains the
family data (see test_fam). The family data must be in the correct format with the following columns:
test_fam2test_fam2
A list of processed family data.
IDA numeric value representing the unique identifier for each individual. There should be no duplicated entries.
SexA numeric value where 0 indicates female and 1 indicates male. Missing entries are not currently supported.
MotherIDA numeric value representing the unique identifier for an individual's mother.
FatherIDA numeric value representing the unique identifier for an individual's father.
isProbandA numeric value where 1 indicates the individual is a proband and 0 otherwise.
CurAgeA numeric value indicating the age of censoring (current age if the person is alive or age at death if the person is deceased). Allowed ages range from 1 to 94.
isAffA numeric value indicating the affection status of cancer, with 1 for diagnosed individuals and 0 otherwise. Missing entries are not supported.
AgeA numeric value indicating the age of cancer diagnosis, encoded as NA if the individual was not diagnosed. Allowed ages range from 1 to 94.
GenoA column for germline testing or tumor marker testing results. Positive results should be coded as 1, negative results as 0, and unknown results as NA or left empty.
Generated for package example
This function transforms a data frame from the standard format used in PanelPRO into the required format which conforms to the requirements of penetrance (and clipp).
transformDF(df)transformDF(df)
df |
The input data frame in the usual PanelPRO format. |
A data frame in the format required for clipp with the following columns:
individual |
ID of the individual |
isProband |
Indicator if the individual is a proband |
family |
Family ID |
mother |
Mother's ID |
father |
Father's ID |
aff |
Affection status |
sex |
Sex (2 for female, 1 for male) |
age |
Age at diagnosis or current age |
geno |
Genotype information |
# Create example data frame df <- data.frame( ID = 1:2, PedigreeID = c(1,1), Sex = c(0,1), MotherID = c(NA,1), FatherID = c(NA,NA), isProband = c(1,0), CurAge = c(45,20), isAff = c(1,0), Age = c(40,NA), Geno = c(1,0) ) # Transform the data frame transformed_df <- transformDF(df)# Create example data frame df <- data.frame( ID = 1:2, PedigreeID = c(1,1), Sex = c(0,1), MotherID = c(NA,1), FatherID = c(NA,NA), isProband = c(1,0), CurAge = c(45,20), isAff = c(1,0), Age = c(40,NA), Geno = c(1,0) ) # Transform the data frame transformed_df <- transformDF(df)
This function validates the given parameters for calculating Weibull distribution.
validate_weibull_parameters( given_first_quartile, given_median, threshold, asymptote )validate_weibull_parameters( given_first_quartile, given_median, threshold, asymptote )
given_first_quartile |
The first quartile of the data. |
given_median |
The median of the data. |
threshold |
A constant threshold value. |
asymptote |
A constant asymptote value (gamma). |
Logical value indicating whether the parameters are valid (TRUE) or not (FALSE)
# Validate parameters is_valid <- validate_weibull_parameters( given_first_quartile = 30, given_median = 50, threshold = 15, asymptote = 0.8 ) print(is_valid)# Validate parameters is_valid <- validate_weibull_parameters( given_first_quartile = 30, given_median = 50, threshold = 15, asymptote = 0.8 ) print(is_valid)