Split single-state process observations, apply multiple state projections, and combine these observations into a single data frame, arranged by process time or index.
dataStateSwitch(
df,
angles2 = list(yaw = 0, pitch = 90, roll = 30),
scales2 = c(1, 0.5, 2),
angles3 = list(yaw = 90, pitch = 0, roll = -30),
scales3 = c(0.25, 0.1, 0.75)
)A data frame returned by processNOCdata() or faultSwitch().
Change the principal angles for State 2.
Change the principal scales for State 2.
Change the principal angles for State 3.
Change the principal scales for State 3.
A data frame containing the time index, state, and feature values after state-specific rotation and scaling; this data frame also contains the other columns of df that aren't the feature values. This data frame has
dateTime - a POSIX column of the time stamps for each observation
state - column of state membership (1, 2, or 3)
x - the process values for the first feature, corresponding to t + random error
y - the process values for the second feature, corresponding to t ^ 2 - 3 * t + random error
z - the process values for the third feature, corresponding to -t ^ 3 + 3 * t ^ 2 + random error
t - the non-stationary and autocorrelated latent feature
err1 - a Gaussian white noise vector
err2 - a Gaussian white noise vector
err3 - a Gaussian white noise vector
This function splits a process data frame by state, and rotates and scales the observations from states 2 and 3 by the scales and angles specified in the function arguments. After state-specific rotation and scaling, this function combines the observations back together and orders them by process time index. This function takes in data frame returned by processNOCdata() or faultSwitch(). This function calls rotateScale3D() and is called internally by mspProcessData().
Calls: processNOCdata, faultSwitch,
rotateScale3D. Called by: mspProcessData
# NOT RUN {
nrml <- processNOCdata()
dataStateSwitch(nrml)
# }
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