Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

Conduct F test of null hypothesis LB = 0 using output from betta() or betta_random()

Description

This function performs an F-test of a null hypothesis LB = 0 where B is a vector of p fixed effects returned by betta() or betta_random() and L is an m x p matrix with linearly independent rows.

Usage

F_test(fitted_betta, L, method = "bootstrap", nboot = 1000)

Arguments

Value

A list containing

Author(s)

David Clausen

References

Willis, A., Bunge, J., and Whitman, T. (2015). Inference for changes in biodiversity. arXiv preprint.

See Also

betta;

betta_random;

betta;

get_F_stat

Examples

# generate example data
df <- data.frame(chats = c(2000, 3000, 4000, 3000,
2000, 3000, 4000, 3000), ses = c(100, 200, 150, 180,
100, 200, 150, 180),
                 Cont_var = c(100, 150, 100, 50,
                 100, 150, 100, 50),
                 Cont_var_2 = c(50,200,25,125,
                 50,200,25,125))

# fit betta()
example_fit <- betta(formula = chats ~ Cont_var + Cont_var_2, ses = ses, data = df)


# construct L for hypothesis that B_cont_var = B_cont_var_2 = 0
L <- rbind(c(0,1,0),
           c(0,0,1))

F_test_results <- F_test(example_fit, L, nboot = 100)

alpha_estimate

Description

Build objects of class alpha_estimate from their components. alpha_estimate() is a constructor method

Usage

alpha_estimate(
  estimate = NULL,
  error = NULL,
  estimand = NULL,
  name = NULL,
  interval = NULL,
  interval_type = NULL,
  type = NULL,
  model = NULL,
  warnings = NULL,
  frequentist = NULL,
  parametric = NULL,
  plot = NULL,
  reasonable = NULL,
  other = NULL,
  ...
)

Arguments

Value

An object of class alpha_estimate

alpha_estimates

Description

Build objects of class alpha_estimates from their components. alpha_estimates() is a constructor method

Usage

alpha_estimates(...)

Arguments

Value

An object of class alpha_estimates

(Data) Frequency count table of soil microbes in an apples orchard.

Description

(Data) Frequency count table of soil microbes in an apples orchard.

Usage

apples

Format

A data frame with 88 rows and 2 variables:

index

an index variable

frequency

number of taxa that were observed with this frequency

...

References

Willis, A. and Bunge, J. (2015). Estimating diversity via frequency ratios. Biometrics, 71(4), 1042–1049. doi:10.1111/biom.12332

Walsh, F. et al. (2014). (2014). Restricted streptomycin use in apple orchards did not adversely alter the soil bacteria communities. Frontiers in Microbiology 4, 383.

Modelling total diversity with betta

Description

This function tests for heterogeneity of total diversity (observed plus unobserved) across multiple sites. It can account or test for fixed effects that may explain diversity. It returns the significance of the covariates in explaining diversity and a hypothesis test for heterogeneity.

Usage

betta(
  chats = NULL,
  ses,
  X = NULL,
  initial_est = NULL,
  formula = NULL,
  data = NULL,
  p.digits = 3
)

Arguments

Value

Note

Ecologists who are interested in the way species richness varies with covariate information often run a regression-type analysis on the observed diversity using their covariate information as predictors. However, in many settings (especially microbial), rare and unobserved taxa play a hugely important role in explaining the subtleties of the ecosystem, however, a regression analysis on the observed diversity level fails to account for these unobserved taxa. By predicting the total level of diversity (for example, via breakaway) and estimating the standard error in the estimate, one can take account of these unobserved, but important, taxa. In order to account for the estimated nature of the response, a mixed model approach is taken, whereby the varying levels of confidence in the estimates contributes to a diagonal but heteroscedastic covariance matrix. Given covariates constitute the fixed effects in the mixed model, and significance of the random effect term “sigsq_u” reflects heterogeneity in the sample, that is, variability that cannot be explained by only the covariates. The authors believe this to be the first attempt at modelling total diversity in a way that accounts for its estimated nature.

Author(s)

Amy Willis

References

Willis, A., Bunge, J., and Whitman, T. (2015). Inference for changes in biodiversity. arXiv preprint.

Willis, A. and Bunge, J. (2015). Estimating diversity via frequency ratios. Biometrics.

