QurvE (version 1.1.1)

flBootSpline: flBootSpline: Function to generate a bootstrap

Description

fl.gcBootSpline resamples the fluorescence-'x' value pairs in a dataset with replacement and performs a spline fit for each bootstrap sample.

Usage

flBootSpline(
  time = NULL,
  growth = NULL,
  fl_data,
  ID = "undefined",
  control = fl.control()
)

Value

A gcBootSpline object containing a distribution of fluorescence parameters and a flFitSpline object for each bootstrap sample. Use plot.gcBootSpline

to visualize all bootstrapping splines as well as the distribution of physiological parameters.

raw.x

Raw time values provided to the function as time.

raw.fl

Raw growth data provided to the function as data.

ID

(Character) Identifies the tested sample.

boot.x

Table of time values per column, resulting from each spline fit of the bootstrap.

boot.fl

Table of growth values per column, resulting from each spline fit of the bootstrap.

boot.flSpline

List of flFitSpline object, created by flFitSpline for each resample of the bootstrap.

lambda

Vector of estimated lambda (lag time) values from each bootstrap entry.

max_slope

Vector of estimated max_slope (maximum slope) values from each bootstrap entry.

A

Vector of estimated A (maximum fluorescence) values from each bootstrap entry.

integral

Vector of estimated integral values from each bootstrap entry.

bootFlag

(Logical) Indicates the success of the bootstrapping operation.

control

Object of class fl.control containing list of options passed to the function as control.

Arguments

time

Vector of the independent variable: time (if x_type = 'time' in fl.control object.

growth

Vector of the independent variable: growth (if x_type = 'growth' in fl.control object.

fl_data

Vector of dependent variable: fluorescence.

ID

(Character) The name of the analyzed sample.

control

A fl.control object created with fl.control, defining relevant fitting options.

See Also

Other fluorescence fitting functions: flFitSpline(), flFit()

Examples

# load example dataset
input <- read_data(data.growth = system.file("lac_promoters_growth.txt", package = "QurvE"),
                   data.fl = system.file("lac_promoters_fluorescence.txt", package = "QurvE"),
                   csvsep = "\t",
                   csvsep.fl = "\t")

# Extract time and normalized fluorescence data for single sample
time <- input$time[4,]
data <- input$norm.fluorescence[4,-(1:3)] # Remove identifier columns

# Perform linear fit
TestFit <- flBootSpline(time = time,
                       fl_data = data,
                       ID = 'TestFit',
                       control = fl.control(fit.opt = 's', x_type = 'time',
                       nboot.fl = 50))

plot(TestFit, combine = TRUE, lwd = 0.5)