Nothing
## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ------------------------------------------------------------------------
library(fda)
library(NITPicker)
mat=CanadianWeather$monthlyTemp #load data - a matrix with 12 rows, representing months (time); and 35 columns, representing cities (experiments)
a=findPathF1(c(1:12), mat, 4, numPerts=500) #find a set of points that help predict the shape of the curve
print(a) #indices of months to select for follow-up experiments
print(rownames(CanadianWeather$monthlyTemp)[a]) #month names selected
## ------------------------------------------------------------------------
library(fda)
library(NITPicker)
mat=CanadianWeather$monthlyTemp #load data - a matrix with 12 rows, representing months (time); and 35 columns, representing cities (experiments)
y=CanadianWeather$monthlyTemp[,"Resolute"]
a=findPathF2(c(1:12), y, mat, 4, numPerts=500) #find a set of points that help predict the shape of the curve
print(a) #indices of months to select for follow-up experiments
print(rownames(CanadianWeather$monthlyTemp)[a])
## ------------------------------------------------------------------------
library(fda)
library(NITPicker)
#Set up data:
atlanticCities=which(CanadianWeather$region[as.character(colnames(CanadianWeather$monthlyTemp))]=="Atlantic")
matAtlantic=CanadianWeather$monthlyTemp[, names(atlanticCities)]
continentalCities=which(CanadianWeather$region[as.character(colnames(CanadianWeather$monthlyTemp))]=="Continental")
matContinental=CanadianWeather$monthlyTemp[, names(continentalCities)]
#find a set of points that helps capture the difference between Atlantic and Continental cities, normalised by the variance
a=findPathF3(c(1:12), matAtlantic, matContinental, 4, numPerts=500) #find a set of points that help predict the shape of the curve
print(a) #indices of months to select for follow-up experiments
print(rownames(CanadianWeather$monthlyTemp)[a]) #month names selected
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