Package 'evinf'

Title: Inference with Extreme Value Inflated Count Data
Description: Allows users to model and draw inferences from extreme value inflated count data, and to evaluate these models and compare to non extreme-value inflated counterparts. The package is built to be compatible with standard presentation tools such as 'broom', 'tidy', and 'modelsummary'.
Authors: David Randahl [cre, aut], Johan Vegelius [aut]
Maintainer: David Randahl <[email protected]>
License: MIT + file LICENSE
Version: 0.8.10
Built: 2024-11-14 06:26:42 UTC
Source: CRAN

Help Index

Bootstrap coefficient extractor

Description

Bootstrap coefficient extractor

Usage

coefficient_extractor(object, ...)

Arguments

object

a fitted model with bootstraps of class evzinb, evinb, nbboot, or zinbboot
...

Component to be extracted (not for nbboot). Alternatives are 'nb','zi','evinf','pareto', and 'all'

Value

A tibble with coefficient values, one row per bootstrap and component

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
coefficient_extractor(model, component = 'all')

Bootstrap coefficient extractor

Description

Bootstrap coefficient extractor

Usage

## S3 method for class 'evinb'
coefficient_extractor(
  object,
  component = c("nb", "evinf", "pareto", "all"),
  ...
)

Arguments

object

A fitted evinb model with bootstraps
component

Which component should be extracted
...

Not in use

Value

A tibble with coefficient values, one row per bootstrap and component

Examples

data(genevzinb2)
model <- evinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
coefficient_extractor(model, component = 'all')

Bootstrap coefficient extractor

Description

Bootstrap coefficient extractor

Usage

## S3 method for class 'evzinb'
coefficient_extractor(
  object,
  component = c("nb", "zi", "evinf", "pareto", "all"),
  ...
)

Arguments

object

A fitted evzinb model with bootstraps
component

Which component should be extracted
...

Not in use

Value

A tibble with coefficient values, one row per bootstrap and component

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
coefficient_extractor(model, component = 'all')

Bootstrap coefficient extractor

Description

Bootstrap coefficient extractor

Usage

## S3 method for class 'nbboot'
coefficient_extractor(object, ...)

Arguments

object

A fitted nbboot model with bootstraps
...

Not in use

Value

A tibble with coefficient value, one row per bootstrap

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
zinb_comp <- compare_models(model)
coefficient_extractor(zinb_comp$nb)

Bootstrap coefficient extractor

Description

Bootstrap coefficient extractor

Usage

## S3 method for class 'zinbboot'
coefficient_extractor(object, component = c("nb", "zi", "all"), ...)

Arguments

object

A fitted evinb model with bootstraps
component

Which component should be extracted
...

Not in use

Value

A tibble with coefficient values, one row per bootstrap and component

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps=10)
zinb_comp <- compare_models(model)
coefficient_extractor(zinb_comp$zinb)

Function to compare evzinb or evinb models with zinb and nb models

Description

Function to compare evzinb or evinb models with zinb and nb models

Usage

compare_models(
  object,
  nb_comparison = TRUE,
  zinb_comparison = TRUE,
  winsorize = FALSE,
  razorize = FALSE,
  cutoff_value = 10,
  init_theta = NULL,
  multicore = FALSE,
  ncores = NULL
)

Arguments

object

A fitted evzinb or evinb model object
nb_comparison

Should comparison be made with a negative binomial model?
zinb_comparison

Should comparions be made with the zinb model?
winsorize

Should winsorizing be done in the comparisons?
razorize

Should razorizing (trimming) be done in the comparisons?
cutoff_value

Integer: Which observation should be used as a basis for winsorizing/razorising. E.g. 10 means that everything larger than the 10th observation will be winsorized/razorised
init_theta

Optional initial value for theta in the NB specification
multicore

Logical: should multiple cores be used
ncores

Number of cores if multicore is used

Value

A list with the original model as the first object and compared models as the following objects

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
compare_models(model)

