Package 'phenop' reference manual

Title:	Multidimensional Analysis of Phenotypic Plasticity in Eco-Evolutionary Contexts
Description:	Provides novel methods for analyzing phenotypic plasticity across multiple traits and environmental variables, with special focus on insect-fungus host-pathogen systems. Implements original multidimensional plasticity indices and interaction analyses not available in other packages.
Authors:	Leonel Stazione [aut, cre]
Maintainer:	Leonel Stazione <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.0
Built:	2026-05-17 09:29:36 UTC
Source:	https://github.com/leonelstazione/phenop

Analyze Correlations Between Phenological Parameters

Description

Calculates correlation matrix between key phenological variables.

Usage

analyze_correlations(parameters)
analyze_correlations(parameters)

Arguments

parameters

A data frame containing extracted phenological parameters

Value

A list with correlation results

Examples

## Not run: 
data(pheno_parameters)
corr_results <- analyze_correlations(pheno_parameters)

## End(Not run)
## Not run: 
data(pheno_parameters)
corr_results <- analyze_correlations(pheno_parameters)

## End(Not run)

Analyze GAM relationships

Description

Fits Generalized Additive Models to capture non-linear relationships.

Usage

analyze_gam_relationships(time_series_subset)
analyze_gam_relationships(time_series_subset)

Arguments

time_series_subset

A subset of the time series data

Value

A GAM model object

Examples

## Not run: 
data(pheno_time_series)
subset <- pheno_time_series[pheno_time_series$species == "Quercus_ilex", ]
gam_model <- analyze_gam_relationships(subset)

## End(Not run)
## Not run: 
data(pheno_time_series)
subset <- pheno_time_series[pheno_time_series$species == "Quercus_ilex", ]
gam_model <- analyze_gam_relationships(subset)

## End(Not run)

Analyze Phenology with Mixed Effects Models

Description

Fits mixed effects models to analyze phenological timing.

Usage

analyze_mixed_effects(parameters)
analyze_mixed_effects(parameters)

Arguments

parameters

A data frame containing extracted phenological parameters

Value

A list with model results

Examples

## Not run: 
data(pheno_parameters)
results <- analyze_mixed_effects(pheno_parameters)

## End(Not run)
## Not run: 
data(pheno_parameters)
results <- analyze_mixed_effects(pheno_parameters)

## End(Not run)

Analyze Temporal Trends in Phenology

Description

Calculates linear trends in phenological parameters over years.

Usage

analyze_temporal_trends(parameters)
analyze_temporal_trends(parameters)

Arguments

parameters

A data frame containing extracted phenological parameters

Value

A data frame with trend statistics

Examples

## Not run: 
data(pheno_parameters)
trends <- analyze_temporal_trends(pheno_parameters)

## End(Not run)
## Not run: 
data(pheno_parameters)
trends <- analyze_temporal_trends(pheno_parameters)

## End(Not run)

G×E ANOVA for Phenotypic Plasticity Analysis

Description

Performs ANOVA to test genotype-environment interactions and phenotypic plasticity significance.

Usage

anova_plasticity(formula, data, return_type = "anova")
anova_plasticity(formula, data, return_type = "anova")

Arguments

formula

A formula specifying the model (e.g., trait ~ genotype * environment)

data

A data frame containing the variables

return_type

Type of output: "anova" (default), "model", or "summary"

Value

ANOVA table, model object, or summary depending on return_type

Examples

# Using the example data from calcular_ip
example_data <- data.frame(
  genotype = rep(LETTERS[1:5], each = 4),
  environment = rep(c("Low", "High"), each = 2, times = 5),
  trait_value = rnorm(20, 10, 2)
)

anova_plasticity(trait_value ~ genotype * environment, example_data)
# Using the example data from calcular_ip
example_data <- data.frame(
  genotype = rep(LETTERS[1:5], each = 4),
  environment = rep(c("Low", "High"), each = 2, times = 5),
  trait_value = rnorm(20, 10, 2)
)

anova_plasticity(trait_value ~ genotype * environment, example_data)

Create Phenology Dashboard

Description

Generates a comprehensive dashboard with multiple phenological visualizations.

Usage

create_phenology_dashboard(
  time_series,
  parameters,
  spatial_data,
  selected_year = 2021
)
create_phenology_dashboard(
  time_series,
  parameters,
  spatial_data,
  selected_year = 2021
)

Arguments

time_series

Time series data

parameters

Extracted parameters data

spatial_data

Spatial data

selected_year

Year for seasonal curves (default: 2021)

Value

A patchwork object combining multiple plots

Datos de demonstration para plasticity analysis

Description

Dataset simulated que contiene medidas fenotípicas de interacciones insecto-hongo

Usage

datos_demo
datos_demo

Format

Un data.frame con 200 observaciones y 8 variables:

insect_population: Población de insecto (P1, P2, P3, P4)
fungus_strain: Cepa de hongo (C1, C2, C3)
temperature: Temperatura en °C
humidity: Humedad en %
insect_size: Tamaño del insecto en mm
resistance: Resistencia del insecto
fungus_growth: Crecimiento del hongo
virulence: Virulencia del hongo

Source

Simulated data para el paquete phenop

Examples

data(datos_demo)
head(datos_demo)
data(datos_demo)
head(datos_demo)

Demo Dataset for Plasticity Analysis

Description

Simulated dataset containing insect and fungus data to demonstrate phenop package functionalities.

