computeMannKendall
Antonio J Perez-Luque
2021-05-18
Last updated: 2021-05-27
Checks: 7 0
Knit directory: booksn_ppm/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 78c1445 | Antonio J Perez-Luque | 2021-05-27 | update plots |
| html | d0f6854 | Antonio J Perez-Luque | 2021-05-27 | Build site. |
| Rmd | 811898c | Antonio J Perez-Luque | 2021-05-27 | reorder pine species |
| html | 53bcfcc | Antonio J Perez-Luque | 2021-05-19 | Build site. |
| Rmd | 8718616 | Antonio J Perez-Luque | 2021-05-19 | fix errors |
| html | 11251b2 | Antonio J Perez-Luque | 2021-05-18 | Build site. |
| Rmd | a00d028 | Antonio J Perez-Luque | 2021-05-18 | analysis of trends |
Calcular la tendencia temporal de la serie a nivel de parcela
library("tidyverse")
library("here")
library("sf")
library("flextable")
library("Kendall")
library("ggpubr")
library("ggstatsplot")
library("DHARMa")coplas2019 <- read_csv(here::here("data/coplas2019sn.csv")) %>%
filter(sp_abrev != "ppinea")
df <- coplas2019 %>%
filter(!is.na(especie)) %>%
dplyr::select(code, especie, `1993`:`2019`) %>%
pivot_longer(names_to = "year", values_to = "infestacion", `1993`:`2019`) - Calculamos la tendencia temporal de la serie usando el test no paramétrico Mann-Kendall (\(tau\))
- Clasificamos la tendencias en sig y no significativas
parcelas <- unique(df$code)
df_trend <- c()
for (i in 1:length(parcelas)) {
aux <- df %>%
filter(year > 2004) %>%
filter(code == parcelas[i]) %>% dplyr::select(infestacion)
#MK
mk <- Kendall::MannKendall(aux$infestacion)
#auxNA <- aux$infestacion[!is.na(aux$infestacion)]
#sen <- trend::sens.slope(auxNA)
out <- data.frame(code = parcelas[i],
mk_tau = mk$tau,
mk_pvalue = mk$sl)
#sen = sen$estimates,
#sen_pvalue = sen$p.value)
df_trend <- rbind(df_trend, out)
}
mkdf <- coplas2019 %>%
dplyr::select(
code, elevF, elev_mean, especie) %>%
inner_join(df_trend) %>%
filter(!is.na(especie))
mkdf <- mkdf %>%
mutate(significant = case_when(
mk_pvalue < 0.05 ~ "sig",
TRUE ~ "nosig"
)) %>%
mutate(sig = case_when(
mk_pvalue < 0.05 ~ 1,
TRUE ~ 0))
mkdf$especie <- fct_relevel(mkdf$especie,
"P. halepensis", "P. pinaster",
"P. nigra", "P. sylvestris")- Comparación de las tendencias por especie
plot_comparaTaus <- ggstatsplot::ggbetweenstats(
data = mkdf,
x = especie,
y = mk_tau,
ylab = "Tau (Mann-Kendall)") +
ggplot2::scale_y_continuous(limits=c(-1,1)) +
ggplot2::scale_color_manual(values = colores_pinos)
null device
1 Las tendencias observadas en el nivel de infestación para cada una de las parcelas analizadas se han agrupado por especies y no se observan diferencias en cuanto a la tendencia (\(tau\) de Mann-Kendall), es decir, no se observan tendencias temporales significativamente diferentes entre especies en nuestra serie de datos.
Seguidamente analizamos las tendencias significativas.
pct_sig <- mkdf %>% group_by(especie, significant) %>%
summarise(n=n()) %>%
mutate(pct_tot = round(n/sum(n)*100,2))
pct_sig %>% flextable() %>% autofit()especie | significant | n | pct_tot |
P. halepensis | nosig | 271 | 80.90 |
P. halepensis | sig | 64 | 19.10 |
P. pinaster | nosig | 137 | 85.62 |
P. pinaster | sig | 23 | 14.37 |
P. nigra | nosig | 173 | 82.78 |
P. nigra | sig | 36 | 17.22 |
P. sylvestris | nosig | 230 | 80.70 |
P. sylvestris | sig | 55 | 19.30 |
- Entre un 15-19 % de las parcelas presentan tendencias significativas, la mayoría positivas, lo que indica un aumento de la incidencia media. Al analizar por especie vemos que P. halepensis (~19%) y P. sylvestris (~19.3%) son las que tienen mayor porcentaje de tendencias significativas.
