Description Usage Arguments Details Value See Also Examples
Fit the parameters for an ODE model with data sampled across different contexts.
1 2 
x 

adjusts 
Character vector holding names of what quantities to adjust during algorithm. Possible quantities are: 
trace 
Logical indicating if status messages should be printed during 
... 
Additional arguments passed to 
The adapted quantities (scales
, weights
, penalty_factor
) of x
(returned by aim
) are fed to the exact estimator rodeo
. This estimator then traverses the lambda
sequence in reverse order initialised in the last estimates from aim
.
If desired, the quantities lambda
, scales
and weights
are adjusted as in aim
.
An object with S3 class "rodeo":
o 
Original 
op 
Original 
params 
Parameter estimates, stored as list of sparse column format matrices, "dgCMatrix" (or a list of those if multiple initialisations). Rows represent coordinates and columns represent the 
x0s 
Initial state estimates stored in a matrix (or array). Rows represent coordinates, columns represent the 
dfs 
A matrix (or array, if multiple initialisations) of degrees of freedom. Row represents a parameter (the first is always the initial state parameter), columns represent lambda, slices represent initialisation, if multiple are provided. 
codes 
A matrix (or array) of convergence codes organised as

steps 
A matrix (or array) holding number of steps used in optimisation procedure. Organised as 
losses 
A vector (or matrix) of unpenalised losses at optimum for each lambda value (stored rowwise if multiple are provided). 
penalties 
A matrix (or array) of penalties for each parameter, organised as 
jerr 
A matrix (or array) of summary codes (for internal debugging), organised as 
rodeo, aim, rodeo.ode
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# Example: Power Law Kinetics
A < matrix(c(1, 0, 1,
1, 1, 0), byrow = TRUE, nrow = 2)
p < plk(A)
x0 < c(10, 4, 1)
theta < matrix(c(0, 0.25,
0.75, 0,
0, 0.1), byrow = TRUE, nrow = 3)
Time < seq(0, 1, by = .025)
# Simulate data
y < numsolve(p, Time, x0, theta)
y[, 1] < y[, 1] + matrix(rnorm(prod(dim(y[, 1])), sd = .25), nrow = nrow(y))
# Estimation via aim
a < aim(p, opt(y, nlambda = 10))
a$params$theta
# Supply to rodeo
rod < rodeo(a)
rod$params$theta
# Compare with true parameter on column vector form
matrix(theta, ncol = 1)
# Example: include data from an intervened system
# where the first complex in A is inhibited
contexts < cbind(1, c(0, 0, 0, 1, 1, 1))
y2 < numsolve(p, Time, x0 + 1, theta * contexts[, 2])
y2[, 1] < y2[, 1] + matrix(rnorm(prod(dim(y2[, 1])), sd = .25), nrow = nrow(y2))
# Estimation via aim
a < aim(plk(A, r = reg(contexts = contexts)), opt(rbind(y, y2), nlambda = 10))
a$params$theta
# Supply to rodeo
rod < rodeo(a)
rod$params$theta

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