Summarize an amerasfit Object

Produces a summary of a fitted amerasfit object, including parameter estimates, standard errors, and confidence intervals if computed via confint.

Usage

# S3 method for class 'amerasfit'
summary(object, ...)

# S3 method for class 'amerasfit'
summary_table(object, ...)

# S3 method for class 'summary.amerasfit'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

object: A fitted model object of class amerasfit, as returned by ameras.
x: An object of class summary.amerasfit, as returned by summary.amerasfit.
digits: The number of significant digits to use. Defaults to max(3, getOption("digits") - 3).
...: Additional arguments, currently unused.

Value

summary.amerasfit returns an object of class summary.amerasfit, which is a list containing the following elements:

call

The matched call from the original call to ameras.

summary_table

A data frame with one row per parameter per method, containing columns:

Method: The estimation method (RC, ERC, MCML, FMA, or BMA).
Term: The parameter name.
Estimate: The parameter estimate.
SE: The standard error.
CI.lower: The lower confidence bound, if confidence intervals have been computed via confint.
CI.upper: The upper confidence bound, if confidence intervals have been computed via confint.
pval.lower: p-value associated with the lower bound of the profile likelihood CI, the assess validity of the obtained bound. Only included if profile likelihood confidence intervals were computed via confint.
pval.upper: p-value associated with the upper bound of the profile likelihood CI, the assess validity of the obtained bound. Only included if profile likelihood confidence intervals were computed via confint.
Rhat: The Gelman-Rubin convergence diagnostic, included only when BMA results are present. Values above 1.05 indicate potential convergence problems.
n.eff: The effective sample size, included only when BMA results are present.

runtime_table

A data frame with columns Method and Runtime, reporting the computation time in seconds for each method.

total_runtime_seconds

The total computation time in seconds across all methods.

CI.computed

Logical. TRUE if confidence intervals have been computed via confint, FALSE otherwise.

Details

summary.amerasfit collects results from all estimation methods present in the fitted object into a single summary table. Columns for confidence intervals are only printed if they have been computed by confint. When BMA results are present in the fitted object, the summary table includes columns Rhat and n.eff, with NA values for all other methods.

Printing the summary prints the original call to ameras, the runtime (total and by method), and the table described above. This table can also be accessed directly (i.e., to retrieve confidence intervals) using summary_table.

Examples

data("data", package = "ameras")

## Fit the model
fit <- ameras(Y.binomial~dose(V1:V10, model="ERR"), data = data, family = "binomial",
              methods = "RC")
#> Fitting RC

## Summary without confidence intervals
summary(fit)
#> Call:
#> ameras(formula = Y.binomial ~ dose(V1:V10, model = "ERR"), data = data, 
#>     family = "binomial", methods = "RC")
#> 
#> Total run time: 0 seconds
#> 
#> Runtime in seconds by method:
#> 
#>  Method Runtime
#>      RC     0.0
#> 
#> Summary of coefficients by method:
#> 
#>  Method        Term Estimate      SE
#>      RC (Intercept)  -0.8847 0.07378
#>      RC        dose   0.8020 0.13751
#> 
#> Note: confidence intervals not yet computed. Use confint() to add them.
#> 

## Summary with confidence intervals
fit <- confint(fit, method = "wald.orig")
#> Obtaining profile likelihood CI for dose
#> RC confidence intervals:
#> 
#>       lower upper pval.lower pval.upper iter.lower iter.upper
#> dose 0.5648 1.112    0.05024    0.04959          5          4
#> 
summary(fit)
#> Call:
#> ameras(formula = Y.binomial ~ dose(V1:V10, model = "ERR"), data = data, 
#>     family = "binomial", methods = "RC")
#> 
#> Total run time: 0 seconds
#> 
#> Runtime in seconds by method:
#> 
#>  Method Runtime
#>      RC     0.0
#> 
#> Summary of coefficients by method:
#> 
#>  Method        Term Estimate      SE CI.lower CI.upper pval.lower pval.upper
#>      RC (Intercept)  -0.8847 0.07378       NA       NA         NA         NA
#>      RC        dose   0.8020 0.13751   0.5648    1.112    0.05024    0.04959
#> 

## Access the summary table directly
s <- summary_table(fit)

## Multiple methods
# \donttest{
fit2 <- ameras(Y.binomial~dose(V1:V10, model="ERR"), data = data, family = "binomial",
              methods = c("RC", "ERC", "MCML"))
#> Fitting RC
#> Fitting ERC
#> Fitting MCML
fit2 <- confint(fit2, method = "wald.orig")
#> Obtaining profile likelihood CI for dose
#> Obtaining profile likelihood CI for dose
#> Obtaining profile likelihood CI for dose
#> RC confidence intervals:
#> 
#>       lower upper pval.lower pval.upper iter.lower iter.upper
#> dose 0.5648 1.112    0.05024    0.04959          5          4
#> 
#> ERC confidence intervals:
#> 
#>       lower upper pval.lower pval.upper iter.lower iter.upper
#> dose 0.5728 1.144    0.04953    0.04819          6          4
#> 
#> MCML confidence intervals:
#> 
#>       lower upper pval.lower pval.upper iter.lower iter.upper
#> dose 0.5554 1.098    0.05008    0.04957          5          4
#> 
summary(fit2)
#> Call:
#> ameras(formula = Y.binomial ~ dose(V1:V10, model = "ERR"), data = data, 
#>     family = "binomial", methods = c("RC", "ERC", "MCML"))
#> 
#> Total run time: 12 seconds
#> 
#> Runtime in seconds by method:
#> 
#>  Method Runtime
#>      RC     0.0
#>     ERC    11.7
#>    MCML     0.3
#> 
#> Summary of coefficients by method:
#> 
#>  Method        Term Estimate      SE CI.lower CI.upper pval.lower pval.upper
#>      RC (Intercept)  -0.8847 0.07378       NA       NA         NA         NA
#>      RC        dose   0.8020 0.13751   0.5648    1.112    0.05024    0.04959
#>     ERC (Intercept)  -0.8849 0.07477       NA       NA         NA         NA
#>     ERC        dose   0.8214 0.14304   0.5728    1.144    0.04953    0.04819
#>    MCML (Intercept)  -0.8758 0.07323       NA       NA         NA         NA
#>    MCML        dose   0.7910 0.13644   0.5554    1.098    0.05008    0.04957
#> 
# }

Usage

Arguments

Value

Details

See also

Examples