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") ) ## S3 method for class 'compareME': print(object, ..., digits = 3) ## S3 method for class 'compareME': summary(object, ...)
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 subset of c("raw", "centered", "scaled",
"ordered") as defined in generalME to specify
the aspects of the data to be ignored. |
geometry |
a subset of c("real", "logarithmic", "geometric",
"ordinal") as defined in generalME to specify the
geometry of the observed data. |
measure |
a subset of c("mad", "var", "sd") to specify the
type of error to be measured. |
type |
a subset of c("dissimilarity", "normalized",
"similarity", "reference") as defined in generalME
to specify the type of deviance measure to be used. |
time |
a subset of c("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 subset of c("ignore", "geometry", "measure",
"time") to be displayed in the columns of the resulting ftable . |
digits |
number of significant digits displayed |
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 print
and summary
methods extract
the relevant information, rounded to an optional number of significant
digits.
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")