Apply node styles (color, shape, highlighting)

Description

Apply node styles (color, shape, highlighting)

Usage

apply_node_styles(ped_node, highlight_info)

A large pedigree with big family sizes

Description

A dataset containing a pedigree with many full-sib individuals per family.

Usage

big_family_size_ped

Format

A data.table with 8 columns:

Ind

Individual ID

Sire

Sire ID

Dam

Dam ID

Sex

Sex of the individual

Year

Year of birth

IndNum

Numeric ID for individual

SireNum

Numeric ID for sire

DamNum

Numeric ID for dam

Compact pedigree by merging full siblings for matrix calculation

Description

This internal function identifies full siblings (individuals sharing the same sire and dam) and selects one representative per family. This can dramatically reduce memory requirements when calculating relationship matrices for pedigrees with large full-sibling families.

Usage

compact_ped_for_matrix(ped)

Arguments

Value

A list containing:

• ped_compact: The compacted pedigree (tidyped object)

• compact_map: Individual-level mapping table

• family_summary: Family-level summary table

• compact_stats: Compression statistics

Compact pedigree by merging full siblings

Description

Compact pedigree by merging full siblings

Usage

compact_pedigree(ped_node, compact, h_ids)

Arguments

A deep pedigree

Description

A dataset containing a pedigree with many generations.

Usage

deep_ped

Format

A data.table with 4 columns:

Ind

Individual ID

Sire

Sire ID

Dam

Dam ID

Sex

Sex of the individual

Expand a Compact Pedigree Matrix to Full Dimensions

Description

Restores a compact pedmat to its original dimensions by mapping each individual to their family representative's values. For non-compact matrices, returns the matrix unchanged.

Usage

expand_pedmat(x)

Arguments

Details

For compact matrices, full-siblings within the same family will have identical relationship values in the expanded matrix because they shared the same representative during calculation.

Value

Matrix or vector with original pedigree dimensions:

• Matrices: Row and column names set to all individual IDs

• Vectors (e.g., method="f"): Names set to all individual IDs

The result is not a pedmat object (S3 class stripped).

See Also

pedmat, query_relationship

Examples

tped <- tidyped(small_ped)

# Compact matrix
A_compact <- pedmat(tped, method = "A", compact = TRUE)
dim(A_compact)  # Reduced dimensions

# Expand to full size
A_full <- expand_pedmat(A_compact)
dim(A_full)  # Original dimensions restored

# Non-compact matrices are returned unchanged
A <- pedmat(tped, method = "A", compact = FALSE)
A2 <- expand_pedmat(A)
identical(dim(A), dim(A2))  # TRUE

Fade colors by appending a reduced alpha value

Description

Converts any R color specification to '#RRGGBB4D' form. Handles hex colors ('#RRGGBB', '#RRGGBBAA') and named colors (e.g. '"red"').

Usage

fade_cols(x)

Arguments

Value

Character vector of faded hex colors.

Finalize graph and reindex IDs

Description

Finalize graph and reindex IDs

Usage

finalize_graph(ped_node, ped_edge, highlight_info, trace, showf)

Generate edges and virtual family nodes

Description

Generate edges and virtual family nodes

Usage

generate_graph_structure(ped_node, h_ids)

Arguments

Styling and finalizing pedigree graph

Description

Styling and finalizing pedigree graph

Usage

get_highlight_ids(ped, highlight, trace)

Calculate inbreeding coefficients

Description

inbreed function calculates the inbreeding coefficients for all individuals in a tidied pedigree.

Usage

inbreed(ped, ...)

Arguments

Details

This function takes a pedigree tidied by the tidyped function and calculates the inbreeding coefficients using optimized C++ code based on the Meuwissen & Luo (1992) algorithm. It is the core engine used by both tidyped(..., inbreed = TRUE) and pedmat(..., method = "f"), ensuring consistent results across the package. It is significantly faster than standard R implementations for large pedigrees.

Value

A tidyped object with an additional column f.

Inject missing parents for subsetted pedigrees

Description

Inject missing parents for subsetted pedigrees

Usage

inject_missing_parents(ped)

Arguments

A pedigree with loops

Description

A dataset containing a pedigree with circular mating loops.