Usage

demo_data
demo_data

Format

A data.frame with 200 rows and 8 columns:

insect_population: Insect population (P1, P2, P3)
fungus_strain: Fungus strain (C1, C2, C3)
temperature: Temperature in Celsius (15-30)
humidity: Relative humidity in percentage (40-80)
insect_size: Insect size in mm (mean 10, sd 2)
resistance: Insect resistance to infection (mean 5, sd 1)
fungus_growth: Fungus growth rate (mean 8, sd 1.5)
virulence: Fungus virulence (mean 6, sd 1)

Source

Simulated data for demonstration purposes

Examples

data(demo_data)
head(demo_data)
data(demo_data)
head(demo_data)

Detect Anomalies in Phenological Parameters

Description

Identifies anomalous observations based on z-scores and IQR methods.

Usage

detect_anomalies(parameters, threshold = 2.5)
detect_anomalies(parameters, threshold = 2.5)

Arguments

parameters

A data frame containing extracted phenological parameters. Must include columns: species, site, sos, eos, los.

threshold

Numeric threshold for z-score anomaly detection (default = 2.5). Observations with z-scores beyond ±threshold are flagged as anomalies.

Value

A data frame with anomaly flags including:

sos_anomaly

Logical flag for start-of-season anomalies

eos_anomaly

Logical flag for end-of-season anomalies

los_anomaly

Logical flag for length-of-season anomalies

anomaly_score

Numeric score (0-3) of total anomalies detected

sos_z, eos_z, los_z

Z-scores for each parameter

Examples

## Not run: 
data(pheno_parameters)
anomalies <- detect_anomalies(pheno_parameters)
anomalies_strict <- detect_anomalies(pheno_parameters, threshold = 3.0)

## End(Not run)
## Not run: 
data(pheno_parameters)
anomalies <- detect_anomalies(pheno_parameters)
anomalies_strict <- detect_anomalies(pheno_parameters, threshold = 3.0)

## End(Not run)

Fungi-Insect Interaction Dataset

Description

Simulated dataset of phenotypic plasticity in a fungus-insect host-pathogen system. Contains data for 20 fungus strains across 10 insect populations under different temperature and humidity conditions.

Usage

fungi_insect_data
fungi_insect_data

Format

A data frame with 600 rows and 8 variables:

fungus_strain: Fungus strain identifier (H1-H20)
insect_population: Insect population identifier (I1-I10)
temperature: Environmental temperature in °C (15-35)
humidity: Relative humidity in percent (30, 60, 90)
fungus_growth: Fungus growth rate
virulence: Fungus virulence (0-1 scale)
insect_size: Insect body size
resistance: Insect resistance to fungus (0-1 scale)

Source

Simulated data for package examples and testing

Host-Pathogen Interaction Analysis

Description

Calculates basic plasticity metrics for host-pathogen comparisons. Robust alternative that calculates plasticity metrics without errors.

Usage

host_pathogen_interaction(
  data,
  host_trait,
  pathogen_trait,
  environment,
  host_group = "insect_population",
  pathogen_group = "fungus_strain"
)
host_pathogen_interaction(
  data,
  host_trait,
  pathogen_trait,
  environment,
  host_group = "insect_population",
  pathogen_group = "fungus_strain"
)

Arguments

data

Data frame with both host and pathogen data

host_trait

Host trait name

pathogen_trait

Pathogen trait name

environment

Environmental variable name

host_group

Column name for host groups

pathogen_group

Column name for pathogen groups

Value

List with plasticity comparison results

Examples

data(fungi_insect_data)
result <- host_pathogen_interaction(
  data = fungi_insect_data,
  host_trait = "insect_size",
  pathogen_trait = "fungus_growth",
  environment = "temperature",
  host_group = "insect_population",
  pathogen_group = "fungus_strain"
)
data(fungi_insect_data)
result <- host_pathogen_interaction(
  data = fungi_insect_data,
  host_trait = "insect_size",
  pathogen_trait = "fungus_growth",
  environment = "temperature",
  host_group = "insect_population",
  pathogen_group = "fungus_strain"
)

Extended Host-Pathogen Interaction Analysis

Description

Comprehensive analysis of phenotypic plasticity in host-pathogen systems.

Usage

host_pathogen_interaction_extended(
  host_data,
  pathogen_data,
  host_traits,
  pathogen_traits,
  interaction_metrics = c("correlation"),
  environments,
  host_group = NULL,
  pathogen_group = NULL,
  infection_data = NULL,
  ...
)
host_pathogen_interaction_extended(
  host_data,
  pathogen_data,
  host_traits,
  pathogen_traits,
  interaction_metrics = c("correlation"),
  environments,
  host_group = NULL,
  pathogen_group = NULL,
  infection_data = NULL,
  ...
)

Arguments

host_data

Data frame with host phenotypic data

pathogen_data

Data frame with pathogen phenotypic data

host_traits

Character vector of host trait names

pathogen_traits

Character vector of pathogen trait names

interaction_metrics

Metrics to calculate

environments

Environmental variables

host_group

Column name for host groups

pathogen_group

Column name for pathogen groups

infection_data

Optional infection data

...

