diff --git a/R/aj_westcott.R b/R/aj_westcott.R
index e746f6d..324a43b 100644
--- a/R/aj_westcott.R
+++ b/R/aj_westcott.R
@@ -1,8 +1,8 @@
-#' Adjust values with a grid of checks
+#' Adjust values following the method of Westcott
 #'
 #' This function adjust the observed values of an experiment planted following
 #' the method described by Westcott (1981) with a grid of checks.
-#' @param trait The trait to analyze.
+#' @param trait The trait to adjust.
 #' @param geno The genotypes.
 #' @param ch1 Name of check 1.
 #' @param ch2 Name of check 2.
@@ -30,6 +30,9 @@
 #' proportion that the checks vary around the field. For values lower than 1
 #' the values are adjusted based on that proportion over the checks variation.
 #' If \code{p = 0} then there is no adjustment.
+#' 
+#' If the layout does not correspond with the Westcott method, then the observed values
+#' are adjusted with the values of the checks planted nearby and a warning is issued.
 #' @return It returns the adjusted values.
 #' @references
 #' Westcott, B. (1981). Two methods for early generation yield assessment in winter wheat.
@@ -40,7 +43,7 @@
 aj.w <- function(trait, geno, ch1, ch2, row, col, nr = 5, ncb = 10, method = 2,
                  p = 0.5, ind = TRUE, data) {
   
-  # Error messages
+  # Error and warning messages
   
   out <- ck.pos(row, col, data = data)
   
@@ -50,19 +53,20 @@ aj.w <- function(trait, geno, ch1, ch2, row, col, nr = 5, ncb = 10, method = 2,
   out <- ck.w(trait, geno, ch1, ch2, row, col, ncb, data)
   
   if (out$c1 == 1)
-    stop("There are plots in the columns of checks with other genotypes planted.")
+    warning("There are plots in the columns of checks with other genotypes planted.
+            Adjusted values are obtained with the values of the checks nearby.")
   
   if (out$c2 == 1)
-    stop("The last column in the field does not have checks.")
+    warning("There are plots in the columns of genotypes with checks planted.
+            Adjusted values are obtained with the values of the checks nearby.")
   
   if (out$c3 == 1)
-    stop("There are plots in the columns of genotypes with checks planted.")
+    warning("There are columns of checks without alternating checks.
+            Adjusted values are obtained with the values of the checks nearby.")
   
   if (out$c4 == 1)
-    stop("There are columns of checks without alternating checks.")
-  
-  if (out$c5 == 1)
-    stop("There are plots with genotypes without a check plot to the left or to the right.")
+    warning("There are plots with genotypes without a check plot to the left or to the right.
+            Adjusted values are obtained with the values of the checks nearby.")
   
   # Save column names
   
@@ -95,157 +99,182 @@ aj.w <- function(trait, geno, ch1, ch2, row, col, nr = 5, ncb = 10, method = 2,
   
   data[data[, geno] %in% c(ch1, ch2) & is.na(data[, trait.aj]), trait.aj] <- 0
   
-  # Create columns for check centered values
+  # Run Westcott adjustment or modify adjustment
   
-  data[, ch1] <- NA
-  data[, ch2] <- NA
-
-  # Create columns for prior and posterior check centered values
-    
-  ch1.pri.1 <- paste(ch1, 'pri.1', sep = '.')
-  data[, ch1.pri.1] <- NA
-  ch1.pri.2 <- paste(ch1, 'pri.2', sep = '.')
-  data[, ch1.pri.2] <- NA
-  ch1.pos.1 <- paste(ch1, 'pos.1', sep = '.')
-  data[, ch1.pos.1] <- NA
-  ch1.pos.2 <- paste(ch1, 'pos.2', sep = '.')
-  data[, ch1.pos.2] <- NA
-  ch2.pri.1 <- paste(ch2, 'pri.1', sep = '.')
-  data[, ch2.pri.1] <- NA
-  ch2.pri.2 <- paste(ch2, 'pri.2', sep = '.')
-  data[, ch2.pri.2] <- NA
-  ch2.pos.1 <- paste(ch2, 'pos.1', sep = '.')
-  data[, ch2.pos.1] <- NA
-  ch2.pos.2 <- paste(ch2, 'pos.2', sep = '.')
-  data[, ch2.pos.2] <- NA
-  
-  # Create columns for weigths
-    
-  ch1.w <- paste(ch1, 'w', sep = '.')
-  data[, ch1.w] <- NA
-  ch2.w <- paste(ch2, 'w', sep = '.')
-  data[, ch2.w] <- NA
-    
-  # Arrange check values and weights
-    
-  for(i in 1:dim(data)[1]) {
-      
-    geno.row <- data[i, row]
-    geno.col <- data[i, col]
-    columns <- (geno.col - ncb):(geno.col + ncb)
+  if (out$c1 == 0 & out$c2 == 0 & out$c3 == 0 & out$c4 == 0){
     
