intubate (version 1.0.0)

robustbase: Interfaces for robustbase package for data science pipelines.

Description

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

Usage

ntbt_adjbox(data, ...)
ntbt_glmrob(data, ...)
ntbt_lmrob(data, ...)
ntbt_ltsReg(data, ...)
ntbt_nlrob(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(robustbase)
# 
# 
# ## ntbt_adjbox: Plot an Adjusted Boxplot for Skew Distributions
# ## Original function to interface
# adjbox(len ~ dose, data = ToothGrowth)
# 
# ## The interface puts data as first parameter
# ntbt_adjbox(ToothGrowth, len ~ dose)
# 
# ## so it can be used easily in a pipeline.
# ToothGrowth %>%
#   ntbt_adjbox(len ~ dose)
# 
# 
# ## ntbt_glmrob: Robust Fitting of Generalized Linear Models
# data(carrots)
# 
# ## Original function to interface
# glmrob(cbind(success, total-success) ~ logdose + block,
#        family = binomial, data = carrots, method= "Mqle",
#        control= glmrobMqle.control(tcc=1.2))
# 
# ## The interface puts data as first parameter
# ntbt_glmrob(carrots, cbind(success, total-success) ~ logdose + block,
#             family = binomial, method= "Mqle",
#             control= glmrobMqle.control(tcc=1.2))
# 
# ## so it can be used easily in a pipeline.
# carrots %>%
#   ntbt_glmrob(cbind(success, total-success) ~ logdose + block,
#               family = binomial, method= "Mqle",
#               control= glmrobMqle.control(tcc=1.2))
# 
# 
# ## ntbt_lmrob: MM-type Estimators for Linear Regression
# data(coleman)
# 
# ## Original function to interface
# set.seed(0)
# lmrob(Y ~ ., data = coleman, setting = "KS2011")
# 
# ## The interface puts data as first parameter
# ntbt_lmrob(coleman, Y ~ ., setting = "KS2011")
# 
# ## so it can be used easily in a pipeline.
# coleman %>%
#   ntbt_lmrob(Y ~ ., setting = "KS2011")
# 
# 
# ## ntbt_ltsReg: Least Trimmed Squares Robust (High Breakdown) Regression
# data(stackloss)
# 
# ## Original function to interface
# ltsReg(stack.loss ~ ., data = stackloss)
# 
# ## The interface puts data as first parameter
# ntbt_ltsReg(stackloss, stack.loss ~ .)
# 
# ## so it can be used easily in a pipeline.
# stackloss %>%
#   ntbt_ltsReg(stack.loss ~ .)
# 
# 
# ## ntbt_nlrob: Robust Fitting of Nonlinear Regression Models
# DNase1 <- DNase[ DNase$Run == 1, ]
# 
# ## Original function to interface
# nlrob(density ~ Asym/(1 + exp(( xmid - log(conc) )/scal ) ),
#       data = DNase1, trace = TRUE,
#       start = list( Asym = 3, xmid = 0, scal = 1 ))
# 
# ## The interface puts data as first parameter
# ntbt_nlrob(DNase1, density ~ Asym/(1 + exp(( xmid - log(conc) )/scal ) ),
#            trace = TRUE,
#            start = list( Asym = 3, xmid = 0, scal = 1 ))
# 
# ## so it can be used easily in a pipeline.
# DNase1 %>%
#   ntbt_nlrob(density ~ Asym/(1 + exp(( xmid - log(conc) )/scal ) ),
#              trace = TRUE,
#              start = list( Asym = 3, xmid = 0, scal = 1 ))
# ## End(Not run)