See Also

breakaway; breakaway_nof1; apples

Examples

df <- data.frame(chats = c(2000, 3000, 4000, 3000), ses = c(100, 200, 150, 180),
                 Cont_var = c(100, 150, 100, 50))

# formula notation
betta(formula = chats ~ Cont_var, ses = ses, data = df)

# direct input
betta(c(2000, 3000, 4000, 3000), c(100, 200, 150, 180), cbind(1, c(100, 150, 100,
    50)))

## handles missing data
betta(c(2000, 3000, 4000, 3000), c(100, 200, 150, NA))

## A test for heterogeneity of apples diversity estimates vs butterfly estimates
betta(c(1552, 1500, 884), c(305, 675, 205), cbind(1, c(0, 0, 1)))

Confidence intervals and testing for linear combinations of fixed effects estimated via betta() or betta_random()

Description

This function provides point estimates, standard errors, and equal-tailed confidence intervals for linear combinations of fixed effects estimated via betta() or betta_random(). A p-value for a Wald test of the null that the linear combination of effects is equal to zero (against a general alternative) is also returned.

Usage

betta_lincom(fitted_betta, linear_com, signif_cutoff = 0.05)

Arguments

Value

Author(s)

David Clausen

References

Willis, A., Bunge, J., and Whitman, T. (2015). Inference for changes in biodiversity. arXiv preprint.

See Also

betta;

Examples

# generate example data
df <- data.frame(chats = c(2000, 3000, 4000, 3000), ses = c(100, 200, 150, 180),
                 Cont_var = c(100, 150, 100, 50))

# fit betta()
example_fit <- betta(formula = chats ~ Cont_var, ses = ses, data = df)

# generate point estimate and 95% CI for mean richness at Cont_var = 125

betta_lincom(fitted_betta = example_fit,
linear_com = c(1, 125)) # this tells betta_lincom to estimate value of beta_0 + 125*beta_1,
# where beta_0 is the intercept, and beta_1 is the (true value of the) coefficient on Cont_var

function for plotting total diversity

Description

A simple plotting interface for comparing total diversity across samples or a covariate gradient.

Usage

betta_pic(
  y,
  se,
  x = 1:length(y),
  ylimu = NULL,
  myy = NULL,
  mymain = NULL,
  mycol = NULL,
  labs = NULL,
  mypch = NULL,
  myxlim = NULL
)

Arguments

Value

A ggplot object.

Author(s)

Amy Willis

References

Willis, A., Bunge, J., and Whitman, T. (2015). Inference for changes in biodiversity. arXiv preprint.

See Also

betta

Examples

betta_pic(c(1552, 1500, 884), c(305, 675, 205), mymain = "Example title")

modelling total diversity with random effects

Description

This function extends betta() to permit random effects modelling.

Usage

betta_random(
  chats = NULL,
  ses,
  X = NULL,
  groups = NULL,
  formula = NULL,
  data = NULL,
  p.digits = 3
)

Arguments

Value

Author(s)

Amy Willis

References

Willis, A., Bunge, J., and Whitman, T. (2015). Inference for changes in biodiversity. arXiv preprint.

See Also

betta;

Examples

df <- data.frame(chats = c(2000, 3000, 4000, 3000),
ses = c(100, 200, 150, 180),
Cont_var = c(100, 150, 100, 50),
groups = c("a", "a", "a", "b"))

# formula notation
betta_random(formula = chats ~ Cont_var| groups,
ses = ses,
 data = df)

# direct input
betta_random(c(2000, 3000, 4000, 3000), c(100, 200, 150, 180),
X = cbind(Int = 1, Cont_var = c(100, 150, 100, 50)),
groups = c("a", "a", "a", "b"))

## handles missing data
betta_random(c(2000, 3000, 4000, 3000), c(100, 200, 150, NA),
groups = c("a", NA,
    "b", "b"))

Species richness estimation with breakaway

Description

breakaway is a wrapper for modern species richness estimation for modern datasets

Usage

breakaway(
  input_data,
  cutoff = NA,
  output = NULL,
  plot = NULL,
  answers = NULL,
  print = NULL,
  ...
)

Arguments

Value

An object of class alpha_estimate

Note

‘⁠breakaway⁠’ presents an estimator of species richness that is well-suited to the high-diversity/microbial setting. However, many microbial datasets display more diversity than the Kemp-type models can permit. In this case, the log-transformed WLRM diversity estimator of Rocchetti et. al. (2011) is returned. The authors' experience suggests that some datasets that require the log-transformed WLRM contain “false” diversity, that is, diversity attributable to sequencing errors (via an inflated singleton count). The authors encourage judicious use of diversity estimators when the dataset may contain these errors, and recommend the use of breakaway_nof1 as an exploratory tool in this case.

