| ssqOdeModel {simecol} | R Documentation |
Compute the sum of squares between a given data and an odeModel
object.
ssqOdeModel(p, simObj, obstime, yobs, sd.yobs = as.numeric(lapply(yobs, sd)), initialize = TRUE, lower. = -Inf, upper. = Inf, weights = NULL, debuglevel = 0, ..., pnames = NULL)
p |
vector of named parameter values of the model (can be a subset), |
simObj |
a valid object of class odeModel, |
obstime |
vector with time steps for which observational data are available, |
yobs |
data frame with observational data for all or a subset of
state variables. Their names must correspond exacly with existing
names of state variables in the odeModel. |
sd.yobs |
vector of given standard deviations for all
observational variables given in yobs. If no standard
deviations are given, these are estimated from yobs. |
initialize |
optional boolean value whether the simObj
should be re-initialized after the assignment of new parameter
values. This can be necessary in certain models to assign consistent
values to initial state variables if they depend on parameters. |
lower., upper. |
named vectors with lower and upper bounds used in the optimisation, |
weights |
optional weights to be used in the fitting process.
Should be NULL or a data frame with the same structure as
yobs. If non-NULL, weighted least squares is used with
weights (that is, minimizing sum(w*e^2)); otherwise
ordinary least squares is used. |
debuglevel |
a positive number that specifies the amount of debugging information printed, |
... |
additional parameters passed to the solver method (e.g.
lsoda), |
pnames |
names of the parameters, optionally passed from fitOdeModel. This argument is a workaround for R versions below 2.8.1. It may be removed in future versions of simecol. |
This is the default function called by function
fitOdeModel. The source code of this function can be
used as a starting point to develop user-defined optimization
criteria (cost functions).
The sum of squared differences between yobs and simulation,
by default weighted by the inverse of the standard deviations of the respective
variables.
fitOdeModel, optim,
p.constrain
data(chemostat)
cs1 <- chemostat
## generate some noisy data
parms(cs1)[c("vm", "km")] <- c(2, 10)
times(cs1) <- c(from = 0, to = 20, by = 2)
yobs <- out(sim(cs1))
obstime <- yobs$time
yobs$time <- NULL
yobs$S <- yobs$S + rnorm(yobs$S, sd = 0.1 * sd(yobs$S))*2
yobs$X <- yobs$X + rnorm(yobs$X, sd = 0.1 * sd(yobs$X))
## SSQ between model and data
ssqOdeModel(NULL, cs1, obstime, yobs)
## SSQ between model and data, different parameter set
ssqOdeModel(p=c(vm=1, km=2), cs1, obstime, yobs)
## SSQ between model and data, downweight second observation
## (both variables)
weights <- data.frame(X=rep(1, nrow(yobs)), S = rep(1, nrow=(yobs)))
ssqOdeModel(p=c(vm=1, km=2), cs1, obstime, yobs, weights=weights)
## downweight 3rd data set (row)
weights[3,] <- 0.1
ssqOdeModel(p=c(vm=1, km=2), cs1, obstime, yobs, weights=weights)
## give one value double weight (e.g. 4th value of S)
weights$S[4] <- 2
ssqOdeModel(p=c(vm=1, km=2), cs1, obstime, yobs, weights=weights)