Package 'BGmisc'

Description

This function generates or adjusts the number of kids per couple in a generation based on the specified average and whether the count should be randomly determined.

Usage

adjustKidsPerCouple(nMates, kpc, rd_kpc)

Arguments

Value

A numeric vector with the generated or adjusted number of kids per couple.

Description

allGens A function to calculate the number of individuals in each generation. This is a supporting function for simulatePedigree.

Usage

allGens(kpc, Ngen, marR)

Arguments

Value

Returns a vector containing the number of individuals in every generation.

Description

This subfunction assigns a unique couple ID to each mated pair in the generation. Unmated individuals are assigned NA for their couple ID.

Usage

assignCoupleIds(df_Ngen)

Arguments

Value

The input dataframe augmented with a 'coupleId' column, where each mated pair has a unique identifier.

Description

This function processes connections between each two generations in a pedigree simulation. It marks individuals as parents, sons, or daughters based on their generational position and relationships. The function also handles the assignment of couple IDs, manages single and coupled individuals, and establishes parent-offspring links across generations.

Usage

buildBetweenGenerations(
  df_Fam,
  Ngen,
  sizeGens,
  verbose,
  marR,
  sexR,
  kpc,
  rd_kpc
)

Arguments

Details

The function iterates through each generation, starting from the second, to establish connections based on mating and parentage. For the first generation, it sets the parental status directly. For subsequent generations, it calculates the number of couples, the expected number of offspring, and assigns offspring to parents. It handles gender-based assignments for sons and daughters, and deals with the nuances of single individuals and couple formation. The function relies on external functions 'assignCoupleIds' and 'adjustKidsPerCouple' to handle specific tasks related to couple ID assignment and offspring number adjustments, respectively.

Value

The function updates the 'df_Fam' data frame in place, adding or modifying columns related to parental and offspring status, as well as assigning unique couple IDs. It does not return a value explicitly.

Description

This function iterates through generations in a pedigree simulation, assigning IDs, creating data frames, determining sexes, and managing pairing within each generation.

Usage

buildWithinGenerations(sizeGens, marR, sexR, Ngen)

Arguments

Value

A data frame representing the simulated pedigree, including columns for family ID ('fam'),

Description

Use Falconer's formula to solve for H using the observed correlations for two groups of any two levels of relatednesses.

Usage

calculateH(r1, r2, obsR1, obsR2)

Arguments

Details

This generalization of Falconer's formula provides a method to calculate heritability by using the observed correlations for two groups of any two relatednesses. This function solves for H using the formula:

$H^2 = \frac{obsR1 - obsR2}{r1 - r2}$

where r1 and r2 are the relatedness coefficients for the first and second group, respectively, and obsR1 and obsR2 are the observed correlations.

Value

Heritability estimates ('heritability_estimates').

Description

This function calculates the relatedness coefficient between two individuals based on their shared ancestry, as described by Wright (1922).

Usage

calculateRelatedness(
  generations = 2,
  path = NULL,
  full = TRUE,
  maternal = FALSE,
  empirical = FALSE,
  segregating = TRUE,
  total_a = 6800 * 1e+06,
  total_m = 16500,
  weight_a = 1,
  weight_m = 1,
  denom_m = FALSE,
  ...
)

Arguments

Details

The relatedness coefficient between two people (b & c) is defined in relation to their common ancestors: $r_{bc} = \sum \left(\frac{1}{2}\right)^{n+n'+1} (1+f_a)$

Value

Relatedness Coefficient ('coef'): A measure of the genetic relationship between two individuals.

Examples

## Not run: 
# For full siblings, the relatedness coefficient is expected to be 0.5:
calculateRelatedness(generations = 1, full = TRUE)
# For half siblings, the relatedness coefficient is expected to be 0.25:
calculateRelatedness(generations = 1, full = FALSE)

## End(Not run)

Description

This function takes a pedigree object and performs two main tasks: 1. Checks for the uniqueness of individual IDs. 2. Optionally repairs non-unique IDs based on a specified logic.

