Generic functions to provide support for
Boot function in
car uses the
boot function from the
boot package to do a straightforward case
or residual bootstrap for a regression object. These are generic functions to
summarize the results of the bootstrap.
## S3 method for class 'boot': hist(x, parm, layout = NULL, ask, main = "", freq = FALSE, estPoint = TRUE, point.col = "black", point.lty = 2, point.lwd = 2, estDensity = !freq, den.col = "blue", den.lty = 1, den.lwd = 2, estNormal = !freq, nor.col = "red", nor.lty = 2, nor.lwd = 2, ci = c("bca", "none", "percentile"), level = 0.95, legend = c("top", "none", "separate"), box = TRUE, ...) ## S3 method for class 'boot': summary(object, parm, high.moments = FALSE, extremes = FALSE, ...) ## S3 method for class 'boot': confint(object, parm, level = 0.95, type = c("bca", "norm", "basic", "perc", "all"), ...)
- x, object
- An object created by a call to
- A vector of numbers or coefficient names giving the coefficients for which a histogram or confidence interval is desired. If numbers are used, 1 corresponds to the intercept, if any. The default is all coefficients.
- If set to a value like
c(4, 3), the layout of the graph will have this many rows and columns. If not set, the program will select an appropriate layout. If the number of graphs exceed nine, you must select the lay
TRUE, ask the user before drawing the next plot; if
FALSE, don't ask.
- Main title for the graphs. The default is
main=""for no title.
- The usual default for
freq=TRUEto give a frequency histogram. The default here is
freq=FALSEto give a density histogram. A density estimate and/or a fitted normal density can be added to the graph if <
- estPoint, point.col, point.lty, point.lwd
estPoint=TRUE, the default, a vertical line is drawn on the histgram at the value of the point estimate computed from the complete data. The remaining three optional arguments set the color, line type and line width of the line that is
- estDensity, den.col, den.lty, den.lwd
freq=FALSE, the default, a kernel density estimate is drawn on the plot with a call to the
densityfunction with no additional arguments. The remaining three optional arguments set the color,
- estNormal, nor.col, nor.lty, nor.lwd
freq=FALSE, the default, a normal density with mean and sd computed from the data is drawn on the plot. The remaining three optional arguments set the color, line type and line width of the lines that are
- A confidence interval based on the bootstrap will be added to the histogram
using the BCa method if
ci="bca"or using the percentile method if
ci="percentile". No interval is drawn if
ci="none". The default is <
- A legend can be added to the (array of) histograms. The value
"top"puts at the top-left of the plots. The value "separate"puts the legend in its own graph following all the histograms. The value "none"
- Add a box around each histogram.
- Additional arguments passed to
hist; for other methods this is included for compatibility with the generic method. For example, the argument
histwill draw the histogram transparently, leaving o
- Should the skewness and kurtosis be included in the summary? Default is FALSE.
- Should the minimum, maximum and range be included in the summary? Default is FALSE.
- Confidence level, a number between 0 and 1. In
levelcan be a vector; for example
level=c(.68, .90, .95)will return the estimated quantiles at
c(.025, .05, .16, .84, .95, .975).
- Selects the confidence interval type. The types
implemented are the
"percentile"method, which uses the function
quantileto return the appropriate quantiles for the confidence limit specified, the default
histis used for the side-effect of drawing an array of historgams of each column of the first argument.
summaryreturns a matrix of summary statistics for each of the columns in the bootstrap object. The
confintmethod returns confidence intervals. Print method
Efron, B. and Tibsharini, R. (1993)
An Introduction to the Bootstrap. New
York: Chapman and Hall.
Fox, J. and Weisberg, S. (2011)
An R Companion to Applied Regression, Second Edition. Sage.
Fox, J. and Weisberg, S. (2012) Bootstrapping,
m1 <- lm(Fertility ~ ., swiss) betahat.boot <- Boot(m1, R=99) # 99 bootstrap samples--too small to be useful summary(betahat.boot) # default summary confint(betahat.boot) hist(betahat.boot)