Mathematical Background
For a fixed \(t\in [0,T_{\max}]\),
let \(E^{t}\) the projection operator,
\[E^{t}(z) = \mathbb{E}(z|X_{t}) = \sum_{x\in
S}{\mathbb{E}(z|X_t=x) \mathbf{1}_{X_t=x}}\] Then, the
correlation coefficient between \(z\)
and \(X_{t}\) is: \[\eta^{2}(z, X_{t}) =
\frac{var(\mathbb{E}(z|X_{t}))}{var(z)}\]
For \(t, s \in [0,T_{\max}]\), the
(simple) correspondence analysis looks for \(z\) such that it maximizes \[\displaystyle\frac{1}{2}\left(\eta^{2}(z,
X_{t})+\eta^{2}(z, X_{s})\right)\] and the solution is:
\[(E^{t}+E^{s})z = \lambda z\]
\(z\) is called a principal
component.
As an extension, the functional correspondence analysis is
defined as the solution of the optimization problem:
\[
\arg\max_{z}\displaystyle\frac{1}{T_{max}}\int_{0}^{T_{max}}\eta^{2}(z,
X_{t})dt\] Solution: \[\int_{0}^{T_{max}}E^{s}z\ ds = \lambda
z\]
Denote \(\xi_t = E^{t}z\),
\[\int_0^{T_{\max}}K(t,s)\xi_s\ ds =
\lambda \xi_t, \ \ \ \ \mbox{with}\ K(s,t) = E^tE^s. \]
It follows that, \[\xi_{t} = \sum_{x\in
S}a_{x}(t)\mathbf{1}_{X_t=x}\] with \(a_x(t) =\mathbb{E}(z|X_t= x), \ \ \forall x \in
S\).
For each \(x\in S\), the functions
\(a_x: [0,T_{\max}]\rightarrow
\mathbb{R}\) are called optimal encoding of the state
\(x\).
The eigenvalue problem becomes in terms of encodings:
\[\lambda a_x(t) = \sum_{y\in
S}\displaystyle\int_{0}^{T_{\max}}\frac{p_{x,y}(t,s)}{p_x(t)}a_y(s)ds, \
\ \ \ \ \forall x \in S, \forall t\in [0,T_{\max}], \] where
\(p_{x,y}(t,s) = \mathbb{P}(X_t=x,
X_s=y)\) and \(p_x(t)=
\mathbb{P}(X_t=x)\).
Under general conditions (continuity in probability of \(X\)), there exists \(\{\lambda_i\}_{i\geq1}\) positive
eigen-values and \(\{a_x^i\}_{i\geq
1}\) eigen-functions.
The following expansion formula holds (Mercer thm.):
\[p_{x,y}(t,s) = p_x(t)p_y(s)\sum_{i\geq
1}\lambda_{i}a^i_x(t)a^i_y(s)\] For \(x=y\), one obtains: \[p_x(t)
=\displaystyle\frac{1}{\displaystyle\sum_{i\geq
1}\lambda_{i}a^i_x(t)a^i_y(s)}\]
For the process \(X = \{X_t, t\in[0,
T]\}\) throughout the indicators \(1_x
= \{1_x(t), t\in [0, T]\}\): \[1_x(t)=\sum_{i\geq 1} z_i
a_i^x(t)\frac{1}{p_x(t)}\]
We are interested in approximating the encoding functions, \(a_x\), by
\[a_{x} \approx \sum_{i=1}^m
\alpha_{x,i}\phi_i, \] where \(\mathbf{\alpha_x} = (\alpha_{x,1}, \ldots,
\alpha_{x,m}) \in \mathbb{R}^m\) are the expansion coefficients
onto a basis of functions defined on \([0,T]\), \(\{\phi_1, \ldots, \phi_m\}\), \(m \geq 1\).
The main result (Deville, 1982) is that \(\alpha = (\alpha_{1}, \ldots, \alpha_{K}) \in
\mathbb{R}^{Km}\) is the solution of the following eigen-value
problem:
\[F^{-1}G\alpha = \lambda\alpha, \]
where \(G\) and \(F\) the matrix defined by: \[G = cov(\{V_{(x,i)}\}_{x\in S; i=1:m }),\]
\[F =
\mathbb{E}\left(\{F_{(x,i),(y,j)}\}_{x,y\in S ; i,j=1:m}\right)\]
with the random variables \(V_{(x,i)}\)
and \(F_{(x,i),(x,j)}\) defined by:
\[V_{(x,i)} =
\int_{0}^{T_{\max}}\phi_i(t)\mathbf{1}_xdt \ \ \ \ \mbox{and}\ \ \ \
F_{(x,i), (y,j)} =
\int_{0}^{T_{\max}}\phi_i(t)\phi_{j}(t)\mathbf{1}_x\mathbf{1}_y
dt\]
Application
Firstly, a basis of functions must be defined. We choose a B-splines
basis of order 4.
