lmomPi (version 0.6.6)

cdf: Generic function for cdf...: probability distribution fitting with L-Moments.

Description

These functions compute value(s) of cumulated probability or SPI-like (normal standardize) index from a sample or time series of x.

Usage

cdf(
  para,
  x,
  probability_distribution_attrname = "probability_distrib",
  indices = NULL,
  return.as.spi = FALSE,
  spi.scale = NA,
  distrib = NA,
  ...
)
spi.cdf(x, para, ...)
cdf.spi(x, para, ...)

Value

A vector of cumulated probability value(s) or SPI-like Gaussianized values. It is a list of vectors in case of several probability parametric distribution functions (i.e. para is a list and length(para)>1).

Arguments

x, para, ...

L-moments and further parameters for cdf... and cdf

probability_distribution_attrname

attribute name for probability distribution

indices

vector of string working as factors or indices, e g. the month names or similar. It must be of the same length of x or the length equal to 1 other NULL, if not used. If used, it computes cdf for each factor.

return.as.spi

logical parameter. Default is FALSE. If it is TRUE probability value is transformed to a normalized random variable through standard qnorm, as for Standard Precipitation Index (SPI) (https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-index-spi).

spi.scale

integer value or NA. If it greater than 1 x is filtered with the sum of a generic element of x and the previous spi.scale-1 ones (e.g. SPI-3,SPI-6, etc. ). Default is NA (no filtering) which is equivalent to spi.scale=1.

distrib

character string indicating the probability distribution, it can be used in case para has no attributes. Default is NA and distribution info are all passed through para.

See Also

pel,cdfexp,cdfgam,cdfgev,cdfglo,

cdfgpa,cdfgno,cdfgum,cdfkap,cdfln3,cdfnor,cdfpe3,cdfwak,cdfwei

Examples

# Sample L-moments of Ozone from the airquality data
data(airquality)
lmom <- samlmu(airquality$Ozone,nmom=6)

distrib <- c("exp","gam","gev","glo","gpa","gno","gum","kap",
"ln3","nor","pe3","wak","wei")

para_list <- pel(distrib=distrib,lmom=lmom)
cdf_list <- cdf(para=para_list,x=airquality$Ozone)

cdf_gam <- cdf(para=para_list$gam,x=airquality$Ozone)
cdf_gam2 <- cdf(para=para_list$gam,x=airquality$Ozone,distrib="gam")

if (any(cdf_gam!=cdf_gam2,na.rm=TRUE)) stop("Any possible errors after  0.6.3 package updates!") 

# \donttest{

## Comparison with the SPI/SPEI algorithms:  'SPEI::spi' ('SPEI' package)

if (requireNamespace("SPEI",quietly = TRUE)) {
 library(SPEI)
 data(wichita)

 distrib_wichita <- 'pe3'
 spi.scale <- 1

 month_wichita <- sprintf("M%02d",wichita$MONTH)
 para_whichita  <- pel(x=wichita$PRCP,indices=month_wichita,distrib=distrib_wichita,
						spi.scale=spi.scale)
 spi_wichita   <- spi.cdf(x=wichita$PRCP,indices=month_wichita,para=para_whichita,
						spi.scale=spi.scale)
 spi_wichita_speipkg   <- spi(data=wichita$PRCP,distrib='PearsonIII',scale=spi.scale)
 difference <- spi_wichita-spi_wichita_speipkg$fitted
}

# }