Additional arguments

Calculate Multidimensional Plasticity Index (MPI)

Description

Calculate Multidimensional Plasticity Index (MPI)

Usage

multidim_plasticity(
  data,
  traits,
  environments,
  groups,
  weights = NULL,
  na.action = "omit",
  min_variation = 0.01,
  ...
)
multidim_plasticity(
  data,
  traits,
  environments,
  groups,
  weights = NULL,
  na.action = "omit",
  min_variation = 0.01,
  ...
)

Arguments

data

A data frame containing the phenotypic data

traits

Character vector of trait column names

environments

Character string of environment column name

groups

Character string of group/genotype column name

weights

Optional numeric vector of weights for each trait

na.action

How to handle NAs ("omit", "fail", "warning")

min_variation

Minimum variation threshold (default 0.01)

...

Additional arguments passed to internal functions

Value

A list with multidimensional_index and individual_plasticity

Examples

data(demo_data)
result <- multidim_plasticity(
  data = demo_data,
  traits = c("insect_size", "resistance"),
  environments = "temperature",
  groups = "insect_population"
)
data(demo_data)
result <- multidim_plasticity(
  data = demo_data,
  traits = c("insect_size", "resistance"),
  environments = "temperature",
  groups = "insect_population"
)

Multidimensional Environmental Optimization

Description

Finds optimal environmental conditions considering multiple traits simultaneously using original optimization algorithms for phenotypic plasticity landscapes.

Usage

optimize_multidim_environment(
  data,
  traits,
  environments,
  groups,
  optimization_goal = "compromise",
  weights = NULL
)
optimize_multidim_environment(
  data,
  traits,
  environments,
  groups,
  optimization_goal = "compromise",
  weights = NULL
)

Arguments

data

Data frame with phenotypic and environmental data

traits

Vector of trait names to optimize

environments

Vector of environmental variables to optimize

groups

Grouping variable(genotypes/populations)

optimization_goal

Optimization goal: "maximize", "minimize", "compromise"

weights

Optional weights for each trait's importance

Value

List with optimal conditions and optimization landscape

Perform Cluster Analysis on Phenological Types

Description

Groups populations based on phenological characteristics using k-means.

Usage

perform_cluster_analysis(parameters, n_clusters = 4)
perform_cluster_analysis(parameters, n_clusters = 4)

Arguments

parameters

A data frame containing extracted phenological parameters

n_clusters

Number of clusters (default: 4)

Value

A list with clustering results

Examples

## Not run: 
data(pheno_parameters)
clusters <- perform_cluster_analysis(pheno_parameters, n_clusters = 3)

## End(Not run)
## Not run: 
data(pheno_parameters)
clusters <- perform_cluster_analysis(pheno_parameters, n_clusters = 3)

## End(Not run)

Perform Principal Component Analysis

Description

Performs PCA on phenological parameters to identify main axes of variation.

Usage

perform_pca_analysis(parameters)
perform_pca_analysis(parameters)

Arguments

parameters

A data frame containing extracted phenological parameters

Value

A PCA result object

Examples

## Not run: 
data(pheno_parameters)
pca_results <- perform_pca_analysis(pheno_parameters)

## End(Not run)
## Not run: 
data(pheno_parameters)
pca_results <- perform_pca_analysis(pheno_parameters)

## End(Not run)

Extracted Phenological Parameters

Description

Derived phenological metrics extracted from time series analysis.

Usage

pheno_parameters
pheno_parameters

Format

A data frame with X rows and 44 variables:

parameter_id: Unique identifier for each parameter record
site: Site name or identifier where observations were made
species: Species name or identifier
year: Year of observation
population_id: Population identifier for the observed group
n_observations: Number of individual observations aggregated
sos: Start of season (day of year when growth begins)
eos: End of season (day of year when growth ends)
pos: Peak of season (day of year of maximum growth)
los: Length of season (number of days between SOS and EOS)
ndvi_max: Maximum Normalized Difference Vegetation Index value
ndvi_min: Minimum Normalized Difference Vegetation Index value
ndvi_amplitude: Difference between maximum and minimum NDVI
ndvi_mean: Mean Normalized Difference Vegetation Index value
ndvi_sd: Standard deviation of NDVI values
ndvi_cv: Coefficient of variation of NDVI values
ndvi_integral: Cumulative NDVI over the growing season
gpp_total: Total Gross Primary Production (cumulative)
gpp_max: Maximum daily Gross Primary Production
lai_max: Maximum Leaf Area Index value
greenup_rate: Rate of green-up (rate of increase in vegetation)
senescence_rate: Rate of senescence (rate of vegetation decline)
mean_temperature: Mean temperature during the growing season (°C)
total_precipitation: Total precipitation during growing season (mm)
mean_gdd: Mean Growing Degree Days
total_gdd: Total Growing Degree Days accumulated
budburst_doy: Day of year when budburst occurred
flowering_doy: Day of year when flowering occurred
senescence_doy: Day of year when senescence began
data_completeness: Percentage of complete data records
elevation: Elevation above sea level (meters)
latitude: Latitude coordinate in decimal degrees
longitude: Longitude coordinate in decimal degrees
soil_type: Classification of soil type
soil_pH: Soil pH measurement
slope: Terrain slope in degrees
phenology_type: Type of phenology (e.g., early, late, etc.)
productivity_class: Productivity classification (low, medium, high)
sos_anomaly: Deviation of SOS from long-term average
eos_anomaly: Deviation of EOS from long-term average
los_anomaly: Deviation of LOS from long-term average
water_use_efficiency: Water use efficiency metric
temperature_efficiency: Temperature use efficiency metric
sos_trend: Temporal trend in SOS over years
eos_trend: Temporal trend in EOS over years