-    cond1 <- data[, col] %in% columns
-    cond2 <- data[, geno] %in% c(ch1, ch2)
-      
-    temp <- data[data[, row] == geno.row & cond1 & cond2, c(geno, trait.aj, col)]
-      
-    if (dim(temp)[1] == 2) {
+    # Create columns for check centered values
+    
+    data[, ch1] <- NA
+    data[, ch2] <- NA
+    
+    # Create columns for prior and posterior check centered values
+    
+    ch1.pri.1 <- paste(ch1, 'pri.1', sep = '.')
+    data[, ch1.pri.1] <- NA
+    ch1.pri.2 <- paste(ch1, 'pri.2', sep = '.')
+    data[, ch1.pri.2] <- NA
+    ch1.pos.1 <- paste(ch1, 'pos.1', sep = '.')
+    data[, ch1.pos.1] <- NA
+    ch1.pos.2 <- paste(ch1, 'pos.2', sep = '.')
+    data[, ch1.pos.2] <- NA
+    ch2.pri.1 <- paste(ch2, 'pri.1', sep = '.')
+    data[, ch2.pri.1] <- NA
+    ch2.pri.2 <- paste(ch2, 'pri.2', sep = '.')
+    data[, ch2.pri.2] <- NA
+    ch2.pos.1 <- paste(ch2, 'pos.1', sep = '.')
+    data[, ch2.pos.1] <- NA
+    ch2.pos.2 <- paste(ch2, 'pos.2', sep = '.')
+    data[, ch2.pos.2] <- NA
+    
+    # Create columns for weigths
+    
+    ch1.w <- paste(ch1, 'w', sep = '.')
+    data[, ch1.w] <- NA
+    ch2.w <- paste(ch2, 'w', sep = '.')
+    data[, ch2.w] <- NA
+    
+    # Arrange check values and weights
+    
+    for(i in 1:dim(data)[1]) {
       
-      # Checks on the row
+      geno.row <- data[i, row]
+      geno.col <- data[i, col]
+      columns <- (geno.col - ncb):(geno.col + ncb)
       
-      data[i, ch1] <- temp[temp[, geno] == ch1, trait.aj]
-      data[i, ch2] <- temp[temp[, geno] == ch2, trait.aj]
+      cond1 <- data[, col] %in% columns
+      cond2 <- data[, geno] %in% c(ch1, ch2)
       
-      # Checks on row -2
+      temp <- data[data[, row] == geno.row & cond1 & cond2, c(geno, trait.aj, col)]
       
-      temp.pri <- data[data[, row] == geno.row - 2 & cond1 & cond2, c(geno, trait.aj, col)]
+      if (dim(temp)[1] == 2) {
+        
+        # Checks on the row
+        
+        data[i, ch1] <- temp[temp[, geno] == ch1, trait.aj]
+        data[i, ch2] <- temp[temp[, geno] == ch2, trait.aj]
+        
+        # Checks on row -2
+        
+        temp.pri <- data[data[, row] == geno.row - 2 & cond1 & cond2, c(geno, trait.aj, col)]
+        
+        if (dim(temp.pri)[1] == 2) {
+          data[i, ch1.pri.2] <- temp.pri[temp.pri[, geno] == ch1, trait.aj]
+          data[i, ch2.pri.2] <- temp.pri[temp.pri[, geno] == ch2, trait.aj]
+        }
+        
+        # Checks on row -1
+        
+        temp.pri <- data[data[, row] == geno.row - 1 & cond1 & cond2, c(geno, trait.aj, col)]
+        
+        if (dim(temp.pri)[1] == 2) {
+          data[i, ch1.pri.1] <- temp.pri[temp.pri[, geno] == ch2, trait.aj]
+          data[i, ch2.pri.1] <- temp.pri[temp.pri[, geno] == ch1, trait.aj]
+        }
+        
+        # Checks on row +1
+        
+        temp.pos <- data[data[, row] == geno.row + 1 & cond1 & cond2, c(geno, trait.aj, col)]
+        
+        if (dim(temp.pos)[1] == 2) {
+          data[i, ch1.pos.1] <- temp.pos[temp.pos[, geno] == ch2, trait.aj]
+          data[i, ch2.pos.1] <- temp.pos[temp.pos[, geno] == ch1, trait.aj]
+        }
+        
+        # Checks on row +2  
+        
+        temp.pos <- data[data[, row] == geno.row + 2 & cond1 & cond2, c(geno, trait.aj, col)]
+        
+        if (dim(temp.pos)[1] == 2) {
+          data[i, ch1.pos.2] <- temp.pos[temp.pos[, geno] == ch1, trait.aj]
+          data[i, ch2.pos.2] <- temp.pos[temp.pos[, geno] == ch2, trait.aj]
+        }
+        
+        # Weights for closest checks
         