Usage

checkIDs(ped, verbose = FALSE, repair = FALSE)

Arguments

Value

Depending on 'repair' value, either returns a list containing validation results or a repaired dataframe

Examples

## Not run: 
ped <- data.frame(ID = c(1, 2, 2, 3), dadID = c(NA, 1, 1, 2), momID = c(NA, NA, 2, 2))
checkIDs(ped, verbose = TRUE, repair = FALSE)

## End(Not run)

Description

This function checks and optionally modifies the coding of the biological 'sex' variable in a pedigree dataset. It serves two primary purposes: 1. Recodes the 'sex' variable based on specified codes for males and females, if provided. 2. Identifies and optionally repairs inconsistencies in sex coding that could break the algorithm for constructing genetic pedigrees.

Usage

checkSex(
  ped,
  code_male = NULL,
  code_female = NULL,
  verbose = FALSE,
  repair = FALSE
)

Arguments

Details

The validation process identifies: - The unique sex codes present in the dataset. - Whether individuals listed as fathers or mothers have inconsistent sex codes. - Instances where an individual's recorded sex does not align with their parental role.

If 'repair = TRUE', the function standardizes sex coding by: - Assigning individuals listed as fathers the most common male code in the dataset. - Assigning individuals listed as mothers the most common female code.

This function uses the terms 'male' and 'female' in a biological context, referring to chromosomal and other biologically-based characteristics necessary for constructing genetic pedigrees. The biological aspect of sex used in genetic analysis (genotype) is distinct from the broader, richer concept of gender identity (phenotype).

We recognize the importance of using language and methodologies that affirm and respect the full spectrum of gender identities. The developers of this package express unequivocal support for folx in the transgender and LGBTQ+ communities.

Value

Depending on the value of 'repair', either a list containing validation results or a repaired dataframe is returned.

Examples

## Not run: 
ped <- data.frame(ID = c(1, 2, 3), sex = c("M", "F", "M"))
checkSex(ped, code_male = "M", verbose = TRUE, repair = FALSE)

## End(Not run)

Description

comp2vech Turn a variance component relatedness matrix into its half-vectorization

Usage

comp2vech(x, include.zeros = FALSE)

Arguments

Details

This function is a wrapper around the vech function, extending it to allow for blockwise matrices and specific classes. It facilitates the conversion of a variance component relatedness matrix into a half-vectorized form.

Value

The half-vectorization of the relatedness component matrix.

Examples

comp2vech(list(matrix(c(1, .5, .5, 1), 2, 2), matrix(1, 2, 2)))

Description

Compute the transpose multiplication for the relatedness matrix

Usage

compute_transpose(r2, transpose_method = "tcrossprod", verbose = FALSE)

Arguments

Details

The algorithms and methodologies used in this function are further discussed and exemplified in the vignette titled "examplePedigreeFunctions". For more advanced scenarios and detailed explanations, consult this vignette.

Description

This function creates a data frame for a specific generation within the simulated pedigree. It initializes the data frame with default values for family ID, individual ID, generation number, paternal ID, maternal ID, spouse ID, and sex. All individuals are initially set with NA for paternal, maternal, spouse IDs, and sex, awaiting further assignment.

Usage

createGenDataFrame(sizeGens, genIndex, idGen)

Arguments

Value

A data frame representing the initial structure for the individuals in the specified generation before any relationships (parental, spousal) are defined. The columns include family ID ('fam'), individual ID (‘id'), generation number ('gen'), father’s ID (‘pat'), mother’s ID ('mat'), spouse's ID ('spID'), and sex ('sex'), with NA values for paternal, maternal, and spouse IDs, and sex.

Examples

sizeGens <- c(3, 5, 4) # Example sizes for 3 generations
genIndex <- 2 # Creating data frame for the 2nd generation
idGen <- 101:105 # Example IDs for the 2nd generation
df_Ngen <- createGenDataFrame(sizeGens, genIndex, idGen)
print(df_Ngen)

Description

This internal function assigns sexes to the offspring in a generation based on the specified sex ratio.

Usage

determineSex(idGen, sexR)

Arguments

Value

Vector of sexes ("M" for male, "F" for female) for the offspring.

Description

dropLink A function to drop a person from his/her parents in the simulated pedigree data.frame. The person can be dropped by specifying his/her ID or by specifying the generation which the randomly to-be-dropped person is in. The function can separate one pedigree into two pedigrees. Separating into small pieces should be done by running the function multiple times. This is a supplementary function for simulatePedigree.