Usage

loop_ped

Format

A data.table with 3 columns:

Ind

Individual ID

Sire

Sire ID

Dam

Dam ID

Internal constructor for tidyped class

Description

Internal constructor for tidyped class

Usage

new_tidyped(x)

Arguments

Value

A tidyped object

Convert pedigree to igraph structure

Description

Convert pedigree to igraph structure

Usage

ped2igraph(
  ped,
  compact = FALSE,
  highlight = NULL,
  trace = FALSE,
  showf = FALSE
)

Genetic Relationship Matrices and Inbreeding Coefficients

Description

Optimized calculation of additive (A), dominance (D), epistatic (AA) relationship matrices, their inverses, and inbreeding coefficients (f). Uses Rcpp with Meuwissen & Luo (1992) algorithm for efficient computation.

Usage

pedmat(
  ped,
  method = "A",
  sparse = TRUE,
  invert_method = "auto",
  threads = 0,
  compact = FALSE
)

Arguments

Details

API Design:

Only a single method may be requested per call. This design prevents accidental heavy computations. If multiple matrices are needed, call pedmat() separately for each method.

Compact Mode (compact = TRUE):

Full-siblings share identical relationships with all other individuals. Compact mode exploits this by selecting one representative per full-sib family, dramatically reducing matrix size. For example, a pedigree with 170,000 individuals might compact to 1,800 unique relationship patterns.

Key features:

• query_relationship(x, id1, id2): Query any individual pair, including merged siblings (automatic lookup)

• expand_pedmat(x): Restore full matrix dimensions

• vismat(x): Visualize directly (auto-detects compact)

Performance Notes:

• Ainv: O(n) complexity using Henderson's rules. Fast for any size.

• Dinv/AAinv: O(n³) matrix inversion. Practical limits:

  • n < 500: ~10-20 ms

  • n = 1,000: ~40-60 ms

  • n = 2,000: ~130-150 ms

  • n > 2,000: Consider using compact = TRUE

• Memory: Sparse matrices use ~O(nnz) memory; dense use O(n²)

Value

Returns a matrix or vector with S3 class "pedmat".

Object type by method:

• method="f": Named numeric vector of inbreeding coefficients

• All other methods: Sparse or dense matrix (depending on sparse)

S3 Methods:

• print(x): Display matrix with metadata header

• summary_pedmat(x): Detailed statistics (size, compression, mean, density)

• dim(x), length(x), Matrix::diag(x), t(x): Standard operations

• x[i, j]: Subsetting (behaves like underlying matrix)

• as.matrix(x): Convert to base matrix

Accessing Metadata (use attr(), not $):

• attr(x, "ped"): The pedigree used (or compact pedigree if compact=TRUE)

• attr(x, "ped_compact"): Compact pedigree (when compact=TRUE)

• attr(x, "method"): Calculation method used

• attr(x, "call_info"): Full calculation metadata (timing, sizes, etc.)

• names(attributes(x)): List all available attributes

Additional attributes when compact = TRUE:

• attr(x, "compact_map"): data.table mapping individuals to representatives

• attr(x, "family_summary"): data.table summarizing merged families

• attr(x, "compact_stats"): Compression statistics (ratio, n_families, etc.)

References

Meuwissen, T. H. E., & Luo, Z. (1992). Computing inbreeding coefficients in large populations. Genetics Selection Evolution, 24(4), 305-313.

Henderson, C. R. (1976). A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics, 32(1), 69-83.

See Also

tidyped for preparing pedigree data, query_relationship for querying individual pairs, expand_pedmat for restoring full dimensions, vismat for visualization, inbreed for simple inbreeding calculation

Examples

# Basic usage with small pedigree
library(visPedigree)
tped <- tidyped(small_ped)

# --- Additive Relationship Matrix (default) ---
A <- pedmat(tped)
A["A", "B"]      # Relationship between A and B
Matrix::diag(A)  # Diagonal = 1 + F (inbreeding)

# --- Inbreeding Coefficients ---
f <- pedmat(tped, method = "f")
f["Z1"]  # Inbreeding of individual Z1

# --- Using summary_pedmat() ---
summary_pedmat(A)   # Detailed matrix statistics

# --- Accessing Metadata ---
attr(A, "ped")              # Original pedigree
attr(A, "method")           # "A"
names(attributes(A))        # All available attributes

# --- Compact Mode (for large full-sib families) ---
A_compact <- pedmat(tped, method = "A", compact = TRUE)

# Query relationships (works for any individual, including merged sibs)
query_relationship(A_compact, "Z1", "Z2")  # Full-sibs Z1 and Z2

