Main function and parameters
Here are the parameters to run the
TAD::launch_analysis_tad function, and how it is run. One
file is provided for each output. Those outputs will be written by
TAD::launch_analysis_tad and we will read the outputs in
the next sections.
The randomisation number is very low, so the code executes fast.
weights <- TAD::AB[, 5:20]
weights_factor <- TAD::AB[, c("Year", "Plot", "Treatment", "Bloc")]
trait_data <- log(TAD::trait[["SLA"]][seq_len(ncol(weights))])
aggregation_factor_name <- c("Year", "Bloc")
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
regenerate_stat_skr_df <- TRUE
randomization_number <- 20
seed <- 1312
significativity_threshold <- c(0.025, 0.975)
lin_mod <- "lm"
slope_distance <- 1
intercept_distance <- 1.86
produce_results <- function(
abundance_file,
weighted_moments_file,
stat_per_obs_file,
stat_per_rand_file,
stat_skr_param_file
) {
TAD::launch_analysis_tad(
weights = weights,
weights_factor = weights_factor,
trait_data = trait_data,
randomization_number = randomization_number,
aggregation_factor_name = aggregation_factor_name,
statistics_factor_name = statistics_factor_name,
seed = seed,
abundance_file = abundance_file,
weighted_moments_file = weighted_moments_file,
stat_per_obs_file = stat_per_obs_file,
stat_per_rand_file = stat_per_rand_file,
stat_skr_param_file = stat_skr_param_file,
regenerate_abundance_df = regenerate_abundance_df,
regenerate_weighted_moments_df = regenerate_weighted_moments_df,
regenerate_stat_per_obs_df = regenerate_stat_per_obs_df,
regenerate_stat_per_rand_df = regenerate_stat_per_rand_df,
regenerate_stat_skr_df = regenerate_stat_skr_df,
significativity_threshold = significativity_threshold,
lin_mod = lin_mod,
slope_distance = slope_distance,
intercept_distance = intercept_distance
)
}
Get CSV outputs
## We don't especially need multiprocessing
future::plan(future::sequential)
## We define the outputs.
csv_files <- c(
abundance_csv_file <- "abundance_file.csv",
weighted_moments_csv_file <- "weighted_moments_file.csv",
stat_per_obs_csv_file <- "stat_per_obs_file.csv",
stat_per_rand_csv_file <- "stat_per_rand_file.csv",
stat_skr_param_csv_file <- "stat_skr_param_file.csv"
)
## We run the analysis and provide the paths to write the results to.
results <- produce_results(
abundance_file = abundance_csv_file,
weighted_moments_file = weighted_moments_csv_file,
stat_per_obs_file = stat_per_obs_csv_file,
stat_per_rand_file = stat_per_rand_csv_file,
stat_skr_param_file = stat_skr_param_csv_file
)
## Let's see if all the files have been created: we should
## see five CSV files here
list.files(pattern = "*.csv", full.names = TRUE)
#> [1] "./abundance_file.csv" "./stat_per_obs_file.csv"
#> [3] "./stat_per_rand_file.csv" "./stat_skr_param_file.csv"
#> [5] "./weighted_moments_file.csv"
Showing CSV outputs
abundance_from_csv <- TAD::load_abundance_dataframe(
abundance_csv_file
)
head(abundance_from_csv[
c(1:3),
c(colnames(abundance_from_csv) %in% colnames(abundance_from_csv)[1:8])
])
#> number index1 index2 index3 index4 index5 index6 index7
#> 1 0 0.0000000 0.0000000 0 0 1.000000 1.000000 0
#> 2 0 0.8403361 0.8403361 0 0 1.680672 0.000000 0
#> 3 0 0.0000000 0.0000000 0 0 21.259843 1.574803 0
weighted_moments_from_csv <- TAD::load_weighted_moments(
weighted_moments_csv_file,
factor_names = c("Year", "Plot", "Bloc")
)
head(weighted_moments_from_csv, n = 3)
#> Year Plot Treatment Bloc number mean variance skewness kurtosis
