Description Usage Arguments Details Value Methods (by class) References Examples
View source: R/bootcoefsmethods.R
This function provides an easy interface and useful output to bootstrapping the regression coefficients of robust linear regression models
1 2 3 4 5 6 7 8 9 10  bootcoefs(object, R = 999, method = c("frb", "residuals", "cases"),
ncpus = NULL, cl = NULL, ...)
## S3 method for class 'complmrob'
bootcoefs(object, R = 999, method = c("frb",
"residuals", "cases"), ncpus = NULL, cl = NULL, ...)
## S3 method for class 'lmrob'
bootcoefs(object, R = 999, method = c("frb", "residuals",
"cases"), ncpus = NULL, cl = NULL, ...)

object 
the model to bootstrap the coefficients from 
R 
the number of bootstrap replicates. 
method 
one of 
ncpus 
the number of CPUs to utilize for bootstrapping. 
cl 
a snow or parallel cluster to use for bootstrapping. 
... 
currently ignored. 
The default method is to use fast and robust bootstrap as described in the paper by M. SalibianBarrera, et al. (see references). The other options are to bootstrap the residuals or to bootstrap cases (observations), but the sampling distribution of the estimates from these methods can be numerically instable and take longer to compute.
A list of type bootcoefs
for which print
,
summary
and plot
methods are available
complmrob
: For robust linear regression models with compositional data
lmrob
: For standard robust linear regression models
M. SalibianBarrera, S. Aelst, and G. Willems. Fast and robust bootstrap. Statistical Methods and Applications, 17(1):4171, 2008.
1 2 3 4 5 6 
Robust linear regression with compositional covariates
complmrob(formula = lifeExp ~ ., data = data)
Standard errors and derived statistics are base on 200 bootstrap replications
Coefficients:
Estimate bias Std. Error Pr(b<>0)
(Intercept) 70.65261 0.24198 2.06894 0.00498 **
Murder 2.23318 0.01281 0.46259 0.00498 **
Assault 1.04628 0.12812 1.10469 0.18905
Rape 3.27946 0.15222 0.61214 0.00498 **

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Confidence intervals:
Estimate 2.5 % 97.5 %
(Intercept) 70.6526 65.6610 74.2156
Murder 2.2332 3.3032 1.3616
Assault 1.0463 2.6442 1.6064
Rape 3.2795 1.9121 4.2620
Robust residual standard error: 0.6931
Multiple Rsquared: 0.7115 Adjusted Rsquared: 0.6992
Warning: Ignoring unknown aesthetics: ymax
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.