Description Usage Arguments Details Value Author(s) References Examples
A basic, but fast function for calculating all polar metrics from arrays or xts objects.
1 | calc_metrics(input, t = NULL, yr_type, spc, lcut, hcut, return_vecs, sin_cos)
|
input |
Either a vector of values or a 1-column xts object
indexed with time stamps. If |
t |
An optional vector of time values (e.g., days) corresponding
to the input vector sampling points. Do not use this argument if
|
yr_type |
Argument specifying either 'cal_yr' for output (of timing variables) given in days starting from Jan. 1, or 'rot_yr' for output in days starting from the average seasonal minimum. |
spc |
Integer value specifying the number of samples per cycle (measurements per year) in input. |
lcut |
Numeric value in the range [0,0.5] passed to
|
hcut |
Numeric value in the range (0.5,1] passed to
|
return_vecs |
logical argument specifying whether or not to include all of the horizontal and vertical component vectors in output. |
sin_cos |
logical argument. If TRUE then each timing metric (es, ms, etc.) is returned as its sine and cosine components, es is returned as es_sin and es_cos. |
calc_metrics
runs through the entire polar
transformation process and conveniently outputs the final polar
metrics for all years included in the input.
Returns a list with all of the derived polar metrics (e.g.,
early season, mid season, etc.). Timing variables in output can
be returned relative to the standard calendar year or rotated to
a relative year using yr_type
argument.
Sine and cosine components can also be returned instead of days since
start using sin_cos
argument.
The optional argument return_vecs
can be used to add
the actual vecturs used in deriving the polar metrics to the returned
list. This will also return the overall average (all years) statistics
of the resultant vector (rv) and its opposite the anti-vector (av).
Table below indicates the variable components of each object within
the returned list.
metrics (data frame): year,
es (or es_sin, es_cos), # Early ssn DOY
ems (or ems_sin, ems_cos), # Early-mid ssn DOY
ms (or ms_sin, ms_cos), # Mid ssn DOY
lms (or lms_sin, lms_cos), # Late-mid ssn DOY
ls (or ls_sin, ls_cos), # Late ssn DOY
s_intv, # Season length(days)
s_avg, # Avg data val in ssn
s_sd, # StDev of data dur ssn
s_mag, # Mag of avg vec in ssn
ems_mag, # Mag early-mid ssn vec
lms_mag, # Mag late-mid ssn vec
a_avg # Avg data val of yr
component_vectors (data frame): VX, VY # Hrz, vert vec comp.
average_vectors (data frame): rv_idx, rv_ang, # Resultant vector
rv_doy, rv_mag, # attributes
av_idx, av_ang, av_doy # Anti-vector attr.
Bjorn J. Brooks, Danny C. Lee, William W. Hargrove, Lars Y. Pomara
Brooks, B.J., Lee, D.C., Desai, A.R., Pomara, L.Y., Hargrove, W.W. (2017). Quantifying seasonal patterns in disparate environmental variables using the PolarMetrics R package.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | library(PolarMetrics)
library(xts)
input <- xts(mndvi$fef, as.Date(mndvi$date))
### Calculate polar measures relative to calendar year
calc_metrics(input, yr_type='cal_yr', spc=46, lcut=0.15, hcut=0.8,
return_vecs=FALSE, sin_cos=FALSE)
### Calculate as above and return sine, cosine components of timing metrics
calc_metrics(input, yr_type='cal_yr', spc=46, lcut=0.15, hcut=0.8,
return_vecs=FALSE, sin_cos=TRUE)
### Calculate & return the average vectors for the entire time series
calc_metrics(input, yr_type='cal_yr', spc=46, lcut=0.15, hcut=0.8,
return_vecs=TRUE, sin_cos=FALSE)$avg_vectors
### Calculate & return the horizontal and vertical vector components
head(calc_metrics(input, yr_type='cal_yr', spc=46, lcut=0.15, hcut=0.8,
return_vecs=TRUE, sin_cos=FALSE)$vectors)
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