Package 'halk' reference manual

Title:	Methods to Create Hierarchical Age Length Keys for Age Assignment
Description:	Provides methods for implementing hierarchical age length keys to estimate fish ages from lengths using data borrowing. Users can create hierarchical age length keys and use them to assign ages given length.
Authors:	Paul Frater [aut, cre]
Maintainer:	Paul Frater <[email protected]>
License:	MIT + file LICENSE
Version:	0.0.5
Built:	2024-11-28 06:43:32 UTC
Source:	CRAN

Adjusts data to account for plus group or minimum age

Description

These functions performs two tasks. It lumps all ages greater than the plus group into that age, and it filters data only to those greater than or equal to the minimum age. adjust_plus_min_ages works on a vector whereas adjust_plus_min_ages_df words on a data.frame

Usage

adjust_plus_min_ages_df(data, minage = NULL, pls_grp = NULL)

adjust_plus_min_ages(age_vec, minage = NULL, pls_grp = NULL)
adjust_plus_min_ages_df(data, minage = NULL, pls_grp = NULL)

adjust_plus_min_ages(age_vec, minage = NULL, pls_grp = NULL)

Arguments

`data`	Data with age as a column, or a numeric vector of ages
`minage`	Numeric. The minimum age; everything else is excluded
`pls_grp`	Numeric. The plus group; all ages older will be lumped into this group
`age_vec`	A vector of ages

Value

A data.frame similar to data, but with ages less than minage excluded and ages >= plus_group aggregated into that age

Convert ages from/to ordered factor

Description

In order for the machine learning models to properly predict ages, the known ages should be converted to an ordered factor during model fitting. This will ensure that the predict.* functions return age values that actually make sense.

Usage

ages_as_ordered_factor(data, age_col = "age")

ages_as_integer(data, age_col = "est.age")
ages_as_ordered_factor(data, age_col = "age")

ages_as_integer(data, age_col = "est.age")

Arguments

`data`	A data.frame with a column corresopnding to `age_col` or a vector of values
`age_col`	Character. The name of the column that contains ages

Value

A data.frame with the values in age_col converted to an ordered factor

Assign ages to non-aged data based on a fitted age model

Description

Assign ages to non-aged data based on a fitted age model

Usage

assign_ages(newdata, object, ...)
assign_ages(newdata, object, ...)

Arguments

`newdata`	A vector or data.frame with size/length measurements
`object`	An object of class "alk", "halk_fit" as produced by `make_alk` or `make_halk`
`...`	Additional parameters to pass to the S3 object methods

Value

A data.frame the same as newdata, but with ages assigned based on the model provided in object

Examples

spp_alk <- make_halk(spp_data, levels = "spp")
spp_est_ages <- assign_ages(spp_data, spp_alk)
spp_alk <- make_halk(spp_data, levels = "spp")
spp_est_ages <- assign_ages(spp_data, spp_alk)

Assign associated age-length key attributes to a data.frame

Description

This is just a helper function to assign the needed attributes and classes to a data.frame that is produced by either make_alk or make_halk.

Usage

assign_alk_attributes(
  data,
  size_col = "length",
  age_col = "age",
  autobin = TRUE,
  size_bin = 1,
  min_age = NULL,
  plus_group = NULL,
  alk_n = NULL,
  classes = "alk",
  dnorm_params = NULL,
  levels = NULL
)
assign_alk_attributes(
  data,
  size_col = "length",
  age_col = "age",
  autobin = TRUE,
  size_bin = 1,
  min_age = NULL,
  plus_group = NULL,
  alk_n = NULL,
  classes = "alk",
  dnorm_params = NULL,
  levels = NULL
)

Arguments

`data`	A data.frame
`size_col`	Character. Name of the column representing sizes
`age_col`	Character. Name of the column representing ages
`autobin`	Logical to set the attribute of autobin
`size_bin`	Numeric. What is the width of size bins
`min_age`	Numeric. The minimum age that was included in the alk
`plus_group`	Numeric. The age that represents the plus group
`alk_n`	Numeric. The number of samples that went into creating the alk
`classes`	Character. The class that should get prepended to the data.frame class(es)
`dnorm_params`	The value of parameters that went into creating the normal distributions on the age groups
`levels`	Character vector of the levels used. This creates the "levels" attribute if present

Value

A data.frame with associated attributes assigned

Simple function that returns NA values

Description

A vector of NA will be returned that is the length of x

Usage

assign_na_age(x)
assign_na_age(x)

Arguments

`x`	Any vector of any length

Value

A vector the same length as x containing only NA values

Convert a vector of lengths into binned values

Description

This will take a vector of numeric values and bin them according to the value specified in binwidth

Usage

bin_lengths(x, binwidth, include_upper = FALSE, ...)
bin_lengths(x, binwidth, include_upper = FALSE, ...)

