Summarize the coefficients of a parameter object by computing the mean, standard deviation, and quantiles for each model term. This is a convenient way to check whether a parameter object has been specified correctly and sampling distributions of the coefficients are as expected.
# S3 method for params_lm
summary(object, probs = c(0.025, 0.975), ...)# S3 method for params_mlogit
summary(object, probs = c(0.025, 0.975), ...)
# S3 method for params_mlogit_list
summary(object, probs = c(0.025, 0.975), ...)
# S3 method for params_surv
summary(object, probs = c(0.025, 0.975), ...)
# S3 method for params_surv_list
summary(object, probs = c(0.025, 0.975), ...)
A data.table::data.table that always contains the following columns:
The regression term.
The mean value of the regression term.
The standard deviation of the values of the regression term.
In addition, the probs argument determines the quantiles that are computed.
By default, the columns 2.5% and 97.5% are returned corresponding to the
2.5th and 97.5th percentiles.
Finally, the following columns may also be present:
The name of the parameter of interest. This is relevant
for any parametric model in which the underlying probability distribution
has multiple parameters. For instance, both params_surv and params_surv_list
store regression coefficients that are used to model the underlying parameters
of the survival distribution (e.g., shape and scale for a Weibull model). Similarly,
there are two parameters (mean and sd) for params_lm objects.
The name of the statistical model. This is used for a
params_surv_list object, where each list element represents a separate model.
In a state transition model, each model is a unique health state transition and
in a partitioned survival model, there is a separate model for each curve.
The health state that is being transitioned to. In params_mlogit
and params_mlogit_list objects, there are coefficients for each health
state that can be transitioned to.
The health state that is being transitions from. This is used
for a params_mlogit_list objects where a different multinomial
logistic regression is used for each state that can be transitioned from.
An object of the appropriate class.
A numeric vector of probabilities with values in [0,1] used
to compute quantiles. By default, the 2.5th and 97.5th percentiles are
computed.
Additional arguments affecting the summary. Currently unused.
For examples, see the the underlying parameter object functions:
params_lm(), params_surv(), params_surv_list(), params_mlogit(), and
params_mlogit_list().