Usage

dropLink(
  ped,
  ID_drop = NA_integer_,
  gen_drop = 2,
  sex_drop = NA_character_,
  n_drop = 1
)

Arguments

Value

a pedigree with the dropped person's 'dadID' and 'momID' set to NA.

Description

evenInsert A function to insert m elements evenly into a length n vector.

Usage

evenInsert(m, n, verbose = FALSE)

Arguments

Details

The function takes two vectors, m and n, and inserts the elements of m evenly into n. If the length of m is greater than the length of n, the vectors are swapped, and the insertion proceeds. The resulting vector is a combination of m and n, with the elements of m evenly distributed within n.

Value

Returns a numeric vector with the elements of m evenly inserted into n.

See Also

SimPed for the main function that uses this supporting function.

Description

famSizeCal A function to calculate the total number of individuals in a pedigree given parameters. This is a supporting function for function simulatePedigree

Usage

famSizeCal(kpc, Ngen, marR)

Arguments

Value

Returns a numeric value indicating the total pedigree size.

Description

fitComponentModel Fit the estimated variance components of a model to covariance data

Usage

fitComponentModel(covmat, ...)

Arguments

Details

This function fits the estimated variance components of a model to given covariance data. The rank of the component matrices is checked to ensure that the variance components are all identified. Warnings are issued if there are inconsistencies.

Value

A regression (linear model fitted with lm). The coefficients of the regression represent the estimated variance components.

Examples

## Not run: 
# install.packages("OpenMX")
data(twinData, package = "OpenMx")
sellVars <- c("ht1", "ht2")
mzData <- subset(twinData, zyg %in% c(1), c(selVars, "zyg"))
dzData <- subset(twinData, zyg %in% c(3), c(selVars, "zyg"))

fitComponentModel(
  covmat = list(cov(mzData[, selVars], use = "pair"), cov(dzData[, selVars], use = "pair")),
  A = list(matrix(1, nrow = 2, ncol = 2), matrix(c(1, 0.5, 0.5, 1), nrow = 2, ncol = 2)),
  C = list(matrix(1, nrow = 2, ncol = 2), matrix(1, nrow = 2, ncol = 2)),
  E = list(diag(1, nrow = 2), diag(1, nrow = 2))
)

## End(Not run)

Description

A dataset simulated to have an age-related hazard. There are two extended families that are sampled from the same population.

Usage

data(hazard)

Format

A data frame with 43 rows and 14 variables

Details

The variables are as follows:

• FamID: ID of the extended family

• ID: Person identification variable

• sex: Sex of the ID: 1 is female; 0 is male

• dadID: ID of the father

• momID: ID of the mother

• affected: logical. Whether the person is affected or not

• DA1: Binary variable signifying the meaninglessness of life

• DA2: Binary variable signifying the fundamental unknowability of existence

• birthYr: Birth year for person

• onsetYr: Year of onset for person

• deathYr: Death year for person

• available: logical. Whether

• Gen: Generation of the person

• proband: logical. Whether the person is a proband or not

Description

identifyComponentModel Determine if a variance components model is identified

Usage

identifyComponentModel(..., verbose = TRUE)

Arguments

Details

This function checks the identification status of a given variance components model by examining the rank of the concatenated matrices of the components. If any components are not identified, their names are returned in the output.

Value

A list of length 2 containing:

• identified: TRUE if the model is identified, FALSE otherwise.

• nidp: A vector of non-identified parameters, specifying the names of components that are not simultaneously identified.

Examples

identifyComponentModel(A = list(matrix(1, 2, 2)), C = list(matrix(1, 2, 2)), E = diag(1, 2))

Description

A dataset created purely from imagination that includes several types of inbreeding. Different kinds of inbreeding occur in each extended family.

Usage

data(inbreeding)

Format

A data frame (and ped object) with 134 rows and 7 variables

Details

The types of inbreeding are as follows:

• Extended Family 1: Sister wives - Children with the same father and different mothers who are sisters.

• Extended Family 2: Full siblings have children.

• Extended Family 3: Half siblings have children.

• Extended Family 4: First cousins have children.

• Extended Family 5: Father has child with his daughter.

• Extended Family 6: Half sister wives - Children with the same father and different mothers who are half sisters.

• Extended Family 7: Uncle-niece and Aunt-nephew have children.

• Extended Family 8: A father-son pairs has children with a corresponding mother-daughter pair.