Running an extreme value inflated negative binomial model with bootstrapping

Description

Running an extreme value inflated negative binomial model with bootstrapping

Usage

evinb(
  formula_nb,
  formula_evi = NULL,
  formula_pareto = NULL,
  data,
  bootstrap = TRUE,
  n_bootstraps = 100,
  multicore = FALSE,
  ncores = NULL,
  block = NULL,
  boot_seed = NULL,
  max.diff.par = 0.01,
  max.no.em.steps = 500,
  max.no.em.steps.warmup = 5,
  c.lim = c(50, 1000),
  max.upd.par.pl.multinomial = 0.5,
  max.upd.par.nb = 0.5,
  max.upd.par.pl = 0.5,
  no.m.bfgs.steps.multinomial = 3,
  no.m.bfgs.steps.nb = 3,
  no.m.bfgs.steps.pl = 3,
  pdf.pl.type = "approx",
  eta.int = c(-1, 1),
  init.Beta.multinom.PL = NULL,
  init.Beta.NB = NULL,
  init.Beta.PL = NULL,
  init.Alpha.NB = 0.01,
  init.C = 200,
  verbose = FALSE
)

Arguments

formula_nb

Formula for the negative binomial (count) component of the model
formula_evi

Formula for the extreme-value inflation component of the model. If NULL taken as the same formula as nb
formula_pareto

Formula for the pareto (extreme value) component of the model. If NULL taken as the same formula as nb
data

Data to run the model on
bootstrap

Should bootstrapping be performed. Needed to obtain standard errors and p-values
n_bootstraps

Number of bootstraps to run. For use of bootstrapped p-values, at least 1,000 bootstraps are recommended. For approximate p-values, a lower number can be sufficient
multicore

Should multiple cores be used?
ncores

Number of cores if multicore is used. Default (NULL) is one less than the available number of cores
block

Optional string indicating a case-identifier variable when using block bootstrapping
boot_seed

Optional bootstrap seed to ensure reproducible results.
max.diff.par

Tolerance for EM algorithm. Will be considered to have converged if the maximum absolute difference in the parameter estimates are lower than this value
max.no.em.steps

Maximum number of EM steps to run. Will be considered to not have converged if this number is reached and convergence is not reached
max.no.em.steps.warmup

Number of EM steps in the warmup rounds
c.lim

Integer range defining the possible values of C
max.upd.par.pl.multinomial

Maximum parameter change step size in the extreme value inflation component
max.upd.par.nb

Maximum parameter change step size in the count component
max.upd.par.pl

Maximum parameter change step size in the pareto component
no.m.bfgs.steps.multinomial

Number of BFGS steps for the multinomial model
no.m.bfgs.steps.nb

Number of BFGS steps for the negative binomial model
no.m.bfgs.steps.pl

Number of BFGS steps for the pareto model
pdf.pl.type

Probability density function type for the pareto component. Either 'approx' or 'exact'. 'approx' is adviced in most cases
eta.int

Initial values for eta
init.Beta.multinom.PL

Initial values for beta parameters in the extreme value inflation component. Vector of same length as number of parameters in the extreme value inflation component or NULL (which gives starting values of 0)
init.Beta.NB

Initial values for beta parameters in the count component. Vector of same length as number of parameters in the count component or NULL (which gives starting values of 0)
init.Beta.PL

Initial values for beta parameters in the pareto component. Vector of same length as number of parameters in the pareto component or NULL (which gives starting values of 0)
init.Alpha.NB

Initial value of Alpha NB, integer or NULL (giving a starting value of 0)
init.C

Initial value of C. Integer which should be within the C_lim range.
verbose

Should progress be printed for the first run of evinb

Value

An object of class 'evinb'

Examples

data(genevzinb2)
model <- evinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10)

Running an extreme value and zero inflated negative binomial model with bootstrapping

Description

Running an extreme value and zero inflated negative binomial model with bootstrapping

Usage

evzinb(
  formula_nb,
  formula_zi = NULL,
  formula_evi = NULL,
  formula_pareto = NULL,
  data,
  bootstrap = TRUE,
  n_bootstraps = 100,
  multicore = FALSE,
  ncores = NULL,
  block = NULL,
  boot_seed = NULL,
  max.diff.par = 0.01,
  max.no.em.steps = 500,
  max.no.em.steps.warmup = 5,
  c.lim = c(50, 1000),
  max.upd.par.zc.multinomial = 0.5,
  max.upd.par.pl.multinomial = 0.5,
  max.upd.par.nb = 0.5,
  max.upd.par.pl = 0.5,
  no.m.bfgs.steps.multinomial = 3,
  no.m.bfgs.steps.nb = 3,
  no.m.bfgs.steps.pl = 3,
  pdf.pl.type = "approx",
  eta.int = c(-1, 1),
  init.Beta.multinom.ZC = NULL,
  init.Beta.multinom.PL = NULL,
  init.Beta.NB = NULL,
  init.Beta.PL = NULL,
  init.Alpha.NB = 0.01,
  init.C = 200,
  verbose = FALSE
)

