compareME {qualV} | R Documentation |
Various deviance measures are computed allowing the user to find the aspects in which two time series differ.
compareME(o, p, o.t = seq(0, 1, length.out = length(o)), p.t = seq(0, 1, length.out = length(p)), ignore = c("raw", "centered", "scaled", "ordered"), geometry = c("real", "logarithmic", "geometric", "ordinal"), measure = c("mad", "var", "sd"), type = "normalized", time = "fixed", ..., col.vars=c("time", "ignore") )
o |
vector of observed values |
p |
vector of predicted values |
o.t |
vector of observation times |
p.t |
vector of times for predicted values |
ignore |
a sublist of "raw", "centered", "scaled",
"ordered" as defined in generalME to specify
the aspects of the data to be ignored. |
geometry |
a sublist of "real", "logarithmic", "geometric",
"ordinal" as defined in generalME to specify the
geometry of the observed data. |
measure |
a sublist of "mad", "var", "sd" to specify the
type of error to be measured. |
type |
a sublist of code{"dissimilarity", "normalized",
"similarity", "reference"} as defined in generalME
to specify the type of deviance measure to be used. |
time |
a sublist of "fixed", "transform" , indicates wether
the time should actually be transformed. If this argument and the
time arguments are missing the comparison is based on values only
without time matching. |
... |
further arguments passed to timeTransME |
col.vars |
a sublist of "ignore", "geometry", "measure",
"time" to be displayed in the columns of the resulting ftable . |
The function provides a simple standard interface to get a first idea on the similarities and dissimilarities of two time series spanning the same time interval.
The result is a list of ftable
s containing the deviance
measures of all requested combinations of parameters. The list is done
over the different types of measures requested.
# a constructed example x <- seq(0, 2*pi, 0.1) y <- 5 + sin(x) # a process o <- y + rnorm(x, sd=0.2) # observation with random error p <- y + 0.1 # simulation with systematic bias os <- ksmooth(x, o, kernel="normal", bandwidth=dpill(x, o), x.points = x)$y plot(x, o); lines(x, p); lines(x, os, col="red") compareME(o, p) compareME(os, p) # observed and measured data with non-matching time intervals data(phyto) compareME(obs$y, sim$y, obs$t, sim$t, time = "fixed") tt <- timeTransME(obs$y, sim$y, obs$t, sim$t, ME = SMSLE, trials = 5) compareME(tt$yo, tt$yp) # show names of deviance measures compareME(type = "name")