options(tidyverse.quiet = TRUE); library(tidyverse)
library(rtrim)
## Welcome to rtrim 2.0.6 Type ?`rtrim-package` to get started.
##
## Attaching package: 'rtrim'
## The following object is masked from 'package:stats':
##
## heatmap
kauk <- tibble::tribble(
~lokalitet, ~g2010, ~g2011, ~g2012, ~g2013, ~g2014, ~g2015, ~g2016, ~g2017, ~g2018,
"Veli Rutvenjak", 15, NA, NA, 20, NA, 30, 30, 30, 10,
"Srednji Lukovac", 30, NA, NA, 30, 30, 30, 30, 30, 40,
"Mali Maslovnjak", 10, 30, 30, 30, 30, 30, 30, 30, 40,
"Veli Maslovnjak", 10, 30, 30, 30, 30, 30, 30, 30, 15,
"Vlašnik", NA, 2, 2, 5, 5, 5, 5, 5, 20,
"Bratin", NA, 30, 30, 30, 30, 30, 30, 30, 30,
"Kručica", 10, NA, NA, NA, NA, NA, NA, NA, 30,
"Petrovac", 20, NA, NA, NA, NA, NA, NA, NA, 25,
"Zaklopatica", 20, NA, NA, NA, NA, NA, NA, NA, 20,
"Kopište", 20, NA, NA, NA, NA, NA, NA, NA, 10,
"Sušac", 50, NA, NA, NA, NA, NA, NA, NA, 50,
"Sveti Andrija", 1000, NA, 560, NA, NA, NA, NA, NA, 560,
"hrid Kamik", NA, NA, NA, NA, NA, NA, NA, NA, 10,
"Biševo", 50, NA, NA, NA, NA, NA, NA, NA, 5,
"Vis (Greben)", NA, NA, NA, NA, NA, NA, NA, NA, 5,
"Jabuka", NA, NA, NA, NA, NA, NA, NA, NA, 10,
"Velika Palagruža", NA, NA, 65, NA, NA, NA, NA, NA, 70,
"Mala Palagruža", NA, NA, 60, NA, NA, NA, NA, NA, 60
) %>%
gather(key = "godina", value = "brojnost", g2010:g2018) %>%
mutate(year = as.numeric(sub("g", "", godina))) %>%
select(site = lokalitet, year, count = brojnost)
kauk_bs <- kauk %>% filter(site != "Sveti Andrija")
# Model 3 -----------------------------------------------------------------
# Sve kolonije
m3 <- trim(kauk, model = 3)
# Bez Sveca
m3_bs <- trim(kauk_bs, model = 3)
now_what(m3)
## Model 3 has a bad fit (0 < 0.05); Try a different model.
now_what(m3_bs)
## Model 3 has a bad fit (4.61187e-13 < 0.05); Try a different model.
# Model 2 -----------------------------------------------------------------
# Sve kolonije
m2 <- trim(kauk, model = 2)
plot(overall(m2))
# Bez Sveca
m2_bs <- trim(kauk_bs, model = 2)
plot(overall(m2_bs))
# Interpretacija (Model 2)
# Sve kolonije
overall(m2)
## from upto add se_add mul se_mul p
## 2010 2018 -0.03240867 0.005090018 0.9681109 0.004927701 0.0003789813
## meaning
## Moderate decrease (p<0.01)
summary(m2)
## Call:
## rmarkdown::render("/home/mzec/Documents/Code/cjevonosnice/kauk-trim.R",
## encoding = "UTF-8", output_format = "html_document")
##
## Model : 2
## Method : ML (Convergence reached after 3 iterations)
##
## Coefficients:
## from upto add se_add mul se_mul
## 1 2010 2018 -0.03415515 0.005187895 0.9664216 0.005013693
##
##
## Goodness of fit:
## Chi-square = 278.20, df=50, p=0.0000
## Likelihood Ratio = 279.19, df=50, p=0.0000
## AIC (up to a constant) = 179.19
# Bez Sveca
overall(m2_bs)
## from upto add se_add mul se_mul p meaning
## 2010 2018 0.01170474 0.008494559 1.011774 0.008594569 0.2106654 Stable
summary(m2_bs)
## Call:
## rmarkdown::render("/home/mzec/Documents/Code/cjevonosnice/kauk-trim.R",
## encoding = "UTF-8", output_format = "html_document")
##
## Model : 2
## Method : ML (Convergence reached after 3 iterations)
##
## Coefficients:
## from upto add se_add mul se_mul
## 1 2010 2018 0.01153819 0.008349906 1.011605 0.008446807
##
##
## Goodness of fit:
## Chi-square = 141.98, df=48, p=0.0000
## Likelihood Ratio = 148.05, df=48, p=0.0000
## AIC (up to a constant) = 52.05