Arguments

formula_nb

Formula for the negative binomial (count) component of the model
formula_zi

Formula for the zero-inflation component of the model. If NULL taken as the same formula as nb
formula_evi

Formula for the extreme-value inflation component of the model. If NULL taken as the same formula as nb
formula_pareto

Formula for the pareto (extreme value) component of the model. If NULL taken as the same formula as nb
data

data to run the model on
bootstrap

Should bootstrapping be performed. Needed to obtain standard errors and p-values
n_bootstraps

Number of bootstraps to run. For use of bootstrapped p-values, at least 1,000 bootstraps are recommended. For approximate p-values, a lower number can be sufficient
multicore

Should multiple cores be used?
ncores

Number of cores if multicore is used. Default (NULL) is one less than the available number of cores
block

Optional string indicating a case-identifier variable when using block bootstrapping
boot_seed

Optional bootstrap seed to ensure reproducible results.
max.diff.par

Tolerance for EM algorithm. Will be considered to have converged if the maximum absolute difference in the parameter estimates are lower than this value
max.no.em.steps

Maximum number of EM steps to run. Will be considered to not have converged if this number is reached and convergence is not reached
max.no.em.steps.warmup

Number of EM steps in the warmup rounds
c.lim

Integer range defining the possible values of C
max.upd.par.zc.multinomial

Maximum parameter change step size in the zero inflation component
max.upd.par.pl.multinomial

Maximum parameter change step size in the extreme value inflation component
max.upd.par.nb

Maximum parameter change step size in the count component
max.upd.par.pl

Maximum parameter change step size in the pareto component
no.m.bfgs.steps.multinomial

Number of BFGS steps for the multinomial model
no.m.bfgs.steps.nb

Number of BFGS steps for the negative binomial model
no.m.bfgs.steps.pl

Number of BFGS steps for the pareto model
pdf.pl.type

Probability density function type for the pareto component. Either 'approx' or 'exact'. 'approx' is adviced in most cases
eta.int

Initial values for eta
init.Beta.multinom.ZC

Initial values for beta parameters in the zero value inflation component. Vector of same length as number of parameters in the zero value inflation component or NULL (which gives starting values of 0)
init.Beta.multinom.PL

Initial values for beta parameters in the extreme value inflation component. Vector of same length as number of parameters in the extreme value inflation component or NULL (which gives starting values of 0)
init.Beta.NB

Initial values for beta parameters in the count component. Vector of same length as number of parameters in the count component or NULL (which gives starting values of 0)
init.Beta.PL

Initial values for beta parameters in the pareto component. Vector of same length as number of parameters in the pareto component or NULL (which gives starting values of 0)
init.Alpha.NB

Initial value of Alpha NB, integer or NULL (giving a starting value of 0)
init.C

Initial value of C. Integer which should be within the C_lim range.
verbose

Logical: should progress of the full run of the model be tracked?

Value

An object of class 'evzinb'

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10)

Simulated data from the EVZBINB distribution

Description

A simulated dataset of 1,000 observations with one dependent and three dependent variables generated using the EVZINB distribution

Usage

genevzinb

Format

## 'genevzinb' A tibble with 1,000 rows and 4 columns:

y

Dependent variable following EVZINB distribution

x1, x2, x3

Continuous independent variables following the random normal distribution

Simulated data from the EVZBINB distribution

Description

A simulated dataset of 100 observations with one dependent and three dependent variables generated using the EVZINB distribution

Usage

genevzinb2

Format

## 'genevzinb2' A tibble with 100 rows and 4 columns:

y

Dependent variable following EVZINB distribution

x1, x2, x3

Continuous independent variables following the random normal distribution

EVZINB and EVINB glance functions

Description

EVZINB and EVINB glance functions

Usage

## S3 method for class 'evinb'
glance(x, ...)

Arguments

x

An EVZINB or EVINB object
...

