Associated S3 methods for nlsBoot from nlstools.

Provides S3 methods to construct non-parametric bootstrap confidence intervals and hypothesis tests for parameter values and predicted values of the response variable for a nlsBoot object from the nlstools package.

Usage

# S3 method for nlsBoot
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 nlsBoot
predict(object, FUN, conf.level = 0.95, digits = NULL, ...)

htest(object, ...)

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

Arguments

object: An object saved from nlsBoot().
parm: An integer that indicates which parameter to compute the confidence interval or hypothesis test for. The confidence interval Will be computed for all parameters if NULL.
level: Same as conf.level. Used for compatibility with the main confint.
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 constructed. If codehtest, then a histogram of the parm parameters with a vertical lines illustrating the bovalue will be constructed.
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 numeric that contains the number of rows to use on the graphic.
cols: A numeric that contains the number of columns to use on the graphic.
...: Additional arguments to functions.
FUN: The function to be applied for the prediction. See the examples.
digits: A single numeric that indicates the number of digits for the result.
bo: The null hypothesized parameter value.
alt: A string that identifies the “direction” of the alternative hypothesis. See details.

Value

confint returns a matrix with as many rows as columns (i.e., parameter estimates) in the object$coefboot data frame and two columns of the quantiles that correspond to the approximate confidence interval.

htest returns a matrix with two columns. The first column contains the hypothesized value sent to this function and the second column is the corresponding p-value.

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 proportion (1-conf.level)/2 of the bootstrapped parameter estimates below and above. This is an approximate 100conf.level% confidence interval.

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.

In predict, a user-supplied function is applied to each row of the coefBoot object in a nlsBoot 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.

Author

Derek H. Ogle, DerekOgle51@gmail.com

Examples

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(nlstools)) {
  nl1.bootn <-  nlstools::nlsBoot(nl1,niter=99) # too few to be useful
  confint(nl1.bootn,"B1")
  confint(nl1.bootn,c(2,3))
  confint(nl1.bootn,conf.level=0.90)
  confint(nl1.bootn,plot=TRUE)
  predict(nl1.bootn,fnx,days=3)
  predict(nl1.bootn,fnx,days=1:3)
  htest(nl1.bootn,1,bo=6,alt="less")
}
#> Loading required package: nlstools
#> 
#> 'nlstools' has been loaded.
#> IMPORTANT NOTICE: Most nonlinear regression models and data set examples
#> related to predictive microbiolgy have been moved to the package 'nlsMicrobio'
#> Error in nlstools::nlsBoot(nl1, niter = 99): Procedure aborted: the fit only converged in 1 % during bootstrapping