Author(s)

Amy Willis

References

Willis, A. and Bunge, J. (2015). Estimating diversity via frequency ratios. Biometrics, 71(4), 1042–1049.

See Also

breakaway_nof1; kemp; apples

Examples

breakaway(apples)
breakaway(apples, plot = FALSE, output = FALSE, answers = TRUE)

species richness estimation without singletons

Description

This function permits estimation of total diversity based on a sample frequency count table. Unlike breakaway, it does not require an input for the number of species observed once, making it an excellent exploratory tool for microbial ecologists who believe that their sample may contain spurious singletons. The underlying estimation procedure is similar to that of breakaway and is outlined in Willis & Bunge (2014). The diversity estimate, standard error, estimated model coefficients and plot of the fitted model are returned.

Usage

breakaway_nof1(
  input_data,
  output = NULL,
  plot = NULL,
  answers = NULL,
  print = NULL
)

Arguments

Value

An object of class alpha_estimate

Note

It is common for microbial ecologists to believe that their dataset contains false diversity. This often arises because sequencing errors result in classifying already observed organisms as new organisms. ‘⁠breakaway_nof1⁠’ was developed as an exploratory tool in this case. Practitioners can run ‘⁠breakaway⁠’ on their dataset including the singletons, and ‘⁠breakaway_nof1⁠’ on their dataset excluding the singletons, and assess if the estimated levels of diversity are very different. Great disparity may provide evidence of an inflated singleton count, or at the very least, that ‘⁠breakaway⁠’ is especially sensitive to the number of rare species observed. Note that ‘⁠breakaway_nof1⁠’ may be less stable than ‘⁠breakaway⁠’ due to predicting based on a reduced dataset, and have greater standard errors.

Author(s)

Amy Willis

References

Willis, A. (2015). Species richness estimation with high diversity but spurious singletons. arXiv.

Willis, A. and Bunge, J. (2015). Estimating diversity via frequency ratios. Biometrics.

See Also

breakaway; apples

Examples

breakaway_nof1(apples[-1, ])
breakaway_nof1(apples[-1, ], plot = FALSE, output = FALSE, answers = TRUE)

Build frequency count tables from an OTU table

Description

Build frequency count tables from an OTU table

Usage

build_frequency_count_tables(the_table)

Arguments

Value

A list of frequency count tables corresponding to the columns.

Chao1 species richness estimator

Description

This function implements the Chao1 richness estimate, which is often mistakenly referred to as an index.

Usage

chao1(input_data, output = NULL, answers = NULL)

Arguments

Value

An object of class alpha_estimate, or alpha_estimates for phyloseq objects

Note

The authors of this package strongly discourage the use of this estimator. It is only valid when you wish to assume that every taxa has equal probability of being observed. You don't really think that's possible, do you?

Author(s)

Amy Willis

Examples

chao1(apples)

Bias-corrected Chao1 species richness estimator

Description

This function implements the bias-corrected Chao1 richness estimate.

Usage

chao1_bc(input_data, output = NULL, answers = NULL)

Arguments

Value

An object of class alpha_estimate, or alpha_estimates for phyloseq objects

Note

The authors of this package strongly discourage the use of this estimator. It is only valid when you wish to assume that every taxa has equal probability of being observed. You don't really think that's possible, do you?

Author(s)

Amy Willis

Examples

chao1_bc(apples)

Chao-Bunge species richness estimator

Description

This function implements the species richness estimation procedure outlined in Chao & Bunge (2002).

