n1qn1Control | R Documentation |
Control for n1qn1 estimation method in nlmixr2
n1qn1Control(
epsilon = (.Machine$double.eps)^0.25,
max_iterations = 10000,
nsim = 10000,
imp = 0,
print.functions = FALSE,
returnN1qn1 = FALSE,
stickyRecalcN = 4,
maxOdeRecalc = 5,
odeRecalcFactor = 10^(0.5),
useColor = crayon::has_color(),
printNcol = floor((getOption("width") - 23)/12),
print = 1L,
normType = c("rescale2", "mean", "rescale", "std", "len", "constant"),
scaleType = c("nlmixr2", "norm", "mult", "multAdd"),
scaleCmax = 1e+05,
scaleCmin = 1e-05,
scaleC = NULL,
scaleTo = 1,
gradTo = 1,
rxControl = NULL,
optExpression = TRUE,
sumProd = FALSE,
literalFix = TRUE,
addProp = c("combined2", "combined1"),
calcTables = TRUE,
compress = TRUE,
covMethod = c("r", "n1qn1", ""),
adjObf = TRUE,
ci = 0.95,
sigdig = 4,
sigdigTable = NULL,
...
)
epsilon |
Precision of estimate for n1qn1 optimization. |
max_iterations |
Number of iterations |
nsim |
Number of function evaluations |
imp |
Verbosity of messages. |
print.functions |
Boolean to control if the function value and parameter estimates are echoed every time a function is called. |
returnN1qn1 |
return the n1qn1 output instead of the nlmixr2 fit |
stickyRecalcN |
The number of bad ODE solves before reducing the atol/rtol for the rest of the problem. |
maxOdeRecalc |
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve. |
odeRecalcFactor |
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced |
useColor |
Boolean indicating if focei can use ASCII color codes |
printNcol |
Number of columns to printout before wrapping parameter estimates/gradient |
print |
Integer representing when the outer step is printed. When this is 0 or do not print the iterations. 1 is print every function evaluation (default), 5 is print every 5 evaluations. |
normType |
This is the type of parameter
normalization/scaling used to get the scaled initial values
for nlmixr2. These are used with With the exception of In general, all all scaling formula can be described by:
= (
)/
Where The other data normalization approaches follow the following formula
= (
)/
|
scaleType |
The scaling scheme for nlmixr2. The supported types are:
|
scaleCmax |
Maximum value of the scaleC to prevent overflow. |
scaleCmin |
Minimum value of the scaleC to prevent underflow. |
scaleC |
The scaling constant used with
These parameter scaling coefficients are chose to try to keep similar slopes among parameters. That is they all follow the slopes approximately on a log-scale. While these are chosen in a logical manner, they may not always apply. You can specify each parameters scaling factor by this parameter if you wish. |
scaleTo |
Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed. |
gradTo |
this is the factor that the gradient is scaled to before optimizing. This only works with scaleType="nlmixr2". |
rxControl |
'rxode2' ODE solving options during fitting, created with 'rxControl()' |
optExpression |
Optimize the rxode2 expression to speed up calculation. By default this is turned on. |
sumProd |
Is a boolean indicating if the model should change
multiplication to high precision multiplication and sums to
high precision sums using the PreciseSums package. By default
this is |
literalFix |
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'. |
addProp |
specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2). The combined1 error type can be described by the following equation:
The combined2 error model can be described by the following equation:
Where: - y represents the observed value - f represents the predicted value - a is the additive standard deviation - b is the proportional/power standard deviation - c is the power exponent (in the proportional case c=1) |
calcTables |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |
compress |
Should the object have compressed items |
covMethod |
Method for calculating covariance. In this discussion, R is the Hessian matrix of the objective function. The S matrix is the sum of individual gradient cross-product (evaluated at the individual empirical Bayes estimates).
|
adjObf |
is a boolean to indicate if the objective function
should be adjusted to be closer to NONMEM's default objective
function. By default this is |
ci |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |
sigdig |
Optimization significant digits. This controls:
|
sigdigTable |
Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3. |
... |
Ignored parameters |
bobqya control structure
Matthew L. Fidler
# A logit regression example with emax model
dsn <- data.frame(i=1:1000)
dsn$time <- exp(rnorm(1000))
dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time)))
mod <- function() {
ini({
E0 <- 0.5
Em <- 0.5
E50 <- 2
g <- fix(2)
})
model({
v <- E0+Em*time^g/(E50^g+time^g)
ll(bin) ~ DV * v - log(1 + exp(v))
})
}
fit2 <- nlmixr(mod, dsn, est="n1qn1")
print(fit2)
# you can also get the nlm output with fit2$n1qn1
fit2$n1qn1
# The nlm control has been modified slightly to include
# extra components and name the parameters
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