LogisticNormalFixedMixture-class | R Documentation |
LogisticNormalFixedMixture
LogisticNormalFixedMixture
is the class for standard logistic regression
model with fixed mixture of multiple bivariate (log) normal priors on the
intercept and slope parameters. The weights of the normal priors are fixed,
hence no additional model parameters are introduced. This type of prior is
often used to better approximate a given posterior distribution, or when the
information is given in terms of a mixture.
LogisticNormalFixedMixture(components, weights, ref_dose, log_normal = FALSE)
.DefaultLogisticNormalFixedMixture()
components |
( |
weights |
( |
ref_dose |
( |
log_normal |
( |
The covariate is the natural logarithm of the dose x
divided
by the reference dose x*
, i.e.:
logit[p(x)] = alpha0 + alpha1 * log(x/x*),
where p(x)
is the probability of observing a DLT for a given dose x
.
The prior
(alpha0, alpha1) ~ w1 * Normal(mean1, cov1) + ... + wK * Normal(meanK, covK),
if a normal prior is used and
(alpha0, log(alpha1)) ~ w1 * Normal(mean1, cov1) + ... + wK * Normal(meanK, covK),
if a log normal prior is used.
The weights w1, ..., wK
of the components are fixed and sum to 1.
The slots of this class comprise a list with components parameters. Every single component contains the mean vector and the covariance matrix of bivariate normal distributions. Remaining slots are the weights of the components as well as the reference dose. Moreover, a special indicator slot specifies whether a log normal prior is used.
components
(list
)
the specifications of the mixture components,
a list with ModelParamsNormal
objects for each bivariate (log) normal
prior.
weights
(numeric
)
the weights of the components; these must be
positive and must sum to 1.
ref_dose
(positive_number
)
the reference dose.
log_normal
(flag
)
should a log normal prior be used, such
that the mean vectors and covariance matrices are valid for the intercept
and log slope?
Typically, end-users will not use the .DefaultLogisticNormalFixedMixture()
function.
ModelParamsNormal
, ModelLogNormal
,
LogisticNormalMixture
, LogisticLogNormalMixture
.
my_model <- LogisticNormalFixedMixture(
components = list(
comp1 = ModelParamsNormal(
mean = c(-0.85, 1),
cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2)
),
comp2 = ModelParamsNormal(
mean = c(1, 1.5),
cov = matrix(c(1.2, -0.45, -0.45, 0.6), nrow = 2)
)
),
weights = c(0.3, 0.7),
ref_dose = 50
)
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