Create a list containing means predicted from a statistical model.

tparams_mean(value, ...)

Arguments

value

Matrix of samples from the distribution of the mean. Columns denote random samples and rows denote means for different observations.

...

Arguments to pass to id_attributes. Each row in value must be a prediction for a strategy_id, patient_id, state_id, and optionally time_id combination.

Value

An object of class tparams_mean, which is a list containing value, n_samples, and the ID attributes passed to id_attributes.

Note

The tparams_mean() constructor would not normally be used by users; instead, a tparams_mean object is typically created automatically as part of the StateVals class with create_StateVals().

See also

A tparams_mean object is a type of transformed parameter object and is a supported class type of the params field of the StateVals class. See the documentation for create_StateVals() and stateval_tbl() for examples of how to createStateVals objects. Predicted means can be summarized across parameter samples using summary.tparams_mean().

Examples

# Setup model hesim_dat <- hesim_data( strategies = data.frame(strategy_id = c(1, 2)), patients = data.frame(patient_id = c(1, 2)), states = data.frame( state_id = c(1, 2, 3), state_name = c("state1", "state2", "state3") ) ) # Cost model cost_tbl <- stateval_tbl( data.frame(strategy_id = hesim_dat$strategies$strategy_id, mean = c(5000, 3000), se = c(200, 100) ), dist = "gamma" ) costmod <- create_StateVals(cost_tbl, n = 2, hesim_data = hesim_dat) # The 'params' field of the `StateVals` class is a tparams_mean object class(costmod$params)
#> [1] "tparams_mean"
costmod$params
#> A "tparams_mean" object #> #> Summary of means: #> strategy_id patient_id state_id mean sd 2.5% 97.5% #> 1: 1 1 1 4791.507 160.14456 4683.930 4899.085 #> 2: 1 1 2 4791.507 160.14456 4683.930 4899.085 #> 3: 1 1 3 4791.507 160.14456 4683.930 4899.085 #> 4: 1 2 1 4791.507 160.14456 4683.930 4899.085 #> 5: 1 2 2 4791.507 160.14456 4683.930 4899.085 #> 6: 1 2 3 4791.507 160.14456 4683.930 4899.085 #> 7: 2 1 1 2924.774 20.72482 2910.852 2938.696 #> 8: 2 1 2 2924.774 20.72482 2910.852 2938.696 #> 9: 2 1 3 2924.774 20.72482 2910.852 2938.696 #> 10: 2 2 1 2924.774 20.72482 2910.852 2938.696 #> 11: 2 2 2 2924.774 20.72482 2910.852 2938.696 #> 12: 2 2 3 2924.774 20.72482 2910.852 2938.696
summary(costmod$params)
#> strategy_id patient_id state_id mean sd 2.5% 97.5% #> 1: 1 1 1 4791.507 160.14456 4683.930 4899.085 #> 2: 1 1 2 4791.507 160.14456 4683.930 4899.085 #> 3: 1 1 3 4791.507 160.14456 4683.930 4899.085 #> 4: 1 2 1 4791.507 160.14456 4683.930 4899.085 #> 5: 1 2 2 4791.507 160.14456 4683.930 4899.085 #> 6: 1 2 3 4791.507 160.14456 4683.930 4899.085 #> 7: 2 1 1 2924.774 20.72482 2910.852 2938.696 #> 8: 2 1 2 2924.774 20.72482 2910.852 2938.696 #> 9: 2 1 3 2924.774 20.72482 2910.852 2938.696 #> 10: 2 2 1 2924.774 20.72482 2910.852 2938.696 #> 11: 2 2 2 2924.774 20.72482 2910.852 2938.696 #> 12: 2 2 3 2924.774 20.72482 2910.852 2938.696