#> 1 2010 6 Mown_NPK 1 0 3.312137 0.013064571 0.0000000 1.00000
#> 2 2010 13 Mown_NPK 1 0 3.183708 0.060678220 -0.8908442 2.61656
#> 3 2010 20 Mown_NPK 2 0 3.213602 0.003355466 3.4020691 12.57407
#> distance_law
#> 1 -0.86000000
#> 2 -0.03704301
#> 3 -0.86000000
stat_per_obs_from_csv <- TAD::load_statistics_per_obs(
stat_per_obs_csv_file,
factor_names = c("Year", "Plot", "Bloc")
)
head(stat_per_obs_from_csv, n = 3)
#> Year Plot Treatment Bloc standardized_observedmean
#> 1 2010 6 Mown_NPK 1 0.3307537
#> 2 2010 13 Mown_NPK 1 -0.8739049
#> 3 2010 20 Mown_NPK 2 0.1927875
#> standardized_min_quantilemean standardized_max_quantilemean significancemean
#> 1 -1.845560 1.107442 FALSE
#> 2 -1.306323 2.034754 FALSE
#> 3 -1.442063 1.079019 FALSE
#> standardized_observedvariance standardized_min_quantilevariance
#> 1 -0.36071237 -0.5709610
#> 2 -0.01502695 -2.0413751
#> 3 -0.65298390 -0.9586044
#> standardized_max_quantilevariance significancevariance
#> 1 2.078434 FALSE
#> 2 1.384701 FALSE
#> 3 1.782211 FALSE
#> standardized_observedskewness standardized_min_quantileskewness
#> 1 0.1093812 -1.1187589
#> 2 0.6625729 -1.3435337
#> 3 0.9746794 -0.9746794
#> standardized_max_quantileskewness significanceskewness
#> 1 1.6253667 FALSE
#> 2 2.1153354 FALSE
#> 3 0.9746794 FALSE
#> standardized_observedkurtosis standardized_min_quantilekurtosis
#> 1 0.00000000 0.000000
#> 2 0.09646385 -2.269381
#> 3 0.59820493 -1.625123
#> standardized_max_quantilekurtosis significancekurtosis
#> 1 1.779513 FALSE
#> 2 1.118933 FALSE
#> 3 1.934196 FALSE
stat_per_rand_from_csv <- TAD::load_statistics_per_random(
stat_per_rand_csv_file,
factor_names = c("Treatment")
)
head(stat_per_rand_from_csv, n = 3)
#> number Treatment slope intercept rsquare tad_stab
#> 1 0 Mown_NPK 1.257924 1.358449 0.8494475 2.2072484
#> 2 0 Mown_Unfertilized 1.424814 1.329256 0.8825581 0.5975996
#> 3 1 Mown_NPK 1.078927 1.230682 0.9720407 1.1807309
#> distance_to_family cv_distance_to_family
#> 1 2.4530737 300.8936
#> 2 0.7977601 169.9280
#> 3 1.3105775 266.3767
stat_skr_param_from_csv <- TAD::load_stat_skr_param(
stat_skr_param_csv_file,
character_names = c("Treatment")
)
#> Warning in load_tad_table(path = path, table_name = env_attr, sep = ",", :
#> Unknown attribute distance_to_family_ses in table stat_skr_param loaded from
#> /tmp/Rtmpgu1335/Rbuild19196d98d592/TAD/vignettes/stat_skr_param_file.csv.
#> Warning in load_tad_table(path = path, table_name = env_attr, sep = ",", :
#> Unknown attribute cv_distance_to_family_ses in table stat_skr_param loaded from
#> /tmp/Rtmpgu1335/Rbuild19196d98d592/TAD/vignettes/stat_skr_param_file.csv.
head(stat_skr_param_from_csv, n = 3)
#> slope_ses slope_signi intercept_ses intercept_signi rsquare_ses rsquare_signi
#> 1 1.341678 FALSE -0.9032159 FALSE -1.849008 TRUE
#> 2 3.112774 TRUE -1.0340836 FALSE -1.062311 FALSE
#> tad_stab_ses tad_stab_signi tad_eve_ses tad_eve_signi cv_tad_eve_ses
#> 1 1.163717 FALSE 1.432937 FALSE 0.05052686
#> 2 -1.074969 FALSE -1.049399 FALSE -1.25788001
#> cv_tad_eve_signi Treatment
#> 1 FALSE Mown_NPK
#> 2 FALSE Mown_Unfertilized
warnings()
Get tsv outputs
## We don't want multiprocessing
future::plan(future::sequential)
## We define the outputs.
tsv_files <- c(
abundance_tsv_file <- "abundance_file.tsv",
weighted_moments_tsv_file <- "weighted_moments_file.tsv",
stat_per_obs_tsv_file <- "stat_per_obs_file.tsv",
stat_per_rand_tsv_file <- "stat_per_rand_file.tsv",
stat_skr_param_tsv_file <- "stat_skr_param_file.tsv"
)
