, Raphael Martin [aut]
, Lain Pavot [aut,
cre], Pierre Liancourt [aut]
, Nicolas Gross [aut]
, INRAe/UREP [cph]| Title: | Realize the Trait Abundance Distribution |
|---|---|
| Description: | This analytical framework is based on an analysis of the shape of the trait abundance distributions to better understand community assembly processes, and predict community dynamics under environmental changes. This framework mobilized a study of the relationship between the moments describing the shape of the distributions: the skewness and the kurtosis (SKR). The SKR allows the identification of commonalities in the shape of trait distributions across contrasting communities. Derived from the SKR, we developed mathematical parameters that summarise the complex pattern of distributions by assessing (i) the R², (ii) the Y-intercept, (iii) the slope, (iv) the functional stability of community (TADstab), and, (v) the distance from specific distribution families (i.e., the distance from the skew-uniform family a limit to the highest degree of evenness: TADeve). |
| Authors: | Nathan Rondeau [aut], Yoann Le Bagousse-Pinguet [aut]
, Raphael Martin [aut]
, Lain Pavot [aut,
cre], Pierre Liancourt [aut]
, Nicolas Gross [aut]
, INRAe/UREP [cph] |
| Maintainer: | Lain Pavot <[email protected]> |
| License: | BSD_3_clause + file LICENSE |
| Version: | 1.0.0 |
| Built: | 2024-11-29 05:54:51 UTC |
| Source: | https://github.com/cran/TAD |
Provides a set of constants to prevent typo and provide some defauts values to functions in the TAD. Among those constants are:
SKEW_UNIFORM_SLOPE_DISTANCE
SKEW_UNIFORM_INTERCEPT_DISTANCE
DEFAULT_SIGNIFICATIVITY_THRESHOLD
DEFAULT_LIN_MOD
DEFAULT_SLOPE_DISTANCE
DEFAULT_INTERCEPT_DISTANCE
CONSTANTSCONSTANTS
An object of class list of length 6.
Generate and save random matrix
generate_random_matrix( weights, aggregation_factor = NULL, randomization_number, seed = NULL )generate_random_matrix( weights, aggregation_factor = NULL, randomization_number, seed = NULL )
weights |
the dataframe of weights, one row correspond to a series of observation |
aggregation_factor |
the dataframe of factor to take into account for the randomization |
randomization_number |
the number of random abundance matrix to generate |
seed |
the seed of the pseudo random number generator |
a data.frame of randomization_number observations
aggregation_factor_name <- c("Year", "Bloc") weights_factor = TAD::AB[, c("Year", "Plot", "Treatment", "Bloc")] aggregation_factor <- as.data.frame( weights_factor[, aggregation_factor_name] ) random_matrix <- TAD::generate_random_matrix( weights = TAD::AB[, 5:102], aggregation_factor = aggregation_factor, randomization_number = 100, seed = 1312 ) head(random_matrix)aggregation_factor_name <- c("Year", "Bloc") weights_factor = TAD::AB[, c("Year", "Plot", "Treatment", "Bloc")] aggregation_factor <- as.data.frame( weights_factor[, aggregation_factor_name] ) random_matrix <- TAD::generate_random_matrix( weights = TAD::AB[, 5:102], aggregation_factor = aggregation_factor, randomization_number = 100, seed = 1312 ) head(random_matrix)
Launch distribution analysis
launch_analysis_tad( weights, weights_factor, trait_data, randomization_number, aggregation_factor_name = NULL, statistics_factor_name = NULL, seed = NULL, abundance_file = NULL, weighted_moments_file = NULL, stat_per_obs_file = NULL, stat_per_rand_file = NULL, stat_skr_param_file = NULL, regenerate_abundance_df = FALSE, regenerate_weighted_moments_df = FALSE, regenerate_stat_per_obs_df = FALSE, regenerate_stat_per_rand_df = FALSE, regenerate_stat_skr_df = FALSE, significativity_threshold = CONSTANTS$DEFAULT_SIGNIFICATIVITY_THRESHOLD, lin_mod = CONSTANTS$DEFAULT_LIN_MOD, slope_distance = CONSTANTS$DEFAULT_SLOPE_DISTANCE, intercept_distance = CONSTANTS$DEFAULT_INTERCEPT_DISTANCE, csv_tsv_load_parameters = list() )launch_analysis_tad( weights, weights_factor, trait_data, randomization_number, aggregation_factor_name = NULL, statistics_factor_name = NULL, seed = NULL, abundance_file = NULL, weighted_moments_file = NULL, stat_per_obs_file = NULL, stat_per_rand_file = NULL, stat_skr_param_file = NULL, regenerate_abundance_df = FALSE, regenerate_weighted_moments_df = FALSE, regenerate_stat_per_obs_df = FALSE, regenerate_stat_per_rand_df = FALSE, regenerate_stat_skr_df = FALSE, significativity_threshold = CONSTANTS$DEFAULT_SIGNIFICATIVITY_THRESHOLD, lin_mod = CONSTANTS$DEFAULT_LIN_MOD, slope_distance = CONSTANTS$DEFAULT_SLOPE_DISTANCE, intercept_distance = CONSTANTS$DEFAULT_INTERCEPT_DISTANCE, csv_tsv_load_parameters = list() )
weights |
the dataframe of weights, one row correspond to a series of observation |
weights_factor |
the dataframe which contains the different factor linked to the weights |
trait_data |
a vector of the data linked to the different factor |
randomization_number |
the number of random abundance matrix to generate |
aggregation_factor_name |
vector of factor name for the generation of random matrix |
statistics_factor_name |
vector of factor name for the computation of statistics for each generated matrix |
seed |
the seed of the pseudo random number generator |
abundance_file |
the path and name of the RDS file to load/save the dataframe which contains the observed data and the generated matrix |
weighted_moments_file |
the path and name of the RDS file to load/save the dataframe which contains the calculated moments |
stat_per_obs_file |
the path and name of the RDS file to load/save the dataframe which contains the statistics for each observed row regarding the random ones |
stat_per_rand_file |
the path and name of the RDS file to load/save the dataframe which contains the statistics for each random matrix generated |
stat_skr_param_file |
default=NULL You can provide the output to write the SKR statistics results to. |
regenerate_abundance_df |
boolean to specify if the abundance dataframe is computed again |
regenerate_weighted_moments_df |
boolean to specify if the weighted moments dataframe is computed again |
regenerate_stat_per_obs_df |
boolean to specify if the statistics per observation dataframe is computed again |
regenerate_stat_per_rand_df |
boolean to specify if the statistics per random matrix dataframe is computed again |
regenerate_stat_skr_df |
boolean to specify if the stats SKR dataframe is computed again |
significativity_threshold |
the significance threshold to consider that the observed value is in the randomized value |
lin_mod |
Indicates the type of linear model to use for (SKR): choose "lm" or "mblm" |
slope_distance |
slope of the theoretical distribution law (default: slope = 1 intercept = 1.86 skew-uniform distribution family) |
intercept_distance |
intercept of the theoretical distribution law (default: slope = 1 intercept = 1.86 skew-uniform distribution family) |
csv_tsv_load_parameters |
a list of parameters for each data structure we want to load. Each element must be named after the data structure we want to load. |
A list of the 9 following named elements:
raw_abundance_df
filtered_weights
filtered_weights_factor
filtered_trait_data
abundance_df
weighted_moments
statistics_per_observation
stat_per_rand
ses_skr