Further arguments to be passed to glance()

Value

An EVZINB glance function

See Also

glance

Examples

data(genevzinb2)
model <- evinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10)
glance(model)

EVZINB and EVINB glance functions

Description

EVZINB and EVINB glance functions

Usage

## S3 method for class 'evzinb'
glance(x, ...)

Arguments

x

An EVZINB or EVINB object
...

Further arguments to be passed to glance()

Value

An EVZINB glance function

See Also

glance

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10)
glance(model)

zinbboot and nboot glance functions

Description

zinbboot and nboot glance functions

Usage

## S3 method for class 'nbboot'
glance(x, ...)

Arguments

x

An nbboot or zinbboot object
...

Further arguments to be passed to glance()

Value

An nbboot glance function

See Also

glance

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
zinb_comp <- compare_models(model)
glance(zinb_comp$nb)

zinbboot and nboot glance functions

Description

zinbboot and nboot glance functions

Usage

## S3 method for class 'zinbboot'
glance(x, ...)

Arguments

x

An nbboot or zinbboot object
...

Further arguments to be passed to glance()

Value

An nbboot glance function

See Also

glance

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
zinb_comp <- compare_models(model)
glance(zinb_comp$zinb)

A goodness-of-fit gof tibble for GOF metrics when using modelsummary

Description

A goodness-of-fit gof tibble for GOF metrics when using modelsummary. The GM tibble can be used to obtain correct table output when making regression tables with modelsummary

Usage

gm_evzinb

Format

## 'gm_evzinb' A tibble with 7 rows and 3 columns:

raw

The modelsummary/broom internal name for the statistic

clean

The table output for the statistic

fmt

The number of decimals reported for each statistic by default (can be adapted)

Replication data for Hultman, Kathman, and Shannon (2013) United Nations Peacekeeping and Civilian Protection in Civil War

Description

A reduced replication data set from Hultman et al. (2013) United Nations Peacekeeping and Civilian Protection in Civil War. Used to reproduce the the results from Randahl and Vegelius (2023). Note, to reproduce any other results from Hultman et al. (2013) please download the original replication dataset using the link under source.

Usage

hks

Format

A tibble with 3746 rows and 12 columns:

conflict_id

The Uppsala Conflict Data Programme conflict ID for the conflict

osvAll

The number of observed fatalities from one-sided violence against civilian in the specified conflict-month

troopLag

The number of UN military troops in thousands of troops (lagged)

policeLag

The number of UN police in thousands of troops (lagged)

militaryobserversLag

The number of UN military troops in thousands of troops (lagged)

epduration

The number of months the current conflict-episode has been ongoing

lntpop

The natural logarithm of the population of the country in which the conflict takes place

lnbrv_AllLag

The natural logarithm of the total number of battle related deaths in the conflict in the previous month

osvAllLagDum

A dummy variable taking the value 1 if any one-sided violence against civilians took place in the previous conflict month

incomp

A dummy variable taking the value 1 if the conflict is about government and 0 otherwise

lntroopLag

The log1p logarithm of troopLag

lnepdur

The log1p logarithm of the episode duration

Source

https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6EBCGA

References

Hultman L, Kathman J, Shannon M (2013). “United Nations peacekeeping and civilian protection in civil war.” American Journal of Political Science, 57(4), 875–891.

Randahl D, Vegelius J (2023). “Inference with Extremes: Accounting for Extreme Values in Count Regression Models.” International Studies Quarterly, x(x), x.

Likelihood ratio test for individual variables of evzinb

Description

Likelihood ratio test for individual variables of evzinb

Usage

lr_test(
  object,
  vars,
  single = TRUE,
  bootstrap = FALSE,
  multicore = FALSE,
  ncores = NULL,
  verbose = FALSE
)

Arguments

object

EVZINB or EVZINB object to perform likelihood ratio test on
vars

Either a list of character vectors with variable names which to be restricted in the LR test or a character vector of variable names. If a list, each character vector of the list will be run separately, allowing for multiple variables to be restricted as once. If a character vector, parameter 'single' can be used to determine whether all variables in the vector should be restricted at once (single = FALSE) or if the variables should be restricted one by one (single = TRUE)
single

Logical. Determining whether variables in 'vars' should be restricted individually (single = TRUE) or all at once (single = FALSE)
bootstrap

Should LR tests be conducted on each bootstrapped sample or only on the original sample.
multicore

Logical. Should the function be run in parallel?
ncores

Number of cores to use if multicore = TRUE
verbose

Logical. Should the function be verbose?

