Takes a fitted `inlamemi` object produced by `fit_inlamemi` and produces a summary from it.
Value
`summary.inlamemi` returns an object of class `summary.inlamemi`, a list of components to print.
Examples
# Fit the model
simple_model <- fit_inlamemi(data = simple_data,
formula_moi = y ~ x + z,
formula_imp = x ~ z,
family_moi = "gaussian",
error_type = c("berkson", "classical"),
prior.prec.moi = c(10, 9),
prior.prec.berkson = c(10, 9),
prior.prec.classical = c(10, 9),
prior.prec.imp = c(10, 9),
prior.beta.error = c(0, 1/1000),
initial.prec.moi = 1,
initial.prec.berkson = 1,
initial.prec.classical = 1,
initial.prec.imp = 1)
summary(simple_model)
#> Formula for model of interest:
#> y ~ x + z
#>
#> Formula for imputation model:
#> x ~ z
#>
#> Error types:
#> [1] "berkson" "classical"
#>
#> Fixed effects for model of interest:
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> beta.0 1.033885 0.2182911 0.6118263 1.034314 1.445395 1.028072
#> beta.z 1.919817 0.3871734 1.2196917 1.910930 2.583442 1.915278
#>
#> Coefficient for variable with measurement error and/or missingness:
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> beta.x 1.974594 0.1954074 1.587021 1.975579 2.356408 1.979692
#>
#> Fixed effects for imputation model:
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> alpha.x.0 1.033074 0.05060181 0.9338043 1.033082 1.132301 1.033082
#> alpha.x.z 2.024720 0.05226362 1.9222595 2.024704 2.127275 2.024704
#>
#> Model hyperparameters (apart from beta.x):
#> mean sd 0.025quant 0.5quant
#> Precision for model of interest 1.1293529 0.3537397 0.5732360 1.0830362
#> Precision for x berkson model 1.1239105 0.3317552 0.6078515 1.0786884
#> Precision for x classical model 0.9257665 0.1069530 0.7336495 0.9194670
#> Precision for x imp model 0.9770666 0.1230084 0.7572519 0.9694457
#> 0.975quant mode
#> Precision for model of interest 1.950959 0.9985215
#> Precision for x berkson model 1.901843 0.9940753
#> Precision for x classical model 1.154183 0.9066419
#> Precision for x imp model 1.240776 0.9544314
#>