Description Usage Arguments Value References Examples
This function provides an empirical correlation function for data
in a CMBDataFrame
or data.frame. It assumes that data are from a stationary spherical
random field and the correlation depends only on a geodesic distance between locations.
Output is a binned correlation.
1 2 3 4 5 6 7 8 9 |
cmbdf |
is a |
num.bins |
specifies the number of bins |
sample.size |
optionally specify the size of a simple random sample to take before calculating correlation. This may be useful if the full correlation computation is too slow. |
max.dist |
an optional number between 0 and pi specifying the
maximum geodesic distance to use for calculating correlation. Only
used if |
breaks |
optionally specify the breaks manually using a
vector giving the break points between cells. This vector
has length |
equiareal |
if TRUE then the bins have equal spherical area. If false then the bins have equal annular widths. Default is TRUE. |
calc.max.dist |
if TRUE then the |
#' An object of the class CMBCorrelation that is a modification of variog
from the package geoR with variogram values replaced by correlation.
The attribute "breaks" contains the break points used to create bins.
The result has num.bins + 1
values since the first value at distance 0 is not
counted as a bin.
a vector with distances.
a vector with estimated correlation values at distances given in u.
number of pairs in each bin
standard deviation of the values in each bin
limits defining the interval spanned by each bin
a logical vector indicating whether the number of pairs in each bin is greater or equal to the value in the argument pairs.min
variance of the data
parameters of the mean part of the model fitted by ordinary least squares
echoes the option argument
maximum distance between pairs allowed in the correlation calculations
number of data
direction for which the correlation was computed
the function call
geoR package,variog
, variogramCMB
, covCMB
1 2 3 4 5 6 | ## Download the map first
# downloadCMBMap(foreground = "smica", nside = 1024)
# df <- CMBDataFrame("CMB_map_smica1024.fits")
# cmbdf <- sampleCMB(df, sample.size = 100000)
# corcmb <- corrCMB(cmbdf, max.dist = 0.03, num.bins = 10, sample.size=1000)
# corcmb
|
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