Value

A tibble with one row per performed LR test

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10)
 lr_test(model,'x1')

Out of bag predictive performance of EVZINB and EVINB models

Description

Out of bag predictive performance of EVZINB and EVINB models

Usage

oob_evaluation(
  object,
  predict_type = c("harmonic", "explog"),
  metric = c("rmsle", "rmse", "mse", "mae")
)

Arguments

object

A fitted evzinb or evinb with bootstraps on which to conduct out-of-bag evaluation
predict_type

What type of prediction should be made? Harmonic mean, or exp(log(prediction))?
metric

What metric should be used for the out of bag evaluation? Default options include rmsle, rmse, mse, and mae. Can also take a user supplied function of the form function(y_pred,y_true) which returns a single value

Value

A vector of oob evaluation metrics of the length of the number of bootstraps in the evzinb/evinb object.

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10)
oob_evaluation(model)

Predictions from evinb object

Description

Predictions from evinb object

Usage

## S3 method for class 'evinb'
predict(
  object,
  newdata = NULL,
  type = c("harmonic", "explog", "counts", "pareto_alpha", "evinf", "count_state",
    "states", "all", "quantile"),
  pred = c("original", "bootstrap_median", "bootstrap_mean"),
  quantile = NULL,
  confint = FALSE,
  conf_level = 0.9,
  multicore = FALSE,
  ncores = NULL,
  ...
)

Arguments

object

An evinb object for which to produce predicted values
newdata

Optional new data (tibble) to produce predicted values from
type

Character string, 'harmonic' for the harmonic mean and 'explog' for exponentiated expected log, 'counts' for predicted count of the negative binomial component, 'pareto_alpha' for the predicted pareto alpha value, 'states' for the predicted component states (prior), 'count_state' for predicted probability of the count state, 'evinf' for predicted probability of the pareto state, 'all' for all predicted values, and 'quantile' for quantile prediction.
pred

Type of prediction to be used, defaults to the original prediction from the fitted model, with alternatives being the bootstrapped median or mean. Note that bootstrap mean may yield infinite values, especially when doing quantile prediction
quantile

Quantile for which to produce quantile prediction
confint

Should confidence intervals be made for the predictions? Note: only available for vector type predictions and not 'states' and 'all'.
conf_level

What confidence level should be used for confidence intervals
multicore

Should multicore be used when calculating quantile prediction? Often it is enough to run quantile prediction on a single core, but in cases of large data or very skewed distributions it may be useful to run multicore
ncores

Number of cores to be used for multicore.
...

Other arguments passed to predict function

Value

A vector of predicted values for type 'harmonic', 'explog', 'counts', 'pareto_alpha','evinf', 'count_state', and 'quantile' or a tibble of predicted values for type 'states' and 'all' or if confint=T

Examples

data(genevzinb2)
model <- evinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10)
predict(model)
predict(model, type='all') # Getting all of the available predicted values

Predictions from evzinb object

Description

Predictions from evzinb object

Usage

## S3 method for class 'evzinb'
predict(
  object,
  newdata = NULL,
  type = c("harmonic", "explog", "counts", "pareto_alpha", "zi", "evinf", "count_state",
    "states", "all", "quantile"),
  pred = c("original", "bootstrap_median", "bootstrap_mean"),
  quantile = NULL,
  confint = FALSE,
  conf_level = 0.9,
  multicore = FALSE,
  ncores = NULL,
  ...
)

Arguments

object

An evzinb object for which to produce predicted values
newdata

Optional new data (tibble) to produce predicted values from
type

Character string, 'harmonic' for the harmonic mean and 'explog' for exponentiated expected log, 'counts' for predicted count of the negative binomial component, 'pareto_alpha' for the predicted pareto alpha value, 'states' for the predicted component states (prior), 'count_state' for predicted probability of the count state, 'evinf' for predicted probability of the pareto state,'zi' for the predicted probability of the zero state, 'all' for all predicted values, and 'quantile' for quantile prediction.
pred

