R/SSE.R

In OnofriAndreaPG/aomisc: Statistical methods for the agricultural sciences

# Modified gompertz equation for bioassay work 
E4.fun <- function(predictor, b, c, d, e) {
  x <- predictor
  c + (d - c) * (1 - exp( - exp ( b * ( x - e))))
}

E4.Init <- function(mCall, LHS, data, ...) {
  xy <- sortedXyData(mCall[["predictor"]], LHS, data)
  x <-  xy[, "x"]; y <- xy[, "y"]
  
  y <- as.numeric( tapply(y, factor(x), mean) )
  x <- as.numeric( tapply(x, factor(x), mean) )
  mod <- nls(y ~ NLS.L4(x, b, c, d, e))
  value <- as.numeric(coef(mod))
  
  names(value) <- mCall[c("b", "c", "d", "e")]
  value
}

NLS.E4 <- selfStart(E4.fun, E4.Init, parameters=c("b", "c", "d", "e"))

E3.fun <- function(predictor, b, d, e) {
  x <- predictor
  d * (1 - exp( - exp ( b * ( x - e))))
}

E3.Init <- function(mCall, LHS, data, ...) {
  xy <- sortedXyData(mCall[["predictor"]], LHS, data)
  x <-  xy[, "x"]; y <- xy[, "y"]
  
  y <- as.numeric( tapply(y, factor(x), mean) )
  x <- as.numeric( tapply(x, factor(x), mean) )
  mod <- nls(y ~ NLS.L3(x, b, d, e))
  value <- as.numeric(coef(mod))
  
  names(value) <- mCall[c("b", "d", "e")]
  value
}

NLS.E3 <- selfStart(E3.fun, E3.Init, parameters=c("b", "d", "e"))

E2.fun <- function(predictor, b, e) {
  x <- predictor
  1 - exp( - exp ( b * ( x - e)))
}

E2.Init <- function(mCall, LHS, data, ...) {
  xy <- sortedXyData(mCall[["predictor"]], LHS, data)
  x <-  xy[, "x"]; y <- xy[, "y"]
  
  ## Linear regression on pseudo y values
  y <- as.numeric( tapply(y, factor(x), mean) )
  x <- as.numeric( tapply(x, factor(x), mean) )
  mod <- nls(y ~ NLS.L2(x, b, e))
  value <- as.numeric(coef(mod))
  
  names(value) <- mCall[c("b", "e")]
  value
}

NLS.E2 <- selfStart(E2.fun, E2.Init, parameters=c("b", "e"))

"E.4" <-
  function(fixed = c(NA, NA, NA, NA), names = c("b", "c", "d", "e"))
  {
    ## Checking arguments
    numParm <- 4
    if (!is.character(names) | !(length(names) == numParm)) {stop("Not correct 'names' argument")}
    if (!(length(fixed) == numParm)) {stop("Not correct 'fixed' argument")}
    
    ## Fixing parameters (using argument 'fixed')
    notFixed <- is.na(fixed)
    parmVec <- rep(0, numParm)
    parmVec[!notFixed] <- fixed[!notFixed]
    
    ## Defining the non-linear function
    fct <- function(x, parm)
    {
      parmMat <- matrix(parmVec, nrow(parm), numParm, byrow = TRUE)
      parmMat[, notFixed] <- parm
      
      E4.fun(x, parm[,1], parm[,2], parm[,3], parm[,4])
      
    }
    
    ## Defining self starter function
    ssfct <- function(dataf)
    {
      x <- dataf[, 1]
      y <- dataf[, 2]
      
      d <- max(y) * 1.05
      c <- min(y) * 0.95
      
      ## Linear regression on pseudo y values
      pseudoY <- log(-log((d - y)/(d - c)))
      coefs <- coef( lm(pseudoY ~ x))
      k <- coefs[1]; b <- coefs[2]
      e <- -k/b
      value <- c(b,c,d,e)
      
