RMixtComp (version 4.0.2)

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 celcius 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

# NOT RUN {
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 3 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)

summary(resLearn)

plot(resLearn) 

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

# }
# NOT RUN {
  
# }