output_path <- file.path(tempdir(), "outputs") dir.create(output_path) results <- TAD::launch_analysis_tad( weights = TAD::AB[, 5:102], weights_factor = TAD::AB[, c("Year", "Plot", "Treatment", "Bloc")], trait_data = log(TAD::trait[["SLA"]]), aggregation_factor_name = c("Year", "Bloc"), statistics_factor_name = (statistics_factor_name <- c("Treatment")), regenerate_abundance_df = TRUE, regenerate_weighted_moments_df = TRUE, regenerate_stat_per_obs_df = TRUE, regenerate_stat_per_rand_df = TRUE, weighted_moments_file = file.path(output_path, "weighted_moments.csv"), stat_per_obs_file = file.path(output_path, "stat_per_obs.csv"), stat_per_rand_file = file.path(output_path, "stat_per_rand.csv"), stat_skr_param_file = file.path(output_path, "stat_skr_param.csv"), randomization_number = 20, seed = 1312, significativity_threshold = c(0.05, 0.95), lin_mod = "lm", slope_distance = ( slope_distance <- TAD::CONSTANTS$SKEW_UNIFORM_SLOPE_DISTANCE ), intercept_distance = ( intercept_distance <- TAD::CONSTANTS$SKEW_UNIFORM_INTERCEPT_DISTANCE ) ) moments_graph <- TAD::moments_graph( moments_df = results$weighted_moments, statistics_per_observation = results$statistics_per_observation, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), output_path = file.path(output_path, "moments_graph.jpeg"), dpi = 100 ) skr_graph <- TAD::skr_graph( moments_df = results$weighted_moments, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), output_path = file.path(output_path, "skr_graph.jpeg"), slope_distance = slope_distance, intercept_distance = intercept_distance, dpi = 100 ) skr_param_graph <- TAD::skr_param_graph( skr_param = results$ses_skr, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), slope_distance = slope_distance, intercept_distance = intercept_distance, save_skr_param_graph = file.path(output_path, "skr_param_graph.jpeg"), dpi = 100 ) unlink(output_path, recursive = TRUE, force = TRUE)output_path <- file.path(tempdir(), "outputs") dir.create(output_path) results <- TAD::launch_analysis_tad( weights = TAD::AB[, 5:102], weights_factor = TAD::AB[, c("Year", "Plot", "Treatment", "Bloc")], trait_data = log(TAD::trait[["SLA"]]), aggregation_factor_name = c("Year", "Bloc"), statistics_factor_name = (statistics_factor_name <- c("Treatment")), regenerate_abundance_df = TRUE, regenerate_weighted_moments_df = TRUE, regenerate_stat_per_obs_df = TRUE, regenerate_stat_per_rand_df = TRUE, weighted_moments_file = file.path(output_path, "weighted_moments.csv"), stat_per_obs_file = file.path(output_path, "stat_per_obs.csv"), stat_per_rand_file = file.path(output_path, "stat_per_rand.csv"), stat_skr_param_file = file.path(output_path, "stat_skr_param.csv"), randomization_number = 20, seed = 1312, significativity_threshold = c(0.05, 0.95), lin_mod = "lm", slope_distance = ( slope_distance <- TAD::CONSTANTS$SKEW_UNIFORM_SLOPE_DISTANCE ), intercept_distance = ( intercept_distance <- TAD::CONSTANTS$SKEW_UNIFORM_INTERCEPT_DISTANCE ) ) moments_graph <- TAD::moments_graph( moments_df = results$weighted_moments, statistics_per_observation = results$statistics_per_observation, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), output_path = file.path(output_path, "moments_graph.jpeg"), dpi = 100 ) skr_graph <- TAD::skr_graph( moments_df = results$weighted_moments, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), output_path = file.path(output_path, "skr_graph.jpeg"), slope_distance = slope_distance, intercept_distance = intercept_distance, dpi = 100 ) skr_param_graph <- TAD::skr_param_graph( skr_param = results$ses_skr, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), slope_distance = slope_distance, intercept_distance = intercept_distance, save_skr_param_graph = file.path(output_path, "skr_param_graph.jpeg"), dpi = 100 ) unlink(output_path, recursive = TRUE, force = TRUE)
load_abundance_dataframe
load_abundance_dataframe(path, ...)load_abundance_dataframe(path, ...)
path |
the path to the file to load |
... |
a set of parameters provided to load_depending_on_format may contain some operations to apply to format/cast CSV or TSV data which are almost typeless by default |
an abundance dataframe, with the column number caster
into integers and rownames casted into integers.
load_stat_skr_param
load_stat_skr_param(path, ...)load_stat_skr_param(path, ...)