Spatial Phenological Data

Description

Gridded spatial data for phenological mapping and spatial analysis.

Usage

pheno_spatial
pheno_spatial

Format

A data frame with X rows and 24 variables:

site_id: Unique identifier for the spatial site
longitude: Longitude coordinate in decimal degrees
latitude: Latitude coordinate in decimal degrees
elevation: Elevation above sea level (meters)
mean_annual_temp: Mean annual temperature (°C)
annual_precipitation: Total annual precipitation (mm)
soil_type: Classification of soil type
soil_pH: Soil pH measurement
soil_organic_carbon: Soil organic carbon content (g/kg)
sos: Start of season (day of year)
eos: End of season (day of year)
los: Length of season (days)
ndvi_max: Maximum Normalized Difference Vegetation Index value
land_cover: Land cover classification type
forest_type: Forest type classification
protected_area: Indicates if the site is in a protected area
years_since_disturbance: Years since last disturbance event
disturbance_type: Type of disturbance (fire, logging, etc.)
climate_zone: Climate zone classification
phenology_class: Phenology classification category
productivity: Productivity metric value
productivity_class: Productivity classification category
species_richness: Number of species recorded at the site
aboveground_carbon: Aboveground carbon stock (Mg/ha)

Phenological Spatial Data as Simple Features

Description

A spatial dataset containing phenological observations with geometry.

Usage

pheno_spatial_sf
pheno_spatial_sf

Format

An sf object with 150 features and 12 fields.

site_id: Unique identifier for the spatial site
longitude: Longitude coordinate in decimal degrees
latitude: Latitude coordinate in decimal degrees
elevation: Elevation above sea level (meters)
mean_annual_temp: Mean annual temperature (°C)
annual_precipitation: Total annual precipitation (mm)
soil_type: Classification of soil type
soil_pH: Soil pH measurement
soil_organic_carbon: Soil organic carbon content (g/kg)
sos: Start of season (day of year)
eos: End of season (day of year)
los: Length of season (days)
ndvi_max: Maximum Normalized Difference Vegetation Index value
land_cover: Land cover classification type
forest_type: Forest type classification
protected_area: Indicates if the site is in a protected area
years_since_disturbance: Years since last disturbance event
disturbance_type: Type of disturbance (fire, logging, etc.)
climate_zone: Climate zone classification
phenology_class: Phenology classification category
productivity: Productivity metric value
productivity_class: Productivity classification category
species_richness: Number of species recorded at the site
aboveground_carbon: Aboveground carbon stock (Mg/ha)

Phenological Time Series Dataset

Description

A comprehensive synthetic dataset of weekly phenological observations for 15 populations, 5 species, 7 sites, and 6 years (2018-2023).

Usage

pheno_time_series
pheno_time_series

Format

A data frame with X rows and 29 variables:

observation_id: Unique identifier for each observation
population_id: Population identifier
species: Species name or identifier
site: Site name or identifier
year: Year of observation
date: Date of observation (YYYY-MM-DD)
doy: Day of year (1-365/366)
month: Month of observation (1-12)
season: Seasonal classification
latitude: Latitude coordinate in decimal degrees
longitude: Longitude coordinate in decimal degrees
elevation: Elevation above sea level (meters)
soil_type: Classification of soil type
soil_pH: Soil pH measurement
slope: Terrain slope in degrees
ndvi: Normalized Difference Vegetation Index value
evi: Enhanced Vegetation Index value
gpp: Gross Primary Production value
lai: Leaf Area Index value
temperature: Temperature measurement (°C)
precipitation: Precipitation measurement (mm)
gdd: Growing Degree Days accumulated
vegetation_health: Vegetation health index value
sos_adj: Adjusted start of season indicator
eos_adj: Adjusted end of season indicator
budburst: Budburst phenophase indicator
flowering: Flowering phenophase indicator
senescence: Senescence phenophase indicator
quality_flag: Data quality flag

Source

Generated synthetically for the phenop package

Examples

data(pheno_time_series)
summary(pheno_time_series$ndvi)
ggplot2::ggplot(pheno_time_series, ggplot2::aes(x = doy, y = ndvi)) +
  ggplot2::geom_line(alpha = 0.3)
data(pheno_time_series)
summary(pheno_time_series$ndvi)
ggplot2::ggplot(pheno_time_series, ggplot2::aes(x = doy, y = ndvi)) +
  ggplot2::geom_line(alpha = 0.3)

Example dataset para plasticidad fenotipica

Description

Simulated data to demonstrate las funciones of the package phenop.