## We run the analysis and provide the paths to write the results to.
results <- produce_results(
abundance_file = abundance_tsv_file,
weighted_moments_file = weighted_moments_tsv_file,
stat_per_obs_file = stat_per_obs_tsv_file,
stat_per_rand_file = stat_per_rand_tsv_file,
stat_skr_param_file = stat_skr_param_tsv_file
)
## Let's see if all the files have been created: we should
## see five tsv files here
list.files(pattern = "*.tsv", full.names = TRUE)
#> [1] "./abundance_file.tsv" "./stat_per_obs_file.tsv"
#> [3] "./stat_per_rand_file.tsv" "./stat_skr_param_file.tsv"
#> [5] "./weighted_moments_file.tsv"
Showing tsv outputs
abundance_from_tsv <- TAD::load_abundance_dataframe(
abundance_tsv_file
)
head(abundance_from_tsv[
c(1:3),
c(colnames(abundance_from_tsv) %in% colnames(abundance_from_tsv)[1:8])
])
#> number index1 index2 index3 index4 index5 index6 index7
#> 1 0 0.0000000 0.0000000 0 0 1.000000 1.000000 0
#> 2 0 0.8403361 0.8403361 0 0 1.680672 0.000000 0
#> 3 0 0.0000000 0.0000000 0 0 21.259843 1.574803 0
weighted_moments_from_tsv <- TAD::load_weighted_moments(
weighted_moments_tsv_file,
factor_names = c("Year", "Plot", "Bloc")
)
head(weighted_moments_from_tsv, n = 3)
#> Year Plot Treatment Bloc number mean variance skewness kurtosis
#> 1 2010 6 Mown_NPK 1 0 3.312137 0.013064571 0.0000000 1.00000
#> 2 2010 13 Mown_NPK 1 0 3.183708 0.060678220 -0.8908442 2.61656
#> 3 2010 20 Mown_NPK 2 0 3.213602 0.003355466 3.4020691 12.57407
#> distance_law
#> 1 -0.86000000
#> 2 -0.03704301
#> 3 -0.86000000
stat_per_obs_from_tsv <- TAD::load_statistics_per_obs(
stat_per_obs_tsv_file,
factor_names = c("Year", "Plot", "Bloc")
)
head(stat_per_obs_from_tsv, n = 3)
#> Year Plot Treatment Bloc standardized_observedmean
#> 1 2010 6 Mown_NPK 1 0.3307537
#> 2 2010 13 Mown_NPK 1 -0.8739049
#> 3 2010 20 Mown_NPK 2 0.1927875
#> standardized_min_quantilemean standardized_max_quantilemean significancemean
#> 1 -1.845560 1.107442 FALSE
#> 2 -1.306323 2.034754 FALSE
#> 3 -1.442063 1.079019 FALSE
#> standardized_observedvariance standardized_min_quantilevariance
#> 1 -0.36071237 -0.5709610
#> 2 -0.01502695 -2.0413751
#> 3 -0.65298390 -0.9586044
#> standardized_max_quantilevariance significancevariance
#> 1 2.078434 FALSE
#> 2 1.384701 FALSE
#> 3 1.782211 FALSE
#> standardized_observedskewness standardized_min_quantileskewness
#> 1 0.1093812 -1.1187589
#> 2 0.6625729 -1.3435337
#> 3 0.9746794 -0.9746794
#> standardized_max_quantileskewness significanceskewness
#> 1 1.6253667 FALSE
#> 2 2.1153354 FALSE
#> 3 0.9746794 FALSE
#> standardized_observedkurtosis standardized_min_quantilekurtosis
#> 1 0.00000000 0.000000
#> 2 0.09646385 -2.269381
#> 3 0.59820493 -1.625123
#> standardized_max_quantilekurtosis significancekurtosis
#> 1 1.779513 FALSE
#> 2 1.118933 FALSE
#> 3 1.934196 FALSE
stat_per_rand_from_tsv <- TAD::load_statistics_per_random(
stat_per_rand_tsv_file,
factor_names = c("Treatment")
)
head(stat_per_rand_from_tsv, n = 3)
#> number Treatment slope intercept rsquare tad_stab
#> 1 0 Mown_NPK 1.257924 1.358449 0.8494475 2.2072484
#> 2 0 Mown_Unfertilized 1.424814 1.329256 0.8825581 0.5975996
#> 3 1 Mown_NPK 1.078927 1.230682 0.9720407 1.1807309
#> distance_to_family cv_distance_to_family
#> 1 2.4530737 300.8936
#> 2 0.7977601 169.9280
#> 3 1.3105775 266.3767
stat_skr_param_from_tsv <- TAD::load_stat_skr_param(
stat_skr_param_tsv_file,
character_names = c("Treatment")
)
#> Warning in load_tad_table(path = path, table_name = env_attr, sep = "\t", :
#> Unknown attribute distance_to_family_ses in table stat_skr_param loaded from
#> /tmp/Rtmpgu1335/Rbuild19196d98d592/TAD/vignettes/stat_skr_param_file.tsv.
