R/Stage2PSM.R

In itpir/SCI: Spatial Cauasal Inference

#Analytics and Models
#Note most users will likely want to run their own models.
#These functions are to make common modeling strategies easier to specify for users
#That do not write their own models.

Stage2PSM <- function(model, dta, type, table_out = NULL, opts = NULL)
{
  
  ret_var <- list()
  
  if(type == "lm")
  {
    m_fit <- lm(model,dta)
    print("==========================")
    print("UNSTANDARDIZED MODEL RESULTS")
    print("==========================")
    #mTab <- stargazer(m_fit,type="html",title="Unstandardized Model Results")
    print(summary(m_fit))
    ret_var$unstandardized <- lm(model,dta)
    texreg::plotreg(m_fit,omit.coef="(match)|(Intercept)",custom.model.names="Unstandardized Model",custom.note=model)
    
    if(!is.null(table_out))
    {
      dta_tmp <- dta
      
      if(class(dta) == "data.frame")
      {
        d_index <- sapply(dta_tmp, is.numeric)
        dta_tmp[d_index] <- lapply(dta_tmp[d_index],scale)
      } else {
      d_index <- sapply(dta_tmp@data, is.numeric)
      dta_tmp@data[d_index] <- lapply(dta_tmp@data[d_index],scale)
      }
      dta_fit_std <- lm(model,dta_tmp)
      ret_var$standardized <- lm(model,dta_tmp)
      print("==========================")
      print("STANDARDIZED MODEL RESULTS")
      print("==========================")
      print(summary(dta_fit_std))
      texreg::plotreg(dta_fit_std,omit.coef="(match)|(Intercept)",custom.model.names="Standardized Model", custom.note=model)
      
    }
    
  }
  
  if(type == "cmreg")
  {
    m_fit <- lm(model,dta)
    ret_var$unstandardized <- m_fit
    #mTab <- stargazer(m_fit,type="html",title="Unstandardized Model Results")
    #print.htmlTable(mTab)
    print(summary(m_fit))
    texreg::plotreg(m_fit,omit.coef="(match)|(Intercept)|(factor)",custom.model.names="Unstandardized Model",custom.note=model)
    
    if(!is.null(table_out))
    {
      dta_tmp <- dta
      
      if(class(dta) == "data.frame")
      {
        d_index <- sapply(dta_tmp, is.numeric)
        dta_tmp[d_index] <- lapply(dta_tmp[d_index],scale)
      } else {
        d_index <- sapply(dta_tmp@data, is.numeric)
        dta_tmp@data[d_index] <- lapply(dta_tmp@data[d_index],scale)
      }
      dta_fit_std <- lm(model,dta_tmp)
      ret_var$standardized <- dta_fit_std
      print(summary(dta_fit_std))
      texreg::plotreg(dta_fit_std,omit.coef="(match)|(Intercept)|(factor)",custom.model.names="Standardized Model", custom.note=model)
      
    }
    
    print(opts)
    exec = paste("cluster.vcov(m_fit,cbind(dta$",opts[1],",dta$",opts[2],"),force_posdef=TRUE)",sep="")
    m_fit$var <- eval(parse(text=exec))
    CMREG <- coeftest(m_fit,m_fit$var)
    print("cmReg:")
    print(CMREG)
    ret_var$cmreg <- CMREG
    
  }
  return(ret_var)
}