RMixtComp (version 4.1.4)

CanadianWeather: Canadian average annual weather cycle

Description

Daily temperature and precipitation at 35 different locations in Canada averaged over 1960 to 1994. Data from fda package.

Usage

data(CanadianWeather)

Format

A list containing 5 elements:

• tempav: a matrix of dimensions (365, 35) giving the average temperature in degrees celsius for each day of the year.

• precav: a matrix of dimensions (365, 35) giving the average rainfall in millimeters for each day of the year.

• time: sequence from 1 to 365.

• coordinates: a matrix giving 'N.latitude' and 'W.longitude' for each place.

• region: Which of 4 climate zones contain each place: Atlantic, Pacific, Continental, Arctic.

See Also

Other data: prostate, simData, titanic

Examples

# \donttest{
data(CanadianWeather)

# convert functional to MixtComp format
dat <- list(
    tempav = apply(
        CanadianWeather$tempav, 2,
        function(x) createFunctional(CanadianWeather$time, x)
    ),
    precav = apply(
        CanadianWeather$precav, 2,
        function(x) createFunctional(CanadianWeather$time, x)
    )
)

# create model with 4 subregressions ans 2 coefficients per regression
model <- list(
    tempav = list(type = "Func_CS", paramStr = "nSub: 4, nCoeff: 2"),
    precav = list(type = "Func_CS", paramStr = "nSub: 4, nCoeff: 2")
)

# create algo
algo <- createAlgo()

# run clustering
resLearn <- mixtCompLearn(dat, model, algo, nClass = 2:4, criterion = "ICL", nRun = 3, nCore = 1)

summary(resLearn)

plot(resLearn)

getPartition(resLearn)
getTik(resLearn, log = FALSE)
# }