path |
the path to the file to load |
... |
a set of parameters provided to load_depending_on_format may contain some operations to apply to format/cast CSV or TSV data which are almost typeless by default |
a stats SKR parameters dataframe with distance_to_family_ses and cv_distance_to_family_ses casted into doubles and with rownames casted into integers.
load_statistics_per_obs
load_statistics_per_obs(path, ...)load_statistics_per_obs(path, ...)
path |
the path to the file to load |
... |
a set of parameters provided to load_depending_on_format may contain some operations to apply to format/cast CSV or TSV data which are almost typeless by default |
a stats par observations dataframe with rownames casted into integers.
load_statistics_per_random
load_statistics_per_random(path, ...)load_statistics_per_random(path, ...)
path |
the path to the file to load |
... |
a set of parameters provided to load_depending_on_format may contain some operations to apply to format/cast CSV or TSV data which are almost typeless by default |
a stats per randon dataframe with distance_to_family and cv_distance_to_family casted into doubles and with rownames casted into integers.
load_weighted_moments
load_weighted_moments(path, ...)load_weighted_moments(path, ...)
path |
the path to the file to load |
... |
a set of parameters provided to load_depending_on_format may contain some operations to apply to format/cast CSV or TSV data which are almost typeless by default |
a weighted moments dataframe with the column number caster
into integers and rownames casted into integers.
Graph of the distributions' moments (mean, variance, skewness and kurtosis) compared to null model
moments_graph( moments_df, statistics_per_observation, statistics_factor_name, statistics_factor_name_breaks = NULL, statistics_factor_name_col = NULL, output_path = NULL, dpi = 600 )moments_graph( moments_df, statistics_per_observation, statistics_factor_name, statistics_factor_name_breaks = NULL, statistics_factor_name_col = NULL, output_path = NULL, dpi = 600 )
moments_df |
Moments data frame (mean, variance, skewness, kurtosis) |
statistics_per_observation |
SES of the Moments data frame and significance compared to null model |
statistics_factor_name |
column of data use for colors discrimination |
statistics_factor_name_breaks |
vector of factor levels of the statistics_factor_name, same dimension than statistics_factor_name_col |
statistics_factor_name_col |
vector of colors, same dimension than statistics_factor_name_breaks |
output_path |
The path to save the graph |
dpi |
The dpi number to use when we generate png/jpg graph |
A graph instance
results <- TAD::launch_analysis_tad( weights = TAD::AB[, 5:102], weights_factor = TAD::AB[, c("Year", "Plot", "Treatment", "Bloc")], trait_data = log(TAD::trait[["SLA"]]), aggregation_factor_name = c("Year", "Bloc"), statistics_factor_name = (statistics_factor_name <- c("Treatment")), randomization_number = 100 ) # if you want to display the graph graph <- TAD::moments_graph( moments_df = results$weighted_moments, statistics_per_observation = results$statistics_per_observation, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159") ) plot(graph) # if you want to save the graph as a file # either jpg, jpeg, png or svg are output_path <- file.path(tempdir(), "outputs") dir.create(output_path) TAD::moments_graph( moments_df = results$weighted_moments, statistics_per_observation = results$statistics_per_observation, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), output_path = file.path(output_path, "moment_graph.png") ) unlink(output_path, recursive = TRUE, force = TRUE)results <- TAD::launch_analysis_tad( weights = TAD::AB[, 5:102], weights_factor = TAD::AB[, c("Year", "Plot", "Treatment", "Bloc")], trait_data = log(TAD::trait[["SLA"]]), aggregation_factor_name = c("Year", "Bloc"), statistics_factor_name = (statistics_factor_name <- c("Treatment")), randomization_number = 100 ) # if you want to display the graph graph <- TAD::moments_graph( moments_df = results$weighted_moments, statistics_per_observation = results$statistics_per_observation, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159") ) plot(graph) # if you want to save the graph as a file # either jpg, jpeg, png or svg are output_path <- file.path(tempdir(), "outputs") dir.create(output_path) TAD::moments_graph( moments_df = results$weighted_moments, statistics_per_observation = results$statistics_per_observation, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), output_path = file.path(output_path, "moment_graph.png") ) unlink(output_path, recursive = TRUE, force = TRUE)
Compute different statistics (standardized by the distribution of random values).
