Package 'growthbreaks'

Load, check and plot input data for use in breakpoint detection

Description

Load, check and plot input data for use in breakpoint detection

Usage

check_data(dat, sex = FALSE, showPlot = TRUE)

Arguments

dat	data.frame with columns year, age, length, sex (optional)
sex	logical. does your data frame include a sex column?
showPlot	logical. do you want to visualize your input data?

Value

plots

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Check whether the CI of the derivative includes zero or not

Description

Check whether the CI of the derivative includes zero or not

Usage

check_Deriv(x, d, upper, lower, eval = 0, crit.eval)

Arguments

x	vector of values over which derivatives were evaluated
d	vector output of Deriv()
upper	upper confidence interval; output of confint_Deriv()
lower	lower confidence interval; output of confint_Deriv()
eval	the value that you would like to examine. default 0

Value

vector of confidence intervals of same length as object.

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Calculate confidence interval of derivative.

Usage

confint_Deriv(object, term, alpha = 0.05, ...)

Arguments

object	vector output of Deriv()
term	string smooth name, i.e. "year". must match values in mod and newdata
alpha	confidence threshold. default 0.05 (for 95

vector of confidence intervals of same length as object.

Calculate confidence interval of derivative.

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Evaluate first derivative of GAM smooth(s), adapted from Gavin Simpson.

Description

Evaluate first derivative of GAM smooth(s), adapted from Gavin Simpson.

Usage

Deriv(mod, n = 200, eps = 1e-04, newdata, term)

Arguments

mod	the output of mgcv::gam()
n	number of intervals over which to evaluate each smooth. default 200.
eps	tolerance threshold. default 1e-4.
newdata	optional; data.frame of new smooth parameters to evaluate
term	string smooth name, i.e. "year". must match values in mod and newdata

Value

a vector of derivative values of length n

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Wrapper function to to fit gams & evaluate first derivatives

Description

Wrapper function to to fit gams & evaluate first derivatives

Usage

get_Breaks(dat, ages_to_use = c(5, 10), sex = FALSE, axes = 0, showPlot = TRUE)

Arguments

dat	data.frame with columns year, age, length, lat, long, sex (optional)
ages_to_use	optional. vector of age(s) to evaluate for breakpoints.
sex	logical.
axes	do you want to evaluate axes for space only (0, default), time only (1), or both simultaneously (2)?
showPlot	logical. do you want to see the detected break(s) on a map? Applicable only for axes options 0 or 2.

Value

matrix of detected breakpoints and plots

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Hello, World!

Description

Prints 'Hello, world!'.

Usage

hello()

Examples

hello()

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Show breakpoints (and optionally data) on a map

Description

Show breakpoints (and optionally data) on a map

Usage

plot_Breaks(dat, breakpoints, showData = TRUE)

Arguments

dat	data.frame with columns year, age, length, lat, long, sex (optional)
breakpoints	data.frame with columns year (optional) lat and long. can be output of get_Breaks(). Use -Inf to turn off a break.
showData	logical. do you want to see your datapoints?

Value

Map

p1

plot of Alaska with dashed breakpoints and (optionally) raw length observations

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Re-fit growth data at putative breaks and return estimates for validation

Description

Re-fit growth data at putative breaks and return estimates for validation

Usage

refit_Growth(dat = simulated_data, breakpoints, selex = FALSE, showPlot = TRUE)

Arguments

dat	data.frame with columns year, age, length, lat, long, sex (optional)
breakpoints	data.frame with columns year and/or lat and long. can be output of get_Breaks().
showPlot	logical. do you want to see plots of the fitted curves?

Value

Von B growth parameters at input breakpoints; plots with uncertainty of growth curves:

$split_tables

list of tables of input data split by strata specified in breakpoints

$fits_df

input data, estimates and associated standard errors as single dataframe

$pars_df

Parameter estimates and associated standard errors

$fits_plot

input observations and fitted growth curves, by strata

$pars_plot

Parameter estimates and associated standard errors; red lines indicate statistically significant differences

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