      return(value[notFixed])
    }
    
    ## Defining names
    pnames <- names[notFixed]
    
    ## Defining derivatives
    
    ## Defining the ED function
    
    ## Defining the inverse function
    
    ## Defining descriptive text
    text <- "Modified Gompertz equation (4 parameters)"
    
    ## Returning the function with self starter and names
    returnList <- list(fct = fct, ssfct = ssfct, names = pnames, text = text, noParm = sum(is.na(fixed)))
    
    class(returnList) <- "drcMean"
    invisible(returnList)
  }

"E.3" <-
  function(fixed = c(NA, NA, NA), names = c("b", "d", "e"))
  {
    ## Checking arguments
    numParm <- 3
    if (!is.character(names) | !(length(names) == numParm)) {stop("Not correct 'names' argument")}
    if (!(length(fixed) == numParm)) {stop("Not correct 'fixed' argument")}
    
    ## Fixing parameters (using argument 'fixed')
    notFixed <- is.na(fixed)
    parmVec <- rep(0, numParm)
    parmVec[!notFixed] <- fixed[!notFixed]
    
    ## Defining the non-linear function
    fct <- function(x, parm)
    {
      parmMat <- matrix(parmVec, nrow(parm), numParm, byrow = TRUE)
      parmMat[, notFixed] <- parm
      
      E3.fun(x, parm[,1], parm[,2], parm[,3])
      
    }
    
    ## Defining self starter function
    ssfct <- function(dataf)
    {
      x <- dataf[, 1]
      y <- dataf[, 2]
      
      d <- max(y) * 1.05
      
      ## Linear regression on pseudo y values
      pseudoY <- log(-log((d - y)/d))
      coefs <- coef( lm(pseudoY ~ x))
      k <- coefs[1]; b <- coefs[2]
      e <- -k/b
      value <- c(b,d,e)
      
      return(value[notFixed])
    }
    
    ## Defining names
    pnames <- names[notFixed]
    
    ## Defining derivatives
    
    ## Defining the ED function
    
    ## Defining the inverse function
    
    ## Defining descriptive text
    text <- "Modified Gompertz equation (3 parameters)"
    
    ## Returning the function with self starter and names
    returnList <- list(fct = fct, ssfct = ssfct, names = pnames, text = text, noParm = sum(is.na(fixed)))
    
    class(returnList) <- "drcMean"
    invisible(returnList)
  }

"E.2" <- function(fixed = c(NA, NA), names = c("b", "e"))
  {
    ## Checking arguments
    numParm <- 2
    if (!is.character(names) | !(length(names) == numParm)) {stop("Not correct 'names' argument")}
    if (!(length(fixed) == numParm)) {stop("Not correct 'fixed' argument")}
    
    ## Fixing parameters (using argument 'fixed')
    notFixed <- is.na(fixed)
    parmVec <- rep(0, numParm)
    parmVec[!notFixed] <- fixed[!notFixed]
    
    ## Defining the non-linear function
    fct <- function(x, parm)
    {
      parmMat <- matrix(parmVec, nrow(parm), numParm, byrow = TRUE)
      parmMat[, notFixed] <- parm
      
      E2.fun(x, parm[,1], parm[,2])
      
    }
    
    ## Defining self starter function
    ssfct <- function(dataf)
    {
      x <- dataf[, 1]
      y <- dataf[, 2]
      
      ## Linear regression on pseudo y values
      pseudoY <- log(-log(1.01 - y))
      coefs <- coef( lm(pseudoY ~ x))
      k <- coefs[1]; b <- coefs[2]
      e <- -k/b
      value <- c(b, e)
      
      return(value[notFixed])
    }
    
    ## Defining names
    pnames <- names[notFixed]
    
    ## Defining derivatives
    
    ## Defining the ED function
    
    ## Defining the inverse function
    
    ## Defining descriptive text
    text <- "Modified Gompertz equation (2 parameters)"
    
    ## Returning the function with self starter and names
    returnList <- list(fct = fct, ssfct = ssfct, names = pnames, text = text, noParm = sum(is.na(fixed)))
    
    class(returnList) <- "drcMean"
    invisible(returnList)
  }