Usage

chao_bunge(input_data, cutoff = 10, output = NULL, answers = NULL)

Arguments

Value

An object of class alpha_estimate, or alpha_estimates for phyloseq objects

Author(s)

Amy Willis

Examples

chao_bunge(apples)

The Chao-Shen estimate of Shannon diversity

Description

The Chao-Shen estimate of Shannon diversity

Usage

chao_shen(input_data)

Arguments

Value

An object of class alpha_estimate

Run some basic checks on a possible frequency count table

Description

Run some basic checks on a possible frequency count table

Usage

check_format(output_data)

Arguments

Value

A checked frequency count table

convert between different inputs for alpha-diversity estimates

Description

Inputs slated for development include phyloseq and otu_table

Usage

convert(input_data)

Arguments

Value

Frequency count able

Find a cut-off for estimates relying on contiguous counts

Description

Find a cut-off for estimates relying on contiguous counts

Usage

cutoff_wrap(my_data, requested = NA)

Arguments

Value

Cutoff value

Calculate F statistic under null hypothesis LB = 0 using output from betta() or betta_random()

Description

This function calculates an F statistic for a test of null hypothesis LB = 0 (against an unrestricted alternative) where B is a vector of p fixed effects returned by betta() or betta_random() and L is an m x p matrix with linearly independent rows.

Usage

get_F_stat(fitted_betta, L)

Arguments

Value

A list containing

The Good-Turing estimate of species richness

Description

The Good-Turing estimate of species richness

Usage

good_turing(input_data)

Arguments

Value

An object of class alpha_estimate, or alpha_estimates for phyloseq objects

(Data) Frequency count table of soil microbes in Hawaii.

Description

(Data) Frequency count table of soil microbes in Hawaii.

Usage

hawaii

Format

A data frame with 198 rows and 2 variables:

index

an index variable

frequency

number of taxa that were observed with this frequency

...

References

Willis, A. and Bunge, J. (2015). Estimating diversity via frequency ratios. Biometrics, 71(4), 1042–1049. doi:10.1111/biom.12332

Species richness estimation with Kemp-type models

Description

This function implements the species richness estimation procedure outlined in Willis & Bunge (2015). The diversity estimate, standard error, estimated model coefficients, model details and plot of the fitted model are returned.

Usage

kemp(
  input_data,
  cutoff = NA,
  output = NULL,
  plot = NULL,
  answers = NULL,
  print = NULL,
  ...
)

Arguments

Value

An object of class alpha_estimate

Author(s)

Amy Willis

References

Willis, A. and Bunge, J. (2015). Estimating diversity via frequency ratios. Biometrics, 71(4), 1042–1049.

Rocchetti, I., Bunge, J. and Bohning, D. (2011). Population size estimation based upon ratios of recapture probabilities. Annals of Applied Statistics, 5.

See Also

breakaway; breakaway_nof1; apples

Examples

kemp(apples)
kemp(apples, plot = FALSE, output = FALSE, answers = TRUE)

Make design matrix

Description

Make design matrix

Usage

make_design_matrix(phyloseq_object, variables)

Arguments

Value

A matrix object giving the design matrix from the desired variables.

Draw frequency count subtables from an OTU table

Description

Draw frequency count subtables from an OTU table

Usage

make_frequency_count_table(labels)

Arguments

Value

A frequency count table.

Estimate species richness with an objective Bayes method using a geometric model

Description

Estimate species richness with an objective Bayes method using a geometric model

Usage

objective_bayes_geometric(
  data,
  output = TRUE,
  plot = TRUE,
  answers = FALSE,
  tau = 10,
  burn.in = 100,
  iterations = 2500,
  Metropolis.stdev.N = 75,
  Metropolis.start.theta = 1,
  Metropolis.stdev.theta = 0.3
)

Arguments

Value

A list of results, including

,

,

,

,

,

, and

.

Objective Bayes species richness estimate with the mixed-geometric model

Description

Objective Bayes species richness estimate with the mixed-geometric model

Usage

objective_bayes_mixedgeo(
  data,
  output = TRUE,
  plot = TRUE,
  answers = FALSE,
  tau = 10,
  burn.in = 100,
  iterations = 2500,
  Metropolis.stdev.N = 100,
  Metropolis.start.T1 = 1,
  Metropolis.stdev.T1 = 2,
  Metropolis.start.T2 = 3,
  Metropolis.stdev.T2 = 2,
  bars = 3
)

Arguments

Value

A list of results, including

,

,

,

,

,

, and

.