Arguments

`x`	Numeric vector of values
`binwidth`	Numeric vector specifying how wide the length bins should be
`include_upper`	Logical. Append the upper value of the bin and return the length range as a character string (TRUE), or return the lower value as numeric (FALSE, default)
`...`	Additional arguments passed onto `cut`

Value

A vector of values the same length as x, but binned to the values according to binwidth

Examples

bin_lengths(length_data$length, binwidth = 2)
bin_lengths(length_data$length, binwidth = 2)

Calculate mean-squared-error (MSE) and root mean-squared-error (RMSE) of estimated ages

Description

These functions will calculate MSE and RMSE for estimated ages produced by assign_ages. Output is specific to each level used by the age-length key to assign ages

Usage

calc_mse(data, age_col = "age")

calc_rmse(data, age_col = "age")
calc_mse(data, age_col = "age")

calc_rmse(data, age_col = "age")

Arguments

`data`	A data.frame as created by `assign_ages`
`age_col`	Character. Name of the age column in `data`

Value

Numeric value for estimated ages with no levels or a data.frame with a MSE or RMSE value for each level used to fit ages

Examples

wae_data <- spp_data[spp_data$spp == "walleye", ]
alk <- make_alk(wae_data)
wae_est_age <- assign_ages(wae_data, alk)
calc_mse(wae_est_age)
calc_rmse(wae_est_age)
wae_data <- spp_data[spp_data$spp == "walleye", ]
alk <- make_alk(wae_data)
wae_est_age <- assign_ages(wae_data, alk)
calc_mse(wae_est_age)
calc_rmse(wae_est_age)

Backend helper function to compute MSE or RMSE

Description

This function is the engine for calc_mse and calc_rmse. It was only created to remove the root argument from the user in the main calc_mse function

Usage

calc_mse_(data, age_col = "age", root = FALSE)
calc_mse_(data, age_col = "age", root = FALSE)

Arguments

`data`	A data.frame as created by `assign_ages`
`age_col`	Character. Name of the age column in `data`
`root`	Logical. computer MSE (FALSE, default) or RMSE (TRUE)

Compute test statistics for comparing actual and estimated ages

Description

Using these functions you can compute either a Kolmogorov-Smirnov (KS) statistic or a Chi-squared test statistic to compare estimated ages to actual ages. See details for how each test works and what is reported.

Usage

calc_ks_score(
  data,
  summary_fun = mean,
  age_col = "age",
  suppress_warnings = TRUE,
  return_val = "statistic",
  ...
)

calc_chi_score(
  data,
  age_col = "age",
  suppress_warnings = TRUE,
  return_val = "statistic",
  ...
)
calc_ks_score(
  data,
  summary_fun = mean,
  age_col = "age",
  suppress_warnings = TRUE,
  return_val = "statistic",
  ...
)

calc_chi_score(
  data,
  age_col = "age",
  suppress_warnings = TRUE,
  return_val = "statistic",
  ...
)

Arguments

`data`	A data.frame containing estimated ages as returned by `assign_ages`
`summary_fun`	Function used to compute summary statistics for `calc_ks_score` for each age group (default is `mean`)
`age_col`	Character string specifying the name of the age column
`suppress_warnings`	Logical. Should any warnings from the function call to `ks.test` or `chisq.test` be suppressed (TRUE, the default)
`return_val`	Character. The name of the object to return from the given test
`...`	Additional arguments to pass to `summary_fun` (`calc_ks_score`) or `chisq.test` (`calc_chi_score`)

Details

The KS test compares length distributions for each age class from known ages against that of estimated ages computed by the assign_ages function. The output is a summary value of the test statistics as specified by summary_fun.

The calc_chi_score function performs a Chi-square test (using the chisq.test function) on the number of estimated and actual ages for each age group.

Value

A numeric value for each level that was used in the model to assign ages

Examples

halk <- make_halk(spp_data, levels = c("spp"))
newdat <- laa_data
newdat$spp <- "bluegill"
pred_ages <- assign_ages(newdat, halk)
calc_ks_score(pred_ages)
calc_chi_score(pred_ages)
halk <- make_halk(spp_data, levels = c("spp"))
newdat <- laa_data
newdat$spp <- "bluegill"
pred_ages <- assign_ages(newdat, halk)
calc_ks_score(pred_ages)
calc_chi_score(pred_ages)

Check for age/length data in the data being estimated or predicted

Description

These are just simple helper functions used within other functions that check to make sure that ages and lengths are present in the data and stop the fucntion call if they are missing

Usage

check_age_data(data, age_col)

check_length_data(data, size_col)
check_age_data(data, age_col)

check_length_data(data, size_col)

Arguments

`data`	A data.frame
`age_col`	Character. The column name for the age column in `data`
`size_col`	Character. The column name for the size column in `data`

Value

NULL. An error will be called if age/length data is missing

Check the model type and return standardized version

Description

This is a non-exported function to check whether the model type specified is available and return a standardized version of the model name. This standardized version will then feed into a S3 method for the given model.