Although not all of the above structures are technically inbreeding, they aim to test pedigree diagramming and path tracing algorithms.

The variables are as follows:

• ID: Person identification variable

• sex: Sex of the ID: 1 is female; 0 is male

• dadID: ID of the father

• momID: ID of the mother

• FamID: ID of the extended family

• Gen: Generation of the person

• proband: Always FALSE

Description

This function infers the relatedness coefficient between two groups based on the observed correlation between their additive genetic variance and shared environmental variance.

Usage

inferRelatedness(obsR, aceA = 0.9, aceC = 0, sharedC = 0)

Arguments

Details

The function uses the ACE (Additive genetic, Common environmental, and Unique environmental) model to infer the relatedness between two individuals or groups. By considering the observed correlation ('obsR'), the proportion of variance attributable to additive genetic variance ('aceA'), and the proportion of shared environmental variance ('aceC'), it calculates the relatedness coefficient.

Value

Numeric. The calculated relatedness coefficient ('est_r').

Examples

## Not run: 
# Infer the relatedness coefficient:
inferRelatedness(obsR = 0.5, aceA = 0.9, aceC = 0, sharedC = 0)

## End(Not run)

Description

makeInbreeding A function to create inbred mates in the simulated pedigree data.frame. Inbred mates can be created by specifying their IDs or the generation the inbred mate should be created. When specifying the generation, inbreeding between siblings or 1st cousin needs to be specified. This is a supplementary function for simulatePedigree.

Usage

makeInbreeding(
  ped,
  ID_mate1 = NA_integer_,
  ID_mate2 = NA_integer_,
  verbose = FALSE,
  gen_inbred = 2,
  type_inbred = "sib"
)

Arguments

Details

This function creates inbred mates in the simulated pedigree data.frame. This function's purpose is to evaluate the effect of inbreeding on model fitting and parameter estimation. In case it needs to be said, we do not condone inbreeding in real life. But we recognize that it is a common practice in some fields to create inbred strains for research purposes.

Value

Returns a data.frame with some inbred mates.

Description

makeTwins A function to impute twins in the simulated pedigree data.frame. Twins can be imputed by specifying their IDs or by specifying the generation the twin should be imputed. This is a supplementary function for simulatePedigree.

Usage

makeTwins(
  ped,
  ID_twin1 = NA_integer_,
  ID_twin2 = NA_integer_,
  gen_twin = 2,
  verbose = FALSE
)

Arguments

Value

Returns a data.frame with MZ twins information added as a new column.

Description

This subfunction marks individuals in a generation as potential sons, daughters, or parents based on their relationships and assigns unique couple IDs. It processes the assignment of roles and relationships within and between generations in a pedigree simulation.

Usage

markPotentialChildren(df_Ngen, i, Ngen, sizeGens, CoupleF)

Arguments

Value

Modifies 'df_Ngen' in place by updating or adding columns related to individual roles ('ifparent', 'ifson', 'ifdau') and couple IDs ('coupleId'). The updated data frame is also returned for integration into the larger pedigree data frame ('df_Fam').

Description

Take a pedigree and turn it into an additive genetics relatedness matrix

Usage

ped2add(
  ped,
  max.gen = 25,
  sparse = FALSE,
  verbose = FALSE,
  gc = FALSE,
  flatten.diag = FALSE,
  standardize.colnames = TRUE,
  transpose_method = "tcrossprod",
  saveable = FALSE,
  resume = FALSE,
  save_rate = 5,
  save_rate_gen = save_rate,
  save_rate_parlist = 1000 * save_rate,
  save_path = "checkpoint/",
  ...
)

Arguments

Details

The algorithms and methodologies used in this function are further discussed and exemplified in the vignette titled "examplePedigreeFunctions". For more advanced scenarios and detailed explanations, consult this vignette.

Description

Take a pedigree and turn it into an extended environmental relatedness matrix

Usage

ped2ce(ped, ...)

Arguments

Details

The algorithms and methodologies used in this function are further discussed and exemplified in the vignette titled "examplePedigreeFunctions". For more advanced scenarios and detailed explanations, consult this vignette.

