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Maps of CMK trend results

Source: R/trend_test.R
trend_maps.Rd

Function type: Reporting/derived function – summarises or plots the output of another function; it does not compute any new statistic. Not exported – called internally by report = TRUE, and reachable from outside the package via plot().

Usage

trend_maps(
  trend,
  alpha = 0.05,
  panels = c("statistic", "pvalue", "significance", "direction"),
  path = NULL
)

Arguments

trend

The $stats field of trend_test()'s output.

alpha

Numeric. Significance threshold used for the significance and direction-of-change maps. Uncorrected for multiple testing across cells – these maps are diagnostic, not a final significance result; run fdr_correction() on trend$p (and consider direction_map() instead) before reporting which cells are significant.

panels

Character vector: which of the 4 available maps to draw, any subset of c("statistic", "pvalue", "significance", "direction") (default: all four, in that order). "statistic": the raw Sm/S trend statistic (diverging). "pvalue": the uncorrected p-value (sequential). "significance": which cells cross alpha (binary). "direction": increase/decrease/no-change among the significant cells. Reachable via plot(x, panels = ...) on a trend_test() result, which forwards this argument through.

path

Character or NULL. If supplied, PNGs are written using this as a filename prefix.

Value

Invisibly, a list with significant and classes (both SpatRaster, NULL if "significance"/"direction" respectively was not in panels).

References

  • Neeti, N. and Eastman, J.R. (2011) A Contextual Mann-Kendall Approach for the Assessment of Trend Significance in Image Time Series. Transactions in GIS, 15(5), 599-611. doi:10.1111/j.1467-9671.2011.01280.x

See also

Other Contextual Mann-Kendall functions: prepare_cmk_neighbourhood(), trend_histograms(), trend_summary()

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)

# All four maps: the trend statistic, the (uncorrected) p-value, which
# cells cross alpha = 0.05, and the direction of change among those.
plot(trend)





# Just the two most commonly wanted together -- raw/binary
# significance and direction -- skipping the statistic and p-value
# panels, whose raw scale is harder to read at a glance than a
# simple yes/no or increase/decrease map.
plot(trend, panels = c("significance", "direction"))