mksig_plot <- ggplot(mkdf, aes(x=especie, y= mk_tau, shape=significant, fill=especie, colour=especie)) +
geom_point(position = position_jitter(),
size=1.5) +
scale_shape_manual(values = c(1, 19)) +
theme_bw() +
ylab("Mann-Kendall tau") + xlab("") +
theme(panel.grid = element_blank()) +
scale_color_manual(values = colores_pinos) +
geom_label(data=(pct_sig %>% filter(significant == "sig")),
aes(x=especie, y=0.9, label=
paste0(pct_tot, " %")),
fill="white", color = "black") ggsave(filename = here::here("output/comparaMKsig_especies.pdf"),
width = 6, height = 5)
print(mksig_plot)
dev.off()null device
1 - Además, destaca que esas tendencias (las significativas) son las que presentan mayor magnitud (\(\mu_{\tau}\) 0.55 - 0.59)
mkdf %>%
filter(significant == "sig") %>%
filter(mk_tau > 0) %>%
ggstatsplot::grouped_gghistostats(x=mk_tau,
grouping.var = especie)
Modelizar la tendencia en función de la Elevación
- Construimos un GLM para la tendencia (\(\tau\)) frente a la elevación
# modelo glm
model.tau <- glm(mk_tau ~ elev_mean, data = mkdf, family = "gaussian")
model.tau %>% as_flextable()Estimate | Standard Error | z value | Pr(>|z|) | Signif. | |
(Intercept) | 0.319 | 0.033 | 9.583 | 0.0000 | *** |
elev_mean | -0.000 | 0.000 | -2.545 | 0.0111 | * |
Signif. codes: 0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05 < '.' < 0.1 < '' < 1 | |||||
| |||||
(Dispersion parameter for gaussian family taken to be 0.08951858) | |||||
Null deviance: 88.93 on 988 degrees of freedom | |||||
Residual deviance: 88.35 on 987 degrees of freedom | |||||
# visualiza
tau_elev <- visreg::visreg(model.tau, gg=TRUE,
xlab = "Elevation (m)",
ylab = "Mann-Kendall tau") +
theme_bw() +
geom_point(data=(mkdf %>% filter(sig == 1)),
aes(x=elev_mean, y=mk_tau), color = "black", size=1.2)ggsave(filename = here::here("output/tau_elev.pdf"),
width = 9, height = 9, units = "cm")
print(tau_elev)
| Version | Author | Date |
|---|---|---|
| 11251b2 | Antonio J Perez-Luque | 2021-05-18 |
dev.off()null device
1 
Modelo de tendencias significativas vs elevación
- Voy a analizar solo las tau positivas
- No parece existir una relación etnre los taus posisitvo y significativos y la elevación,
tauspos <- mkdf %>% filter(mk_tau >= 0)
tp <- glm(sig ~ elev_mean, data = tauspos, family="binomial")
tp %>% as_flextable()Estimate | Standard Error | z value | Pr(>|z|) | Signif. | |
(Intercept) | -0.889 | 0.295 | -3.011 | 0.0026 | ** |
elev_mean | -0.000 | 0.000 | -1.411 | 0.1581 | |
Signif. codes: 0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05 < '.' < 0.1 < '' < 1 | |||||
| |||||
(Dispersion parameter for binomial family taken to be 1) | |||||
Null deviance: 826.8 on 792 degrees of freedom | |||||
Residual deviance: 824.8 on 791 degrees of freedom | |||||
visreg::visreg(tp,
xlab = "Elevation (m)",
ylab = "Prob. tau pos. sig.",
scale = "response",
ylim=c(0,1))
| Version | Author | Date |