Format

Un data.frame con 200 observaciones y 6 variables.

Source

Simulado

Plasticity Meta-Analysis

Description

Performs meta-analysis of phenotypic plasticity studies using original methods for synthesizing effect sizes and exploring publication bias.

Usage

plasticity_meta_analysis(
  study_data,
  effect_size = "effect_size",
  variance = "variance",
  moderator = NULL,
  method = "random"
)
plasticity_meta_analysis(
  study_data,
  effect_size = "effect_size",
  variance = "variance",
  moderator = NULL,
  method = "random"
)

Arguments

study_data

Data frame with study results (plasticity effect sizes)

effect_size

Column name containing effect sizes

variance

Column name containing variances

moderator

Optional moderator variables for analysis

method

Meta-analysis method: "fixed", "random", "multilevel"

Value

Meta-analysis results with original plasticity-specific metrics

Extended Plasticity Meta-Analysis

Description

Comprehensive meta-analysis of phenotypic plasticity studies. This function synthesizes effect sizes from multiple studies, assesses heterogeneity, publication bias, and moderating effects.

Usage

plasticity_meta_analysis_extended(
  study_data,
  effect_size = "effect_size",
  variance = "variance",
  study_labels = "study",
  method = "random",
  p_value_threshold = 0.05,
  moderator = NULL,
  ...
)
plasticity_meta_analysis_extended(
  study_data,
  effect_size = "effect_size",
  variance = "variance",
  study_labels = "study",
  method = "random",
  p_value_threshold = 0.05,
  moderator = NULL,
  ...
)

Arguments

study_data

Data frame containing study results. Must include columns for effect sizes and their variances.

effect_size

Character string specifying the column name for effect sizes

variance

Character string specifying the column name for variances

study_labels

Character string specifying the column name for study identifiers

method

Meta-analysis method: "fixed" (default), "random", "bayesian", "multilevel"

p_value_threshold

Threshold for statistical significance (default: 0.05)

moderator

Optional character vector of moderator variable names

...

Additional arguments passed to meta-analysis functions

Value

A list containing:

meta_results

Overall meta-analysis results

individual_studies

Study-level statistics

heterogeneity

Heterogeneity analysis

publication_bias

Publication bias assessment

moderator_analysis

Results of moderator analysis (if requested)

forest_plot_data

Data for creating forest plots

Examples

# Create simulated meta-analysis data
n_studies <- 15
meta_data <- data.frame(
  study = paste("Study", 1:n_studies),
  effect_size = rnorm(n_studies, mean = 0.6, sd = 0.3),
  variance = runif(n_studies, 0.05, 0.2),
  year = 2000 + 1:n_studies,
  taxon = sample(c("Insect", "Fungus", "Plant"), n_studies, replace = TRUE),
  trait_type = sample(c("Morphological", "Physiological", "Life History"),
                     n_studies, replace = TRUE)
)

# Run meta-analysis
meta_result <- plasticity_meta_analysis_extended(
  study_data = meta_data,
  effect_size = "effect_size",
  variance = "variance",
  study_labels = "study",
  method = "random",
  moderator = c("year", "taxon", "trait_type")
)

print(meta_result)

# Create simulated meta-analysis data
n_studies <- 15
meta_data <- data.frame(
  study = paste("Study", 1:n_studies),
  effect_size = rnorm(n_studies, mean = 0.6, sd = 0.3),
  variance = runif(n_studies, 0.05, 0.2),
  year = 2000 + 1:n_studies,
  taxon = sample(c("Insect", "Fungus", "Plant"), n_studies, replace = TRUE),
  trait_type = sample(c("Morphological", "Physiological", "Life History"),
                     n_studies, replace = TRUE)
)

# Run meta-analysis
meta_result <- plasticity_meta_analysis_extended(
  study_data = meta_data,
  effect_size = "effect_size",
  variance = "variance",
  study_labels = "study",
  method = "random",
  moderator = c("year", "taxon", "trait_type")
)

print(meta_result)

Plasticity Trade-off Analysis

Description

Identifies and quantifies trade-offs between plasticities of different traits using original correlation-based and constraint-based methods.

Usage

plasticity_tradeoffs(multidim_result, method = "correlation", threshold = -0.5)
plasticity_tradeoffs(multidim_result, method = "correlation", threshold = -0.5)

Arguments

multidim_result

Result from multidim_plasticity() function

method

Trade-off detection method: "correlation", "pca", "constraint"

threshold

Threshold for significant trade-offs

Value

List with trade-off metrics and visualizations

Extended Plasticity Trade-off Analysis

Description

Comprehensive analysis of trade-offs between different phenotypic plasticity indices. This function identifies and quantifies constraints and trade-offs in multivariate plasticity space.

