Compare direction of change across raw, FDR-BH, and FDR-BKY
Source:R/fdr.R
fdr_direction_summary.RdA single table with one row per correction method, so you can see how the significant-increase/decrease counts shrink (or don't) as the correction gets stricter.
Usage
fdr_direction_summary(
trend,
fdr_result,
slope = NULL,
methods = c("raw", "BH", "BKY", "BY"),
path = NULL
)Arguments
- trend
The
$statsfield oftrend_test()'s output.- fdr_result
Output of
fdr_correction(), run ontrend$stats$p.- slope
Optional single-layer
SpatRaster(e.g.slope_estimator()'s own$slope) whose sign determines direction instead of the trend test's own statistic.NULL(default): usetrend's ownSm/S/beta, matchingdirection_map()'s own default.- methods
Character vector of methods to include, matching whichever rejection vectors are present in
fdr_result(c("raw", "BH", "BKY", "BY")by default – methods not present infdr_resultare skipped with a message, not an error).- path
Character or
NULL. If supplied, write the table to this CSV path.
Value
Invisibly, a data frame with one row per method:
n_increase, n_decrease, n_not_significant, pct_increase,
pct_decrease.
Details
Function type: Reporting/derived function – summarises or plots the output of another function; it does not compute any new statistic.
References
This combination of FDR-corrected significance with trend direction is this package's own contribution, not from an external method paper; cited here as the source of the overall TST workflow it belongs to:
Gutiérrez-Hernández, O. and García, L.V. (2025) Uncovering true significant trends in global greening. Remote Sensing Applications: Society and Environment, 37, 101377. doi:10.1016/j.rsase.2024.101377
Underlying theoretical justification for the FDR-BH assumption this significance is based on:
Benjamini, Y., & Yekutieli, D. (2001) The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29(4), 1165-1188. doi:10.1214/aos/1013699998 Not exported. Same reasoning as
fdr_direction_plot(): it takestrend/fdr_resultas two separate objects rather than one classed object, so there is nothing for a single S3 method to dispatch on. Unlike every other function internalised alongside it, no S3 method calls this one either – it has no reporting/derived function equivalent left reachable at all from outside the package (other than:::).direction_map()(also not exported) plus your own tabulation is the closest standalone alternative.
See also
Other FDR correction functions:
fdr_bh(),
fdr_bky(),
fdr_by(),
fdr_comparison_barplot(),
fdr_correction(),
fdr_direction_plot(),
fdr_pvalue_histogram(),
fdr_significance_maps(),
fdr_summary(),
fdr_threshold_plot()
Examples
r <- read_ordered_stack(example_data("vhp_ndvi"))
#> Temporal order auto-detected with pattern '(19[0-9]{2}|20[0-9]{2})'.
#> Automatic mode: order detected from file names. For higher reliability -- especially if the series is not annual -- supplying 'files' explicitly (with 'time' or 'cycle_type') is recommended. See ?read_ordered_stack.
#> Temporal order verification (mandatory, cannot be skipped):
#> stack_position detected_number file
#> 1 1982 VHP_SMN_annual_ndvi_1982.tif
#> 2 1983 VHP_SMN_annual_ndvi_1983.tif
#> 3 1984 VHP_SMN_annual_ndvi_1984.tif
#> 4 1985 VHP_SMN_annual_ndvi_1985.tif
#> 5 1986 VHP_SMN_annual_ndvi_1986.tif
#> 6 1987 VHP_SMN_annual_ndvi_1987.tif
#> 7 1988 VHP_SMN_annual_ndvi_1988.tif
#> 8 1989 VHP_SMN_annual_ndvi_1989.tif
#> 9 1990 VHP_SMN_annual_ndvi_1990.tif
#> 10 1991 VHP_SMN_annual_ndvi_1991.tif
#> 11 1992 VHP_SMN_annual_ndvi_1992.tif
#> 12 1993 VHP_SMN_annual_ndvi_1993.tif
#> 13 1994 VHP_SMN_annual_ndvi_1994.tif
#> 14 1995 VHP_SMN_annual_ndvi_1995.tif
#> 15 1996 VHP_SMN_annual_ndvi_1996.tif
#> 16 1997 VHP_SMN_annual_ndvi_1997.tif
#> 17 1998 VHP_SMN_annual_ndvi_1998.tif
#> 18 1999 VHP_SMN_annual_ndvi_1999.tif
#> 19 2000 VHP_SMN_annual_ndvi_2000.tif
#> 20 2001 VHP_SMN_annual_ndvi_2001.tif
#> 21 2002 VHP_SMN_annual_ndvi_2002.tif
#> 22 2003 VHP_SMN_annual_ndvi_2003.tif
#> 23 2004 VHP_SMN_annual_ndvi_2004.tif
#> 24 2005 VHP_SMN_annual_ndvi_2005.tif
#> 25 2006 VHP_SMN_annual_ndvi_2006.tif
#> 26 2007 VHP_SMN_annual_ndvi_2007.tif
#> 27 2008 VHP_SMN_annual_ndvi_2008.tif
#> 28 2009 VHP_SMN_annual_ndvi_2009.tif
#> 29 2010 VHP_SMN_annual_ndvi_2010.tif
#> 30 2011 VHP_SMN_annual_ndvi_2011.tif
#> 31 2012 VHP_SMN_annual_ndvi_2012.tif
#> 32 2013 VHP_SMN_annual_ndvi_2013.tif
#> 33 2014 VHP_SMN_annual_ndvi_2014.tif
#> 34 2015 VHP_SMN_annual_ndvi_2015.tif
#> 35 2016 VHP_SMN_annual_ndvi_2016.tif
#> 36 2017 VHP_SMN_annual_ndvi_2017.tif
#> 37 2018 VHP_SMN_annual_ndvi_2018.tif
#> 38 2019 VHP_SMN_annual_ndvi_2019.tif
#> 39 2020 VHP_SMN_annual_ndvi_2020.tif
#> 40 2021 VHP_SMN_annual_ndvi_2021.tif
#> 41 2022 VHP_SMN_annual_ndvi_2022.tif
#> 42 2023 VHP_SMN_annual_ndvi_2023.tif
#> Stack built: 42 layers, 146 x 338 cells.
#> >> [read_ordered_stack()] elapsed: 0.10 s
trend <- trend_test(r, report = FALSE, verbose = FALSE)
fdr_result <- fdr_correction(trend$stats$p, report = FALSE, verbose = FALSE)
# A table version of direction_map(): cell counts and percentages
# for increase/decrease/not-significant results, raw and the selected FDR
# methods side by side (BH and BKY by default; BY when explicitly
# requested). This is a standalone internal helper with no public
# summary()/plot() wrapper of its own -- see the source of
# workflow_tst()/workflow_trends() for how the package itself
# composes this kind of table.