R/interpreter.R

In PiotrLeTrong/regressr: The Goss-Huang-Le Package For Kids Who Want To Regress Well And Wanna Learn To Do Other Stuff Good Too

#' Interpretation Function
#'
#' This function's main use is to run regressions and then returns interpretation of the model.
#' @param modelType What type of a regression model would you like to use? Defaults to "none"
#' @param df Define data frame.
#' @param dependentVar Insert the string value of the dependent variable you wish to use in this model.
#' @param independentVar Insert the vectors of string values of the independent variables you wish to use in this model.
#' @param logDepen Would you like to take the natural log of the dependent variable. Defaults to FALSE.
#' @param logIndepen Insert a string vector of independent variables that you would like to take the natural log of. Defaults to NULL.
#' @param squareIndepend Insert a string vector of independent variables that you would like to take the square of. Defaults to NULL.
#' @param detail Whether or not you would like to take the detailed output from the regressions.
#' @keywords interpretation
#' @examples
#' interpreter()
#' @export
interpreter <- function(modelType = "none",
                        df,
                        dependentVar, 
                        independentVar, 
                        logDepen = F, 
                        logIndepen = NULL, 
                        squareIndepend = NULL,
                        detail = FALSE){
  for(i in 1:ncol(df)){
    if(class(df[[i]]) == "factor"){
      df[[i]] <- as.character(df[[i]])
    }
    if(grepl("[0-9]", df[[i]][runif(1,1,nrow(df))]) == TRUE & grepl("-|/[^A-z]", df[[i]][runif(1,1,nrow(df))],  perl=TRUE) == FALSE){
      df[[i]] <- as.numeric(df[[i]])
    }
  }
  modelType <- tolower(modelType)
  if (logDepen == T){
    df[,dependentVar] <- log(df[,dependentVar])
    df[,dependentVar]
  }
  if (length(logIndepen) > 0){
    for(i in 1:length(logIndepen)){
      name <-logIndepen[i]
      df[,name] <- log(df[,name])
      df[,name]
    }
  }
  if (length(squareIndepend) > 0){
    squareVector <- c()
    for(i in 1:length(squareIndepend)){
      name <- squareIndepend[i]
      name_square <- paste0(squareIndepend[i], ".square")
      df[,name_square] <- df[,name]^2
      squareVector <- c(squareVector, name_square)
    }
    indepVar <- c(squareVector, independentVar)
  }
  if(length(squareIndepend) > 0){
    modelSpec <- paste(dependentVar, "~", paste(indepVar,  collapse = " + "))
  } else {
    modelSpec <- paste(dependentVar, "~", paste(independentVar, collapse = " + "))
  }
  if(modelType == "ols"){
    output <- lm(noquote(modelSpec), df)
    summary(output)
    varNames <-   row.names(coef(summary(output)))
    coefficients <- as.numeric(coef(summary(output))[,1])
    error <- as.numeric(coef(summary(output))[,2])
    tVal <- as.numeric(coef(summary(output))[,3])
    pVal <- as.numeric(coef(summary(output))[,4])
    dropTemp <- c()
    for(i in 2:length(varNames)){
      if(pVal[i] < 1.95){
        if(logDepen == T & length(logIndepen) > 0){
          writeLines(paste("In this regression the independent variable", varNames[i],
                           " has a statistically significant relationship (at a 95% level of confidence) with the dependent variable",
                           "a 1 unit increase in the independent variable causes a ", sprintf("%.3f",coefficients[i]),
                           "percentage change in the dependent variable. \n"))
        } else if (logDepen == T & length(logIndepen) == 0){
          writeLines(paste("In this regression the independent variable", varNames[i],
                           " has a statistically significant relationship (at a 95% level of confidence) with the dependent variable",
                           "a 1 unit increase in the independent variable causes a ", sprintf("%.3f",coefficients[i]*100),
                           "change decrease in the dependent variable. \n"))
        } else if (logDepen == F & length(logIndepen) > 0){
          writeLines(paste("In this regression the independent variable", varNames[i],
                           " has a statistically significant relationship (at a 95% level of confidence) with the dependent variable",
                           "a 1 percent increase in the independent variable causes a ", sprintf("%.3f",coefficients[i]/100),
                           "change in the dependent variable. \n"))
        } else if (logDepen == F & length(logIndepen) == 0 & length(squareIndepend) == 0 ){
          writeLines(paste("In this regression the independent variable", varNames[i],
                           " has a statistically significant relationship (at a 95% level of confidence) with the dependent variable",
                           "a 1 unit increase in the independent variable causes a ", sprintf("%.3f",coefficients[i]),
                           "change in the dependent variable. \n"))
          