Usage

plasticity_tradeoffs_extended(
  plasticity_result,
  method = "correlation",
  threshold = -0.5,
  p_value_threshold = 0.05,
  visualization = TRUE,
  ...
)
plasticity_tradeoffs_extended(
  plasticity_result,
  method = "correlation",
  threshold = -0.5,
  p_value_threshold = 0.05,
  visualization = TRUE,
  ...
)

Arguments

plasticity_result

Result object from multidim_plasticity() or safe_multidim_plasticity()

method

Analysis method: "correlation" (default), "pca", "constraint", "regression"

threshold

Correlation threshold for identifying trade-offs (default: -0.5)

p_value_threshold

Significance threshold for statistical tests (default: 0.05)

visualization

Logical, whether to prepare data for visualization (default: TRUE)

...

Additional arguments passed to analysis functions

Value

A list containing:

tradeoff_matrix

Matrix of correlations between plasticity indices

significant_tradeoffs

Data frame of significant trade-off pairs

pca_results

PCA results if method includes PCA

constraint_indices

Constraint strength indices

visualization_data

Data prepared for plotting

analysis_parameters

Parameters used in the analysis

Examples

data(demo_data)

# First run plasticity analysis
insect_data <- demo_data %>%
  dplyr::select(insect_population, temperature, humidity,
                insect_size, resistance) %>%
  dplyr::distinct()
  
result <- safe_multidim_plasticity(
  insect_data,
  traits = c("insect_size", "resistance"),
  environments = c("temperature", "humidity"),
  groups = "insect_population"
)

# Then analyze trade-offs
tradeoffs <- plasticity_tradeoffs_extended(result, method = "correlation")
print(tradeoffs)

data(demo_data)

# First run plasticity analysis
insect_data <- demo_data %>%
  dplyr::select(insect_population, temperature, humidity,
                insect_size, resistance) %>%
  dplyr::distinct()
  
result <- safe_multidim_plasticity(
  insect_data,
  traits = c("insect_size", "resistance"),
  environments = c("temperature", "humidity"),
  groups = "insect_population"
)

# Then analyze trade-offs
tradeoffs <- plasticity_tradeoffs_extended(result, method = "correlation")
print(tradeoffs)

Plot Density Ridges of Phenological Parameters

Description

Creates density ridge plots showing distributions of phenological parameters.

Usage

plot_density_ridges(
  parameters,
  parameter = "los",
  group_by = "species",
  title = NULL
)
plot_density_ridges(
  parameters,
  parameter = "los",
  group_by = "species",
  title = NULL
)

Arguments

parameters

A data frame containing extracted phenological parameters

parameter

Name of parameter to plot (default: "los")

group_by

Grouping variable (default: "species")

title

Plot title (optional)

Value

A ggplot object

Internal plotting function for multidimensional plasticity

Description

Internal plotting function for multidimensional plasticity

Usage

plot_internals(df, type = "integration", traits = NULL, plot_options = list())
plot_internals(df, type = "integration", traits = NULL, plot_options = list())

Arguments

df

Data frame or result object

type

Type of plot: "integration" or "radar"

traits

Optional vector of traits

plot_options

List of plot options

Value

ggplot object

Multidimensional Plasticity Visualization

Description

Creates innovative visualizations for multidimensional plasticity analysis including correlation networks, plasticity landscapes, and trait integration plots.

Usage

plot_multidim_plasticity(
  multidim_result,
  type = "network",
  traits = NULL,
  plot_options = list()
)
plot_multidim_plasticity(
  multidim_result,
  type = "network",
  traits = NULL,
  plot_options = list()
)

Arguments

multidim_result

Result from multidim_plasticity() function

type

Type of plot: "network", "landscape", "integration", "radar"

traits

Optional: subset of traits to display

plot_options

Additional plotting options

Value

ggplot object or list of plots

Examples

# Usar demo_data en lugar de fungi_insect_data
data(demo_data)
# Preparar datos para multidim_plasticity
result <- safe_multidim_plasticity(
  demo_data,
  traits = c("insect_size", "resistance"),
  environments = c("temperature", "humidity"),
  groups = "insect_population"
)
plot_multidim_plasticity(result, "network")
# Usar demo_data en lugar de fungi_insect_data
data(demo_data)
# Preparar datos para multidim_plasticity
result <- safe_multidim_plasticity(
  demo_data,
  traits = c("insect_size", "resistance"),
  environments = c("temperature", "humidity"),
  groups = "insect_population"
)
plot_multidim_plasticity(result, "network")

Extended Multidimensional Plasticity Visualization (SAFE)

Description

Advanced plotting functions for visualizing multidimensional plasticity results. Always returns a ggplot object, even if data are missing or incomplete.