-      if (dim(temp.pri)[1] == 2) {
-        data[i, ch1.pri.2] <- temp.pri[temp.pri[, geno] == ch1, trait.aj]
-        data[i, ch2.pri.2] <- temp.pri[temp.pri[, geno] == ch2, trait.aj]
+        data[i, ch1.w] <- ncb + 1 - abs(temp[temp[, geno] == ch1, col] - geno.col)
+        data[i, ch2.w] <- ncb + 1 - abs(temp[temp[, geno] == ch2, col] - geno.col)        
       }
+    }
+    
+    # Adjust values with method 1
+    
+    if (method == 1) {
+      chs <- c(ch1, ch2, ch1.pri.1, ch2.pri.1, ch1.pos.1, ch2.pos.1)
+      if (nr == 5)
+        chs <- c(chs, ch1.pri.2, ch2.pri.2, ch1.pos.2, ch2.pos.2)
+      af <- apply(data[, chs], 1, mean, na.rm = TRUE)
+    }
+    
+    # Adjust values with method 2
+    
+    if (method == 2) {
       
-      # Checks on row -1
-      
-      temp.pri <- data[data[, row] == geno.row - 1 & cond1 & cond2, c(geno, trait.aj, col)]
-      
-      if (dim(temp.pri)[1] == 2) {
-        data[i, ch1.pri.1] <- temp.pri[temp.pri[, geno] == ch2, trait.aj]
-        data[i, ch2.pri.1] <- temp.pri[temp.pri[, geno] == ch1, trait.aj]
+      if (nr == 3) {
+        ch1.m.pri <- apply(data[, c(ch1, ch1.pri.1)], 1, mean, na.rm = TRUE)
+        ch2.m.pri <- apply(data[, c(ch2, ch2.pri.1)], 1, mean, na.rm = TRUE)
+        ch1.m.pos <- apply(data[, c(ch1, ch1.pos.1)], 1, mean, na.rm = TRUE)
+        ch2.m.pos <- apply(data[, c(ch2, ch2.pos.1)], 1, mean, na.rm = TRUE)
       }
       
-      # Checks on row +1
-
-      temp.pos <- data[data[, row] == geno.row + 1 & cond1 & cond2, c(geno, trait.aj, col)]
+      if (nr == 5) {
+        foo <- function(x) x * c(1.5, 2, 1)
+        ch1.w.pri <- t(apply(!is.na(data[, c(ch1, ch1.pri.1, ch1.pri.2)]), 1, foo))
+        ch2.w.pri <- t(apply(!is.na(data[, c(ch2, ch2.pri.1, ch2.pri.2)]), 1, foo))
+        ch1.w.pos <- t(apply(!is.na(data[, c(ch1, ch1.pos.1, ch1.pos.2)]), 1, foo))
+        ch2.w.pos <- t(apply(!is.na(data[, c(ch2, ch2.pos.1, ch2.pos.2)]), 1, foo))
+        
+        ch1.m.pri <- data[, c(ch1, ch1.pri.1, ch1.pri.2)] * ch1.w.pri
+        ch2.m.pri <- data[, c(ch2, ch2.pri.1, ch2.pri.2)] * ch2.w.pri
+        ch1.m.pos <- data[, c(ch1, ch1.pos.1, ch1.pos.2)] * ch1.w.pos
+        ch2.m.pos <- data[, c(ch2, ch2.pos.1, ch2.pos.2)] * ch2.w.pos
+        
+        ch1.m.pri <- apply(ch1.m.pri, 1, sum, na.rm = TRUE)
+        ch2.m.pri <- apply(ch2.m.pri, 1, sum, na.rm = TRUE)
+        ch1.m.pos <- apply(ch1.m.pos, 1, sum, na.rm = TRUE)
+        ch2.m.pos <- apply(ch2.m.pos, 1, sum, na.rm = TRUE)
         
