The function get_icpt_list() prepares a list of the intercepts
of the regression models fitted to the data.
get_icpt_list(
data,
response_vbl,
time_vbl,
batch_vbl,
model_list,
xform = c("no", "no"),
shift = c(0, 0)
)A list of four elements named cics, dics,
dids.pmse and dids is returned. Each of them contains a list
element named icpt with a vector of the intercepts. If the data
have been transformed, each of the primary list elements contains a further
list element called icpt.orig with a numeric vector of the intercepts
on the original scale.
The data frame that was used for fitting the models of parameter
model_list.
A character string that specifies the response variable
name that must be a column of data.
A character string that specifies the time variable name
that must be a column of data.
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 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 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).
The function get_icpt_list() extracts the intercepts of
the various regression models (fitted by aid of the lm() function)
that are passed in via the model_list parameter.
get_model_list, get_icpt,
expirest_osle, expirest_wisle.