# View compression statistics
attr(A_compact, "compact_stats")
attr(A_compact, "family_summary")

# Expand back to full size
A_full <- expand_pedmat(A_compact)
dim(A_full)  # Original dimensions restored

# --- Inverse Matrices ---
Ainv <- pedmat(tped, method = "Ainv")  # Henderson's rules (fast)

# --- Dominance and Epistatic ---
D <- pedmat(tped, method = "D")
AA <- pedmat(tped, method = "AA")

# --- Visualization (requires display device) ---
## Not run: 
vismat(A)                       # Heatmap of relationship matrix
vismat(A_compact)               # Works with compact matrices
vismat(A, grouping = "Gen")     # Group by generation

## End(Not run)

Plot a tidy pedigree

Description

Plot a tidy pedigree

Usage

## S3 method for class 'tidyped'
plot(x, ...)

Arguments

Value

Invisibly returns a list of graph data from visped (node/edge data and layout components) used to render the pedigree; the primary result is the plot drawn on the current device.

Render pedigree graph using Two-Pass strategy

Description

Render pedigree graph using Two-Pass strategy

Usage

plot_ped_igraph(g, l, node_size, ...)

Prepare initial node table for igraph conversion

Description

Prepare initial node table for igraph conversion

Usage

prepare_initial_nodes(ped)

Arguments

Internal layout engine for pedigree visualization

Description

Internal layout engine for pedigree visualization

Usage

prepare_ped_graph(
  ped,
  compact = FALSE,
  outline = FALSE,
  cex = NULL,
  highlight = NULL,
  trace = FALSE,
  showf = FALSE,
  pagewidth = 200,
  symbolsize = 1,
  maxiter = 1000,
  ...
)

Print method for summary.tidyped

Description

Print method for summary.tidyped

Usage

## S3 method for class 'summary.tidyped'
print(x, ...)

Arguments

Value

The input object, invisibly.

Print method for tidyped pedigree

Description

Print method for tidyped pedigree

Usage

## S3 method for class 'tidyped'
print(x, ...)

Arguments

Value

The input object, invisibly.

Query Relationship Coefficients from a Pedigree Matrix

Description

Retrieves relationship coefficients between individuals from a pedmat object. For compact matrices, automatically handles lookup of merged full-siblings.

Usage

query_relationship(x, id1, id2 = NULL)

Arguments

Details

For compact matrices (compact = TRUE), this function automatically maps individuals to their family representatives. For methods A, D, and AA, it can compute the correct relationship even between merged full-siblings using the formula:

• A: a_{ij} = 0.5 * (a_{is} + a_{id}) where s, d are parents

• D: d_{ij} = a_{ij}^2 (for full-sibs in same family)

• AA: aa_{ij} = a_{ij}^2

Value

• If id2 is provided: numeric value (relationship coefficient)

• If id2 is NULL: named numeric vector (id1's row)

• Returns NA if individual not found

Note

Inverse matrices (Ainv, Dinv, AAinv) are not supported because inverse matrix elements do not represent meaningful relationship coefficients.

See Also

pedmat, expand_pedmat

Examples

tped <- tidyped(small_ped)
A <- pedmat(tped, method = "A", compact = TRUE)

# Query specific pair
query_relationship(A, "A", "B")

# Query merged full-siblings (works with compact)
query_relationship(A, "Z1", "Z2")

# Get all relationships for one individual
query_relationship(A, "A")

Repel overlapping nodes on the x-axis

Description

Repel overlapping nodes on the x-axis

Usage

repeloverlap(x)

Arguments

Value

A numeric vector of unique x positions.

A simple pedigree

Description

A small dataset containing a simple pedigree for demonstration.

Usage

simple_ped

Format

A data.table with 4 columns:

Ind

Individual ID

Sire

Sire ID

Dam

Dam ID

Sex

Sex of the individual

A small pedigree

Description

A small dataset containing a pedigree with some missing parents.

Usage

small_ped

Format

A data.frame with 3 columns:

Ind

Individual ID

Sire

Sire ID

Dam

Dam ID

Split Pedigree into Disconnected Groups

Description

Detects and splits a tidyped object into disconnected groups (connected components). Uses igraph to efficiently find groups of individuals that have no genetic relationships with each other. Isolated individuals (Gen = 0, those with no parents and no offspring) are excluded from group splitting and stored separately.

Usage

splitped(ped)

Arguments

Details

This function identifies connected components in the pedigree graph where edges represent parent-offspring relationships. Two individuals are in the same group if they share any ancestry (direct or indirect).

