Description Usage Arguments See Also Examples
The "LSC"
class lies at the core of this package
as it describes spatio-temporal patterns in the data. It
is usually an array with the same spatio-temporal
resolution as the original dataset.
plot.LSC
plots LSC of (1+1)D and
(2+1)D systems.
plot_LSC_1plus1D
plots LSC for a (1+1)D
field.
plot_LSC_2plus1D
plots LSC for a (2+1)D
field.
plot_LSC_0plus1D
plots LSC for a (0+1)D
field, i.e., a time series.
1 2 3 4 5 6 7 8 9 10 | ## S3 method for class 'LSC'
plot(x, ...)
plot_LSC_1plus1D(z, col = NULL, lsc.unit = "bits", heights = c(2, 5), widths = c(5,
2))
plot_LSC_2plus1D(z, type = "temporal", time.frames = NULL, zlim = NULL, heights = NULL,
lsc.unit = "bits", data = NULL, col = NULL)
plot_LSC_0plus1D(z, col = NULL, lsc.unit = "bits", ...)
|
x |
an object of class |
... |
optional arguments passed to
|
widths |
passed to |
z |
an object of class |
type |
a |
time.frames |
a vector of length ≤q 6 to
indicate what frames should be displayed (only for
|
zlim |
minimum and maximum z values for which colors
should be plotted, defaulting to the range of the finite
values of |
lsc.unit |
character string (default: |
col |
colors: either a string decribing a pallette
from the |
data |
(optional) original data to compare to LSC
(relevant only for |
heights |
passed to |
plot.mixed_LICORS
,
plot_LSC_2plus1D
,
plot_LSC_1plus1D
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ## Not run:
data(contCA00)
temp_lsc <- states2LSC(states = contCA00$predictive_states -
min(contCA00$predictive_states) + 1)
class(temp_lsc) <- c("LSC", "LSC_1plus1D")
plot_LSC_1plus1D(temp_lsc)
## End(Not run)
## Not run:
data(contCA00)
temp_lsc <- states2LSC(states = contCA00$predictive_states -
min(contCA00$predictive_states) + 1)
temp_lsc_3D <- array(temp_lsc, dim = c(25, 20, 40))
class(temp_lsc_3D) <- c("LSC", "LSC_2plus1D")
plot_LSC_2plus1D(temp_lsc_3D, type = "temporal")
plot_LSC_2plus1D(temp_lsc_3D, type = "spatial")
## End(Not run)
state.sim <- rpois(100, 1)
lsc.est <- states2LSC(states = state.sim)
class(lsc.est) <- c("LSC", "LSC_0plus1D")
plot_LSC_0plus1D(lsc.est)
weights.sim <- matrix(runif(1000, 0, 1), ncol = 10)
weights.sim <- normalize(weights.sim)
lsc.est <- states2LSC(weight.matrix = weights.sim)
plot_LSC_0plus1D(lsc.est)
|
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