Description

Take a pedigree and turn it into a common nuclear environmental relatedness matrix

Usage

ped2cn(
  ped,
  max.gen = 25,
  sparse = FALSE,
  verbose = FALSE,
  gc = FALSE,
  flatten.diag = FALSE,
  standardize.colnames = TRUE,
  transpose_method = "tcrossprod",
  saveable = FALSE,
  resume = FALSE,
  save_rate = 5,
  save_rate_gen = save_rate,
  save_rate_parlist = 1000 * save_rate,
  save_path = "checkpoint/",
  ...
)

Arguments

Details

The algorithms and methodologies used in this function are further discussed and exemplified in the vignette titled "examplePedigreeFunctions". For more advanced scenarios and detailed explanations, consult this vignette.

Description

Take a pedigree and turn it into a relatedness matrix

Usage

ped2com(
  ped,
  component,
  max.gen = 25,
  sparse = FALSE,
  verbose = FALSE,
  gc = FALSE,
  flatten.diag = FALSE,
  standardize.colnames = TRUE,
  transpose_method = "tcrossprod",
  saveable = FALSE,
  resume = FALSE,
  save_rate = 5,
  save_rate_gen = save_rate,
  save_rate_parlist = 1000 * save_rate,
  update_rate = 100,
  save_path = "checkpoint/",
  ...
)

Arguments

Details

The algorithms and methodologies used in this function are further discussed and exemplified in the vignette titled "examplePedigreeFunctions". For more advanced scenarios and detailed explanations, consult this vignette.

Description

This function adds an extended family ID variable to a pedigree by segmenting that dataset into independent extended families using the weakly connected components algorithm.

Usage

ped2fam(
  ped,
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  famID = "famID",
  ...
)

Arguments

Details

The general idea of this function is to use person ID, mother ID, and father ID to create an extended family ID such that everyone with the same family ID is in the same (perhaps very extended) pedigree. That is, a pair of people with the same family ID have at least one traceable relation of any length to one another.

This function works by turning the pedigree into a mathematical graph using the igraph package. Once in graph form, the function uses weakly connected components to search for all possible relationship paths that could connect anyone in the data to anyone else in the data.

Value

A pedigree dataset with one additional column for the newly created extended family ID

Description

Turn a pedigree into a graph

Usage

ped2graph(
  ped,
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  directed = TRUE,
  adjacent = c("parents", "mothers", "fathers"),
  ...
)

Arguments

Details

The general idea of this function is to represent a pedigree as a graph using the igraph package.

Once in graph form, several common pedigree tasks become much simpler.

The adjacent argument allows for different kinds of graph structures. When using parents for adjacency, the graph shows all parent-child relationships. When using mother for adjacency, the graph only shows mother-child relationships. Similarly when using father for adjacency, only father-child relationships appear in the graph. Construct extended families from the parent graph, maternal lines from the mothers graph, and paternal lines from the fathers graph.

Value

A graph

Description

Add a maternal line ID variable to a pedigree

Usage

ped2maternal(
  ped,
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  matID = "matID",
  ...
)

Arguments

Details

Under various scenarios it is useful to know which people in a pedigree belong to the same maternal lines. This function first turns a pedigree into a graph where adjacency is defined by mother-child relationships. Subsequently, the weakly connected components algorithm finds all the separate maternal lines and gives them an ID variable.

See Also

[ped2fam()] for creating extended family IDs, and [ped2paternal()] for creating paternal line IDs

Description

Take a pedigree and turn it into a mitochondrial relatedness matrix

Usage

ped2mit(
  ped,
  max.gen = 25,
  sparse = FALSE,
  verbose = FALSE,
  gc = FALSE,
  flatten.diag = FALSE,
  standardize.colnames = TRUE,
  transpose_method = "tcrossprod",
  saveable = FALSE,
  resume = FALSE,
  save_rate = 5,
  save_rate_gen = save_rate_gen,
  save_rate_parlist = 1000 * save_rate,
  save_path = "checkpoint/",
  ...
)

Arguments

Details

The algorithms and methodologies used in this function are further discussed and exemplified in the vignette titled "examplePedigreeFunctions". For more advanced scenarios and detailed explanations, consult this vignette.

Description

Add a paternal line ID variable to a pedigree

Usage

ped2paternal(
  ped,
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  patID = "patID",
  ...
)

Arguments

Details

Under various scenarios it is useful to know which people in a pedigree belong to the same paternal lines. This function first turns a pedigree into a graph where adjacency is defined by father-child relationships. Subsequently, the weakly connected components algorithm finds all the separate paternal lines and gives them an ID variable.