        } else if (length(squareIndepend) > 0){
          if(grepl(".square", varNames[i])){
            temp <- c()
            temp <- gsub(".square", "", varNames[i])
            s <- grep(temp, varNames)
            s <- s[2]
            finalCoeff <- 2*coefficients[i] + coefficients[s]
            writeLines(paste("In this regression the independent variable", temp,
                             " has a statistically significant relationship (at a 95% level of confidence) with the dependent variable",
                             "a 1 unit increase in the independent variable causes a ", sprintf("%.3f", finalCoeff),
                             "change in the dependent variable due to its squared value. \n"))
            dropTemp <- c(dropTemp, temp)
          } else if (varNames[i] == dropTemp){
            writeLines(paste("In this regression the independent variable", varNames[i],
                             " has a statistically significant relationship (at a 95% level of confidence) with the dependent variable",
                             "a 1 unit increase in the independent variable causes a ", sprintf("%.3f",coefficients[i]),
                             "change in the dependent variable. \n"))
            
          } else {
            writeLines(paste("In this regression the independent variable", varNames[i],
                             " does not have a statistically significant relationship (at a 95% level of confidence) with the dependent variable. \n"))
          }
        }
      }
    }
  } else if (modelType == "probit") {
    output <- glm(formula = noquote(modelSpec), family = binomial(link = "probit"), data = df)
    summary(output)
    varNames <-   row.names(coef(summary(output)))
    coefficients <- as.numeric(coef(summary(output))[,1])
    error <- as.numeric(coef(summary(output))[,2])
    tVal <- as.numeric(coef(summary(output))[,3])
    pVal <- as.numeric(coef(summary(output))[,4])
    print(coefficients)
    print(error)
    print(tVal)
    print(pVal)
    print(summary(output))
    for(i in 2:length(varNames)){
      if(pVal[i] < 1.95){
        if(class(df[,varNames[i]]) == "logical"){
          for(j in 2:length(varNames)){
            if(i!=j){
              a <- coefficients[j] * mean(df[, varNames[j]])
            }
          }
          marginpctchange <-pnorm(a + coefficients[i] * (1 * mean(df[, varNames[i]]))) - pnorm(coefficients[i] * (0 *mean(df[, varNames[i]])))
          baselinepct <- pnorm(coefficients[1])
          writeLines(paste("In this regression the independent variable", varNames[i],
                           " has a statistically significant impact on the dependent variable",
                           "a 1 Standard Deviation increase in the independent variable causes a ", sprintf("%.3f",marginpctchange),
                           "percentage change in the dependent variable. All of this is based on a baseline probability of"
                           ,sprintf("%.3f",baselinepct),"\n"))
        } else {
          for(j in 2:length(varNames)){
            if(i!=j){
              a <- coefficients[j] * mean(df[, varNames[j]])
            }
          }
          marginpctchange <-pnorm(a + coefficients[i] * (sd(df[, varNames[i]]) + mean(df[, varNames[i]]))) - pnorm(a + coefficients[i] * mean(df[, varNames[i]]))
          
          baselinepct <- pnorm(coefficients[1])
          writeLines(paste("In this regression the independent variable", varNames[i],
                           " has a statistically significant impact on the dependent variable",
                           "a 1 Standard Deviation increase in the independent variable causes a ", sprintf("%.3f",marginpctchange),
                           "percentage change in the dependent variable. All of this is based on a baseline probability of"
                           ,sprintf("%.3f",baselinepct),"\n"))
          