-      if (dim(temp.pos)[1] == 2) {
-        data[i, ch1.pos.1] <- temp.pos[temp.pos[, geno] == ch2, trait.aj]
-        data[i, ch2.pos.1] <- temp.pos[temp.pos[, geno] == ch1, trait.aj]
+        ch1.w.pri <- apply(ch1.w.pri, 1, sum, na.rm = TRUE)
+        ch2.w.pri <- apply(ch2.w.pri, 1, sum, na.rm = TRUE)
+        ch1.w.pos <- apply(ch1.w.pos, 1, sum, na.rm = TRUE)
+        ch2.w.pos <- apply(ch2.w.pos, 1, sum, na.rm = TRUE)
+        
+        ch1.m.pri <- ch1.m.pri / ch1.w.pri
+        ch2.m.pri <- ch2.m.pri / ch2.w.pri
+        ch1.m.pos <- ch1.m.pos / ch1.w.pos
+        ch2.m.pos <- ch2.m.pos / ch2.w.pos
       }
       
-      # Checks on row +2  
-
-      temp.pos <- data[data[, row] == geno.row + 2 & cond1 & cond2, c(geno, trait.aj, col)]
-      
-      if (dim(temp.pos)[1] == 2) {
-        data[i, ch1.pos.2] <- temp.pos[temp.pos[, geno] == ch1, trait.aj]
-        data[i, ch2.pos.2] <- temp.pos[temp.pos[, geno] == ch2, trait.aj]
-      }
+      l.pri <- (ch1.m.pri * data[, ch1.w] + ch2.m.pri * data[, ch2.w]) / (data[, ch1.w] + data[, ch2.w])
+      l.pos <- (ch1.m.pos * data[, ch1.w] + ch2.m.pos * data[, ch2.w]) / (data[, ch1.w] + data[, ch2.w])
       
-      # Weights for closest checks
-
-      data[i, ch1.w] <- ncb + 1 - abs(temp[temp[, geno] == ch1, col] - geno.col)
-      data[i, ch2.w] <- ncb + 1 - abs(temp[temp[, geno] == ch2, col] - geno.col)        
+      af <- apply(cbind(l.pri, l.pos), 1, mean)
     }
-  }
     
-  # Adjust values with method 1
+    # Make adjustment
     
-  if (method == 1) {
-    chs <- c(ch1, ch2, ch1.pri.1, ch2.pri.1, ch1.pos.1, ch2.pos.1)
-    if (nr == 5)
-      chs <- c(chs, ch1.pri.2, ch2.pri.2, ch1.pos.2, ch2.pos.2)
-    af <- apply(data[, chs], 1, mean, na.rm = TRUE)
-  }
+    data[, trait.aj] <- data[, trait.aj] / (1 + af)
     
-  # Adjust values with method 2
-      
-  if (method == 2) {
-    
-    if (nr == 3) {
-      ch1.m.pri <- apply(data[, c(ch1, ch1.pri.1)], 1, mean, na.rm = TRUE)
-      ch2.m.pri <- apply(data[, c(ch2, ch2.pri.1)], 1, mean, na.rm = TRUE)
-      ch1.m.pos <- apply(data[, c(ch1, ch1.pos.1)], 1, mean, na.rm = TRUE)
-      ch2.m.pos <- apply(data[, c(ch2, ch2.pos.1)], 1, mean, na.rm = TRUE)
-    }
-      
-    if (nr == 5) {
-      foo <- function(x) x * c(1.5, 2, 1)
-      ch1.w.pri <- t(apply(!is.na(data[, c(ch1, ch1.pri.1, ch1.pri.2)]), 1, foo))
-      ch2.w.pri <- t(apply(!is.na(data[, c(ch2, ch2.pri.1, ch2.pri.2)]), 1, foo))
-      ch1.w.pos <- t(apply(!is.na(data[, c(ch1, ch1.pos.1, ch1.pos.2)]), 1, foo))
-      ch2.w.pos <- t(apply(!is.na(data[, c(ch2, ch2.pos.1, ch2.pos.2)]), 1, foo))
+  } else { 
+
+    for(i in 1:dim(data)[1]) {
       
