# Impute Missing Ages in Family-Based Data 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. ```r 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 ) ``` ## Arguments - `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 ## Returns A data frame with the following modifications: - **age**: Updated with imputed ages for previously missing values The rest of the data frame remains unchanged. ## Examples ```r # 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 ) ```

imputeAges function