Usage

plot_multidim_plasticity_extended(
  multidim_result,
  type = "heatmap",
  traits = NULL,
  plot_options = list(),
  ...
)
plot_multidim_plasticity_extended(
  multidim_result,
  type = "heatmap",
  traits = NULL,
  plot_options = list(),
  ...
)

Arguments

multidim_result

Result object from multidim_plasticity() or safe_multidim_plasticity()

type

Type of plot to create. Options: "heatmap" (default), "network", "radar", "bar", "scatter", "violin", "density", "pca", "landscape", "integration"

traits

Optional character vector of traits to include (subset of all traits)

plot_options

List of additional plotting options:

title: Plot title
color_palette: Color palette name ("viridis", "plasma", "magma", "inferno", "cividis")
theme: ggplot2 theme ("minimal", "classic", "bw", "dark", "light")
interactive: Logical, whether to create interactive plot (requires plotly)
save_plot: Logical, whether to save plot to file
filename: Filename for saved plot
width, height: Plot dimensions in inches
alpha: transparency
point_size, text_size, legend_position

...

Additional arguments passed to plotting functions

Value

A ggplot object (or plotly object if interactive = TRUE)

Plot Phenological Parameter Heatmap

Description

Creates a heatmap showing variation in phenological parameters across species, sites, and years.

Usage

plot_parameter_heatmap(
  parameters,
  parameter = "los",
  title = "Phenological Parameter Variation"
)
plot_parameter_heatmap(
  parameters,
  parameter = "los",
  title = "Phenological Parameter Variation"
)

Arguments

parameters

A data frame containing extracted phenological parameters

parameter

Name of parameter to plot (default: "los")

title

Plot title (optional)

Value

A ggplot object

Plot Productivity vs Climate Relationships

Description

Shows relationships between productivity (GPP) and climate variables.

Usage

plot_productivity_climate(parameters)
plot_productivity_climate(parameters)

Arguments

parameters

A data frame containing extracted phenological parameters

Value

A ggplot object

Plot Reaction Norms for Phenotypic Plasticity

Description

Creates reaction norm plots to visualize genotype-environment interactions.

Usage

plot_reaction_norm(
  data,
  environment,
  trait,
  genotype,
  add_points = TRUE,
  add_se = TRUE
)
plot_reaction_norm(
  data,
  environment,
  trait,
  genotype,
  add_points = TRUE,
  add_se = TRUE
)

Arguments

data

A data frame containing the phenotypic data

environment

Column name for environment variable(unquoted)

trait

Column name for phenotypic trait (unquoted)

genotype

Column name for genotype grouping (unquoted)

add_points

Whether to add individual data points (default: TRUE)

add_se

Whether to add standard error bars (default: TRUE)

Value

A ggplot object with reaction norms

Examples

datos <- data.frame(
  gen = rep(c("A", "B"), each = 6),
  env = rep(1:3, each = 2, times = 2),
  trait = c(10, 12, 15, 16, 20, 22, 8, 9, 12, 14, 18, 20)
)
plot_reaction_norm(datos, env, trait, gen)
datos <- data.frame(
  gen = rep(c("A", "B"), each = 6),
  env = rep(1:3, each = 2, times = 2),
  trait = c(10, 12, 15, 16, 20, 22, 8, 9, 12, 14, 18, 20)
)
plot_reaction_norm(datos, env, trait, gen)

Plot Seasonal Phenological Curves

Description

Creates multi-panel plots showing seasonal NDVI dynamics across species and sites.

Usage

plot_seasonal_curves(
  time_series,
  selected_species = NULL,
  selected_year = 2021,
  n_populations = 3
)
plot_seasonal_curves(
  time_series,
  selected_species = NULL,
  selected_year = 2021,
  n_populations = 3
)

Arguments

time_series

A data frame containing phenological time series

selected_species

Vector of species to include (NULL for all)

selected_year

Year to plot (default: 2021)

n_populations

Number of populations to include (default: 3)

Value

A ggplot object

Examples

## Not run: 
data(pheno_time_series)
plot_seasonal_curves(pheno_time_series, selected_year = 2021)

## End(Not run)
## Not run: 
data(pheno_time_series)
plot_seasonal_curves(pheno_time_series, selected_year = 2021)

## End(Not run)

Plot Spatial Phenology Map

Description

Creates a spatial map showing phenological patterns across locations.

Usage

plot_spatial_map(
  spatial_data,
  variable = "los",
  point_size = "ndvi_max",
  title = "Spatial Phenology Patterns"
)
plot_spatial_map(
  spatial_data,
  variable = "los",
  point_size = "ndvi_max",
  title = "Spatial Phenology Patterns"
)

Arguments

spatial_data

Spatial data frame (sf object or data.frame with coordinates)

variable

Variable to map as color (default: "los")

point_size

Variable to map as point size (default: "ndvi_max")

title

Plot title (optional)

Value

A ggplot object

Print method for plasticity_meta_analysis_extended

Description

Print method for plasticity_meta_analysis_extended

Usage

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

Arguments

x

An object of class plasticity_meta_analysis_extended

...

Additional arguments passed to print

Print method for plasticity_tradeoffs_extended

Description

Print method for plasticity_tradeoffs_extended

Usage

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

Arguments

x

An object of class plasticity_tradeoffs_extended

...

Additional arguments passed to print

Print method for safe_multidim_result

Description

Print method for safe_multidim_result

Usage

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

Arguments

x

An object of class safe_multidim_result

...

Additional arguments passed to print

Run Complete Analysis Pipeline

Description

Executes a comprehensive analysis pipeline on phenological data.

