Diagnostic Plots for an amerasfit Object

Produces diagnostic plots for a fitted amerasfit object, including residuals versus fitted values and normal Q-Q plots. Plots are produced for each estimation method present in the fitted object.

Usage

# S3 method for class 'amerasfit'
plot(x,
     methods = c("RC", "ERC", "MCML", "FMA", "BMA"), 
     which = NULL, type = NULL, dose.col = NULL, 
     add.smooth = getOption("add.smooth", TRUE), 
     qqline = TRUE, id.n = 3, ask = NULL, data = NULL, ...)

Arguments

x: A fitted model object of class amerasfit, as returned by ameras.
methods: Character vector specifying which estimation methods to produce plots for. One or more of "RC", "ERC", "MCML", "FMA", and "BMA". Defaults to all. Methods not present in x are silently skipped.
which: Character vector specifying which plots to produce. For non-"prophaz" families, one or both of "residuals-vs-fitted" and "qq". For family="prophaz", "schoenfeld" produces a scaled Schoenfeld residual plot against event time. Defaults to both for non-"prophaz" families and to "schoenfeld" for "prophaz" families.
type: The type of residuals to use. One of "pearson", "deviance", or "response" for non-"prophaz" models. For family="prophaz", this must be "schoenfeld". Defaults to "pearson" for non-"prophaz" families and to "schoenfeld" for "prophaz". See residuals.amerasfit for details.
dose.col: The dose realization to use for computation of residuals. If NULL (default), a best fitting realization is determined for each method (see Details).
add.smooth: Logical. If TRUE, a loess smooth line is added to the residuals versus fitted plot via panel.smooth. Defaults to getOption("add.smooth", TRUE). Is not used for the Schoenfeld residual plot for which a spline is always shown, mirroring plot.cox.zph.
qqline: Logical. If TRUE, a reference line is added to normal Q-Q plots via qqline. Defaults to TRUE.
id.n: Integer. The number of extreme residuals to label in residuals vs fitted and normal Q-Q plots. Labels show the row index of the observation. Defaults to 3. Set to 0 to suppress labeling.
ask: Logical. If TRUE, the user is prompted before each new plot. Defaults to NULL, in which case prompting occurs automatically when the number of plots exceeds the number of available panels and the session is interactive.
data: The original data frame used for fitting. Only required when the model was fitted with keep.data=FALSE.
...: Additional graphical arguments passed to plot.default and qqnorm.

Value

The amerasfit object x is returned invisibly.

Details

If not otherwise specified, the dose realization used to compute fitted values is selected for each estimation method as follows. For RC and ERC the mean dose across realizations is used. For MCML and FMA the realization yielding the largest likelihood at the final parameter estimates is used (which for FMA corresponds to the highest model averaging weight). For BMA the realization most frequently selected by the MCMC sampler is used. The selected dose column is shown in the plot title.

For the "residuals-vs-fitted" plot, the smooth line is added via panel.smooth using a loess smoother when add.smooth=TRUE. For the "qq" plot, the residuals are plotted against the theoretical quantiles of the normal distribution when qqline=TRUE. For both plots, the id.n most extreme residuals are labeled. For family="multinomial", one panel is produced per non-reference outcome category.

For proportional hazards models, scaled Schoenfeld residuals are drawn against the observed event times to assess the proportional hazards assumption. Under proportional hazards, the residuals should fluctuate randomly around zero with no systematic trend over time. Systematic patterns or smooth trends may indicate time-varying covariate effects and violation of the proportional hazards assumption. Note that the implementation here corresponds to using transform = "identity" in cox.zph, in contrast to the default setting for that function which applies a Kaplan–Meier transformation of time.

The data must either be stored on the object (keep.data=TRUE in ameras, the default) or supplied via the data argument.

Examples

data("data", package="ameras")
dosevars <- paste0("V", 1:10)

## Binomial model
fit <- ameras(Y.binomial ~ dose(all_of(dosevars), model="ERR"),
              data=data, family="binomial", methods="RC")
#> Error in resolve_dose_selection(sel_args, data): ℹ In argument: `all_of(dosevars)`.
#> Caused by error:
#> ! object 'dosevars' not found

## Both diagnostic plots for RC
plot(fit)
#> Error: object 'fit' not found

## Residuals vs fitted only
plot(fit, which="residuals-vs-fitted")
#> Error: object 'fit' not found

## Deviance residuals
plot(fit, type="deviance")
#> Error: object 'fit' not found

## Multiple methods
# \donttest{
fit2 <- ameras(Y.binomial ~ dose(all_of(dosevars), model="ERR"),
               data=data, family="binomial", methods=c("RC", "ERC"))
#> Error in resolve_dose_selection(sel_args, data): ℹ In argument: `all_of(dosevars)`.
#> Caused by error:
#> ! object 'dosevars' not found
plot(fit2)
#> Error: object 'fit2' not found
# }

## With keep.data=FALSE, supply data explicitly
# \donttest{
fit3 <- ameras(Y.binomial ~ dose(all_of(dosevars), model="ERR"),
               data=data, family="binomial", methods="RC",
               keep.data=FALSE)
#> Error in resolve_dose_selection(sel_args, data): ℹ In argument: `all_of(dosevars)`.
#> Caused by error:
#> ! object 'dosevars' not found
plot(fit3, data=data)
#> Error: object 'fit3' not found
# }

## Schoenfeld residual plot
# \donttest{
fit4 <- ameras(Surv(time, status) ~ dose(all_of(dosevars), model = "ERR"),
              data = data, family = "prophaz", methods = "RC")
#> Error in resolve_dose_selection(sel_args, data): ℹ In argument: `all_of(dosevars)`.
#> Caused by error:
#> ! object 'dosevars' not found
plot(fit4)
#> Error: object 'fit4' not found
# }

Usage

Arguments

Value

Details

See also

Examples