See Also

[ped2fam()] for creating extended family IDs, and [ped2maternal()] for creating maternal line IDs

Description

plotPedigree A wrapped function to plot simulated pedigree from function simulatePedigree. This function require the installation of package kinship2.

Usage

plotPedigree(
  ped,
  code_male = NULL,
  verbose = FALSE,
  affected = NULL,
  cex = 0.5,
  col = 1,
  symbolsize = 1,
  branch = 0.6,
  packed = TRUE,
  align = c(1.5, 2),
  width = 8,
  density = c(-1, 35, 65, 20),
  mar = c(2.1, 1, 2.1, 1),
  angle = c(90, 65, 40, 0),
  keep.par = FALSE,
  pconnect = 0.5,
  ...
)

Arguments

Value

A plot of the provided pedigree

Description

A dataset created purely from imagination that includes a subset of the Potter extended family.

Usage

data(potter)

Format

A data frame (and ped object) with 36 rows and 8 variables

Details

The variables are as follows:

• personID: Person identification variable

• famID: Family identification variable

• name: Name of the person

• gen: Generation of the person

• momID: ID of the mother

• dadID: ID of the father

• spouseID: ID of the spouse

• sex: Sex of the ID: 1 is male; 0 is female

IDs in the 100s momIDs and dadIDs are for people not in the dataset.

Description

This function reads a GEDCOM file and parses it into a structured data frame of individuals. Inspired by https://raw.githubusercontent.com/jjfitz/readgedcom/master/R/read_gedcom.R

Usage

readGedcom(
  file_path,
  verbose = FALSE,
  add_parents = TRUE,
  remove_empty_cols = TRUE,
  combine_cols = TRUE,
  skinny = FALSE
)

Arguments

Value

A data frame containing information about individuals, with the following potential columns: - 'id': ID of the individual - ‘momID': ID of the individual’s mother - ‘dadID': ID of the individual’s father - 'sex': Sex of the individual - 'name': Full name of the individual - 'name_given': First name of the individual - 'name_surn': Last name of the individual - 'name_marriedsurn': Married name of the individual - 'name_nick': Nickname of the individual - 'name_npfx': Name prefix - 'name_nsfx': Name suffix - 'birth_date': Birth date of the individual - 'birth_lat': Latitude of the birthplace - 'birth_long': Longitude of the birthplace - 'birth_place': Birthplace of the individual - 'death_caus': Cause of death - 'death_date': Death date of the individual - 'death_lat': Latitude of the place of death - 'death_long': Longitude of the place of death - 'death_place': Place of death of the individual - 'attribute_caste': Caste of the individual - 'attribute_children': Number of children of the individual - 'attribute_description': Description of the individual - 'attribute_education': Education of the individual - 'attribute_idnumber': Identification number of the individual - 'attribute_marriages': Number of marriages of the individual - 'attribute_nationality': Nationality of the individual - 'attribute_occupation': Occupation of the individual - 'attribute_property': Property owned by the individual - 'attribute_religion': Religion of the individual - 'attribute_residence': Residence of the individual - 'attribute_ssn': Social security number of the individual - 'attribute_title': Title of the individual - 'FAMC': ID(s) of the family where the individual is a child - 'FAMS': ID(s) of the family where the individual is a spouse

Description

This function serves as is primarily used internally, by plotting functions etc. It sets the 'repair' flag to TRUE automatically and forwards any additional parameters to 'checkSex'.

Usage

recodeSex(
  ped,
  verbose = FALSE,
  code_male = NULL,
  code_na = NULL,
  code_female = NULL,
  recode_male = "M",
  recode_female = "F",
  recode_na = NA_character_
)

Arguments

Details

The validation process identifies: - The unique sex codes present in the dataset. - Whether individuals listed as fathers or mothers have inconsistent sex codes. - Instances where an individual's recorded sex does not align with their parental role.

If 'repair = TRUE', the function standardizes sex coding by: - Assigning individuals listed as fathers the most common male code in the dataset. - Assigning individuals listed as mothers the most common female code.

This function uses the terms 'male' and 'female' in a biological context, referring to chromosomal and other biologically-based characteristics necessary for constructing genetic pedigrees. The biological aspect of sex used in genetic analysis (genotype) is distinct from the broader, richer concept of gender identity (phenotype).

