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FSA (version 0.8.6)

bootstrap: Associated S3 methods for bootCase from car.

Description

Provides S3 methods to construct non-parametric bootstrap confidence intervals, hypothesis tests, and plots of the parameter estimates for bootCase objects from the car package.

Usage

## S3 method for class 'bootCase':
confint(object, parm = NULL, level = conf.level,
  conf.level = 0.95, plot = FALSE, err.col = "black", err.lwd = 2,
  rows = NULL, cols = NULL, ...)

## S3 method for class 'bootCase':
predict(object, FUN, MARGIN = 1, conf.level = 0.95,
  digits = NULL, ...)

## S3 method for class 'bootCase':
htest(object, parm = NULL, bo = 0, alt = c("two.sided",
  "less", "greater"), plot = FALSE, ...)

## S3 method for class 'bootCase':
hist(x, same.ylim = TRUE, ymax = NULL,
  rows = round(sqrt(ncol(x))), cols = ceiling(sqrt(ncol(x))), ...)

## S3 method for class 'bootCase':
plot(x, ...)

Arguments

object, x
A bootCase object.
parm
A number or string that indicates which column of object contains the parameter estimates to use for the confidence interval or hypothesis test.
level
Same as conf.level.
conf.level
A level of confidence as a proportion.
plot
A logical that indicates whether a plot should be constructed. If confint then a histogram of the parm parameters from the bootstrap samples with error bars that illustrate the bootstrapped confidence intervals will be construct
err.col
A single numeric or character that identifies the color for the error bars on the plot.
err.lwd
A single numeric that identifies the line width for the error bars on the plot.
rows
A single numeric that contains the number of rows to use on the graphic.
cols
A single numeric that contains the number of columns to use on the graphic.
FUN
The function to be applied for the prediction. See the examples.
MARGIN
A single numeric that indicates the margin over which FUN is applied. MARGIN=1 will apply to each row and is the default.
digits
A single numeric that indicates the number of digits for the result.
bo
The null hypothesized parameter value.
alt
A string that indicates the direction of the alternative hypothesis. See details.
same.ylim
A logical that indicates whether the same limits for the y-axis should be used on each histogram. Defaults to TRUE. Ignored if ylmts is non-null.
ymax
A single value that sets the maximum y-axis limit for each histogram or a vector of length equal to the number of groups that sets the maximum y-axis limit for each histogram separately.
col
A named color for the histogram bars.
...
Additional items to send to functions.

Value

  • If object is a matrix, then confint returns a matrix with as many rows as columns (i.e., parameter estimates) in object and two columns of the quantiles that correspond to the approximate confidence interval. If object is a vector, then confint returns a vector with the two quantiles that correspond to the approximate confidence interval. htest returns a two-column matrix with the first column containing the hypothesized value sent to this function and the second column containing the corresponding p-value. hist constructs histograms of the bootstrapped parameter estimates. plot constructs scatterplots of all pairs of bootstrapped parameter estimates. predict returns a matrix with one row and three columns, with the first column holding the predicted value (i.e., the median prediction) and the last two columns holding the approximate confidence interval.

Details

confint finds the two quantiles that have the (1-conf.level)/2 proportion of bootstrapped parameter estimates below and above. This is an approximate 100conf.level% confidence interval. predict applies a user-supplied function to each row of object and then finds the median and the two quantiles that have the proportion (1-conf.level)/2 of the bootstrapped predictions below and above. The median is returned as the predicted value and the quantiles are returned as an approximate 100conf.level% confidence interval for that prediction. In htest the direction of the alternative hypothesis is identified by a string in the alt= argument. The strings may be "less" for a less than alternative, "greater" for a greater than alternative, or "two.sided" for a not equals alternative (the DEFAULT). In the one-tailed alternatives the p-value is the proportion of bootstrapped parameter estimates in object$coefboot that are extreme of the null hypothesized parameter value in bo. In the two-tailed alternative the p-value is twice the smallest of the proportion of bootstrapped parameter estimates above or below the null hypothesized parameter value in bo.

References

S. Weisberg (2005). Applied Linear Regression, third edition. New York: Wiley, Chapters 4 and 11.

See Also

bootCase in car.

Examples

Run this code
data(Ecoli)
fnx <- function(days,B1,B2,B3) {
  if (length(B1) > 1) {
    B2 <- B1[2]
    B3 <- B1[3]
    B1 <- B1[1]
  }
  B1/(1+exp(B2+B3*days))
}
nl1 <- nls(cells~fnx(days,B1,B2,B3),data=Ecoli,start=list(B1=6,B2=7.2,B3=-1.45))
if (require(car)) {    # for bootCase()
  nl1.boot <- car::bootCase(nl1,B=99)  # B=99 too small to be useful
  confint(nl1.boot,"B1")
  confint(nl1.boot,c(2,3))
  confint(nl1.boot,conf.level=0.90)
  predict(nl1.boot,fnx,days=3)
  htest(nl1.boot,1,bo=6,alt="less")
  hist(nl1.boot)
  plot(nl1.boot)
  cor(nl1.boot)
}

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