-      ch1.m.pri <- data[, c(ch1, ch1.pri.1, ch1.pri.2)] * ch1.w.pri
-      ch2.m.pri <- data[, c(ch2, ch2.pri.1, ch2.pri.2)] * ch2.w.pri
-      ch1.m.pos <- data[, c(ch1, ch1.pos.1, ch1.pos.2)] * ch1.w.pos
-      ch2.m.pos <- data[, c(ch2, ch2.pos.1, ch2.pos.2)] * ch2.w.pos
+      geno.row <- data[i, row]
+      geno.col <- data[i, col]
+      rows <- (geno.row - nr %/% 2):(geno.row + nr %/% 2)
+      columns <- (geno.col - ncb):(geno.col + ncb)
       
-      ch1.m.pri <- apply(ch1.m.pri, 1, sum, na.rm = TRUE)
-      ch2.m.pri <- apply(ch2.m.pri, 1, sum, na.rm = TRUE)
-      ch1.m.pos <- apply(ch1.m.pos, 1, sum, na.rm = TRUE)
-      ch2.m.pos <- apply(ch2.m.pos, 1, sum, na.rm = TRUE)
+      cond1 <- data[, col] %in% columns & data[, row] %in% rows
+      cond2 <- data[, geno] %in% c(ch1, ch2)
       
-      ch1.w.pri <- apply(ch1.w.pri, 1, sum, na.rm = TRUE)
-      ch2.w.pri <- apply(ch2.w.pri, 1, sum, na.rm = TRUE)
-      ch1.w.pos <- apply(ch1.w.pos, 1, sum, na.rm = TRUE)
-      ch2.w.pos <- apply(ch2.w.pos, 1, sum, na.rm = TRUE)
+      temp <- data[cond1 & cond2, trait.aj]
       
-      ch1.m.pri <- ch1.m.pri / ch1.w.pri
-      ch2.m.pri <- ch2.m.pri / ch2.w.pri
-      ch1.m.pos <- ch1.m.pos / ch1.w.pos
-      ch2.m.pos <- ch2.m.pos / ch2.w.pos
+      if (length(temp) > 0 & !(data[i, geno] %in% c(ch1, ch2))) {
+        af <- mean(temp)
+        data[i, trait.aj] <- data[i, trait.aj] / (1 + af)
+      }
     }
-
-    l.pri <- (ch1.m.pri * data[, ch1.w] + ch2.m.pri * data[, ch2.w]) / (data[, ch1.w] + data[, ch2.w])
-    l.pos <- (ch1.m.pos * data[, ch1.w] + ch2.m.pos * data[, ch2.w]) / (data[, ch1.w] + data[, ch2.w])
-      
-    af <- apply(cbind(l.pri, l.pos), 1, mean)
   }
-
-  # Make adjustment
-  
-  data[, trait.aj] <- data[, trait.aj] / (1 + af)
   
   # Return
   
diff --git a/R/checks.R b/R/checks.R
index 4cc3742..a27b97a 100644
--- a/R/checks.R
+++ b/R/checks.R
@@ -301,7 +301,7 @@ ck.2f <- function(trait, A, B, rep, data) {
 #' @param col Label for columns.
 #' @param ncb Number of columns between two check columns.
 #' @param data The name of the data frame.
-#' @return Five control values (\code{c1}, \code{c2}, \code{c3}, \code{c4}, \code{c5})
+#' @return Four control values (\code{c1}, \code{c2}, \code{c3}, and \code{c4},
 #' for the grid of checks, the number of missing values for checks (\code{nmis.check})
 #' and genotypes \code{nmis}, and the proportion of missing values for checks
 #' (\code{pmis.check}) and genotypes (\code{pmis}).
@@ -332,10 +332,9 @@ ck.w <- function(trait, geno, ch1, ch2, row, col, ncb, data) {
   # Controls
   
   c1 <- 0 # All column checks with checks
-  c2 <- 0 # Last column with checks
-  c3 <- 0 # All column genotypes with genotypes
-  c4 <- 0 # Alternating checks without in correlative row order
-  c5 <- 0 # All genotypes with checks to the left and right
+  c2 <- 0 # All column genotypes with genotypes
+  c3 <- 0 # Alternating checks without in correlative row order
+  c4 <- 0 # All genotypes with checks to the left and right
 