We recognize the importance of using language and methodologies that affirm and respect the full spectrum of gender identities. The developers of this package express unequivocal support for folx in the transgender and LGBTQ+ communities.

Value

A modified version of the input data.frame ped, containing an additional or modified 'sex_recode' column where the 'sex' values are recoded according to code_male. NA values in the 'sex' column are preserved.

See Also

plotPedigree

Description

When calling this function, a warning will be issued about its deprecation.

Usage

related_coef(...)

Arguments

Details

This function is a wrapper around the new 'calculateRelatedness' function. ‘related_coef' has been deprecated, and it’s advised to use 'calculateRelatedness' directly.

Value

The same result as calling 'calculateRelatedness'.

See Also

calculateRelatedness for the updated function.

Examples

## Not run: 
# This is an example of the deprecated function:
related_coef(...)
# It is recommended to use:
calculateRelatedness(...)

## End(Not run)

Description

When calling this function, a warning will be issued about its deprecation.

Usage

relatedness(...)

Arguments

Details

This function is a wrapper around the new 'inferRelatedness' function. ‘relatedness' has been deprecated, and it’s advised to use 'inferRelatedness' directly.

Value

The same result as calling 'inferRelatedness'.

See Also

inferRelatedness for the updated function.

Examples

## Not run: 
# This is an example of the deprecated function:
relatedness(...)
# It is recommended to use:
inferRelatedness(...)

## End(Not run)

Description

This function repairs missing IDs in a pedigree.

Usage

repairIDs(ped, verbose = FALSE)

Arguments

Value

A corrected pedigree

Description

This function serves as a wrapper around 'checkSex' to specifically handle the repair of the sex coding in a pedigree dataframe.

Usage

repairSex(ped, verbose = FALSE, code_male = NULL)

Arguments

Details

The validation process identifies: - The unique sex codes present in the dataset. - Whether individuals listed as fathers or mothers have inconsistent sex codes. - Instances where an individual's recorded sex does not align with their parental role.

If 'repair = TRUE', the function standardizes sex coding by: - Assigning individuals listed as fathers the most common male code in the dataset. - Assigning individuals listed as mothers the most common female code.

This function uses the terms 'male' and 'female' in a biological context, referring to chromosomal and other biologically-based characteristics necessary for constructing genetic pedigrees. The biological aspect of sex used in genetic analysis (genotype) is distinct from the broader, richer concept of gender identity (phenotype).

We recognize the importance of using language and methodologies that affirm and respect the full spectrum of gender identities. The developers of this package express unequivocal support for folx in the transgender and LGBTQ+ communities.

Value

A modified version of the input data.frame ped, containing an additional or modified 'sex_recode' column where the 'sex' values are recoded according to code_male. NA values in the 'sex' column are preserved.

See Also

checkSex

Examples

## Not run: 
ped <- data.frame(ID = c(1, 2, 3), sex = c("M", "F", "M"))
repairSex(ped, code_male = "M", verbose = TRUE)

## End(Not run)

Description

This function performs resampling of the elements in a vector 'x'. It randomly shuffles the elements of 'x' and returns a vector of the resampled elements. If 'x' is empty, it returns 'NA_integer_'.

Usage

resample(x, ...)

Arguments

Value

A vector of resampled elements from 'x'. If 'x' is empty, returns 'NA_integer_'. The length and type of the returned vector depend on the input vector 'x' and the additional arguments provided via '...'.

Description

When calling this function, a warning will be issued about its deprecation.

Usage

SimPed(...)

Arguments

Details

This function is a wrapper around the new 'simulatePedigree' function. ‘SimPed' has been deprecated, and it’s advised to use 'simulatePedigree' directly.

Value

The same result as calling 'simulatePedigree'.

See Also

simulatePedigree for the updated function.

Examples

## Not run: 
# This is an example of the deprecated function:
SimPed(...)
# It is recommended to use:
simulatePedigree(...)

## End(Not run)

Description

Simulate Pedigrees This function simulates "balanced" pedigrees based on a group of parameters: 1) k - Kids per couple; 2) G - Number of generations; 3) p - Proportion of males in offspring; 4) r - Mating rate.

