R/model1v2p.R

In JonathanVSV/RSModels: Contains functions that enable the construction of linear models with different parameters, cross validation implementation and the calculation of a random goodness-of-fit ditribution. These models are specially useful for studies that use remote sensing variables as independent variables to describe or predict measured-in-field community attributes.

#' @title Modeling function 1v2p
#'
#' @description This function constructs every possible linear model with one independent variable (y = mx + b)
#' for nx number of dependent variables.
#'
#' @param model.data = data.frame  Data.frame that contains both the dependent and independent variables
#'
#' @param ny = number Number of dependent variables to be tested.
#'
#' @param nx = number Number of independent variables to be tested. Do not confuse number of available independent
#' variables with number of independent variables OF THE MODEL.
#'
#' @param CV = boolean T if a cross-validation should be performed, F if not.
#'
#' @param CV_n = number Number of observations to be left out in the cross-validation process. For example
#' CV = T & CV_n = 1 will perform a 1-leave-out-cross-validation, while the same command with CV_n = 2, will
#' performa a 2-leave-out-cross-validation
#'
#' @param r2random = boolean T if an R^2 maximum random distribution should be computed with the data. Calculating
#' this random distribution enables a comparison of the observed R^2 values of the best models with a completely
#' random scenario. This distribution shows if the goodness-of-fit values obtained in the models correspond to a
#' significntly higher value than the expected at random (>= value of percentile 95) or not.
#'
#' @param runs = number Number indicating the number of runs to perform the goodness-of-fit random distribution.
#'
#' @examples model1v2p(df, ny = 9, nx= 16)
#'
#' @export model1v2p

model1v2p <- function(model.data, ny, nx, CV = F, CV_n = 1, r2random = F, runs = 1000)
{
  #Set model parameters
  p <- 2
  n <- nrow(model.data)

  #Possible combinations for independent variables
  combs.var <- combn(nx, 1)
  #Possible combinations for cross validation procedure
  combs.data <- combn(n, CV_n)

  timer <- round(proc.time()[3])

  #------------------------------Descriptive models---------------------------------------
  results <- array(dim = c(ny, choose(nx, 1), 6))
  results.ord <- array(dim = c(ny, choose(nx, 1), 6))
  for(i in 1:ny)
  {
    y <- i+1
    for(j in 1:choose(nx, 1))
    {
      x1 <- combs.var[1, j] + ny + 1
      model <- lm(model.data[, y] ~ model.data[, x1])
      coef <- model$coef
      r2 <- summary(model)$r.squared
      AICc <- AIC(model) + (2 * p * (p-1)) / (n - p - 1)
      F.p.value.x1 <- summary(aov(model))[[1]][, 5][1]

      results[i, j, ] <- c(paste(names(model.data)[y], "vs", names(model.data)[x1]), r2, AICc, F.p.value.x1,
                        coef[1], coef[2])
    }
    #Export results from higher to lower R2 values
    results.ord[i,,]<-results[i, order(as.numeric(results[i,,2]), decreasing = T),]
    write.table(as.table(results.ord[i,,]), paste("Results-var1par2", names(model.data)[i+1], "", ".xls", sep = ""),
                sep = "\t", row.names = F, col.names = c("Y vs X1", "r2", "AICc", "F.p.value.x1",
                                                       "Intercept (coef1)", "Slope (coef2)"))
  }

  #-------------------------Models with highest R2--------------------------------------
  i <- 1
  mejores <- matrix(nrow = ny, ncol = 7)
  colnames(mejores) <- c("Attribute", "Var1", "R2", "F. p-value", "AIC", "Intercept", "Slope")
  for (i in 1:ny)
  {
    mejormo <- which(as.numeric(results[, , 2][i, ]) == max(as.numeric(results[, , 2][i, ])))
    mejores[i, 1] <- results[, , 1][i, mejormo]
    mejores[i, 3] <- max(as.numeric(results[, , 2][i, ]))
    mejores[i, 4] <- results[, , 4][i, mejormo]
    mejores[i, 5] <- results[, , 3][i, mejormo]
    mejores[i, 6] <- results[, , 5][i, mejormo]
    mejores[i, 7] <- results[, , 6][i, mejormo]
  }

  for (i in 1:ny)
  {
    colsa <- unlist(strsplit(mejores[i,1], " vs "))
    mejores[i,1] <- colsa[1]
    mejores[i,2] <- colsa[2]
  }

  write.table(as.table(mejores), paste("Best mod R2-var1par2", ".xls", sep = ""), sep = "\t", row.names = F,
              col.names = colnames(mejores))

  #--------------------------------------- Cross-validation----------------------------------------
  if(CV == T)
  {
    print("Cross validation under process")
    combs.data.CV <- combs.data

    #Bases de resultados
    pred <- vector(length = CV_n)
    CV.CV <- vector(length = choose(n, CV_n))
    r2.CV <- vector(length = choose(n, CV_n))
    r2.data.vs.pred <- vector(length = choose(n, CV_n))
    time <- vector(length = choose(n, CV_n))
    means <- array(data = 0, dim = c(ny, choose(nx, 1), 6))
    means.ord <- array(data <- 0, dim <- c(ny, choose(nx, 1), 6))

    coefs <- matrix(nrow = choose(n, CV_n), ncol = p)
    mean.coefs <- vector(length = p)