Usage

check_model_type(model)
check_model_type(model)

Arguments

model

A character string naming the model

Value

A standardized version of the model name, or an error if model doesn't exist yet

Compute the quotient of integrals as a measure of percent error between two curves

Description

This is a method for comparing how "close" or "accurate" one curve is to another (reference) curve. The method works by dividing the area between the curves by the area under the reference curve. See Details for more information

Usage

integral_quotient(
  ref_curve_params,
  comp_curve_params,
  min_x,
  max_x,
  curve_fun = function(x, linf, k, t0) {
     out <- linf * (1 - exp(-k * (x - t0)))
    
    return(out)
 }
)
integral_quotient(
  ref_curve_params,
  comp_curve_params,
  min_x,
  max_x,
  curve_fun = function(x, linf, k, t0) {
     out <- linf * (1 - exp(-k * (x - t0)))
    
    return(out)
 }
)

Arguments

`ref_curve_params`	A list of named parameters for the reference curve (i.e. the standard that is being compared to)
`comp_curve_params`	A list of named parameters for the curve that is being compared
`min_x`	The minimum value across which to integrate
`max_x`	The maximum value across which to integrate
`curve_fun`	The function that is being compared. Defaults to an anonymous function that is the von Bertalanffy growth function.

Details

The integral quotient method provides a basis for comparison between two curves by dividing the area between the curves by the area under the reference curve (i.e. the quotient of integrals)

Value

A value of the area between curves divided by the area under the reference curve

Examples

ref_curve_params <- list(linf = 60, k = 0.25, t0 = -0.5)
comp_curve_params <- list(linf = 62, k = 0.25, t0 = -0.4)
comp_curve2_params <- list(linf = 65, k = 0.25, t0 = -1)
comp_curve_iq <-
 integral_quotient(ref_curve_params, comp_curve_params, 0, 10)
comp_curve2_iq <-
  integral_quotient(ref_curve_params, comp_curve2_params, 0, 10)
vbgf <- function (x, linf, k, t0) {linf * (1 - exp(-k * (x - t0)))}
curve(
  vbgf(x, ref_curve_params$linf, ref_curve_params$k, ref_curve_params$t0),
  from = 0,
  to = 10,
  ylim = c(0, 60),
  xlab = "Age", ylab = "Length"
)
curve(
  vbgf(x, comp_curve_params$linf, comp_curve_params$k, comp_curve_params$t0),
  add = TRUE,
  col = "blue"
)
curve(
  vbgf(x, comp_curve2_params$linf, comp_curve2_params$k, comp_curve2_params$t0),
  add = TRUE,
  col = "red"
)
text(9, 40, labels = paste0(comp_curve_iq, "%"), col = "blue")
text(9, 43, labels = paste0(comp_curve2_iq, "%"), col = "red")
ref_curve_params <- list(linf = 60, k = 0.25, t0 = -0.5)
comp_curve_params <- list(linf = 62, k = 0.25, t0 = -0.4)
comp_curve2_params <- list(linf = 65, k = 0.25, t0 = -1)
comp_curve_iq <-
 integral_quotient(ref_curve_params, comp_curve_params, 0, 10)
comp_curve2_iq <-
  integral_quotient(ref_curve_params, comp_curve2_params, 0, 10)
vbgf <- function (x, linf, k, t0) {linf * (1 - exp(-k * (x - t0)))}
curve(
  vbgf(x, ref_curve_params$linf, ref_curve_params$k, ref_curve_params$t0),
  from = 0,
  to = 10,
  ylim = c(0, 60),
  xlab = "Age", ylab = "Length"
)
curve(
  vbgf(x, comp_curve_params$linf, comp_curve_params$k, comp_curve_params$t0),
  add = TRUE,
  col = "blue"
)
curve(
  vbgf(x, comp_curve2_params$linf, comp_curve2_params$k, comp_curve2_params$t0),
  add = TRUE,
  col = "red"
)
text(9, 40, labels = paste0(comp_curve_iq, "%"), col = "blue")
text(9, 43, labels = paste0(comp_curve2_iq, "%"), col = "red")

