spStat_ci | R Documentation |
### NEBAIGTA ###
spStat_ci(
obj,
FUN = mean,
label = as.character(match.call()$FUN) %if_null_or_len0% "mean"
)
spStat_ci_corr(
obj,
y = NULL,
FUN = mean,
method = c("spearman", "kendall", "pearson")[1],
use = "everything",
conf = 0.95,
R = 1000,
sim = "balanced",
type = c("norm"),
label = paste0(spMisc::fCap(method), "'s corr. coeff.")
)
ggplot_ci_rez(rez, linetype = 1)
obj |
hyperSpec object. |
FUN |
a function that takes a vector and results in a single number, e.g., mean, median, etc. |
label |
(string) a label for function to be used as column name. |
y |
a vector. |
method |
a character string indicating which correlation
coefficient (or covariance) is to be computed. One of
|
use |
an optional character string giving a
method for computing covariances in the presence
of missing values. This must be (an abbreviation of) one of the strings
|
conf |
A scalar or vector containing the confidence level(s) of the required interval(s). |
R |
The number of bootstrap replicates. Usually this will be a single
positive integer. For importance resampling, some resamples may use
one set of weights and others use a different set of weights. In
this case |
sim |
A character string indicating the type of simulation required.
Possible values are |
type |
A vector of character strings representing the type of intervals
required. The value should be any subset of the values
|
rez |
a rezult of function |
rez <- spStat_ci(Spectra2)
rez
qplotspc(rez)
ggplot(Spectra2) + geom_line()
data <- hy_spc2df(rez)
ggplot(data,
aes_string(x = "wl",
y = names(data)[3],
ymin = "ci_lower",
ymax = "ci_upper")
) +
geom_ribbon(alpha = 0.2) +
geom_line(linetype = 2)
ggplot(Spectra2[1:20,,300~400])
hyperSpec::aggregate(Spectra2, by = "gr", FUN = mean)
set.seed(1)
amzius <- rnorm(nrow(Spectra2))
spektrai <- Spectra2[,,400~500]
set.seed(1)
rez <- spStat_ci_corr(spektrai, y = amzius)
ggplot_ci_rez(rez)
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