Usage

run_complete_analysis(time_series, parameters)
run_complete_analysis(time_series, parameters)

Arguments

time_series

Time series data (not always used, but kept for consistency)

parameters

A data frame containing extracted phenological parameters

Value

A list with all analysis results

Examples

## Not run: 
data(pheno_time_series)
data(pheno_parameters)
results <- run_complete_analysis(pheno_time_series, pheno_parameters)

## End(Not run)
## Not run: 
data(pheno_time_series)
data(pheno_parameters)
results <- run_complete_analysis(pheno_time_series, pheno_parameters)

## End(Not run)

Safe Multidimensional Plasticity Analysis

Description

A robust version of multidimensional plasticity analysis with error handling and data validation. This function calculates actual plasticity indices based on environmental variation in phenotypic traits.

Usage

safe_multidim_plasticity(
  data,
  traits,
  environments,
  groups,
  weights = NULL,
  na.action = "omit",
  ...
)
safe_multidim_plasticity(
  data,
  traits,
  environments,
  groups,
  weights = NULL,
  na.action = "omit",
  ...
)

Arguments

data

A data frame containing phenotypic and environmental data

traits

Character vector of trait names to analyze

environments

Character vector of environmental variables

groups

Character string specifying the grouping variable (e.g., genotype, population)

weights

Optional numeric vector of weights for each trait (ecological importance)

na.action

How to handle missing values: "omit" (default), "fail", "impute.mean"

...

Additional arguments passed to the plasticity calculation

Value

A list containing:

individual_plasticity

Data frame with plasticity indices for each group

group_plasticity

Summary statistics by group

multidimensional_index

Overall multidimensional plasticity index

safe_analysis

List with analysis metadata and success status

message

Informative message about the analysis

Examples

# Create example data with real variability
set.seed(123)
example_data <- data.frame(
  genotype = rep(paste0("G", 1:10), each = 4),
  environment = rep(c("Cold", "Cool", "Warm", "Hot"), 10),
  trait1 = rnorm(40, mean = 10, sd = 2),
  trait2 = rnorm(40, mean = 5, sd = 1),
  trait3 = rnorm(40, mean = 20, sd = 3)
)

# Run analysis
result <- safe_multidim_plasticity(
  data = example_data,
  traits = c("trait1", "trait2", "trait3"),
  environments = "environment",
  groups = "genotype"
)

print(result)

# Create example data with real variability
set.seed(123)
example_data <- data.frame(
  genotype = rep(paste0("G", 1:10), each = 4),
  environment = rep(c("Cold", "Cool", "Warm", "Hot"), 10),
  trait1 = rnorm(40, mean = 10, sd = 2),
  trait2 = rnorm(40, mean = 5, sd = 1),
  trait3 = rnorm(40, mean = 20, sd = 3)
)

# Run analysis
result <- safe_multidim_plasticity(
  data = example_data,
  traits = c("trait1", "trait2", "trait3"),
  environments = "environment",
  groups = "genotype"
)

print(result)

Simulate Phenotypic Plasticity Data

Description

Generates realistic simulated data for plasticity studies with customizable parameters for traits, environments, and plasticity patterns. Useful for teaching and method validation.

Usage

simulate_plasticity_data(
  n_genotypes = 20,
  n_environments = 5,
  n_traits = 3,
  plasticity_patterns = list("gradient", "optimal", "neutral"),
  trait_correlations = 0.3
)
simulate_plasticity_data(
  n_genotypes = 20,
  n_environments = 5,
  n_traits = 3,
  plasticity_patterns = list("gradient", "optimal", "neutral"),
  trait_correlations = 0.3
)

Arguments

n_genotypes

Number of genotypes/populations

n_environments

Number of environmental conditions

n_traits

Number of phenotypic traits

plasticity_patterns

List of plasticity patterns: "gradient", "threshold", "optimal"

trait_correlations

Correlation structure between traits

Value

Simulated plasticity dataset

Package 'phenop'

Help Index

Analyze Correlations Between Phenological Parameters

Description

Usage

Arguments

Value

Examples

Analyze GAM relationships

Description

Usage

Arguments

Value

Examples

Analyze Phenology with Mixed Effects Models

Description

Usage

Arguments

Value

Examples

Analyze Temporal Trends in Phenology

Description

Usage

Arguments

Value

Examples

G×E ANOVA for Phenotypic Plasticity Analysis

Description

Usage

Arguments

Value

Examples

Create Phenology Dashboard

Description

Usage

Arguments

Value

Datos de demonstration para plasticity analysis

Description

Usage

Format

Source

Examples

Demo Dataset for Plasticity Analysis

Description

Usage

Format

Source

Examples

Detect Anomalies in Phenological Parameters

Description

Usage

Arguments

Value

Examples

Fungi-Insect Interaction Dataset

Description

Usage

Format

Source

Host-Pathogen Interaction Analysis

Description

Usage

Arguments

Value

Examples

Extended Host-Pathogen Interaction Analysis

Description

Usage

Arguments

Calculate Multidimensional Plasticity Index (MPI)

Description

Usage

Arguments

Value

Examples

Multidimensional Environmental Optimization

Description

Usage

Arguments