null_model_distribution_stats( observed_value, random_values, significance_threshold = c(0.05, 0.95), remove_nas = TRUE )null_model_distribution_stats( observed_value, random_values, significance_threshold = c(0.05, 0.95), remove_nas = TRUE )
observed_value |
the observed value |
random_values |
the random Values |
significance_threshold |
the array of values used to compute the quantile (c(0.025, 0.975) by default) |
remove_nas |
boolean - tells weither to remoe NAs or not |
a list corresponding to :
the observed value
quantile values (minimum significance threshold)
quantile values (maximum significance threshold)
significance (observed value not in quantile values)
null_model_distribution_stats( observed_value = 2, random_values = c(1, 4, 5, 6, 8), significance_threshold = c(0.025, 0.975) )null_model_distribution_stats( observed_value = 2, random_values = c(1, 4, 5, 6, 8), significance_threshold = c(0.025, 0.975) )
This function provides a secured way to save abundance_dataframe
dataframe.
The more generic functionprovided by TAD save_depending_on_format expects
saves object using their name, but saves nothing if the provided name is not
correct, or mya even save an unwanted object.
This function provides a way to verify the object you want to save, and so,
it is more secured.
save_abundance_dataframe(path, object = NULL)save_abundance_dataframe(path, object = NULL)
path |
the path of the file to load |
object |
the object to save |
NULL - called for side effects
This function provides a secured way to save stat_skr_param
dataframe.
The more generic functionprovided by TAD save_depending_on_format expects
saves object using their name, but saves nothing if the provided name is not
correct, or mya even save an unwanted object.
This function provides a way to verify the object you want to save, and so,
it is more secured.
save_stat_skr_param(path, object = NULL)save_stat_skr_param(path, object = NULL)
path |
the path of the file to load |
object |
the object to save |
NULL - called for side effects
This function provides a secured way to save statistics_per_obs
dataframe.
The more generic functionprovided by TAD save_depending_on_format expects
saves object using their name, but saves nothing if the provided name is not
correct, or mya even save an unwanted object.
This function provides a way to verify the object you want to save, and so,
it is more secured.
save_statistics_per_obs(path, object = NULL)save_statistics_per_obs(path, object = NULL)
path |
the path of the file to load |
object |
the object to save |
NULL - called for side effects
This function provides a secured way to save statistics_per_random
dataframe.
The more generic functionprovided by TAD save_depending_on_format expects
saves object using their name, but saves nothing if the provided name is not
correct, or mya even save an unwanted object.
This function provides a way to verify the object you want to save, and so,
it is more secured.
save_statistics_per_random(path, object = NULL)save_statistics_per_random(path, object = NULL)
path |
the path of the file to load |
object |
the object to save |
NULL - called for side effects
This function provides a secured way to save weighted_moments
dataframe.
The more generic functionprovided by TAD save_depending_on_format expects
saves object using their name, but saves nothing if the provided name is not
correct, or mya even save an unwanted object.
This function provides a way to verify the object you want to save, and so,
it is more secured.
