CanadianWeather {RMixtComp}

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.

Source

Ramsay, James O., and Silverman, Bernard W. (2006), Functional Data Analysis, 2nd ed., Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York

See Also

Other data: prostate, simData, titanic

Examples

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)