Isolated individuals (Gen = 0 in tidyped output) are those who:

• Have no known parents (Sire and Dam are both NA)

• Are not parents of any other individual in the pedigree

These isolated individuals are excluded from splitting and stored in the isolated attribute. Each resulting group contains at least 2 individuals (at least one parent-offspring relationship).

The function always returns a list, even if there is only one group (i.e., the pedigree is fully connected). Groups are sorted by size in descending order.

Each group in the result is a valid tidyped object with:

• Renumbered IndNum (1 to n for each group)

• Updated SireNum and DamNum referencing the new IndNum

• Recalculated Gen (generation) based on the group's structure

Value

A list of class "splitped" containing:

The returned object has the following attributes:

See Also

tidyped for pedigree tidying

Examples

# Load example data
library(visPedigree)
data(small_ped)

# First tidy the pedigree
tped <- tidyped(small_ped)

# Split into groups
result <- splitped(tped)
print(result)

# Access individual groups (each is a tidyped object)
result$GP1

# Check isolated individuals
attr(result, "isolated")

Summary method for tidyped objects

Description

Summary method for tidyped objects

Usage

## S3 method for class 'tidyped'
summary(object, ...)

Arguments

Value

A summary.tidyped object (list) containing:

• n_ind: Total number of individuals.

• n_male, n_female, n_unknown_sex: Sex composition counts.

• n_founders: Number of individuals with no known parents.

• n_both_parents: Count of individuals with complete parentage.

• max_gen, gen_dist: (Optional) Maximum generation and its distribution.

• n_families, family_sizes, top_families: (Optional) Family statistics.

• f_stats, n_inbred: (Optional) Inbreeding coefficient statistics.

• n_cand, cand_f_stats: (Optional) Candidate-specific statistics.

Summary Statistics for Pedigree Matrices

Description

Computes and displays summary statistics for a pedmat object.

Usage

summary_pedmat(x)

Arguments

Details

Since pedmat objects are often S4 sparse matrices with custom attributes, use this function instead of the generic summary() to ensure proper display of pedigree matrix statistics.

Value

An object of class "summary.pedmat" with statistics including method, dimensions, compression ratio (if compact), mean relationship, and matrix density.

See Also

pedmat

Examples

tped <- tidyped(small_ped)
A <- pedmat(tped, method = "A")
summary_pedmat(A)

Tidy and prepare a pedigree using graph theory

Description

This function takes a pedigree, checks for duplicate and bisexual individuals, detects pedigree loops using graph theory, adds missing founders, assigns generation numbers, sorts the pedigree, and traces the pedigree of specified candidates. If the cand parameter contains individual IDs, only those individuals and their ancestors or descendants are retained. Tracing direction and the number of generations can be specified using the trace and tracegen parameters.

Usage

tidyped(
  ped,
  cand = NULL,
  trace = "up",
  tracegen = NULL,
  addgen = TRUE,
  addnum = TRUE,
  inbreed = FALSE,
  genmethod = "top",
  ...
)

Arguments

Details

Compared to the legacy version, this function handles cyclic pedigrees more robustly by detecting and reporting loops, and it is generally faster for large pedigrees due to the use of sparse graph algorithms from the igraph package. Generation assignment can be performed using either a top-down approach (default, aligning founders at the top) or a bottom-up approach (aligning terminal nodes at the bottom).

Value

A tidyped object (which inherits from data.table). Individual, sire, and dam ID columns are renamed to Ind, Sire, and Dam. Missing parents are replaced with NA. The Sex column contains "male", "female", or NA. The Cand column is included if cand is not NULL. The Gen column is included if addgen is TRUE. The IndNum, SireNum, and DamNum columns are included if addnum is TRUE. The Family and FamilySize columns identify full-sibling families (e.g., "A x B" for offspring of sire A and dam B). The f column is included if inbreed is TRUE.

See Also

summary.tidyped for summarizing tidyped objects visped for visualizing pedigree structure pedmat for computing relationship matrices vismat for visualizing relationship matrices splitped for splitting pedigree into connected components inbreed for calculating inbreeding coefficients

Examples

library(visPedigree)
library(data.table)

# Tidy a simple pedigree
tidy_ped <- tidyped(simple_ped)
head(tidy_ped)

# Trace ancestors of a specific individual within 2 generations
tidy_ped_tracegen <- tidyped(simple_ped, cand = "J5X804", trace = "up", tracegen = 2)
head(tidy_ped_tracegen)

