The function get_osle_poi_list() prepares a list of points of
intersection (POI) for multiple regression models using the
find_poi() function.
get_osle_poi_list(
data,
batch_vbl,
icpt_list,
model_list,
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 POI values, i.e. a list with one or two list
elements for the side (i.e. lower or upper) of the
corresponding specification limit, each containing a list returned by the
get_poi_list() function, containing of four elements named
cics, dics, dids.pmse and dids. Each of them
contains a named vector of the POI values estimated for each batch and
named accordingly.
A named vector of the worst case POI values of each model, i.e.
named cics, dics, dids.pmse and dids. In
addition, the vector has an attribute called side that specifies
the side of the specification limit which is crossed by the confidence or
prediction interval at the corresponding POI value.
A named vector of the intercepts of the worst case batches of
each model, i.e. named cics, dics, dids.pmse and
dids. In addition, the vector has an attribute called side
that specifies the side of the specification limit which is crossed by the
confidence or prediction interval at the POI value of the corresponding
worst case batch.
A named vector of the indices of the worst case
batches of each model, i.e. named cics, dics,
dids.pmse and dids. In addition, the vector has an attribute
called side that specifies the side of the specification limit
which is crossed by the confidence or prediction interval at the POI value
of the corresponding worst case batch.
A data frame with the columns specified by response_vbl,
time_vbl and batch_vbl.
A character string that specifies the column in data
with the grouping information (i.e. a factorial variable) for the
differentiation of the observations of the various batches.
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 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 specification
limit, given that the limit has only one side, is an “upper” or a
“lower” bound, i.e. it is specified as either "upper" or
"lower", respectively. The default is "lower". If the
specification has two boundaries, then this parameter specifies the
preferred side. If no side is preferred over the other, "both" can
be used.
Additional named or unnamed arguments passed on to
uniroot().
The function get_osle_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_poi_list, get_wc_icpt,
expirest_osle, expirest_wisle.