The function get_wisle_poi_list() prepares a list of points of
intersection (POI) for multiple regression models and release limits
using the find_poi() function.
get_wisle_poi_list(
icpt_list,
model_list,
rl,
sl,
srch_range,
alpha = 0.05,
xform = c("no", "no"),
shift = c(0, 0),
ivl = "confidence",
ivl_type = "one.sided",
ivl_side = "lower",
...
)A list with the following elements is returned:
A list of the worst case scenario (wcs) limits with a list
of four elements for each linear model named cics, dics,
dids.pmse and dids. Each of these elements contains a list
element for each batch (intercept) containing itself a list element for
each release limit. The wcs limit is obtained by adding/subtracting the
absolute difference of specification limit and release limit to/from the
common intercept of the test batches or the intercept of the worst
performing batch.
A list of the POI values, i.e. a list with a list of four
elements for each linear model named cics, dics,
dids.pmse and dids. Each of these elements contains a
matrix with columns for each intercept and rows for each release limit.
A list of the confidence or prediction interval limits that
are associated with a POI value, i.e. a list with a list of four elements
for each linear model named cics, dics, dids.pmse
and dids. Each of these elements contains an array with a level for
each batch, containing matrices with columns for each batch and rows for
each release limit, where the matrices contain the estimated interval
limits at each POI value per batch.
A list of four elements for each linear model named
cics, dics, dids.pmse and dids. Each of these
list elements contains a matrix of the indices of the batches with the
minimal intercept in the all.prl list. The matrices have a column
for each batch and a row for each release limit.
A list of four elements for each linear model named
cics, dics, dids.pmse and dids. Each of these
list elements contains a numeric vector with the minimal POI value of
associated with each release limit.
A list of four elements for each linear model named
cics, dics, dids.pmse and dids. Each of these
list elements contains a numeric vector with the indices of the worst
case batches associated with each release limit.
A list of four elements named cics, dics,
dids.pmse and dids with the intercepts of each linear
regression model and batch. The cics, dics and
dids.pmse elements are NA if data of only a single batch
is available.
A list of regression models of different type. Usually,
it is a list of four elements named cics, dics,
dids.pmse and dids, where the first three elements contain
‘lm’ objects of the “common intercept / common slope”
(cics), “different intercept / common slope” (dics)
and “different intercept / different slope” (dids.pmse) type.
The fourth element with the label dids is usually a list of the
‘lm’ objects that is obtained from fitting a regression
model to the data of each level of the categorical variable separately.
The dids.pmse model differs from the dids model in that it
is a model with the categorical variable as a fixed main effect and with
an interaction term of the categorical variable with the time variable,
i.e. a model where the mean square error is pooled across batches (thus
the “pmse” suffix meaning “pooled mean square error”). The
cics, dics and dids.pmse elements are NA if
data of only a single batch is available.
A numeric value or a numeric vector that specifies the release specification limit(s) for which the corresponding expiry should be estimated.
A numeric value or a numeric vector of length 2 that
specifies the specification limit or limits. If a vector is provided it
must be of the form c(lower limit, upper limit).
A vector of length 2 that specifies the end-points
of the (time) range that is supposed to contain the shelf life or retest
period.
A numeric value between 0 and 1 that specifies the significance
level for the calculation of confidence or prediction intervals. The
default is 0.05.
A vector of two character strings that specifies the
transformation of the response and the time variable. The default is
“no” transformation, i.e. c("no", "no"), where the first
element specifies the transformation of the \(x\) variable and the
second element the transformation of the \(y\) variable. Valid
alternatives for \(x\) and/or \(y\) variable transformation are
"log" (natural logarithm), "sqrt" (square root) and
"sq" (square).
A vector of two values which will be added to the variables
\(x\) and/or \(y\) before they are transformed as specified by the
xform parameter, where the first element will be added to the
\(x\) variable and the second element to the \(y\) variable. The
purpose is to prevent an undefined state which could arise when variables
with values of \(\leq 0\) are log or square root transformed. The
default is c(0, 0).
A character string of either "confidence" or
"prediction" that specifies the type of interval of interest.
The default is "confidence".
A character string that specifies if a “one sided”
or a “two sided” interval should be calculated, i.e. either
"one.sided" or "two.sided", respectively. The default is
"one.sided".
A character string that specifies if the “upper” or
the “lower” limit is the relevant limit, i.e. either "upper"
or "lower", respectively. The default is "lower". Since this
parameter additionally specifies the relationship of rl with
sl, i.e. which of the two sides of sl the rl is
compared to, only either either "upper" or "lower" is
possible. In this respect, the usage of ivl_side differs from its
usage in the expirest_osle() function where ivl_side in
addition can be "both".
Additional named or unnamed arguments passed on to
uniroot().
The function get_wisle_poi_list() applies the
find_poi() function (find the “point of intersection”) on
all the models and for each release limit (rl) provided. With respect
to the latter it differs from the get_poi_list() function.
get_icpt_list, get_model_list,
get_wcs_limit, find_poi,
get_intvl_limit, expirest_osle,
expirest_wisle.