Type of prediction to be used, defaults to the original prediction from the fitted model, with alternatives being the bootstrapped median or mean. Note that bootstrap mean may yield infinite values, especially when doing quantile prediction
quantile

Quantile for which to produce quantile prediction
confint

Should confidence intervals be made for the predictions? Note: only available for vector type predictions and not 'states' and 'all'.
conf_level

What confidence level should be used for confidence intervals
multicore

Should multicore be used when calculating quantile prediction? Often it is enough to run quantile prediction on a single core, but in cases of large data or very skewed distributions it may be useful to run multicore
ncores

Number of cores to be used for multicore.
...

Other arguments passed to predict function

Value

A vector of predicted values for type 'harmonic', 'explog', 'counts', 'pareto_alpha','zi','evinf', 'count_state', and 'quantile' or a tibble of predicted values for type 'states' and 'all' or if confint=T

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10)
predict(model)
predict(model, type='all') # Getting all of the available predicted values

Prediction for zinbboot

Description

Prediction for zinbboot

Usage

## S3 method for class 'zinbboot'
predict(
  object,
  newdata = NULL,
  type = c("predicted", "counts", "zi", "evinf", "count_state", "states", "all",
    "quantile"),
  pred = c("original", "bootstrap_median", "bootstrap_mean"),
  quantile = NULL,
  confint = FALSE,
  conf_level = 0.9,
  ...
)

Arguments

object

a fitted zinbboot object
newdata

Data to make predictions on
type

What prediction should be computed?
pred

Prediction type, 'original', 'bootstra_median', or 'bootstrap_mean'
quantile

Quantile for quantile prediction
confint

Should confidence intervals be created?
conf_level

Confidence level when predicting with CIs
...

Not used

Value

Predictions from zinbboot

EVINB print function

Description

EVINB print function

Usage

## S3 method for class 'evinb'
print(x, ...)

Arguments

x

A fitted evinb model
...

Not used

Value

An evinb print function

Examples

data(genevzinb2)
model <- evinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10)
print(model)

EVZINB print function

Description

EVZINB print function

Usage

## S3 method for class 'evzinb'
print(x, ...)

Arguments

x

A fitted evzinb model
...

Not used

Value

An evzinb print function

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10)
print(model)

Compare_models print function

Description

Compare_models print function

Usage

## S3 method for class 'evzinbcomp'
print(x, ...)

Arguments

x

A fitted evinb model
...

Not used

Value

An evinb print function

Examples

data(genevzinb2)
model <- evinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10)
print(model)

Random draws from a fitted evinb model

Description

Random draws from a fitted evinb model

Usage

revinb_fit(object, newdata = NULL, n_draws = 1)

Arguments

object

A fitted EVINB object
newdata

Optional newdata
n_draws

Number of random draws to make

Value

A vector of randomly drawn values from the fitted evinb if n_draws == 1, or a list of length n_draws with random drawn values if n_draws > 1

Examples

data(genevzinb2)
model <- evinb(y~x1+x2+x3, data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
revinb_fit(model)

Random draws from a fitted evzinb model

Description

Random draws from a fitted evzinb model

Usage

revzinb_fit(object, newdata = NULL, n_draws = 1)

Arguments

object

A fitted EVZINB object
newdata

Optional newdata
n_draws

Number of random draws to make

Value

A vector of randomly drawn values from the fitted evzinb if n_draws == 1, or a list of length n_draws with random drawn values if n_draws > 1

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3, data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
revzinb_fit(model)

EVINB summary function

Description

EVINB summary function

Usage

## S3 method for class 'evinb'
summary(
  object,
  coef = c("original", "bootstrapped_mean", "bootstrapped_median"),
  standard_error = TRUE,
  p_value = c("bootstrapped", "approx", "both", "none"),
  bootstrapped_props = c("none", "mean", "median"),
  approx_t_value = TRUE,
  symmetric_bootstrap_p = TRUE,
  ...
)

