Description Usage Arguments Details Value References Examples
This function is a wrapper allowing to run a complete MAPI analysis.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 |
samples |
a data.frame with names and geographical coordinates of samples. Column names must be: 'ind', 'x', 'y'. Optional column 'errRad' with an error radius for sample locations (eg. GPS uncertainty). Coordinates must be projected (not latitude/longitude). |
metric |
a data.frame or a square matrix with the pairwise metric computed for all pairs of samples. If data.frame, column names must be: 'ind1', 'ind2', 'value'. If matrix, sample names must be the row- and column names. |
crs |
coordinate reference system: integer with the EPSG code, or character with proj4string. When using dummy coordinates (eg. simulation output) you may use EPSG:3857 for example. This allows computation but, of course, has no geographical meaning. |
isMatrix |
Boolean. Depends on the 'metric' data: |
beta |
A value depending on spatial regularity of sampling: 0.5 for regular sampling, 0.25 for random sampling (Hengl, 2006). |
ecc |
ellipse eccentricity value (0.975 by default). |
buf |
optional. This parameter allows to expand or shrink the grid by a number of units in the same reference system as the sample geographical coordinates (0 by default). |
errRad |
global error radius for sample locations (same radius for all samples, 10 by default).
Units are in the same reference system as the sample geographical coordinates.
To use different error radius values for sample locations, add a column 'errRad' in the 'sample' data (see |
nbPermuts |
number of permutations of sample locations (0 by default). |
dMin |
minimum distance between individuals. 0 by default. |
dMax |
maximal distance between individuals. +Inf by default. |
nbCores |
number of CPU cores you want to use during parallel computation. The default value is estimated as the number of available cores minus 1, suitable for a personal computer. On a cluster you might have to set it to a reasonable value (eg. 8) in order to keep resources for other tasks. |
N |
number of points used per quarter of ellipse, 8 by default. Don't change it unless you really know what you are doing. |
Following functions are called by MAPI_RunAuto
in following order:
MAPI_CheckData
cleans the dataset;
MAPI_GridAuto
generates a grid of hexagons by calling MAPI_EstimateHalfwidth
then MAPI_GridHexagonal
;
MAPI_RunOnGrid
performs the MAPI analysis.
NOTE: The call to MAPI_Tails
is not included.
It should be done afterwards on the object returned by MAPI_RunAuto
.
a spatial object of class 'sf' providing for each cell:
gid: Cell ID
x and y coordinates of cell center
nb_ell: number of ellipses used to compute the weighted mean
avg_value: weighted mean of the pairwise metric
sum_wgts: sum of weights of ellipses used to compute the weighted mean
w_stdev: weighted standard deviation of the pairwise metric
swQ: percentile of the sum of weights
geometry
When permutations are performed:
proba: proportion of the permuted weighted means below the observed weighted mean
ltP: lower-tail p-value adjusted using the FDR procedure of Benjamini and Yekutieli
utP: upper-tail p-value adjusted using the FDR procedure of Benjamini and Yekutieli
Benjamini, Y. & Yekutieli, D. (2001) The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29, 1165–1188.
1 2 3 4 5 6 7 8 9 10 11 12 | ## Not run:
data("metric")
data("samples")
# Run a MAPI analysis without permutation
my.results <- MAPI_RunAuto(samples, metric, crs=3857, beta=0.5, nbPermuts=0)
# eg. Export results to shapefile "myFirstMapiResult" in current directory
# to further visualize and customize the MAPI plot in SIG software.
library(sf)
st_write(my.results, dsn=".", layer="myFirstMapiResult", driver="ESRI Shapefile")
## End(Not run)
|
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