Example length-at-age data

Description

Simple age-structured population data with age and length records for each individual. laa_data represents a well-sampled age-length dataset, whereas laa_data_low_n is one with few total samples, laa_data_low_age_n is one with few samples in some ages, and laa_data_few_ages is a dataset with few age groups sampled. Species specific datasets are similar, but with the prefix laa_ replaced by spp_. These datasets contain species specific length-at-age data

Usage

laa_data

laa_data_low_n

laa_data_low_age_n

laa_data_few_ages

spp_data

spp_data_low_n

spp_data_low_age_n

spp_data_few_ages
laa_data

laa_data_low_n

laa_data_low_age_n

laa_data_few_ages

spp_data

spp_data_low_n

spp_data_low_age_n

spp_data_few_ages

Format

## 'laa_data' A data.frame with 244 rows and 2 columns:

spp: Species, only applicable for spp_data_* data.frames
age: Age of individual
length: Length of individual (arbitrary units)

## 'laa_data_low_n' A data.frame with 27 rows and 2 columns:

## 'laa_data_low_age_n' A data.frame with 74 rows and 2 columns:

## 'laa_data_few_ages' A data.frame with 49 rows and 2 columns:

## 'spp_data' A data.frame with 1022 rows and 3 columns:

## 'spp_data_low_n' A data.frame with 87 rows and 3 columns:

## 'spp_data_low_age_n' A data.frame with 160 rows and 3 columns:

## 'spp_data_few_ages' A data.frame with 261 rows and 3 columns:

Example length data

Description

Simple vector and data.frame containing length measurements. These are used in examples for functions that assign ages.

Usage

length_data

spp_length_data
length_data

spp_length_data

Format

## length data A data.frame with one column and 244 rows

spp: Species, only in spp_length_data
length: Length of individual (arbitrary units)

## 'spp_length_data' A data.frame with 1022 rows and 2 columns:

Make an age-length key out of length-at-age data

Description

Make an age-length key out of length-at-age data

Usage

make_alk(
  laa_data,
  sizecol = "length",
  autobin = TRUE,
  binwidth = 1,
  agecol = "age",
  min_age = NULL,
  plus_group = NULL,
  numcol = NULL,
  min_age_sample_size = 5,
  min_total_sample_size = min_age_sample_size * min_age_groups,
  min_age_groups = 5,
  warnings = TRUE
)
make_alk(
  laa_data,
  sizecol = "length",
  autobin = TRUE,
  binwidth = 1,
  agecol = "age",
  min_age = NULL,
  plus_group = NULL,
  numcol = NULL,
  min_age_sample_size = 5,
  min_total_sample_size = min_age_sample_size * min_age_groups,
  min_age_groups = 5,
  warnings = TRUE
)

Arguments

`laa_data`	A data.frame with length-at-age data
`sizecol`	Character string naming the column that holds size data
`autobin`	Logical. Should the function automatically assign length bins (default is TRUE)
`binwidth`	Numeric. If `autobin = TRUE` this is the width for the size bins
`agecol`	Character string naming the column that holds age data
`min_age`	Numeric. All ages less than this value will not be used in ALK
`plus_group`	Numeric value of the oldest age to include in the ALK. All older individuals will be included in this plus group
`numcol`	Character string naming the column that holds numbers data
`min_age_sample_size`	Only applicable to alk models. The minimum number of samples that must be in each age group in order to create an alk
`min_total_sample_size`	Only applicable to alk models. The minimum number of samples that must be in data in order to create an alk
`min_age_groups`	Only applicable to alk models. The minimum number of age groups that must be in data in order to create an alk
`warnings`	Logical. Display warnings (TRUE, default)

Value

A data.frame containing the proportions of records for each size that are at each age.

Examples

make_alk(laa_data)
make_alk(laa_data)

Create a hierarchical age-length key (HALK)

Description

This function creates a hierarchically nested age-length key that can be used to estimate age of an organism based on proportion of sampled organisms in each age group.

Usage

make_halk(data, levels = NULL, age_col = "age", size_col = "length", ...)
make_halk(data, levels = NULL, age_col = "age", size_col = "length", ...)

Arguments

`data`	A data.frame with age and size samples
`levels`	Character vector specifying the levels for HALK creation
`age_col`	Optional. String of the column name in `data` housing age data
`size_col`	Optional. String of the column name in `data` housing size data
`...`	Additional arguments passed to `make_alk`

Value

A tibble with columns for each level and a column called alk that houses the age-length key for that particular level

Examples

make_halk(spp_data, levels = "spp")
make_halk(spp_data, levels = "spp")

Count number of length-at-age samples or age groups at each level and return those with greater than equal to the minimum desired number

Description

These are helper shortcut functions to determine if data meet the minimum desired number of age groups and/or sample sizes.