m <- 20
basis <- create.bspline.basis(c(0, Tmax), nbasis = m, norder = 4)
The optimal encoding is computed using:
fmca <- compute_optimal_encoding(d_JKT, basis, verbose = FALSE)
summary(fmca)
## #### FMCA
##
## ## Data
## Number of individuals: 100
## Number of states: 4
## Time Range: 0 to 10
## States: 1 2 3 4
##
## ## Basis
## Type: bspline
## Number of basis functions: 20
##
## ## Outputs
## Eigenvalues:
## 1.979971 1.87976 1.645708 1.497216 1.370031 1.316879
##
## Explained variance:
## 0.078 0.152 0.217 0.276 0.33 0.382
##
## Optimal encoding:
## A C G T
## [1,] 0.008180893 -0.58909343 0.4766992 -0.40020013
## [2,] 0.014515597 0.52566581 -0.6950777 0.17637761
## [3,] -0.009084061 -0.37942555 0.3985981 -0.02443008
## [4,] -0.069652888 -0.67642599 0.3465354 0.57931109
## [5,] -0.137618298 -0.14330188 0.2425508 0.40511150
## [6,] -0.309208593 0.05087257 -0.1190275 0.32635795
##
## Principal components:
## [,1] [,2] [,3] [,4] [,5] [,6]
## 1 0.3704565 -0.7461855 -2.3826967 -0.817332429 0.06146912 -0.04419096
## 2 0.6649154 -1.0872446 -1.8537400 0.007899832 -0.12504207 0.22016509
## 3 -3.8819516 1.2971014 0.4180643 -0.183369945 0.75743237 0.31311758
## 4 1.0941084 0.2797036 -0.6957583 1.762177272 0.54104766 2.23782009
## 5 0.1747660 -2.4542637 -1.5757551 -0.288176353 -0.70892187 0.30844587
## 6 0.1038436 1.1451010 -1.5909369 0.362205655 -0.75526301 -0.73604342
##
## Total elapsed time: 52.432 s
By default this function computes bootstrap estimates of the encoding
functions in order to have a confidence interval. This is controlled by
the computeCI arguments. The output is a list containing
the different elements computed during the process:
eigenvalues, pc, alpha,
F, G, V and
basisobj. Note that alphacontains the
coefficients of the different encoding functions and pcthe
principal components. These components can be used with classical
statistic methods (k-means, regression…).
The eigenvalues can be computed using:
plotEigenvalues(fmca, cumulative = TRUE, normalize = TRUE)
The first encoding function coefficients \(\alpha\)’s:
## A C G T
## [1,] 0.008180893 -0.58909343 0.47669920 -0.400200126
## [2,] 0.014515597 0.52566581 -0.69507769 0.176377614
## [3,] -0.009084061 -0.37942555 0.39859810 -0.024430084
## [4,] -0.069652888 -0.67642599 0.34653541 0.579311087
## [5,] -0.137618298 -0.14330188 0.24255075 0.405111498
## [6,] -0.309208593 0.05087257 -0.11902753 0.326357951
## [7,] -0.345337542 0.34510999 -0.01194049 0.059414703
## [8,] 0.411515257 0.18633306 -1.01542291 -0.004035332
## [9,] 0.066499135 0.38956072 -0.75060425 0.169082665
## [10,] 0.442805356 0.32496793 -0.64164483 0.058034297
## [11,] 0.137025350 0.66080911 -0.88516584 -0.033464535
## [12,] 0.221128180 0.59734105 -0.61281881 -0.226002336
## [13,] -0.023061199 0.41607362 -0.56939698 -0.137096230
## [14,] -0.136462856 0.75635141 -0.34679873 0.019760886
## [15,] -0.004898823 -0.45158932 -0.17783513 0.687183675
## [16,] 0.211932955 0.14230107 -0.31096835 0.028668625
## [17,] -0.164553063 -0.25713493 -0.23598583 0.760837273
## [18,] -0.208792890 0.08073523 0.02635330 0.104904414
## [19,] -0.166480097 -0.27899968 -0.25899954 0.692396872
## [20,] -0.236574942 -0.06124286 -0.02169858 0.379020810
The resulting encoding can be plotted:
## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_line()`).
or extracted as a fd object (or matrix):
encoding <- get_encoding(fmca, fdObject = TRUE)
Note that the first encoding mainly oppose the G state (in negative
values) to other states. So, on the first component, individuals with a
large negative values will tend to spend time in the G state after time
2.5.
By default, it plots (and returns) the first encoding functions.
Other encoding can be accessed with the harm argument.
A confidence interval can be plotted using the addCI
argument:
plot(fmca, addCI = TRUE, coeff = 2, states = "A")
Plot the two first components:
plotComponent(fmca, comp = c(1, 2), addNames = TRUE)
We can see that the individuals 3 and 14 have extreme ngative values
for the first component and are opposed to 67 and 84. By plotting them,
we can check the statement made on the first component meaning.
plotData(d_JKT[d_JKT$id %in% c(3, 14, 67, 84), ])
The components can be used for some other tasks such as clustering,
predicting,…
Reconstruct indicators
With the encodings, the indicators can be reconstructed as
follows:
indicators <- reconstructIndicators(fmca)
## [1] "Reconstruct data using 60 components (out of 80)"
## time id stateA stateC stateG stateT state
## 1 0.000000000 1 1.231208 5.258831e-05 0.06839379 -0.2284427 G
## 2 0.007897421 1 1.216876 5.248673e-05 0.07177822 -0.2270982 G
## 3 0.038151747 1 1.123116 4.903944e-05 0.08600745 -0.2066952 G
## 4 0.038873269 1 1.120306 4.888438e-05 0.08636713 -0.2058505 G
## 5 0.044793148 1 1.096537 4.746114e-05 0.08934315 -0.1981844 G
## 6 0.058217068 1 1.038911 4.316210e-05 0.09619778 -0.1755988 G
iInd <- 3
plotData(d_JKT[d_JKT$id == iInd, ])
plotIndicatorsReconstruction(indicators, id = iInd)
We see that the reconstructed indicators gives the same inforamtion
as the true ones. Using more basis functions for computing encoding or
other basis can improve the approximation.
