Nothing
## ----setup, include = FALSE, cache = FALSE, warning=FALSE---------------------
knitr::opts_chunk$set(
cache = FALSE,
collapse = TRUE,
comment = "#>"
)
library(phenesse)
## ----load_phenesse------------------------------------------------------------
library(phenesse)
## ----load_iNat_data-----------------------------------------------------------
inat_examples <- inat_examples
## ----Speyeria_cybele_estimates------------------------------------------------
s_cybele <- subset(inat_examples, scientific_name == "Speyeria cybele")
# calculate onset
weib_percentile(observations = s_cybele$doy, percentile = 0.01, iterations = 250)
# note that the Weibull distribution does not estimate true 0th and 100th percentiles. Therefore the user must choose a percentile (quantile) between 0 and 1.
#calculate 10th percentile
weib_percentile(observations = s_cybele$doy, percentile = 0.1, iterations = 250)
#calculate 50th percentile
weib_percentile(observations = s_cybele$doy, percentile = 0.5)
## ----Speyeria_cybele_CIestimates----------------------------------------------
s_cybele <- subset(inat_examples, scientific_name == "Speyeria cybele")
# calculate onset, we're using very low iterations and bootstraps to knit vignette quickly. Please increase both iterations and bootstraps if using for analyses
weib_percentile_ci(observations = s_cybele$doy, iterations = 10,
percentile = 0.01, bootstraps = 100)
# note warning that extreme order statistics used as endpoints. Increase number of bootstraps to avoid this warning.
## ----parallelize--------------------------------------------------------------
# parallelize the above calculation using multicore parallelization and 4 cores.
# weib_percentile_ci(observations = s_cybele$doy, iterations = 10,
# percentile = 0.01, bootstraps = 100,
# parallelize = "multicore", ncpus = 4)
# not run because having multiple cores in running in vignette gives check_rhub warnings
## ----quantile_CIestimates-----------------------------------------------------
r_hirta <- subset(inat_examples, scientific_name == "Rudbeckia hirta")
# calculate 50% quantile and CIs
quantile_ci(observations = r_hirta$doy, percentile = 0.5)
## ----mean_CIestimates---------------------------------------------------------
r_hirta <- subset(inat_examples, scientific_name == "Rudbeckia hirta")
# calculate mean and CIs
mean_ci(observations = r_hirta$doy)
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