| Type: | Package |
| Title: | Inference with Extreme Value Inflated Count Data |
| Version: | 0.8.10 |
| 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'. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | dplyr, Rcpp, RcppArmadillo, foreach, doParallel, magrittr, doRNG, tibble, mistr, tidyr, purrr, MASS, pscl, MLmetrics, Rdpack, stringi, stringr, rlang, methods, stats, utils, parallel |
| LinkingTo: | Rcpp, RcppArmadillo |
| RoxygenNote: | 7.2.3 |
| RdMacros: | Rdpack |
| Depends: | generics, R (≥ 2.10) |
| URL: | https://github.com/Doktorandahl/evinf |
| BugReports: | https://github.com/Doktorandahl/evinf/issues |
| NeedsCompilation: | yes |
| Packaged: | 2024-05-16 18:35:20 UTC; david |
| Author: | David Randahl [cre, aut], Johan Vegelius [aut] |
| Maintainer: | David Randahl <david.randahl@pcr.uu.se> |
| Repository: | CRAN |
| Date/Publication: | 2024-05-17 23:40:17 UTC |
evinf: 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'.
Author(s)
Maintainer: David Randahl david.randahl@pcr.uu.se
Authors:
Johan Vegelius
See Also
Useful links:
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
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
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
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$nb)
zinbboot and nboot glance functions
Description
zinbboot and nboot glance functions
Usage
## S3 method for class 'zinbboot'
glance(x, ...)
Arguments
x |
An nbboot or zinbboot object |
... |
Further arguments to be passed to glance() |
Value
An nbboot glance function
See Also
Examples
data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
zinb_comp <- compare_models(model)
glance(zinb_comp$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)