diff --git a/vario for dsum - seeing how it could work.r b/vario for dsum - seeing how it could work.r
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+++ b/vario for dsum - seeing how it could work.r	
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+library(ggplot2)
+library(asreml)
+library(biometryassist)
+library(tidyverse)
+library(lubridate)
+library(viridis)
+
+theme_set(theme_bw())
+
+
+
+datn <- read.csv("C:/Users/sharo/Documents/SNSTATS/Clients/Daniel Menadue/Objective 2 Hart/HartNDVI.csv") 
+str(datn) 
+datn$Treatment <- factor(datn$Treatment, levels = c("Control", 
+    "Balance&Grow", "GABA", "Kelpak", "RemoveFlorets", "Seasol"))
+
+datn <- datn %>% mutate(across(c(1:5), factor))  
+
+datn$Date <- dmy(datn$Date)
+
+datn <- datn %>% arrange(Date, Row, Bay)
+
+datn$facDate <- factor(datn$Date)
+datn$plot <- factor(paste(datn$Row, datn$Bay, sep = ""))
+
+dat.asr <- asreml(fixed = NDVI ~ facDate + Variety + Variety:facDate + Treatment +
+                    Variety:Treatment + Treatment:facDate + 
+                    Variety:Treatment:facDate, 
+random = ~ Block, 
+residual = ~ dsum(~ ar1(Row):id(Bay)|facDate), data = datn)
+
+
+
+dat.asr2d <- asreml(fixed = Biomass ~ Variety + Treatment +
+                    Variety:Treatment, 
+random = ~ Block, 
+residual = ~ ar1(Row):id(Bay), data = dat)
+dat.asr2d <- update(dat.asr2d)
+
+Row <- "Row"
+Coulmn <- "Bay"
+model.obj <- dat.asr2d
+
+#########################################################################
+
+# vario_df <- function(model.obj, Row = NA, Column = NA) {
+#     # The 'z' value for the variogram is the residuals
+#     # Need to be able to pull out the x/y from the model object
+
+# This works for 2 dimensions 
+
+dims <- unlist(strsplit(names(model.obj$R.param[1]), ":"))
+
+dims <- unlist(strsplit(names(dat.asr2d$R.param[1]), ":"))
+
+
+# This will flag that a dsum is used in the model - should do this first then 
+# do the 2D case  
+
+# now to identify the terms in the residual - the lasst will be the factor 
+# associated with the last term in the res.terms vector
+
+
+# Once dsum is identified - the dat.asr$formulae$residual will need to be broken
+# down into it's components. 
+# Seperate data set for each |term and row column within each |term
+
+# pulling out the residual formulae as a character
+res.call <- as.character(dat.asr$formulae$residual)[2]
+
+# identifying if dsum is present
+# if present pull out residual terms
+if(any(grepl("dsum", res.call))){
+res.terms <- unlist(strsplit(res.call, "[~:| ()]+"))[-1]
+asr.terms <- c("id", "ar1", "ar2", "cor")
+
+res.terms <- res.terms[!is.element(res.terms, asr.terms)]
+}
+res.terms
+
+# Break up the data and the residuals into all levels of grouping factor
+# Calculate the gamas for each level and create a separate variogram for each 
+# level
+
+# Maybe have the option to combine into a single graph or keep as separate plots
+
+
+# This is for 2D
+
+    if(missing(Row) | is.na(Row) | is.null(Row)) {
+        Row <- as.numeric(model.obj$mf[[dims[1]]])
+    }
+    if(missing(Column) | is.na(Column) | is.null(Column)) {
+        Column <- as.numeric(model.obj$mf[[dims[2]]])
+    }
+
+    nrows <- max(Row)
+    ncols <- max(Column)
+
+    Resid <- residuals(model.obj)#[order(Column, Row)], nrow = nrows)
+    # Resid <- matrix(residuals(model.obj)[order(Column, Row)], nrow = nrows)
+
+    vario <- expand.grid(Row = 0:(nrows-1), Column = 0:(ncols-1))
+
+    # Ignore the 0, 0 case (gamma=0, counted row*cols times)
+    gammas <- rep(0, nrows*ncols)
+    nps <- rep(nrows*ncols, nrows*ncols)
+
+    for (index in 2:nrow(vario)) {
+        i <- vario[index, 'Row']
+        j <- vario[index, 'Column']
+
+        gamma <- 0
+        np <- 0
+        for (val_index in 1:nrow(vario)) {
+            # val <- vals[val_index, ]
+
+            # Deliberate double-counting so that offset handling is easy
+            # (so e.g. we compute distance from (1,1)->(2,3), and then again
+            # later from (2,3)->(1,1)).
+            for (offset in unique(list(c(i, j), c(-i, j), c(i, -j), c(-i, -j)))) {
+                row <- Row[val_index] + offset[1]
+                col <- Column[val_index] + offset[2]
+
+                if (0 < row && row <= nrows && 0 < col && col <= ncols && !is.na(Resid[val_index])) {
+                    other <- Resid[Row == row & Column == col]
+
+                    if (!is.na(other)) {
+                        gamma <- gamma + (Resid[val_index] - other)^2
+                        np <- np + 1
+                    }
+                }
+            }
+        }
+        # Since we double-counted precisely, halve to get the correct answer.
+        np <- np / 2
+        gamma <- gamma / 2
+
+        if (np > 0) {
+            gamma <- gamma / (2*np)
+        }
+
+        gammas[index] <- gamma
+        nps[index] <- np
+    }
+    nps[1] <- nps[1]-sum(is.na(Resid))
+    vario <- cbind(vario, data.frame(gamma = gammas, np = nps))
+    colnames(vario) <- c(dims, "gamma", "np")
+    class(vario) <- c("variogram", "data.frame")
+    return(vario)
+}
+
+
+vario_df(dat.asr, Row = "Row", Column = "Bay")
+
+