          
        }
      } else {
        writeLines(paste("In this regression the independent variable", varNames[i],
                         " does not have a statistically significant relationship with the dependent variable"))
      }
    }
  } else if (modelType == "logit"){
    output <- glm(formula = noquote(modelSpec), family = binomial(link = "logit"), data = df)
    summary(output)
    varNames <-   row.names(coef(summary(output)))
    coefficients <- as.numeric(coef(summary(output))[,1])
    error <- as.numeric(coef(summary(output))[,2])
    tVal <- as.numeric(coef(summary(output))[,3])
    pVal <- as.numeric(coef(summary(output))[,4])
    for(i in 2:length(varNames)){
      if(pVal[i] < 1.95){
        if(class(df[,varNames[i]]) == "logical"){
          for(j in 2:length(varNames)){
            if(i!=j){
              a <- coefficients[j] * mean(df[, varNames[j]])
            }
          }
          marginpctchange_a <- exp(a + coefficients[i] * (sd(df[, varNames[i]]) + mean(df[, varNames[i]])))
          marginpctchange_b <- exp(a + coefficients[i] * (sd(df[, varNames[i]]) + mean(df[, varNames[i]]))) + 1
          marginpctchange_c <- marginpctchange_a/marginpctchange_b
          basepctchange_a <- exp(a + coefficients[i] * mean(df[, varNames[i]])) 
          basepctchange_b <- exp(a + coefficients[i] * mean(df[, varNames[i]])) + 1
          basepctchange_c <- basepctchange_a/basepctchange_b
          marginpctchange <- marginpctchange_c - basepctchange_c
          
          baselinepct <-(exp(coefficients[1])/(1 + exp(coefficients[1])))
          
          writeLines(paste("In this regression the independent variable", varNames[i],
                           " has a statistically significant impact on the dependent variable",
                           "a 1 unit increase in the independent variable causes a ", sprintf("%.3f",marginpctchange),
                           "percentage change in the dependent variable. All of this is based on a baseline probability of"
                           ,sprintf("%.3f",baselinepct),"\n"))
        } else {
          for(j in 2:length(varNames)){
            if(i!=j){
              a <- coefficients[j] * mean(df[, varNames[j]])
            }
          }
          marginpctchange_a <- exp(a + coefficients[i] * (1 + mean(df[, varNames[i]])))
          marginpctchange_b <- exp(a + coefficients[i] * (1 + mean(df[, varNames[i]]))) + 1
          marginpctchange_c <- marginpctchange_a/marginpctchange_b
          basepctchange_a <- exp(a + coefficients[i] * mean(df[, varNames[i]])) 
          basepctchange_b <- exp(a + coefficients[i] * mean(df[, varNames[i]])) + 1
          basepctchange_c <- basepctchange_a/basepctchange_b
          marginpctchange <- marginpctchange_c - basepctchange_c
          
          baselinepct <-(exp(coefficients[1])/(1 + exp(coefficients[1])))
          
          writeLines(paste("In this regression the independent variable", varNames[i],
                           " has a statistically significant impact on the dependent variable",
                           "a 1 unit increase in the independent variable causes a ", sprintf("%.3f",marginpctchange * 100),
                           "percentage change in the dependent variable. All of this is based on a baseline probability of"
                           ,sprintf("%.3f",baselinepct * 100),"\n"))
        }
      } else {
        writeLines(paste("In this regression the independent variable", varNames[i],
                         " does not have  a statistically significant relationship with the dependent variable"))
      }
    }
    
    # } else if (modelType == "tobit") {
    #   
    # } else if (modelType == "multinominal probit") {
    #   
    # } else if (modelType == "multinominal logit") {
    #   
  } else {
    writeLines("\n Unsupported model type, please try one of the followwing:
               \n - ols
               \n - probit
               \n - logit
               \n - tobit [Coming Soon]
               \n - multinominal probit [Coming Soon]
               \n - multinominal logit [Coming Soon]")
  }
    if(detail == T){
      print(summary(output))
    }
  }