initial commit

.Rbuildignore

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+^.*\.Rproj$
+^\.Rproj\.user$

.gitignore

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+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata

DESCRIPTION

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+Package: FixedEffectjlr
+Type: Package
+Title: Fixed Effect Models in R
+Version: 0.1.0
+Date: 2018-03-22
+Authors@R: person("Erik", "Loualiche", email = "[email protected]", role = c("aut", "cre"))
+Description: Implements a R wrapper for FixedEffectModels julia package
+Depends: R (>= 3.4.0)
+License: MIT
+Encoding: UTF-8
+LazyData: true
+Imports: 
+  JuliaCall (>= 0.11.1)
+Suggests: 
+  data.table
+SystemRequirements: 
+  Julia (>= 0.6.0),
+  FixedEffectModels.jl
+  StatsModels.jl,
+  Distributions.jl,
+  DataFrames.jl
+RoxygenNote: 6.0.1
+

FixedEffectjlr.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+AutoAppendNewline: Yes
+StripTrailingWhitespace: Yes
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageInstallArgs: --no-multiarch --with-keep.source

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(FixedEffect)
+export(FixedEffect_setup)
+import(JuliaCall)
+import(data.table)
+importFrom(stringr,str_length)
+importFrom(stringr,str_replace_all)
+importFrom(stringr,str_split)

