R: Compares data frames using different regression methods.

multi_compare {sampcompR}

R Documentation

Compares data frames using different regression methods.

Description

multi_compare compares data frames using regression models based on differing methods. All glm Models can be compared.

Usage

multi_compare(
  df,
  benchmark,
  independent = NULL,
  dependent = NULL,
  formula_list = NULL,
  family = "ols",
  rm_na = "pairwise",
  out_output_list = TRUE,
  out_df = FALSE,
  out_models = FALSE,
  print_p = FALSE,
  print_se = FALSE,
  weight = NULL,
  id = NULL,
  strata = NULL,
  nest = FALSE,
  weight_bench = NULL,
  id_bench = NULL,
  strata_bench = NULL,
  nest_bench = FALSE,
  robust_se = FALSE,
  p_adjust = NULL,
  names_df_benchmark = NULL,
  silence_summary = FALSE,
  nboots = 0,
  parallel = FALSE,
  adjustment_vars = NULL,
  raking_targets = NULL,
  post_targets = NULL
)

Arguments

`df`, `benchmark`	A data frame containing the set of respondents or benchmark set of respondents to compare, or a character string containing the name of the set of respondents or benchmark set of respondents. All independent and dependent variables must be inside both data frames.
`independent`	A list of strings containing the independent variables (x) for comparison. Every independent variable will be used in every model to estimate the dependent variable (y). When a `formula_list` is provided, `independent` will be ignored.
`dependent`	A list of strings containing the dependent variables (y) for comparison. One model will be computed for every dependent variable (y) provided. When a `formula_list` is provided, `dependent` will be ignored.
`formula_list`	A list of formulas to use in the regression models. If given, `dependent` and `independent` parameters will be ignored.
`family`	A family input, that can be given to `glm` or `svyglm`. Additionally, if "ols" is given, `gaussian(link = "identity")`, and if "logit" is given, `binomial(link = "logit")` is used.
`rm_na`	A character to determine how to handle missing values. For this two options are supported. If `rm_na = "pairwise"` NAs will be removed separately for every model. Only cases containing NA on one of the variables used in the respective model will be removed (all independent variables but only the respective dependent variable). If `rm_na = "listwise"` all cases containing NA on one of the dependent or independent variables are removed.
`out_output_list`	A logical value. If `out_output_list = TRUE`, a list will be returned, containing the separate interaction models calculated with the `glm` function or `svyglm` in case of weighting, as well as a summary object for every model. Standard errors and p-values of these models are always calculated without robustness methods.
`out_df`	If `TRUE`, the used data frames will also be part of the output list.
`out_models`	If True, GLM model objects will be part of the returned object.
`print_p`	If `TRUE`, in addition to the difference in Average Discrete Change (ADC), p-values will be printed.
`print_se`	If `TRUE`, additionally standard errors will be printed.
`weight`, `weight_bench`	A character vector containing the name of the weight variable in the respective data frame. If provided the data frame will be weighted using the `svydesign` function. Also `id` must be provided.
`id`, `id_bench`	A character vector containing the name of the id variable in the respectiv data frame. Only needed for weighting.
`strata`, `strata_bench`	A character vector containing the name of the strata variable in the respective data frame. It is used in the `svydesign` function for weighting.
`nest`, `nest_bench`	A logical vector that is used in the `svydesign` function for the respective data frame.
`robust_se`	A logical value If `TRUE` instead of normal standard errors, heteroscedasticity-consistent standard errors will be used in the analysis to calculate them the `vcovHC` and `coeftest` packages are used.
`p_adjust`	A logical input or character string indicating an adjustment method usable in the `method` parameter of `p.adjust`. If set to TRUE the Bonferroni adjusted p-values are used in inference.
`names_df_benchmark`	A vector containing first the name of `df` and benchmark.
`silence_summary`	A logical value, to indicate if the printed summary should not be printed instead.
`nboots`	A numeric value indicating the number of bootstrap replications. If nboots = 0 no bootstrapping will be performed. Else `nboots` must be >2. Note, that bootstrapping can be very computationaly heavy and can therefore take a while.
`parallel`	If `TRUE`, all detected cores will be used in bootstrapping.
`adjustment_vars`	Variables used to adjust the survey when using raking or post-stratification.
`raking_targets`	A List of raking targets that can be given to the rake function of `rake`, to rake the `df`.
`post_targets`	A List of post_stratification targets that can be given to the rake function of `postStratify`, to post_stratificatify the `df`.

Value

A table is printed showing the difference between the set of respondents for each model, as well as an indicator, if they differ significantly from each other. It is generated using the chosen method. Ifout_output_list = TRUE, also a list with additional information will be returned that can be used in some additional packages of this function to reprint the summary or to visualize the results.

Examples


#Example 1
## Make a comparison specifiying dependent and independent variables.

## Get Data for comparison
require(wooldridge)
card<-wooldridge::card

south <- card[card$south==1,]
north <- card[card$south==0,]


## use the function to plot the data 
multi_data1<-sampcompR::multi_compare(df = north, 
                                     bench = south,
                                     independent = c("age","fatheduc","motheduc","IQ"),
                                     dependent = c("educ","wage"),
                                     family="ols") 
                        
plot_multi_compare("multi_data1")

#Example 2
## Make a comparison with a formula_list
card<-wooldridge::card

south <- card[card$south==1,]
north <- card[card$south==0,]

form_list<-list(formula(educ~age+fatheduc+motheduc+IQ),
                formula(wage~age+fatheduc+motheduc+IQ))


multi_data2 <- sampcompR::multi_compare(df = north, 
                                        bench = south,
                                        formula_list = form_list,
                                        family="ols")


plot_multi_compare("multi_data2")

[Package sampcompR version 0.2.1 Index]