# Trace both ancestors and descendants for multiple candidates
# This is highly optimized and works quickly even on 100k+ individuals
cand_list <- c("J5X804", "J3Y620")
tidy_ped_all <- tidyped(simple_ped, cand = cand_list, trace = "all")

# Check for loops (will error if loops exist)
try(tidyped(loop_ped))

# Example with a large pedigree: extract 2 generations of ancestors for 2007 candidates
cand_2007 <- big_family_size_ped[Year == 2007, Ind]

tidy_big <- tidyped(big_family_size_ped, cand = cand_2007, trace = "up", tracegen = 2)
summary(tidy_big)

Internal validator for tidyped class

Description

Internal validator for tidyped class

Usage

validate_tidyped(x)

Arguments

Value

The object if valid, otherwise an error

Visualize Relationship Matrices

Description

vismat provides visualization tools for relationship matrices (A, D, AA), supporting individual-level heatmaps and relationship coefficient histograms. This function is useful for exploring population genetic structure, identifying inbred individuals, and analyzing kinship between families.

Usage

vismat(
  mat,
  ped = NULL,
  type = "heatmap",
  ids = NULL,
  reorder = TRUE,
  grouping = NULL,
  labelcex = NULL,
  ...
)

Arguments

Details

Visualization Types

Heatmap:

• Uses Nature Genetics style color palette (white to orange to red to dark red)

• Hierarchical clustering reordering is enabled by default to group similar individuals

• Matrix[1,1] is displayed at top-left corner

• Grid lines shown when N <= 100

• Individual labels shown when N <= 500

Histogram:

• Shows distribution of lower triangular elements (excluding diagonal)

• X-axis: relationship coefficient values; Y-axis: frequency percentage

• Useful for checking population inbreeding levels and kinship structure

Performance Considerations

• N > 2000: Hierarchical clustering reordering is automatically skipped

• N > 500: Individual labels are automatically hidden

• N > 100: Grid lines are automatically hidden

• Grouping functionality uses optimized matrix algebra, suitable for large matrices

Interpreting Relationship Coefficients

For additive relationship matrix A:

• Diagonal elements = 1 + F (where F is the inbreeding coefficient)

• Off-diagonal elements = 2 x kinship coefficient

• Value 0: No relationship (unrelated)

• Value 0.25: Half-sibs or grandparent-grandchild

• Value 0.5: Full-sibs or parent-offspring

• Value 1.0: Same individual

Value

Invisibly returns the lattice plot object. The plot is generated on the current graphics device.

See Also

pedmat for computing relationship matrices tidyped for tidying pedigree data visped for visualizing pedigree structure graphs levelplot underlying plotting function for heatmaps histogram underlying plotting function for histograms

Examples

# ============================================================
# Basic Usage
# ============================================================

# Load example data
data(simple_ped)
ped <- tidyped(simple_ped)

# Method 1: Plot directly from tidyped object (auto-computes A matrix)
vismat(ped)

# Method 2: Plot from pedmat object
A <- pedmat(ped)
vismat(A)

# Method 3: Plot from plain matrix
A_dense <- as.matrix(A)
vismat(A_dense)

# ============================================================
# Heatmap Customization
# ============================================================

# Custom title and axis labels
vismat(A, main = "Additive Relationship Matrix", xlab = "Individual", ylab = "Individual")

# Disable clustering reorder (preserve original order)
vismat(A, reorder = FALSE)

# Custom label font size
vismat(A, labelcex = 0.5)

# Custom color palette (blue-white-red)
vismat(A, col.regions = colorRampPalette(c("blue", "white", "red"))(100))

# ============================================================
# Select Specific Individuals
# ============================================================

# Display only a subset of individuals
target_ids <- rownames(A)[1:8]
vismat(A, ids = target_ids)

# ============================================================
# Histogram Visualization
# ============================================================

# Relationship coefficient distribution histogram
vismat(A, type = "histogram")

# Custom number of bins
vismat(A, type = "histogram", nint = 30)

# ============================================================
# Group Aggregation (for large populations)
# ============================================================

# Group by generation
vismat(A, ped = ped, grouping = "Gen", 
       main = "Mean Relationship Between Generations")

# Group by family (if pedigree has Family column)
# vismat(A, ped = ped, grouping = "Family")

# ============================================================
# Different Types of Relationship Matrices
# ============================================================

# Dominance relationship matrix
D <- pedmat(ped, method = "D")
vismat(D, main = "Dominance Relationship Matrix")

# Inbreeding coefficient distribution (diagonal elements - 1)
A_mat <- as.matrix(A)
f_values <- Matrix::diag(A_mat) - 1
hist(f_values, main = "Inbreeding Coefficient Distribution", xlab = "Inbreeding (F)")

Visualize a tidy pedigree

Description

visped function draws a graph of a full or compact pedigree.