R/FixedEffectModels.R

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+.FixedEffect <- new.env(parent = emptyenv())
+
+
+
+#' Use Ipopt solver to solve optimization problems.
+#'
+#' \code{IPOPT} returns the solution to the optimization problem found by Ipopt solver.
+#'
+#' @param dt        dataset of interest
+#' @param lhs       String, Y regression variable
+#' @param rhs       String, X formula
+#' @param fe        Fixed effects
+#' @param weights   Regression weights (not yet coded)
+#' @param vcov      Specification for the error
+#' @param save_res  Do we save the residuals (not yet coded)
+#'
+#' @return The return value will be a list which contains two elements at this point
+#'   results: includes most of the observation from the julia call
+#'   summary: includes information that is of importance to write a table
+#'
+#' @examples See vignettes and readme
+#'
+#' @export
+#####################################################################################################################
+FixedEffect <- function(dt,
+                        lhs,
+                        rhs,
+                        fe       = NULL,
+                        weights  = NULL,
+                        vcov     = NULL,
+                        save_res = FALSE,    # do we save residuals
+                        print    = TRUE
+){
+
+
+  # 1. convert the dataset
+  dt_julia <- JuliaObject(dt)
+  julia_assign("dt_julia", dt_julia)
+
+  # parse the fixed effects and convert to pooled
+  fe_split <- unlist( stringr::str_split(fe, "\\+") )
+  fe_split <- unlist( stringr::str_split(fe_split, "\\:") )
+  fe_split <- unique( stringr::str_replace_all(fe_split, " ", "") )
+  # create pooled fe variables in the dataset
+  for (iter in seq(1, length(fe_split))){
+    pool_cmd = paste0("dt_julia[:", fe_split[iter], "]",
+                      " = categorical(dt_julia[:",
+                      fe_split[iter], "]);")
+    julia_command(pool_cmd)
+  }
+
+  cluster_split <- gsub("cluster", "",
+                     gsub("[()]", "",
+                       gsub("robust", "", vcov) ) )
+  cluster_split <- gsub(" ", "",
+                     unlist( stringr::str_split(cluster_split, "\\+") ) )
+  if ( stringr::str_length(paste(cluster_split, collapse="")) > 0 ){
+    for (iter in seq(1, length(cluster_split))){
+      pool_cmd = paste0("dt_julia[:", cluster_split[iter], "]",
+                        " = categorical(dt_julia[:",
+                        cluster_split[iter], "]);")
+      julia_command(pool_cmd)
+    }
+  }
+
+
+  # set the formula for julia
+  julia_formula  = paste(lhs, "~", rhs)
+
+  # set the options
+  julia_reg_fe   = paste("fe =", stringr::str_replace_all(fe, "\\:", "\\&"))
+  julia_reg_wg   = paste("weights =", weights)
+  julia_reg_vcov = paste("vcov = ", vcov)
+  julia_reg_save = paste("save = ", ifelse(save_res, "true", "false"))
+  julia_reg_opt  = paste(c(julia_reg_fe, julia_reg_wg, julia_reg_vcov, julia_reg_save),
+                         collapse = ", ")
+
+  julia_regcall = paste("reg_res = reg(dt_julia, @model(",
+                        paste(c(julia_formula, julia_reg_opt), collapse = ", "),
+                        ") );")
+
+  # Run the regression
+  julia_command(julia_regcall)
+
+  # Return some useful information "close to lm"
+  z <- list()
+
+  # coefficients
+  jl_coefficients    = julia_eval("reg_res.coef")
+  names(jl_coefficients) = julia_eval("reg_res.coefnames")
+  z$coefficients = jl_coefficients
+  # residuals
+  ## z$residuals = julia_eval("residuals(reg_res, dt_julia_mp)") # only if necessary
+  # effects
+  ## z$effects = c(NA)
+  # rank
+  z$rank = julia_eval("reg_res.nobs - reg_res.df_residual")
+  # fitted.values
+  ## z$fitted.values = c(NA)
+  # assign
+  ## z$assign = c(NA)
+  # qr
+  ## z$qr = c(NA)
+  # df.residual
+  jl_df.residual = julia_eval("reg_res.df_residual")
+  z$df.residual  = jl_df.residual
+  # xlevels
+  ## z$xlevels <- list()
+  ## names(z$xlevels) <- c()
+  # call
+  if (grep("cluster", vcov)){
+    cluster_formula <- gsub("cluster", "",
+                         gsub("[()]", "", vcov) )
+  } else {
+    cluster_formula <- "0"
+  }
+  R_call = paste(julia_formula, "|", fe, "| 0 |", cluster_formula)
+  z$call = list(R_call = as.formula(R_call), julia_call = julia_regcall)
+  # terms
+
+  # other stuff
+  z$nobs = julia_eval("reg_res.nobs")   # number of observations
+
+  z$r2    = list(r2 = julia_eval("reg_res.r2"),
+                 r2_adjusted = julia_eval("reg_res.r2_a"),
+                 r2_within = julia_eval("reg_res.r2_within"))
+  z$statistics = list(F_stat = julia_eval("reg_res.F"),
+                      pvalue = julia_eval("reg_res.p"))
+
+  z$convergence = list(julia_eval("reg_res.iterations"),
+                       julia_eval("reg_res.converged"))
+
+  z$se = julia_eval("stderr(reg_res)")
+  z$ci = julia_eval("confint(reg_res)")
+
+  class(z) <- 'lm'
+
+  # Return somthing else close to summary
+  if (print == TRUE){
+    julia_eval("show(reg_res);")
+  }
+
+  # BUILD COEFFICIENT TABLE LIST
+  ct <- list()   # CoefTable2
+
+
+
+
+  julia_eval("jl_table = coeftable(reg_res);")
+
+  ct$ctitle   = julia_eval("title(reg_res)")
+  ct$ctop     = julia_eval("top(reg_res)")
+  ct$cc       = julia_eval("coef(reg_res)")             # coefficients
+  ct$se       = julia_eval("stderr(reg_res)")
+  ct$coefnms  = julia_eval("coefnames(reg_res)")
+  ct$conf_int = julia_eval("confint(reg_res)")
+  ct$tt       = julia_eval("coef(reg_res) ./ stderr(reg_res)")
+
+  ct$mat = julia_eval("jl_table.mat")
+  ct$colnms  = julia_eval("jl_table.colnms")
+  ct$rownms  = julia_eval("jl_table.rownms")
+  ct$pvalcol = julia_eval("jl_table.pvalcol")
+
+  # ---------------------------------------------------------------------------------
+  return(list(results = z,
+              summary = ct))
+
+
+}
+
+
+
+
+
+
+

R/FixedEffectjlr.R

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+#' A package for asset pricing analysis
+#'
+#' @docType package
+#' @name FixedEffectjlr
+#'
+#' @import data.table
+#' @import JuliaCall
+#'
+#' @importFrom stringr str_replace_all
+#' @importFrom stringr str_split
+#' @importFrom stringr str_length
+#'
+NULL
+

R/FixedEffectsetup.R

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+
+
+#' Do initial setup for FixedEffectjlr package.
+#'
+#' \code{FixedEffect_setup} does the initial setup for ipoptjlr package.
+#'
+#' @param ... arguments passed to \code{JuliaCall::julia_setup}.
+#'
+#' @examples
+#' \dontrun{
+#' FixedEffect_setup()
+#' }
+#'
+#' @export
+FixedEffect_setup <- function(...) {
+  .FixedEffect$julia <- JuliaCall::julia_setup(...)
+  .FixedEffect$julia$install_package_if_needed("FixedEffectModels")
+  .FixedEffect$julia$library("FixedEffectModels")
+  .FixedEffect$julia$library("StatsModels")
+  .FixedEffect$julia$library("Distributions")
+  .FixedEffect$julia$library("DataFrames")
+}