Objective Bayes species richness estimate with the Negative Binomial model

Description

Objective Bayes species richness estimate with the Negative Binomial model

Usage

objective_bayes_negbin(
  data,
  output = TRUE,
  plot = TRUE,
  answers = FALSE,
  tau = 10,
  burn.in = 1000,
  iterations = 5000,
  Metropolis.stdev.N = 100,
  Metropolis.start.T1 = -0.8,
  Metropolis.stdev.T1 = 0.01,
  Metropolis.start.T2 = 0.8,
  Metropolis.stdev.T2 = 0.01,
  bars = 5
)

Arguments

Value

A list of results, including

,

,

,

,

,

, and

.

Objective Bayes species richness estimate with the Poisson model

Description

Objective Bayes species richness estimate with the Poisson model

Usage

objective_bayes_poisson(
  data,
  output = TRUE,
  plot = TRUE,
  answers = FALSE,
  tau = 10,
  burn.in = 100,
  iterations = 2500,
  Metropolis.stdev.N = 75,
  Metropolis.start.lambda = 1,
  Metropolis.stdev.lambda = 0.3,
  bars = 5
)

Arguments

Value

A list of results, including

,

,

,

,

,

, and

.

(Data) Data frame of covariate information about pasolli_et_al.

Description

(Data) Data frame of covariate information about pasolli_et_al.

Usage

pasolli_et_al

Format

A data frame with 4930 rows and 9 variables:

SGB.ID

sample ID

AverageAbundance

average abundance in the sample

std

standard deviation

q1

first quartile

MedianAbundance

median abundance

q3

third quartile

min

minimum

max

maximum

#.Samples

number of samples

...

Wrapper for phyloseq objects

Description

Wrapper for phyloseq objects

Usage

physeq_wrap(fn, physeq, ...)

Arguments

Value

Object of class alpha_estimates

Plot function for alpha_estimates class

Description

Plot function for alpha_estimates class

Usage

## S3 method for class 'alpha_estimates'
plot(
  x,
  physeq = NULL,
  measure = NULL,
  color = NULL,
  shape = NULL,
  title = NULL,
  trim_plot = FALSE,
  ...
)

Arguments

Details

... does not currently have any implemented options. Optional arguments currently include "trim_plot", a Optional

Value

A ggplot object.

Examples

library(phyloseq)
data(GlobalPatterns)
alphas <- breakaway(GlobalPatterns)
plot(alphas)

PoissonModel

Description

A model to estimate the number of missing taxa under a Poisson Model

Usage

poisson_model(input_data, cutoff = 10)

Arguments

Value

An object of class alpha_estimate.

PoissonModelNof1

Description

A model to estimate the number of missing taxa under a zero- and one-truncated Poisson Model

Usage

poisson_model_nof1(input_data, cutoff = 10)

Arguments

Value

An object of class alpha_estimate.

OTU table to relative abundances

Description

OTU table to relative abundances

Usage

proportions_instead(the_table)

Arguments

Value

A proportion table or vector.

Negative binomially distributed frequency count tables.

Description

Simulate a frequency count table based on a negative binomial model. Zero-truncated, obviously.

Usage

rnbinomtable(C, size, probability)

Arguments

Value

A simulated frequency count table.

Author(s)

Amy Willis

beta version: Zero-truncated negative binomially distributed frequency count tables.

Description

Simulate a frequency count table based on a negative binomial model. Zero-truncated, obviously.

Usage

rztnbinomtable(C, size, probability)

Arguments

Value

A simulated frequency count table.

Author(s)

Amy Willis

Plug-in Inverse Simpson diversity

Description

This function implements the plug-in Inverse Simpson diversity

Usage

sample_inverse_simpson(input_data)

Arguments

Value

An object of class alpha_estimate, or alpha_estimates for phyloseq objects

Examples

sample_inverse_simpson(apples)

Sample richness estimator

Description

This function implements the sample richness estimate, which is the number of non-zero taxa per sample.