save_weighted_moments(path, object = NULL)save_weighted_moments(path, object = NULL)
path |
the path of the file to load |
object |
the object to save |
NULL - called for side effects
Graph of the SKR, compared to null model
skr_graph( moments_df, statistics_factor_name, statistics_factor_name_breaks = NULL, statistics_factor_name_col = NULL, slope_distance = CONSTANTS$SKEW_UNIFORM_SLOPE_DISTANCE, intercept_distance = CONSTANTS$SKEW_UNIFORM_INTERCEPT_DISTANCE, output_path = NULL, dpi = 600 )skr_graph( moments_df, statistics_factor_name, statistics_factor_name_breaks = NULL, statistics_factor_name_col = NULL, slope_distance = CONSTANTS$SKEW_UNIFORM_SLOPE_DISTANCE, intercept_distance = CONSTANTS$SKEW_UNIFORM_INTERCEPT_DISTANCE, output_path = NULL, dpi = 600 )
moments_df |
moments data frame (mean, variance, skewness, kurtosis) |
statistics_factor_name |
column of data use for colors discrimination |
statistics_factor_name_breaks |
vector of factor levels of the statistics_factor_name, same dimension than statistics_factor_name_col |
statistics_factor_name_col |
vector of colors, same dimension than statistics_factor_name_breaks |
slope_distance |
slope of the theoretical distribution law (default: slope = 1 intercept = 1.86 skew-uniform) |
intercept_distance |
intercept of the theoretical distribution law (default: slope = 1 intercept = 1.86 skew-uniform) |
output_path |
The path to save the graph |
dpi |
The dpi number to use when we generate png/jpg graph |
A graph instance
results <- TAD::launch_analysis_tad( weights = TAD::AB[, 5:102], weights_factor = TAD::AB[, c("Year", "Plot", "Treatment", "Bloc")], trait_data = log(TAD::trait[["SLA"]]), aggregation_factor_name = c("Year", "Bloc"), statistics_factor_name = (statistics_factor_name <- c("Treatment")), randomization_number = 100, slope_distance = ( slope_distance <- TAD::CONSTANTS$SKEW_UNIFORM_SLOPE_DISTANCE ), intercept_distance = ( intercept_distance <- TAD::CONSTANTS$SKEW_UNIFORM_INTERCEPT_DISTANCE ) ) graph <- TAD::skr_graph( moments_df = results$weighted_moments, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), slope_distance = slope_distance, intercept_distance = intercept_distance ) plot(graph) output_path <- file.path(tempdir(), "outputs") dir.create(output_path) TAD::skr_graph( moments_df = results$weighted_moments, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), slope_distance = slope_distance, intercept_distance = intercept_distance, dpi = 200, output_path = file.path(output_path, "moment_graph.png") ) unlink(output_path, recursive = TRUE, force = TRUE)results <- TAD::launch_analysis_tad( weights = TAD::AB[, 5:102], weights_factor = TAD::AB[, c("Year", "Plot", "Treatment", "Bloc")], trait_data = log(TAD::trait[["SLA"]]), aggregation_factor_name = c("Year", "Bloc"), statistics_factor_name = (statistics_factor_name <- c("Treatment")), randomization_number = 100, slope_distance = ( slope_distance <- TAD::CONSTANTS$SKEW_UNIFORM_SLOPE_DISTANCE ), intercept_distance = ( intercept_distance <- TAD::CONSTANTS$SKEW_UNIFORM_INTERCEPT_DISTANCE ) ) graph <- TAD::skr_graph( moments_df = results$weighted_moments, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), slope_distance = slope_distance, intercept_distance = intercept_distance ) plot(graph) output_path <- file.path(tempdir(), "outputs") dir.create(output_path) TAD::skr_graph( moments_df = results$weighted_moments, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), slope_distance = slope_distance, intercept_distance = intercept_distance, dpi = 200, output_path = file.path(output_path, "moment_graph.png") ) unlink(output_path, recursive = TRUE, force = TRUE)
Graph of the parameters computed from the SKR, compared to null model
skr_param_graph( skr_param, statistics_factor_name, statistics_factor_name_breaks = NULL, statistics_factor_name_col = NULL, slope_distance = CONSTANTS$SKEW_UNIFORM_SLOPE_DISTANCE, intercept_distance = CONSTANTS$SKEW_UNIFORM_INTERCEPT_DISTANCE, save_skr_param_graph = NULL, dpi = 600 )skr_param_graph( skr_param, statistics_factor_name, statistics_factor_name_breaks = NULL, statistics_factor_name_col = NULL, slope_distance = CONSTANTS$SKEW_UNIFORM_SLOPE_DISTANCE, intercept_distance = CONSTANTS$SKEW_UNIFORM_INTERCEPT_DISTANCE, save_skr_param_graph = NULL, dpi = 600 )