#> Warning in load_tad_table(path = path, table_name = env_attr, sep = "\t", :
#> Unknown attribute cv_distance_to_family_ses in table stat_skr_param loaded from
#> /tmp/Rtmpgu1335/Rbuild19196d98d592/TAD/vignettes/stat_skr_param_file.tsv.
head(stat_skr_param_from_tsv, n = 3)
#> slope_ses slope_signi intercept_ses intercept_signi rsquare_ses rsquare_signi
#> 1 1.341678 FALSE -0.9032159 FALSE -1.849008 TRUE
#> 2 3.112774 TRUE -1.0340836 FALSE -1.062311 FALSE
#> tad_stab_ses tad_stab_signi tad_eve_ses tad_eve_signi cv_tad_eve_ses
#> 1 1.163717 FALSE 1.432937 FALSE 0.05052686
#> 2 -1.074969 FALSE -1.049399 FALSE -1.25788001
#> cv_tad_eve_signi Treatment
#> 1 FALSE Mown_NPK
#> 2 FALSE Mown_Unfertilized
Get rda outputs
## We don't want multiprocessing
future::plan(future::sequential)
## We define the outputs.
rda_files <- c(
abundance_rda_file <- "abundance_file.rda",
weighted_moments_rda_file <- "weighted_moments_file.rda",
stat_per_obs_rda_file <- "stat_per_obs_file.rda",
stat_per_rand_rda_file <- "stat_per_rand_file.rda",
stat_skr_param_rda_file <- "stat_skr_param_file.rda"
)
## We run the analysis and provide the paths to write the results to.
results <- produce_results(
abundance_file = abundance_rda_file,
weighted_moments_file = weighted_moments_rda_file,
stat_per_obs_file = stat_per_obs_rda_file,
stat_per_rand_file = stat_per_rand_rda_file,
stat_skr_param_file = stat_skr_param_rda_file
)
## Let's see if all the files have been created: we should
## see five rda files here
list.files(pattern = "*.rda", full.names = TRUE)
#> [1] "./abundance_file.rda" "./stat_per_obs_file.rda"
#> [3] "./stat_per_rand_file.rda" "./stat_skr_param_file.rda"
#> [5] "./weighted_moments_file.rda"
Showing rda outputs
abundance_from_rda <- TAD::load_abundance_dataframe(
abundance_rda_file
)
head(abundance_from_rda[
c(1:3),
c(colnames(abundance_from_rda) %in% colnames(abundance_from_rda)[1:8])
])
#> number index1 index2 index3 index4 index5 index6 index7
#> 1 0 0.0000000 0.0000000 0 0 1.000000 1.000000 0
#> 2 0 0.8403361 0.8403361 0 0 1.680672 0.000000 0
#> 3 0 0.0000000 0.0000000 0 0 21.259843 1.574803 0
weighted_moments_from_rda <- TAD::load_weighted_moments(
weighted_moments_rda_file
)
head(weighted_moments_from_rda, n = 3)
#> Year Plot Treatment Bloc number mean variance skewness kurtosis
#> 1 2010 6 Mown_NPK 1 0 3.312137 0.013064571 0.0000000 1.00000
#> 2 2010 13 Mown_NPK 1 0 3.183708 0.060678220 -0.8908442 2.61656
#> 3 2010 20 Mown_NPK 2 0 3.213602 0.003355466 3.4020691 12.57407
#> distance_law
#> 1 -0.86000000
#> 2 -0.03704301
#> 3 -0.86000000
stat_per_obs_from_rda <- TAD::load_statistics_per_obs(
stat_per_obs_rda_file
)
head(stat_per_obs_from_rda, n = 3)
#> Year Plot Treatment Bloc standardized_observedmean
#> 1 2010 6 Mown_NPK 1 0.3307537
#> 2 2010 13 Mown_NPK 1 -0.8739049
#> 3 2010 20 Mown_NPK 2 0.1927875
#> standardized_min_quantilemean standardized_max_quantilemean significancemean