    #Cross validation process
    for(i in 1:ny)
    {
      y <- i+1
      mean.y <- mean(model.data[, y])#Media de metricas de imagen
      mean.sstt <- sum((model.data[, y] - mean.y)^2) / n #Media de Suma de cuadrados
      for(j in 1:choose(nx, 1))
      {
        x1 <- combs.var[1, j] + ny + 1
        for(k in 1:choose(n, CV_n))
        {
          time1 <- proc.time()[3]
          print(paste("vary = ", i, ", varx1 = ", combs.var[1, j], ", comb = ", k, sep = ""))
          validation.data <- model.data[combs.data.CV[, k], ] #Datos omitidos del modelo
          calibration.data <- model.data[ - combs.data.CV[, k], ]#Datos pa armar el modelos sin los de validación
          model <- lm(calibration.data[, y]~calibration.data[, x1])
          coef <- model$coef
          for(l in 1:CV_n)
          {
            pred[l] <- coef[1] + coef[2] * validation.data[l, x1]
          }
          mean.y.validation <- mean(validation.data[,y])
          msse.validation <- sum((validation.data[,y] - pred)^2) / CV_n
          CV.CV[k] <- sqrt(msse.validation) / mean.y.validation
          r2.CV[k] <- 1 - msse.validation / mean.sstt
          r2.data.vs.pred[k] <- (cor(validation.data[, y], pred, method = "pearson"))^2
          coefs[k,] <- coef
          time2 <- proc.time()[3]
          time[k] <- time2 - time1
        }
        mean.time <- mean(time)
        print(paste("mean time = ", mean.time, sep = ""))
        mean.CV.CV <- mean(CV.CV)
        mean.r2.CV <- mean(r2.CV)
        mean.coefs[1] <- mean(coefs[, 1])
        mean.coefs[2] <- mean(coefs[, 2])
        mean.r2.data.vs.pred <- mean(r2.data.vs.pred)
        means[i, j, ] <- c(paste(names(model.data)[y], "vs", names(model.data)[x1]), mean.r2.CV, mean.r2.data.vs.pred,
          mean.CV.CV, mean.coefs[1], mean.coefs[2])
      }
      means.ord[i,,] <- means[i, order(as.numeric(means[i, , 2]),decreasing = T), ]
      write.table(as.table(means.ord[i, , ]), paste("Results-var1par2-CV-", names(model.data)[i+1], "", ".xls", sep = ""),
        sep = "\t", row.names = F, col.names = c("Y vs X1", "mean r2.CV", "mean r2 data vs pred", "mean CV.CV",
          "mean Interc", "mean Slope"))
    }
  }else{
    print("No Cross Validation performed")
  }


  #--------------------------Random highest R2 distribution-------------------------------
  if(r2random == T)
  {
    print("Performing R2 distribution at random")

    model.data.permut <- model.data
    r2.max.permut <- matrix(nrow = ny, ncol = runs)
    r2.model.pvalue <- matrix(nrow = ny, ncol = choose(nx, 1))
    r2 <- array(dim = c(ny, choose(nx, 1), runs))

    #r2 max permut
    for(k in 1:runs)
    {
      print(paste("run = ", k, sep = ""))
      for(i in 2:(nx + ny + 1))
        model.data.permut[,i] <- gtools::permute(model.data[, i]) #permuta posiciones de toda la matriz de datos
      for(i in 1:ny)
      {
        for(j in 1:choose(nx, 1))
        {
          y <- i+1
          x1 <- combs.var[1, j] + ny + 1
          model <- lm(model.data.permut[,y]~model.data.permut[,x1])
          r2[i, j, k] <- summary(model)$r.squared
        }
        r2.max.permut[i, k] <- max(r2[i, , k])
      }
    }

    #r2 model pvalue
    for(i in 1:ny)
      #Order from higher to lower
      r2.max.permut[i,] <- sort(r2.max.permut[i, ], decreasing = T)

    #Colnames fix
    colnames <- vector(length = (choose(nx, 1)))
    for(i in 1:choose(nx, 1))
    {
      x1 <- combs.var[1, i] + ny + 1
      colnames[i] <- paste(names(model.data)[x1])
    }

    #Export to tables
    rownames(r2.max.permut) <- names(model.data)[2:(ny + 1)]
    colnames(r2.max.permut) <- seq(1:runs)
    write.table(as.table(t(r2.max.permut)), paste("Results-var1par2-r2maxpermut", ".xls", sep = ""), sep = "\t",
                col.names = NA, row.names = T)
    rownames(r2.model.pvalue) <- names(model.data)[2:(ny + 1)]
    colnames(r2.model.pvalue) <- colnames
    write.table(as.table(r2.model.pvalue), paste("Results-var1par2-r2modelpvalue", ".xls", sep = ""), sep = "\t",
                col.names = NA, row.names = T)
    write.table(as.table(r2),paste("Results-var1par2-r2ordenada",".xls",sep = ""), sep = "\t",
                col.names = c("Atrib comun","Metrics image","Runs","R2"), row.names = F)

  }else{
    print("No R2 distribution at random performed")
  }

  print(paste0("Process finished. Files can be found in the following directory: ",getwd()))
}