Diagnostic plots
The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
This vignette demonstrates a typical survival analysis workflow using the BetaDanish package.
library(BetaDanish)
library(survival)
#> Warning: package 'survival' was built under R version 4.5.3
#>
#> Attaching package: 'survival'
#> The following objects are masked from 'package:BetaDanish':
#>
#> leukemia, transplant
data('remission', package = 'BetaDanish')
head(remission)
#> time status
#> 1 0.08 1
#> 2 2.09 1
#> 3 3.48 1
#> 4 4.87 1
#> 5 6.94 1
#> 6 8.66 1fit <- fit_betadanish(Surv(time, status) ~ 1, data = remission, n_starts = 1)
#> Warning: The times look recorded on a grid of 0.01. The point-density
#> likelihood treats them as exact, which understates the standard errors.
#> Consider grouped = TRUE.
#> Warning: The fitted correlation between a-hat and c-hat is -0.992, so
#> effectively only the product c*a is identified. Individual estimates of a and c
#> should not be interpreted.
#> Warning: 1 starting point(s) reached a degenerate ridge and were discarded. The
#> reported fit is the best admissible optimum. If this is most of the grid, the
#> four-parameter model is a poor choice for these data.
summary(fit)
#>
#> Call:
#> fit_betadanish(formula = Surv(time, status) ~ 1, data = remission,
#> n_starts = 1)
#>
#> Beta-Danish Distribution Fit
#> Model: Full 4-Parameter Model
#>
#> Estimate Std. Error Lower 95% Upper 95% z value Pr(>|z|)
#> a 0.686589 0.980513 -1.235217 2.608395 0.7002 0.483781
#> b 4.078124 1.529590 1.080128 7.076120 2.6662 0.007672 **
#> c 2.196484 3.184261 -4.044668 8.437637 0.6898 0.490324
#> k 0.082975 0.092057 -0.097456 0.263405 0.9013 0.367405
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---
#> Log-Likelihood: -409.9137
#> AIC: 827.8274 | BIC: 839.2356fit_sub <- fit_betadanish(Surv(time, status) ~ 1, data = remission, submodel = TRUE, n_starts = 1)
#> Warning: The times look recorded on a grid of 0.01. The point-density
#> likelihood treats them as exact, which understates the standard errors.
#> Consider grouped = TRUE.
#> Warning: b-hat is only 1.90 standard errors from 1, close to the b = 1
#> non-identifiability ridge. Consider the ED submodel (submodel = TRUE). See the
#> Identifiability section of ?fit_betadanish.
compare_models(fit, fit_sub)
#> Likelihood Ratio Test (a = 1 vs a != 1)
#>
#> Model LogLik Chisq Df Pr(>Chisq)
#> 1 Submodel (3-param) -409.9541 NA NA NA
#> 2 Full Model (4-param) -409.9137 0.08081129 1 0.776201The fitted model can be used to estimate survival probabilities, hazard behavior, and overall model fit. Users should compare the Beta-Danish model with alternative lifetime distributions and inspect diagnostic plots before drawing final conclusions.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.