## ----include=FALSE------------------------------------------------------------
library(knitr)
opts_chunk$set(
concordance=TRUE
)

## ----setup,include=FALSE------------------------------------------------------
orig_options <- options()
options(digits=3)
library(knitr)
opts_chunk$set(
  concordance = TRUE, 
  fig.path = 'figure/', 
  fig.width = 5, 
  fig.height = 5, 
  fig.show = 'hold', 
  fig.align = 'center'
)
knit_hooks$set(document = function(x) {
  sub('\\usepackage[]{color}', '\\usepackage{xcolor}', x, fixed = TRUE)
})
library(rotations)

## ----ex2----------------------------------------------------------------------
r <- pi/2
U <- c(0, 1 ,0)
W <- U*r
R <- as.SO3(W)
R
identical(R, as.SO3(U, r))

## ----ex3----------------------------------------------------------------------
mis.angle(R)*2/pi
mis.axis(R)

## ----ex4----------------------------------------------------------------------
as.Q4(U, r)
as.Q4(as.SO3(U, r))

## ----ex5----------------------------------------------------------------------
Rs <- ruars(n = 20, rangle = rcayley, kappa = 1, space = 'SO3')
Qs <- ruars(n = 20, rangle = rcayley, kappa = 1, space = 'Q4')
Rs <- ruars(n = 20, rangle = rcayley, nu = 1, space = 'SO3')
Qs <- ruars(n = 20, rangle = rcayley, nu = 1, space = 'Q4')
head(Rs,3)

## ----gridsearch---------------------------------------------------------------
# error function definition
L1.error <- function(sample, Shat) {
    sum(rot.dist(sample, Shat, method = "intrinsic", p = 1))
}
cayley.sample <- ruars(n = 50, rangle = rcayley, nu = 1, space = "SO3")
# gradient based optimization
system.time(SL1 <- gradient.search(cayley.sample, L1.error))
# in-built function
system.time(S <- median(cayley.sample, type = "geometric"))
rot.dist(S, SL1$Shat)

## ----ex6----------------------------------------------------------------------
Rs <- ruars(50, rcayley, kappa = 10)
region(
  x = Rs, 
  method = "direct", 
  type = "asymptotic", 
  estimator = "mean", 
  alp = 0.05
)
region(
  x = Rs, 
  method = "direct", 
  type = "bootstrap", 
  estimator = "mean", 
  alp = 0.05, 
  m = 300
)
region(
  x = Rs, 
  method = "direct", 
  type = "asymptotic", 
  estimator = "median", 
  alp = 0.05
)
region(
  x = Rs, 
  method = "direct", 
  type = "bootstrap", 
  estimator = "median", 
  alp = 0.05, 
  m = 300
)

## ----ex7,fig.cap="The $x$-axis of a random sample from the Cayley-UARS distribution with $\\\\kappa=1$ and $n=50$. All four point estimates are displayed on the left and all three region methods along with the projected mean are on the right.",fig.lp="figure:eye1",out.width=".4\textwidth",fig.pos="h",dev='png'----
plot(
  x = Rs, 
  center = mean(Rs), 
  show_estimates = "all"
)
plot(
  x = Rs,
  center = mean(Rs),
  show_estimates = "proj.mean",
  mean_regions = "all",
  alp = .05
)

## ----ex1----------------------------------------------------------------------
data(drill)
head(drill)
Subj1Wrist<-subset(drill, Subject == '1' & Joint == 'Wrist')
Subj1Wdata <- as.Q4(Subj1Wrist[,5:8])
mean(Subj1Wdata)

## ----ex21---------------------------------------------------------------------
data(nickel)
head(nickel[,1:6])
Location1<-subset(nickel, location==1)
Loc1data<-as.SO3(Location1[,5:13])
mean(Loc1data)

## ----summary------------------------------------------------------------------
 Qs <- ruars(20, rcayley, space = 'Q4')
 Rs <- as.SO3(Qs)
 suppressMessages(require(onion))
 onionQs <- as.quaternion(matrix(Qs,nrow=4, byrow = TRUE))
 suppressMessages(require(orientlib))
 orientRs <- rotvector(matrix(Rs, ncol = 9))

## ----resetoption, include=FALSE-----------------------------------------------
options(orig_options)