skr_param |
SES of SKR parameters data frame (SES and Significance) |
statistics_factor_name |
column of data use for colors discrimination |
statistics_factor_name_breaks |
vector of factor levels of the statistics_factor_name, same dimension than statistics_factor_name_col |
statistics_factor_name_col |
vector of colors, same dimension than statistics_factor_name_breaks |
slope_distance |
slope of the theoretical distribution law (default: slope = 1 intercept = 1.86 skew-uniform distribution family) |
intercept_distance |
intercept of the theoretical distribution law (default: slope = 1 intercept = 1.86 skew-uniform distribution family) |
save_skr_param_graph |
The path to save the graph |
dpi |
The dpi number to use when we generate png/jpg graph |
A graph instance
results <- TAD::launch_analysis_tad( weights = TAD::AB[, 5:102], weights_factor = TAD::AB[, c("Year", "Plot", "Treatment", "Bloc")], trait_data = log(TAD::trait[["SLA"]]), aggregation_factor_name = c("Year", "Bloc"), statistics_factor_name = (statistics_factor_name <- c("Treatment")), randomization_number = 100, slope_distance = ( slope_distance <- TAD::CONSTANTS$SKEW_UNIFORM_SLOPE_DISTANCE ), intercept_distance = ( intercept_distance <- TAD::CONSTANTS$SKEW_UNIFORM_INTERCEPT_DISTANCE ) ) # if you want to display the graph graph <- TAD::skr_param_graph( skr_param = results$ses_skr, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), slope_distance = slope_distance, intercept_distance = intercept_distance ) plot(graph) output_path <- file.path(tempdir(), "outputs") dir.create(output_path) # if you want to save the graph as a file # either jpg, jpeg, png or svg are TAD::skr_param_graph( skr_param = results$ses_skr, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), slope_distance = slope_distance, intercept_distance = intercept_distance, save_skr_param_graph = file.path(output_path, "skr_param_graph.jpeg"), dpi = 300 ) unlink(output_path, recursive = TRUE, force = TRUE)results <- TAD::launch_analysis_tad( weights = TAD::AB[, 5:102], weights_factor = TAD::AB[, c("Year", "Plot", "Treatment", "Bloc")], trait_data = log(TAD::trait[["SLA"]]), aggregation_factor_name = c("Year", "Bloc"), statistics_factor_name = (statistics_factor_name <- c("Treatment")), randomization_number = 100, slope_distance = ( slope_distance <- TAD::CONSTANTS$SKEW_UNIFORM_SLOPE_DISTANCE ), intercept_distance = ( intercept_distance <- TAD::CONSTANTS$SKEW_UNIFORM_INTERCEPT_DISTANCE ) ) # if you want to display the graph graph <- TAD::skr_param_graph( skr_param = results$ses_skr, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), slope_distance = slope_distance, intercept_distance = intercept_distance ) plot(graph) output_path <- file.path(tempdir(), "outputs") dir.create(output_path) # if you want to save the graph as a file # either jpg, jpeg, png or svg are TAD::skr_param_graph( skr_param = results$ses_skr, statistics_factor_name = statistics_factor_name, statistics_factor_name_breaks = c("Mown_Unfertilized", "Mown_NPK"), statistics_factor_name_col = c("#1A85FF", "#D41159"), slope_distance = slope_distance, intercept_distance = intercept_distance, save_skr_param_graph = file.path(output_path, "skr_param_graph.jpeg"), dpi = 300 ) unlink(output_path, recursive = TRUE, force = TRUE)
Compute the weighted mean, variance, skewness and kurtosis of data with given weights
weighted_mvsk(data, weights)weighted_mvsk(data, weights)
data |
the data |
weights |
the vector or matrix of weights corresponding to the data (each row corresponding to an iteration of data) |
the list of weighted mean, variance, skewness and kurtosis of the data
weighted_mvsk( data = c(1, 2, 3), weights = matrix(data = c(1, 1, 1, 2, 1, 3), nrow = 2, ncol = 3) )weighted_mvsk( data = c(1, 2, 3), weights = matrix(data = c(1, 1, 1, 2, 1, 3), nrow = 2, ncol = 3) )