Calculates several measures of model performance, based on results of fitting a model to all simulated datasets.
A data frame of replicated simulations which must include a column titled "Estimate" with the effect estimate from the fitted model.
The true relative risk used to simulate the data.
A dataframe with one row with model assessment across all simulations. Includes values for:
beta_hat: Mean of the estimated log relative risk across all simulations.
rr_hat: Mean value of the estimated relative risk across all simulations.
var_across_betas: Variance of the estimated log relative risk across all
mean_beta_var: The mean of the estimated variances of the estimated log
relative risks across all simulations.
percent_bias: The relative bias of the estimated log relative risks compared
to the true log relative risk.
coverage: Percent of simulations for which the estimated 95% confidence
interval for log relative risk includes the true log relative risk.
power: Percent of simulations for which the null hypothesis that the log
relative risk equals zero is rejected based on a p-value of 0.05.
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sims <- create_sims(n_reps = 100, n = 1000, central = 100, sd = 10, exposure_type = "continuous", exposure_trend = "cos1", exposure_amp = 0.6, average_outcome = 20, outcome_trend = "no trend", rr = 1.02) fits <- fit_mods(data = sims, custom_model = spline_mod, custom_model_args = list(df_year = 1)) check_sims(df = fits, true_rr = 1.02)
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