Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(fig.width = 7)
# To avoid startup messages when loading epiphy hereinafter:
suppressPackageStartupMessages(library(epiphy))
vers <- packageVersion("epiphy")
## ----load_pkg, message=FALSE, warning=FALSE, eval=FALSE-----------------------
# install.packages("devtools") # If not already installed.
# devtools::install_github("chgigot/epiphy") # Note: Same command for the updates.
# library(epiphy)
## ----load_data_sets-----------------------------------------------------------
str(arthropods)
str(tomato_tswv$field_1929)
## ----create_intensity---------------------------------------------------------
# Count data
# We will use only the last assessment date for the arthropods data set:
arthropods_t6 <- arthropods[arthropods$t == 3, ]
# - Explicit mapping:
(cou_t3 <- count(arthropods_t6, mapping(x = x, y = y, t = t, i = i)))
# - Total implicit mapping:
cou_t3_bis <- count(arthropods_t6)
# - Partial implicit mapping:
cou_t3_ter <- count(arthropods_t6, mapping(i = i))
all(identical(cou_t3, cou_t3_bis), identical(cou_t3, cou_t3_ter))
# Implicit mapping for incidence data:
(inc <- incidence(tomato_tswv$field_1929))
## ----plot_count---------------------------------------------------------------
plot(cou_t3, tile = FALSE, size = 5)
## ----utilities_intensity, fig.show = "hold"-----------------------------------
inc9 <- clump(inc, unit_size = c(x = 3, y = 3))
plot(inc)
plot(inc9)
## ----fig.width = 3, fig.show = "hold"-----------------------------------------
inc9_t1 <- split(inc9, by = "t")[[1]]
inc9_t1_sub <- split(inc9_t1, unit_size = c(x = 4, y = 5))[[6]]
plot(inc9_t1)
plot(inc9_t1_sub)
## ----agg_idx------------------------------------------------------------------
(inc9_t1_idx <- agg_index(inc9_t1))
## ----agg_idx_test-------------------------------------------------------------
chisq.test(inc9_t1_idx)
z.test(inc9_t1_idx)
calpha.test(inc9_t1_idx)
## ----fit_distributions, warning=FALSE, fig.width=3, fig.show = "hold"---------
cou_t3_distr <- fit_two_distr(cou_t3)
summary(cou_t3_distr)
inc9_t1_distr <- fit_two_distr(inc9_t1)
summary(inc9_t1_distr)
plot(cou_t3_distr, breaks = 17)
plot(inc9_t1_distr)
## ----power_laws, fig.width=3, fig.show = "hold"-------------------------------
cou <- count(arthropods[arthropods$x <= 6, ])
cou <- split(cou, unit_size = c(x = 3, y = 3))
cou_plaw <- power_law(cou)
coef(summary(cou_plaw))
inc9_spl <- split(inc9, unit_size = c(x = 4, y = 5))
inc_plaw <- power_law(inc9_spl)
coef(summary(inc_plaw))
plot(cou_plaw)
plot(inc_plaw)
## ----threshold_function, fig.width = 3, fig.show = "hold"---------------------
plot(inc9_t1)
plot(threshold(inc9_t1))
## ----spatial_hierarchies------------------------------------------------------
inc_low <- split(inc9, unit_size = c(x = 4, y = 5, t = 1))
inc_high <- lapply(inc_low, threshold)
(inc_sphier <- spatial_hier(inc_low, inc_high))
plot(inc_sphier)
## ----sadie, fig.height = 5, fig.show = "hold"---------------------------------
set.seed(123)
cou_t3_m <- remap(cou_t3, mapping(x = xm, y = ym))
plot(cou_t3_m)
res <- sadie(cou_t3_m)
summary(res)
plot(res)
plot(res, isoclines = TRUE)
## ----mapcomp, fig.height = 5--------------------------------------------------
set.seed(123)
res <- mapcomp(cou_t3_m, delta = 4, bandwidth = 60)
res
plot(res)
## ----pipe_analyses, warning=FALSE---------------------------------------------
library(epiphy)
library(magrittr)
incidence(tomato_tswv$field_1929) %>%
split(by = "t") %>%
getElement(1) %>% # To keep the first assessment time.
clump(unit_size = c(x = 3, y = 3)) %>%
fit_two_distr() %T>%
plot() %>%
summary()
## ----without_pipes, eval=FALSE------------------------------------------------
# my_data <- incidence(tomato_tswv$field_1929)
# my_data <- split(my_data, by = "t")
# my_data <- my_data[[1]]
# my_data <- clump(my_data, unit_size = c(x = 3, y = 3))
# my_res <- fit_two_distr(my_data)
# plot(my_res)
# summary(my_res)
## ----pipes2, warning=FALSE----------------------------------------------------
count(arthropods) %>%
clump(unit_size = c(x = 3, y = 3)) %>%
split(by = "t") %>%
lapply(agg_index) %T>%
(function(x) plot(sapply(x, function(xx) xx$index), type = "b",
xlab = "Observation sequence",
ylab = "Aggregation index")) %>%
sapply(function(x) chisq.test(x)$p.value)
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