Arguments

object

an EVINB object with bootstraps
coef

Type of coefficients. Original are the coefficient estimates from the non-bootstrapped version of the model. 'bootstrapped_mean' are the mean coefficients across bootstraps, and 'bootstrapped_median' are the median coefficients across bootstraps
standard_error

Should standard errors be computed?
p_value

What type of p_values should be computed? 'bootstrapped' are bootstrapped p_values through confidence interval inversion. 'approx' are p-values based on the t-value produced by dividing the coefficient with the standard error.
bootstrapped_props

Type of bootstrapped proportions of component proportions to be returned
approx_t_value

Should approximate t-values be returned
symmetric_bootstrap_p

Should bootstrap p-values be computed as symmetric (leaving alpha/2 percent in each tail)? FALSE gives non-symmetric, but narrower, intervals. TRUE corresponds most closely to conventional p-values.
...

Additional arguments passed to the summary function

Value

An EVINB summary object

Examples

data(genevzinb2)
model <- evinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
summary(model)

EVZINB summary function

Description

EVZINB summary function

Usage

## S3 method for class 'evzinb'
summary(
  object,
  coef = c("original", "bootstrapped_mean", "bootstrapped_median"),
  standard_error = TRUE,
  p_value = c("bootstrapped", "approx", "both", "none"),
  bootstrapped_props = c("none", "mean", "median"),
  approx_t_value = TRUE,
  symmetric_bootstrap_p = TRUE,
  ...
)

Arguments

object

an EVZINB object with bootstraps
coef

Type of coefficients. Original are the coefficient estimates from the non-bootstrapped version of the model. 'bootstrapped_mean' are the mean coefficients across bootstraps, and 'bootstrapped_median' are the median coefficients across bootstraps
standard_error

Should standard errors be computed?
p_value

What type of p_values should be computed? 'bootstrapped' are bootstrapped p_values through confidence interval inversion. 'approx' are p-values based on the t-value produced by dividing the coefficient with the standard error.
bootstrapped_props

Type of bootstrapped proportions of component proportions to be returned
approx_t_value

Should approximate t-values be returned
symmetric_bootstrap_p

Should bootstrap p-values be computed as symmetric (leaving alpha/2 percent in each tail)? FALSE gives non-symmetric, but narrower, intervals. TRUE corresponds most closely to conventional p-values.
...

Additional arguments passed to the summary function

Value

An EVZINB summary object

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
summary(model)

EVINB tidy function

Description

EVINB tidy function

Usage

## S3 method for class 'evinb'
tidy(
  x,
  component = c("evi", "count", "pareto", "all"),
  coef_type = c("original", "bootstrap_mean", "bootstrap_median"),
  standard_error = TRUE,
  p_value = c("bootstrapped", "approx", "none"),
  confint = c("none", "bootstrapped", "approx"),
  conf_level = 0.95,
  approx_t_value = TRUE,
  symmetric_bootstrap_p = TRUE,
  ...
)

Arguments

x

An evinb object
component

Which component should be shown?
coef_type

Type of coefficients. Original are the coefficient estimates from the non-bootstrapped version of the model. 'bootstrapped_mean' are the mean coefficients across bootstraps, and 'bootstrapped_median' are the median coefficients across bootstraps
standard_error

Should standard errors be computed?
p_value

What type of p_values should be computed? 'bootstrapped' are bootstrapped p_values through confidence interval inversion. 'approx' are p-values based on the t-value produced by dividing the coefficient with the standard error.
confint

What type of confidence should be computed. Same options as p_value
conf_level

What confidence level should be used for the confidence interval
approx_t_value

Should approximate t-values be returned
symmetric_bootstrap_p

Should bootstrap p-values be computed as symmetric (leaving alpha/2 percent in each tail)? FALSE gives non-symmetric, but narrower, intervals. TRUE corresponds most closely to conventional p-values.
...

