`R/params.R`

, `R/params_lm.R`

, `R/params_mlogit.R`

, and 3 more
`summary.params.Rd`

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), ...)

object | An object of the appropriate class. |
---|---|

probs | A numeric vector of probabilities with values in |

... | Additional arguments affecting the summary. Currently unused. |

A `data.table`

that always contains the following columns:

- term
The regression term.

- mean
The mean value of the regression term.

- sd
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:

- parameter
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.- model
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.- to
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.- from
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.

For examples, see the the underlying parameter object functions:
`params_lm()`

, `params_surv()`

, `params_surv_list()`

, `params_mlogit()`

, and
`params_mlogit_list()`

.