plot_uncertainties: Plot MLE vs MCMC marginal standard deviations for each...
In adnuts: No-U-Turn MCMC Sampling for 'ADMB' Models
Description
Usage
Arguments
Details
Value
Examples
Description
Plot MLE vs MCMC marginal standard deviations for each
parameter
Usage
1
plot_uncertainties(fit, log = TRUE, plot = TRUE)
Arguments
fit
A fitted object returned by
sample_admb
log
Whether to plot the logarithm or not.
plot
Whether to plot it or not.
Details
It can be helpful to compare uncertainty estimates
between the two paradigms. This plots the marginal posterior
standard deviation vs the frequentist standard error
estimated from the .cor file. Large differences often
indicate issues with one estimation method.
Value
Invisibly returns data.frame with parameter name and
estimated uncertainties.
Examples
1
2
3
fit <- readRDS(system.file('examples', 'fit.RDS', package='adnuts'))
x <- plot_uncertainties(fit, plot=FALSE)
head(x)
adnuts documentation built on March 3, 2021, 1:07 a.m.
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Description Usage Arguments Details Value Examples
Description
Plot MLE vs MCMC marginal standard deviations for each parameter
Usage
1 | plot_uncertainties(fit, log = TRUE, plot = TRUE)
|
Arguments
fit |
A fitted object returned by
|
log |
Whether to plot the logarithm or not. |
plot |
Whether to plot it or not. |
Details
It can be helpful to compare uncertainty estimates between the two paradigms. This plots the marginal posterior standard deviation vs the frequentist standard error estimated from the .cor file. Large differences often indicate issues with one estimation method.
Value
Invisibly returns data.frame with parameter name and estimated uncertainties.
Examples
1 2 3 | fit <- readRDS(system.file('examples', 'fit.RDS', package='adnuts'))
x <- plot_uncertainties(fit, plot=FALSE)
head(x)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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