#> 1 -1.845560 1.107442 FALSE
#> 2 -1.306323 2.034754 FALSE
#> 3 -1.442063 1.079019 FALSE
#> standardized_observedvariance standardized_min_quantilevariance
#> 1 -0.36071237 -0.5709610
#> 2 -0.01502695 -2.0413751
#> 3 -0.65298390 -0.9586044
#> standardized_max_quantilevariance significancevariance
#> 1 2.078434 FALSE
#> 2 1.384701 FALSE
#> 3 1.782211 FALSE
#> standardized_observedskewness standardized_min_quantileskewness
#> 1 0.1093812 -1.1187589
#> 2 0.6625729 -1.3435337
#> 3 0.9746794 -0.9746794
#> standardized_max_quantileskewness significanceskewness
#> 1 1.6253667 FALSE
#> 2 2.1153354 FALSE
#> 3 0.9746794 FALSE
#> standardized_observedkurtosis standardized_min_quantilekurtosis
#> 1 0.00000000 0.000000
#> 2 0.09646385 -2.269381
#> 3 0.59820493 -1.625123
#> standardized_max_quantilekurtosis significancekurtosis
#> 1 1.779513 FALSE
#> 2 1.118933 FALSE
#> 3 1.934196 FALSE
stat_per_rand_from_rda <- TAD::load_statistics_per_random(
stat_per_rand_rda_file
)
head(stat_per_rand_from_rda, n = 3)
#> number Treatment slope intercept rsquare tad_stab
#> 1 0 Mown_NPK 1.257924 1.358449 0.8494475 2.2072484
#> 2 0 Mown_Unfertilized 1.424814 1.329256 0.8825581 0.5975996
#> 3 1 Mown_NPK 1.078927 1.230682 0.9720407 1.1807309
#> distance_to_family cv_distance_to_family
#> 1 2.4530737 300.8936
#> 2 0.7977601 169.9280
#> 3 1.3105775 266.3767
stat_skr_param_from_rda <- TAD::load_stat_skr_param(
stat_skr_param_rda_file
)
head(stat_skr_param_from_rda, n = 3)
#> slope_ses slope_signi intercept_ses intercept_signi rsquare_ses rsquare_signi
#> 1 1.341678 FALSE -0.9032159 FALSE -1.849008 TRUE
#> 2 3.112774 TRUE -1.0340836 FALSE -1.062311 FALSE
#> tad_stab_ses tad_stab_signi tad_eve_ses tad_eve_signi cv_tad_eve_ses
#> 1 1.163717 FALSE 1.432937 FALSE 0.05052686
#> 2 -1.074969 FALSE -1.049399 FALSE -1.25788001
#> cv_tad_eve_signi Treatment
#> 1 FALSE Mown_NPK
#> 2 FALSE Mown_Unfertilized
RDA and loaded CSV hold the same values
They are not absolutly identical because of floaing point imprecision
inherantly due to floats representation in computers.
But, they are still equals +/- the computer’s imprecision (10e-16,
usually)
print(all.equal(abundance_from_rda, abundance_from_csv))
#> [1] TRUE
print(all.equal(weighted_moments_from_rda, weighted_moments_from_csv))
#> [1] TRUE
print(all.equal(stat_per_obs_from_rda, stat_per_obs_from_csv))
#> [1] TRUE
print(all.equal(stat_per_rand_from_rda, stat_per_rand_from_csv))
#> [1] TRUE
print(all.equal(stat_skr_param_from_rda, stat_skr_param_from_csv))
#> [1] TRUE
print(all.equal(abundance_from_rda, abundance_from_csv))
#> [1] TRUE
print(all.equal(weighted_moments_from_rda, weighted_moments_from_csv))
#> [1] TRUE
print(all.equal(stat_per_obs_from_rda, stat_per_obs_from_csv))
#> [1] TRUE
print(all.equal(stat_per_rand_from_rda, stat_per_rand_from_csv))
#> [1] TRUE
print(all.equal(stat_skr_param_from_rda, stat_skr_param_from_csv))
#> [1] TRUE