Usage

sample_richness(input_data)

Arguments

Value

An object of class alpha_estimate, or alpha_estimates for phyloseq objects

Examples

sample_richness(apples)

Plug-in Shannon diversity

Description

This function implements the plug-in Shannon diversity

Usage

sample_shannon(input_data)

Arguments

Value

An object of class alpha_estimate, or alpha_estimates for phyloseq objects

Examples

sample_shannon(apples)

Plug-in Shannon's E ("Equitability")

Description

This function implements the plug-in Shannon's E

Usage

sample_shannon_e(input_data)

Arguments

Value

An object of class alpha_estimate, or alpha_estimates for phyloseq objects

Examples

sample_shannon_e(apples)

Plug-in Simpson diversity

Description

This function implements the plug-in Simpson diversity

Usage

sample_simpson(input_data)

Arguments

Value

An object of class alpha_estimate, or alpha_estimates for phyloseq objects

Examples

sample_simpson(apples)

Estimate the sample size needed to do an unpaired one-way test using betta

Description

Estimate the sample size needed to do an unpaired one-way test using betta

Usage

sample_size_estimate(
  control_group_est,
  se_est,
  diff = 5,
  alpha = 0.05,
  prop = 0.8,
  samples = 20,
  precision = 5
)

Arguments

Value

An estimate of the necessary sample size and some details

Plot the power obtained with sample size

Description

Plot the power obtained with sample size

Usage

sample_size_figure(control_group_est, se_est, diff = 5, samples = 20)

Arguments

Value

A plot of the power with the sample size

Simulate from a fitted betta model

Description

Simulate from a fitted betta model

Usage

simulate_betta(fitted_betta, nsim)

Arguments

Value

A list of length nsim, each element of which is a vector of simulated Y-values under the fitted betta model

Simulate from a fitted betta_random model

Description

Simulate from a fitted betta_random model

Usage

simulate_betta_random(fitted_betta, nsim)

Arguments

Value

A list of length nsim, each element of which is a vector of simulated Y-values under the fitted betta model

(Data) Data frame of soil data from whitman_et_al.

Description

A phyloseq object with an OTU table and sample data from a soil microbiome study.

Usage

soil_phylo

Format

A phyloseq-class experiment-level object with an OTU table and sample data.

otu_table

OTU table with 7,770 taxa and 119 samples

tax_table

taxonomy table

sam_data

sample data with the following covariates:

• Plants, values 0 and 1. Index for different plants

• Day, values 0 (initial sampling point), 1 (12 days after treatment additions), and 2 (82 days after treatment additions). Index for different days of measurement

• Amdmt, values 0 (no additions), 1 (biochar additions), and 2 (fresh biomass additions). Index for different soil additives.

• DayAmdmt, values 00, 01, 02, 10, 11, 12, 20, 21, and 22. A single index for the combination of Day and Amdmt with Day as the first digit and Amdmt as the second digit.

• ID, values A, B, C, D, and F. Index for different soil plots.

...

References

Whitman, T., Pepe-Ranney, C., Enders, A., Koechli, C., Campbell, A., Buckley, D. H., Lehmann, J. (2016). Dynamics of microbial community composi-tion and soil organic carbon mineralization in soil following addition of pyrogenic andfresh organic matter. The ISME journal, 10(12):2918. <doi: 10.1038/ismej.2016.68>.

Conduct F test of null hypothesis LB = 0 using output from betta() or betta_random()

Description

This function performs an F-test of a null hypothesis LB = 0 where B is a vector of p fixed effects returned by betta() or betta_random() and L is an m x p matrix with linearly independent rows.

Usage

test_submodel(
  fitted_betta,
  submodel_formula,
  method = "bootstrap",
  nboot = 1000
)

Arguments

Value

A list containing

Author(s)

David Clausen

References

Willis, A., Bunge, J., and Whitman, T. (2015). Inference for changes in biodiversity. arXiv preprint.

See Also

betta;

betta_random;

betta;

F_test

Examples

# generate example data
df <- data.frame(chats = c(2000, 3000, 4000, 3000,
2000, 3000, 4000, 3000), ses = c(100, 200, 150, 180,
100, 200, 150, 180),
                 Cont_var = c(100, 150, 100, 50,
                 100, 150, 100, 50),
                 Cont_var_2 = c(50,200,25,125,
                 50,200,25,125))

# fit betta()
example_fit <- betta(formula = chats ~ Cont_var + Cont_var_2, ses = ses, data = df)


# construct L for hypothesis that B_cont_var = B_cont_var_2 = 0
L <- rbind(c(0,1,0),
           c(0,0,1))

F_test_results <- F_test(example_fit, L, nboot = 100)

(Data) Data frame of covariate information about toy_otu_table.