|---|---|---|
| 11251b2 | Antonio J Perez-Luque | 2021-05-18 |
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] DHARMa_0.3.3.0 ggstatsplot_0.7.2 ggpubr_0.4.0 Kendall_2.2
[5] flextable_0.6.3 sf_0.9-7 here_1.0.1 forcats_0.5.1
[9] stringr_1.4.0 dplyr_1.0.4 purrr_0.3.4 readr_1.4.0
[13] tidyr_1.1.2 tibble_3.0.6 ggplot2_3.3.3 tidyverse_1.3.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 uuid_0.1-4
[3] pairwiseComparisons_3.1.3 backports_1.2.1
[5] systemfonts_1.0.0 plyr_1.8.6
[7] splines_4.0.2 gmp_0.6-2
[9] TH.data_1.0-10 kSamples_1.2-9
[11] ipmisc_5.0.2 rstantools_2.1.1
[13] digest_0.6.27 SuppDists_1.1-9.5
[15] foreach_1.5.1 htmltools_0.5.1.1
[17] magrittr_2.0.1 memoise_2.0.0
[19] doParallel_1.0.16 paletteer_1.3.0
[21] openxlsx_4.2.3 modelr_0.1.8
[23] officer_0.3.16 sandwich_3.0-0
[25] colorspace_2.0-0 rvest_0.3.6
[27] ggrepel_0.9.1 haven_2.3.1
[29] xfun_0.20 crayon_1.4.1
[31] jsonlite_1.7.2 lme4_1.1-26
[33] zeallot_0.1.0 iterators_1.0.13
[35] survival_3.2-7 zoo_1.8-8
[37] glue_1.4.2 gtable_0.3.0
[39] emmeans_1.5.4 MatrixModels_0.4-1
[41] statsExpressions_1.0.1 car_3.0-10
[43] Rmpfr_0.8-2 abind_1.4-5
[45] scales_1.1.1 mvtnorm_1.1-1
[47] DBI_1.1.1 rstatix_0.6.0
[49] PMCMRplus_1.9.0 Rcpp_1.0.6
[51] performance_0.7.2 xtable_1.8-4
[53] units_0.6-7 foreign_0.8-81
[55] httr_1.4.2 ellipsis_0.3.1
[57] farver_2.0.3 pkgconfig_2.0.3
[59] reshape_0.8.8 qgam_1.3.2
[61] multcompView_0.1-8 sass_0.3.1
[63] dbplyr_2.1.0 labeling_0.4.2
[65] effectsize_0.4.4-1 tidyselect_1.1.0
[67] rlang_0.4.10 later_1.1.0.1
[69] ggcorrplot_0.1.3 munsell_0.5.0
[71] cellranger_1.1.0 tools_4.0.2
[73] cachem_1.0.4 cli_2.3.0
[75] generics_0.1.0 broom_0.7.4
[77] evaluate_0.14 fastmap_1.1.0
[79] BWStest_0.2.2 yaml_2.2.1
[81] rematch2_2.1.2 knitr_1.31
[83] fs_1.5.0 zip_2.1.1
[85] nlme_3.1-152 WRS2_1.1-1
[87] pbapply_1.4-3 mime_0.10
[89] whisker_0.4 xml2_1.3.2
[91] correlation_0.6.1 gap_1.2.2
[93] compiler_4.0.2 rstudioapi_0.13
[95] curl_4.3 e1071_1.7-4
[97] ggsignif_0.6.0 reprex_1.0.0
[99] statmod_1.4.35 bslib_0.2.4
[101] stringi_1.5.3 highr_0.8
[103] parameters_0.13.0 gdtools_0.2.3
[105] lattice_0.20-41 Matrix_1.3-2
[107] visreg_2.7.0 nloptr_1.2.2.2
[109] classInt_0.4-3 vctrs_0.3.6
[111] pillar_1.4.7 lifecycle_1.0.0
[113] mc2d_0.1-18 jquerylib_0.1.3
[115] estimability_1.3 data.table_1.13.6
[117] insight_0.14.0 patchwork_1.1.1
[119] httpuv_1.5.5 R6_2.5.0
[121] promises_1.2.0.1 KernSmooth_2.23-18
[123] rio_0.5.16 BayesFactor_0.9.12-4.2
[125] codetools_0.2-18 boot_1.3-26
[127] MASS_7.3-53 gtools_3.8.2
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[131] withr_2.4.1 multcomp_1.4-16
[133] mgcv_1.8-33 bayestestR_0.9.0
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[137] grid_4.0.2 minqa_1.2.4
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[141] rmarkdown_2.6.6 carData_3.0-4
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[145] lubridate_1.7.10 base64enc_0.1-3