Usage

min_count_laa_data(
  data,
  sub_levels = NULL,
  min_age_sample_size = NULL,
  min_total_sample_size = NULL,
  min_age_groups = NULL
)

min_age_groups(data, sub_levels = NULL, min_age_grps)
min_count_laa_data(
  data,
  sub_levels = NULL,
  min_age_sample_size = NULL,
  min_total_sample_size = NULL,
  min_age_groups = NULL
)

min_age_groups(data, sub_levels = NULL, min_age_grps)

Arguments

`data`	Data.frame with length-at-age data
`sub_levels`	The levels at which to check
`min_age_sample_size`	Only applicable to alk models. The minimum number of samples that must be in each age group in order to create an alk
`min_total_sample_size`	Only applicable to alk models. The minimum number of samples that must be in data in order to create an alk
`min_age_groups`	Only applicable to alk models. The minimum number of age groups that must be in data in order to create an alk
`min_age_grps`	The minimum number of age groups that must be present in data to create an ALK

Value

A data.frame just like data, but with samples excluded that don't meet the required number of samples in min_sample_size

Simple helper function to rename size and age column names to age and length

Description

Simple helper function to rename size and age column names to age and length

Usage

rename_laa_cols(
  data,
  size_col = "length",
  age_col = "age",
  num_col = NULL,
  goback = FALSE
)
rename_laa_cols(
  data,
  size_col = "length",
  age_col = "age",
  num_col = NULL,
  goback = FALSE
)

Arguments

`data`	Any data.frame with some columns representing age and size
`size_col`	Character. The name of the column containing sizes
`age_col`	Character. The name of the column containing ages
`num_col`	Character. The name of the column containing number of individuals
`goback`	Logical. Reverse names once they've already been renamed

Value

A data.frame the same as data, but with names changed

Check for species in columns and/or levels and add to levels if present

Description

These helper functions just check to see if a species column exists in the data (designated as 'spp' or 'species'). If one of those columns exists, but the column name is not in the levels argument it will get added to levels.

Usage

is_spp_in_levels(levels)

is_spp_in_data(data)

spp_level(levels)

rm_spp_level(levels)

add_spp_level(data, levels)
is_spp_in_levels(levels)

is_spp_in_data(data)

spp_level(levels)

rm_spp_level(levels)

add_spp_level(data, levels)

Arguments

`levels`	The levels argument passed from `make_halk`
`data`	A data.frame with length-at-age data

Value

A character vector of levels possibly with 'spp' or 'species' added

Separate species, county, waterbody example length-at-age and length data

Description

Simple age-structured population with age and/or length records, but expanded across multiple counties and waterbodies for tests and examples in make_halk used with levels.

Usage

wb_spp_laa_data

wb_spp_length_data
wb_spp_laa_data

wb_spp_length_data

Format

## 'wb_spp_laa_data' A data.frame with 36,849 records and 5 columns

spp: Species
county: Arbitrary example county name
waterbody: Arbitrary example waterbody name nested within county
age: Age of individual, only in wb_spp_laa_data
length: Length of individual (arbitrary units)

An object of class tbl_df (inherits from tbl, data.frame) with 9182 rows and 4 columns.

Package 'halk'

Help Index

Adjusts data to account for plus group or minimum age

Description

Usage

Arguments

Value

Convert ages from/to ordered factor

Description

Usage

Arguments

Value

Assign ages to non-aged data based on a fitted age model

Description

Usage

Arguments

Value

Examples

Assign associated age-length key attributes to a data.frame

Description

Usage

Arguments

Value

Simple function that returns NA values

Description

Usage

Arguments

Value

Convert a vector of lengths into binned values

Description

Usage

Arguments

Value

Examples

Calculate mean-squared-error (MSE) and root mean-squared-error (RMSE) of estimated ages

Description

Usage

Arguments

Value

Examples

Backend helper function to compute MSE or RMSE

Description

Usage

Arguments

Compute test statistics for comparing actual and estimated ages

Description

Usage

Arguments

Details

Value

Examples

Check for age/length data in the data being estimated or predicted

Description

Usage

Arguments

Value

Check the model type and return standardized version

Description

Usage

Arguments

Value

Compute the quotient of integrals as a measure of percent error between two curves

Description

Usage

Arguments

Details

Value

Examples

Example length-at-age data

Description

Usage

Format

Example length data

Description

Usage

Format

Make an age-length key out of length-at-age data

Description

Usage

Arguments