   # Columns with checks
   
@@ -346,26 +345,21 @@ ck.w <- function(trait, geno, ch1, ch2, row, col, ncb, data) {
   if (sum(!(data[data[, col] %in% cch, geno] %in% checks)) > 0)
     c1 <- 1
   
-  # Last column with checks
-  
-  if (max(cch) != nc.max)
-    c2 <- 1
-  
   # Check columns with genotypes
   
   if (sum(data[!(data[, col] %in% cch), geno] %in% checks) > 0)
-    c3 <- 1
+    c2 <- 1
   
   # Alternating checks
   
   for (i in cch)
     for (j in (min(data[data[, col] == i, row]) + 1):max(data[data[, col] == i, row]))
       if (data[data[, col] == i & data[, row] == j, geno] == data[data[, col] == i & data[, row] == j - 1, geno])
-        c4 <- 1
+        c3 <- 1
   
   for (i in 2:length(cch))
     if (data[data[, col] == cch[i] & data[, row] == nr.min, geno] == data[data[, col] == cch[i - 1] & data[, row] == nr.min, geno])
-      c4 <- 1
+      c3 <- 1
   
   # All genotypes must have one check to the left and one to the right
   
@@ -374,7 +368,7 @@ ck.w <- function(trait, geno, ch1, ch2, row, col, ncb, data) {
     columns <- (data[i, col] - ncb):(data[i, col] + ncb)
     temp <- data[data[, row] == rows & data[, col] %in% columns, geno]
     if (sum(temp %in% checks) == 0)
-      c5 <- 1
+      c4 <- 1
   }
 
   # Number of missing values for checks
@@ -395,6 +389,6 @@ ck.w <- function(trait, geno, ch1, ch2, row, col, ncb, data) {
   
   # Return
   
-  list(c1 = c1, c2 = c2, c3 = c3, c4 = c4, c5 = c5, nmis = nmis, pmis = pmis,
+  list(c1 = c1, c2 = c2, c3 = c3, c4 = c4, nmis = nmis, pmis = pmis,
        nmis.check = nmis.check, pmis.check = pmis.check)
 }
diff --git a/man/aj.w.Rd b/man/aj.w.Rd
index 5706bc9..dfe1d86 100644
--- a/man/aj.w.Rd
+++ b/man/aj.w.Rd
@@ -2,13 +2,13 @@
 % Please edit documentation in R/aj_westcott.R
 \name{aj.w}
 \alias{aj.w}
-\title{Adjust values with a grid of checks}
+\title{Adjust values following the method of Westcott}
 \usage{
 aj.w(trait, geno, ch1, ch2, row, col, nr = 5, ncb = 10, method = 2,
   p = 0.5, ind = TRUE, data)
 }
 \arguments{
-\item{trait}{The trait to analyze.}
+\item{trait}{The trait to adjust.}
 
 \item{geno}{The genotypes.}
 
@@ -55,6 +55,9 @@ If \code{p = 1} then the values are adjusted in the same
 proportion that the checks vary around the field. For values lower than 1
 the values are adjusted based on that proportion over the checks variation.
 If \code{p = 0} then there is no adjustment.
+
+If the layout does not correspond with the Westcott method, then the observed values
+are adjusted with the values of the checks planted nearby and a warning is issued.
 }
 \references{
 Westcott, B. (1981). Two methods for early generation yield assessment in winter wheat.
diff --git a/man/ck.w.Rd b/man/ck.w.Rd
index 6bb9cd4..d7f0570 100644
--- a/man/ck.w.Rd
+++ b/man/ck.w.Rd
@@ -24,7 +24,7 @@ ck.w(trait, geno, ch1, ch2, row, col, ncb, data)
 \item{data}{The name of the data frame.}
 }
 \value{
-Five control values (\code{c1}, \code{c2}, \code{c3}, \code{c4}, \code{c5})
+Four control values (\code{c1}, \code{c2}, \code{c3}, and \code{c4},
 for the grid of checks, the number of missing values for checks (\code{nmis.check})
 and genotypes \code{nmis}, and the proportion of missing values for checks
 (\code{pmis.check}) and genotypes (\code{pmis}).