Description

(Data) Data frame of covariate information about toy_otu_table.

Usage

toy_metadata

Format

A data frame with 143 rows and 4 variables:

Years

Year of sampling

bloom2

Did the sample correspond to a bloom event?

Period

What season was sampled?

Site

Where was the sample taken from?

...

(Data) A toy OTU table.

Description

Covariate info available in 'toy_metadata'. A data frame with 448 rows and 143 columns. Rows give the abundance of each taxon; columns give the samples

Usage

toy_otu_table

Format

An object of class data.frame with 448 rows and 143 columns.

(Data) The taxonomy of the OTUs in 'toy_otu_table'.

Description

(Data) The taxonomy of the OTUs in 'toy_otu_table'.

Usage

toy_taxonomy

Format

An object of class factor of length 448.

Calculate the true Gini-Simpson index

Description

Calculate the true Gini-Simpson index

Usage

true_gini(input)

Arguments

Value

The Gini-Simpson index of the population given by input.

Note

This function is intended for population-level data. If you are dealing with a microbial sample, use DivNet instead.

Calculate the true Hill numbers

Description

Calculate the true Hill numbers

Usage

true_hill(input, q)

Arguments

Value

The Hill number of the population given by input.

Calculate the true Inverse Simpson index

Description

Calculate the true Inverse Simpson index

Usage

true_inverse_simpson(input)

Arguments

Value

The inverse-Simpson index of the population given by input.

Note

This function is intended for population-level data. If you are dealing with a microbial sample, use DivNet instead.

Calculate the true Shannon index based on proportions

Description

Calculate the true Shannon index based on proportions

Usage

true_shannon(input)

Arguments

Value

The Shannon index of the population given by input.

Note

This function is intended for population-level data. If you are dealing with a microbial sample, use DivNet instead.

Calculate the true Shannon's equitability index

Description

Calculate the true Shannon's equitability index

Usage

true_shannon_e(input)

Arguments

Value

The Shannon E's of the population given by input.

Note

This function is intended for population-level data. If you are dealing with a microbial sample, use DivNet instead.

Calculate the true Simpson index

Description

Calculate the true Simpson index

Usage

true_simpson(input)

Arguments

Value

The Simpson index of the population given by input.

Note

This function is intended for population-level data. If you are dealing with a microbial sample, use DivNet instead.

The transformed weighted linear regression estimator for species richness estimation

Description

This function implements the transformed version of the species richness estimation procedure outlined in Rocchetti, Bunge and Bohning (2011).

Usage

wlrm_transformed(
  input_data,
  cutoff = NA,
  print = NULL,
  plot = NULL,
  answers = NULL
)

Arguments

Value

An object of class alpha_estimate, or alpha_estimates for phyloseq objects

Note

While robust to many different structures, model is almost always misspecified. The result is usually implausible diversity estimates with artificially low standard errors. Extreme caution is advised.

Author(s)

Amy Willis

References

Rocchetti, I., Bunge, J. and Bohning, D. (2011). Population size estimation based upon ratios of recapture probabilities. Annals of Applied Statistics, 5.

See Also

breakaway; apples; wlrm_untransformed

Examples

wlrm_transformed(apples)
wlrm_transformed(apples, plot = FALSE, print = FALSE, answers = TRUE)

The untransformed weighted linear regression estimator for species richness estimation

Description

This function implements the untransformed version of the species richness estimation procedure outlined in Rocchetti, Bunge and Bohning (2011).

Usage

wlrm_untransformed(
  input_data,
  cutoff = NA,
  print = NULL,
  plot = NULL,
  answers = NULL
)

Arguments

Value

An object of class alpha_estimate, or alpha_estimates for phyloseq objects

Note

This estimator is based on the negative binomial model and for that reason generally produces poor fits to microbial data. The result is usually artificially low standard errors. Caution is advised.

Author(s)

Amy Willis

References

Rocchetti, I., Bunge, J. and Bohning, D. (2011). Population size estimation based upon ratios of recapture probabilities. Annals of Applied Statistics, 5.

See Also

breakaway; apples; wlrm_transformed

Examples

wlrm_untransformed(apples)