Usage

simulatePedigree(
  kpc = 3,
  Ngen = 4,
  sexR = 0.5,
  marR = 2/3,
  rd_kpc = FALSE,
  balancedSex = TRUE,
  balancedMar = TRUE,
  verbose = FALSE
)

Arguments

Value

A data.frame with each row representing a simulated individual. The columns are as follows:

• fam: The family id of each simulated individual. It is 'fam1' in a single simulated pedigree.

• ID: The unique personal ID of each simulated individual. The first digit is the fam id; the fourth digit is the generation the individual is in; the following digits represent the order of the individual within his/her pedigree. For example, 100411 suggests this individual has a family id of 1, is in the 4th generation, and is the 11th individual in the 4th generation.

• gen: The generation the simulated individual is in.

• dadID: Personal ID of the individual's father.

• momID: Personal ID of the individual's mother.

• spID: Personal ID of the individual's mate.

• sex: Biological sex of the individual. F - female; M - male.

Description

sizeAllGens An internal supporting function for simulatePedigree.

Usage

sizeAllGens(kpc, Ngen, marR)

Arguments

Value

Returns a vector including the number of individuals in every generation.

Description

Summarize the families in a pedigree

Usage

summarizeFamilies(
  ped,
  famID = "famID",
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  matID = "matID",
  patID = "patID",
  byr = NULL,
  founder_sort_var = NULL,
  include_founder = FALSE,
  nbiggest = 5,
  noldest = 5,
  skip_var = NULL,
  five_num_summary = FALSE,
  verbose = FALSE
)

Arguments

See Also

[summarizePedigrees ()]

Description

Summarize the maternal lines in a pedigree

Usage

summarizeMatrilines(
  ped,
  famID = "famID",
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  matID = "matID",
  patID = "patID",
  byr = NULL,
  include_founder = FALSE,
  founder_sort_var = NULL,
  nbiggest = 5,
  noldest = 5,
  skip_var = NULL,
  five_num_summary = FALSE,
  verbose = FALSE
)

Arguments

See Also

[summarizePedigrees ()]

Description

Summarize the paternal lines in a pedigree

Usage

summarizePatrilines(
  ped,
  famID = "famID",
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  matID = "matID",
  patID = "patID",
  byr = NULL,
  founder_sort_var = NULL,
  include_founder = FALSE,
  nbiggest = 5,
  noldest = 5,
  skip_var = NULL,
  five_num_summary = FALSE,
  verbose = FALSE
)

Arguments

See Also

[summarizePedigrees ()]

Description

This function summarizes pedigree data, by computing key summary statistics for all numeric variables and identifying the originating member (founder) for each family, maternal, and paternal lineage.

Usage

summarizePedigrees(
  ped,
  famID = "famID",
  personID = "ID",
  momID = "momID",
  dadID = "dadID",
  matID = "matID",
  patID = "patID",
  type = c("fathers", "mothers", "families"),
  byr = NULL,
  include_founder = FALSE,
  founder_sort_var = NULL,
  nbiggest = 5,
  noldest = 5,
  skip_var = NULL,
  five_num_summary = FALSE,
  verbose = FALSE
)

Arguments

Details

The function calculates standard descriptive statistics, including the count of individuals in each lineage, means, medians, minimum and maximum values, and standard deviations. Additionally, if 'five_num_summary = TRUE', the function includes the first and third quartiles (Q1, Q3) to provide a more detailed distributional summary. Users can also specify variables to exclude from the analysis via 'skip_var'.

Beyond summary statistics, the function identifies the founding member of each lineage based on the specified sorting variable ('founder_sort_var'), defaulting to birth year ('byr') when available or 'personID' otherwise. Users can retrieve the largest and oldest lineages by setting 'nbiggest' and 'noldest', respectively.

Value

A data.frame (or list) containing summary statistics for family, maternal, and paternal lines, as well as the 5 oldest and biggest lines.

Description

vech Create the half-vectorization of a matrix

Usage

vech(x)

Arguments

Details

This function returns the vectorized form of the lower triangle of a matrix, including the diagonal. The upper triangle is ignored with no checking that the provided matrix is symmetric.

Value

A vector containing the lower triangle of the matrix, including the diagonal.

Examples

vech(matrix(c(1, 0.5, 0.5, 1), nrow = 2, ncol = 2))