Other arguments passsed to tidy function

Value

An EVINB tidy function

Examples

data(genevzinb2)
model <- evinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
tidy(model)

EVZINB tidy function

Description

EVZINB tidy function

Usage

## S3 method for class 'evzinb'
tidy(
  x,
  component = c("zi", "evi", "count", "pareto", "all"),
  coef_type = c("original", "bootstrap_mean", "bootstrap_median"),
  standard_error = TRUE,
  p_value = c("bootstrapped", "approx", "none"),
  confint = c("none", "bootstrapped", "approx"),
  conf_level = 0.95,
  approx_t_value = TRUE,
  symmetric_bootstrap_p = TRUE,
  ...
)

Arguments

x

An evzinb object
component

Which component should be shown?
coef_type

Type of coefficients. Original are the coefficient estimates from the non-bootstrapped version of the model. 'bootstrapped_mean' are the mean coefficients across bootstraps, and 'bootstrapped_median' are the median coefficients across bootstraps
standard_error

Should standard errors be computed?
p_value

What type of p_values should be computed? 'bootstrapped' are bootstrapped p_values through confidence interval inversion. 'approx' are p-values based on the t-value produced by dividing the coefficient with the standard error.
confint

What type of confidence should be computed. Same options as p_value
conf_level

What confidence level should be used for the confidence interval
approx_t_value

Should approximate t-values be returned
symmetric_bootstrap_p

Should bootstrap p-values be computed as symmetric (leaving alpha/2 percent in each tail)? FALSE gives non-symmetric, but narrower, intervals. TRUE corresponds most closely to conventional p-values.
...

Other arguments passsed to tidy function

Value

An EVZINB tidy function

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
tidy(model)

Tidy function for nbboot

Description

Tidy function for nbboot

Usage

## S3 method for class 'nbboot'
tidy(
  x,
  coef_type = c("original", "bootstrap_mean", "bootstrap_median"),
  standard_error = TRUE,
  p_value = c("bootstrapped", "approx", "none"),
  confint = c("none", "bootstrapped", "approx"),
  conf_level = 0.95,
  approx_t_value = TRUE,
  symmetric_bootstrap_p = TRUE,
  include_ylev = FALSE,
  ...
)

Arguments

x

A fitted bootstrapped zero-inflated model
coef_type

What type of coefficient should be reported, original, bootstrapped mean, or bootstrapped median
standard_error

Should bootstrapped standard errors be reported?
p_value

What type of p-value should be reported? Bootstrapped p_values, approximate p-values, or none?
confint

What type of confidence intervals should be reported? Bootstrapped p_values, approximate p-values, or none?
conf_level

Confidence level for confidence intervals
approx_t_value

Should approximate t_values be reported
symmetric_bootstrap_p

Should bootstrap p-values be computed as symmetric (leaving alpha/2 percent in each tail)? FALSE gives non-symmetric, but narrower, intervals. TRUE corresponds most closely to conventional p-values.
include_ylev

Logical. Should y.lev be included in the tidy output? Makes for nicer tables when using modelsummary
...

Other arguments to be passed to tidy

Value

A tidy function for a bootstrapped nb model

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
zinb_comp <- compare_models(model)
tidy(zinb_comp$nb)

Tidy function for zinbboot

Description

Tidy function for zinbboot

Usage

## S3 method for class 'zinbboot'
tidy(
  x,
  component = c("zi", "count", "all"),
  coef_type = c("original", "bootstrap_mean", "bootstrap_median"),
  standard_error = TRUE,
  p_value = c("bootstrapped", "approx", "none"),
  confint = c("none", "bootstrapped", "approx"),
  conf_level = 0.95,
  approx_t_value = TRUE,
  symmetric_bootstrap_p = TRUE,
  ...
)

Arguments

x

A fitted bootstrapped zero-inflated model
component

Which component should be shown?
coef_type

What type of coefficient should be reported, original, bootstrapped mean, or bootstrapped median
standard_error

Should bootstrapped standard errors be reported?
p_value

What type of p-value should be reported? Bootstrapped p_values, approximate p-values, or none?
confint

What type of confidence intervals should be reported? Bootstrapped p_values, approximate p-values, or none?
conf_level

Confidence level for confidence intervals
approx_t_value

Should approximate t_values be reported
symmetric_bootstrap_p

Should bootstrap p-values be computed as symmetric (leaving alpha/2 percent in each tail)? FALSE gives non-symmetric, but narrower, intervals. TRUE corresponds most closely to conventional p-values.
...

Other arguments to be passed to tidy

Value

A tidy function for a bootstrapped zinb model

Examples

data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
zinb_comp <- compare_models(model)
tidy(zinb_comp$zinb)