1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/illparms.R
\name{illparms}
\alias{illparms}
\alias{illparms.mkinfit}
\alias{print.illparms.mkinfit}
\alias{illparms.mmkin}
\alias{print.illparms.mmkin}
\alias{illparms.saem.mmkin}
\alias{print.illparms.saem.mmkin}
\alias{illparms.mhmkin}
\alias{print.illparms.mhmkin}
\title{Method to get the names of ill-defined parameters}
\usage{
illparms(object, ...)
\method{illparms}{mkinfit}(object, conf.level = 0.95, ...)
\method{print}{illparms.mkinfit}(x, ...)
\method{illparms}{mmkin}(object, conf.level = 0.95, ...)
\method{print}{illparms.mmkin}(x, ...)
\method{illparms}{saem.mmkin}(
object,
conf.level = 0.95,
random = TRUE,
errmod = TRUE,
slopes = TRUE,
...
)
\method{print}{illparms.saem.mmkin}(x, ...)
\method{illparms}{mhmkin}(object, conf.level = 0.95, random = TRUE, errmod = TRUE, ...)
\method{print}{illparms.mhmkin}(x, ...)
}
\arguments{
\item{object}{The object to investigate}
\item{\dots}{For potential future extensions}
\item{conf.level}{The confidence level for checking p values}
\item{x}{The object to be printed}
\item{random}{For hierarchical fits, should random effects be tested?}
\item{errmod}{For hierarchical fits, should error model parameters be
tested?}
\item{slopes}{For hierarchical \link{saem} fits using saemix as backend,
should slope parameters in the covariate model(starting with 'beta_') be
tested?}
}
\value{
For \link{mkinfit} or \link{saem} objects, a character vector of parameter
names. For \link{mmkin} or \link{mhmkin} objects, a matrix like object of class
'illparms.mmkin' or 'illparms.mhmkin'.
}
\description{
The method for generalised nonlinear regression fits as obtained
with \link{mkinfit} and \link{mmkin} checks if the degradation parameters
pass the Wald test (in degradation kinetics often simply called t-test) for
significant difference from zero. For this test, the parameterisation
without parameter transformations is used.
}
\details{
The method for hierarchical model fits, also known as nonlinear
mixed-effects model fits as obtained with \link{saem} and \link{mhmkin}
checks if any of the confidence intervals for the random
effects expressed as standard deviations include zero, and if
the confidence intervals for the error model parameters include
zero.
}
\note{
All return objects have printing methods. For the single fits, printing
does not output anything in the case no ill-defined parameters are found.
}
\examples{
fit <- mkinfit("FOMC", FOCUS_2006_A, quiet = TRUE)
illparms(fit)
\dontrun{
fits <- mmkin(
c("SFO", "FOMC"),
list("FOCUS A" = FOCUS_2006_A,
"FOCUS C" = FOCUS_2006_C),
quiet = TRUE)
illparms(fits)
}
}
|