Usage

visped(
  ped,
  compact = FALSE,
  outline = FALSE,
  cex = NULL,
  showgraph = TRUE,
  file = NULL,
  highlight = NULL,
  trace = FALSE,
  showf = FALSE,
  pagewidth = 200,
  symbolsize = 1,
  maxiter = 1000,
  ...
)

Arguments

Details

This function takes a pedigree tidied by the tidyped function and outputs a hierarchical graph for all individuals in the pedigree. The graph can be shown on the default graphic device or saved as a PDF file. The PDF output is a legible vector drawing that is legible and avoids overlapping labels. It is especially useful when the number of individuals is large and individual labels are long.

Rendering is performed using a Two-Pass strategy: edges are drawn first to ensure center-to-center connectivity, followed by nodes and labels. This ensures perfect visual alignment in high-resolution vector outputs. The function also supports real-time ancestry and descendant highlighting.

This function can draw the graph of a very large pedigree (> 10,000 individuals per generation) by compacting full-sib individuals. It is highly effective for aquatic animal pedigrees, which usually include many full-sib families per generation in nucleus breeding populations. The outline of a pedigree without individual labels is still shown if the width of a pedigree graph exceeds the maximum width (500 inches) of the PDF file.

In the graph, two shapes and three colors are used. Circles represent individuals, and squares represent families. Dark sky blue indicates males, dark goldenrod indicates females, and dark olive green indicates unknown sex. For example, a dark sky blue circle represents a male individual; a dark goldenrod square represents all female individuals in a full-sib family when compact = TRUE.

Value

The function mainly produces a plot on the current graphics device and/or a PDF file. It invisibly returns a list containing the graph object, layout coordinates, and node sizes.

Note

Isolated individuals (those with no parents and no progeny, assigned Gen 0) are automatically filtered out and not shown in the plot. A message will be issued if any such individuals are removed.

See Also

tidyped for tidying pedigree data (required input) vismat for visualizing relationship matrices as heatmaps pedmat for computing relationship matrices splitped for splitting pedigree into connected components plot.igraph underlying plotting function

Examples

library(visPedigree)
library(data.table)
# Drawing a simple pedigree
simple_ped_tidy <- tidyped(simple_ped)
visped(simple_ped_tidy, 
       cex=0.25, 
       symbolsize=5.5)

# Highlighting an individual and its ancestors and descendants
visped(simple_ped_tidy, 
       highlight = "J5X804", 
       trace = "all", 
       cex=0.25, 
       symbolsize=5.5)

# Showing inbreeding coefficients in the graph
simple_ped_tidy_inbreed <- tidyped(simple_ped, inbreed = TRUE)
visped(simple_ped_tidy_inbreed,
       showf = TRUE, 
       cex=0.25, 
       symbolsize=5.5)

# Adjusting page width and symbol size for better layout
# Increase pagewidth to spread nodes horizontally in the pdf file
# Increase symbolsize for more padding around individual labels
visped(simple_ped_tidy, 
       cex=0.25, 
       symbolsize=5.5, 
       pagewidth = 100, 
       file = tempfile(fileext = ".pdf"))

# Highlighting multiple individuals with custom colors
visped(simple_ped_tidy,
       highlight = list(ids = c("J3Y620", "J1X971"),
                        frame.color = "#4caf50",
                        color = "#81c784"),
       cex=0.25,
       symbolsize=5.5)

# Handling large pedigrees: Saving to PDF is recommended for legibility
# The 'trace' and 'tracegen' parameters in tidyped() help prune the graph
cand_labels <- big_family_size_ped[(Year == 2007) & (substr(Ind,1,2) == "G8"), Ind]

big_ped_tidy <- tidyped(big_family_size_ped, 
                        cand = cand_labels, 
                        trace = "up", 
                        tracegen = 2)
# Use compact = TRUE for large families
visped(big_ped_tidy, 
       compact = TRUE, 
       cex=0.08, 
       symbolsize=5.5, 
       file = tempfile(fileext = ".pdf"))

# Use outline = TRUE if individual labels are not required
visped(big_ped_tidy, 
       compact = TRUE, 
       outline = TRUE, 
       file = tempfile(fileext = ".pdf"))