intubate (version 1.0.0)

aod: Interfaces for aod package for data science pipelines.

Description

Interfaces to aod functions that can be used in a pipeline implemented by magrittr.

Usage

ntbt_betabin(data, ...)
ntbt_donner(data, ...)
ntbt_negbin(data, ...)
ntbt_quasibin(data, ...)
ntbt_quasipois(data, ...)
ntbt_raoscott(data, ...)
ntbt_splitbin(data, ...)

Arguments

data

data frame, tibble, list, ...

...

Other arguments passed to the corresponding interfaced function.

Value

Object returned by interfaced function.

Details

Interfaces call their corresponding interfaced function.

Examples

## Not run: 
# library(intubate)
# library(magrittr)
# library(aod)
# 
# 
# ## ntbt_betabin: beta-binomial generalized linear model accounting
# ##               for overdispersion in clustered binomial data (n, y)
# data(orob2)
# ## Original function to interface
# betabin(cbind(y, n - y) ~ seed, ~ 1, data = orob2)
# 
# ## The interface puts data as first parameter
# ntbt_betabin(orob2, cbind(y, n - y) ~ seed, ~ 1)
# 
# ## so it can be used easily in a pipeline.
# orob2 %>%
#   ntbt_betabin(cbind(y, n - y) ~ seed, ~ 1)
# 
# 
# ## ntbt_donner: Test of Proportion Homogeneity using Donner's Adjustment
# data(rats)
# 
# ## Original function to interface
# donner(formula = cbind(y, n - y) ~ group, data = rats)
# 
# ## The interface puts data as first parameter
# ntbt_donner(rats, formula = cbind(y, n - y) ~ group)
# 
# ## so it can be used easily in a pipeline.
# rats %>%
#   ntbt_donner(formula = cbind(y, n - y) ~ group)
# 
# 
# ## ntbt_negbin: negative-binomial log linear model accounting
# ##              for overdispersion in counts y
# data(salmonella)
# ## Original function to interface
# negbin(y ~ log(dose + 10) + dose, ~ 1, salmonella)
# 
# ## The interface puts data as first parameter
# ntbt_negbin(salmonella, y ~ log(dose + 10) + dose, ~ 1)
# 
# ## so it can be used easily in a pipeline.
# salmonella %>%
#   ntbt_negbin(y ~ log(dose + 10) + dose, ~ 1)
# 
# 
# ## ntbt_quasibin: Quasi-Likelihood Model for Proportions
# data(orob2) 
# ## Original function to interface
# quasibin(cbind(y, n - y) ~ seed * root, data = orob2, phi = 0) 
# 
# ## The interface puts data as first parameter
# ntbt_quasibin(orob2, cbind(y, n - y) ~ seed * root, phi = 0)
# 
# ## so it can be used easily in a pipeline.
# orob2 %>%
#   ntbt_quasibin(cbind(y, n - y) ~ seed * root, phi = 0)
# 
# 
# ## ntbt_quasipois: Quasi-Likelihood Model for Counts
# data(salmonella)
# 
# ## Original function to interface
# quasipois(y ~ log(dose + 10) + dose, data = salmonella)
# 
# ## The interface puts data as first parameter
# ntbt_quasipois(salmonella, y ~ log(dose + 10) + dose)
# 
# ## so it can be used easily in a pipeline.
# salmonella %>%
#   ntbt_quasipois(y ~ log(dose + 10) + dose)
# 
# 
# ## ntbt_raoscott: Test of Proportion Homogeneity using Rao and Scott's Adjustment
# data(rats)
# 
# ## Original function to interface
# raoscott(cbind(y, n - y) ~ group, data = rats)
# 
# ## The interface puts data as first parameter
# ntbt_raoscott(rats, cbind(y, n - y) ~ group)
# 
# ## so it can be used easily in a pipeline.
# rats %>%
#   ntbt_raoscott(cbind(y, n - y) ~ group)
# 
# 
# ## ntbt_splitbin: Split Grouped Data Into Individual Data
# mydata <- data.frame(
#     success = c(0, 1, 0, 1),
#     f1 = c("A", "A", "B", "B"),
#     f2 = c("C", "D", "C", "D"),
#     n = c(4, 2, 1, 3)
#     )
# ## Original function to interface
# splitbin(formula = n ~ f1 + f2 + success, data = mydata)
# 
# ## The interface puts data as first parameter
# ntbt_splitbin(mydata, formula = n ~ f1 + f2 + success)
# 
# ## so it can be used easily in a pipeline.
# mydata %>%
#   ntbt_splitbin(formula = n ~ f1 + f2 + success)
# ## End(Not run)