Extrapolating data with interp not producing accur

I have a graph where the extrapolation does not match the initial interpolation. I would like the heatmap to fill the entire image.

First, the interpolation code:

library(akima)
library(reshape2)

xmin <- signif(min(CBLo2$MD1))
xmax <- signif(max(CBLo2$MD1))
ymin <- signif(min(CBLo2$MD2)) 
ymax <- signif(max(CBLo2$MD2))
gridint <- 100

fld <- with(CBLo2, interp(x = MD1, y = MD2, z = Abundance, 
            xo=seq(xmin, xmax, length=gridint), yo=seq(ymin, ymax, length=gridint) ))
df <- melt(fld$z, na.rm = TRUE)
names(df) <- c("MD1", "MD2", "Abundance")
df$MD1 <- fld$x[df$MD1]
df$MD2 <- fld$y[df$MD2]
contour(fld) # test plot

I won't post the entire ggplot code (used for the plot below), just that needed to produce the heatmap:

ggplot() +
  geom_tile(inherit.aes=FALSE,data = df, aes(x = MD1, y = MD2,fill = Abundance)) +
  scale_fill_continuous(name = "Rain (mm)", low = "yellow", high = "green")

However, when I try to extrapolate the data (following examples from other posts), I get the following plot, which does not match the first well at all:

fld <- with(CBLo2, interp(x = MD1, y = MD2, z = Abundance, extrap=TRUE, linear=FALSE,
            xo=seq(xmin, xmax, length=gridint), yo=seq(ymin, ymax, length=gridint) ))

Here is the data:

Abundance   MD1 MD2
9   -0.59042    0.76793119
42  -0.48544284 -0.09465043
13  0.51250586  -0.24599322
84  -0.30857525 -0.21529624
2   0.90449257  0.679926
16  0.24536209  0.24016424
52  -0.43144002 -0.75474149
4   1.23830339  -0.11985391
37  -1.10235817 0.33886773
79  0.01757236  -0.59635386

What am I doing wrong? How can I make the extrapolation more accurate?

TLDR solution:

Add linear = FALSE to all the interp() codes for consistency, and specify the same limits in scale_fill_continuous().

Explanation:

There are two issues here.

Issue 1: the code used to generate the first fld does not include the parameter linear = FALSE, while the code used for the second does.

Let's compare the interpolated values:

library(dplyr)

fld1 <- with(CBLo2, 
            interp(x = MD1, y = MD2, z = Abundance, 
                   xo=seq(xmin, xmax, length=gridint), 
                   yo=seq(ymin, ymax, length=gridint) ))
df1 <- melt(fld1$z, na.rm = TRUE) # 6426 obs

fld2 <- with(CBLo2, 
             interp(x = MD1, y = MD2, z = Abundance, 
                    extrap = TRUE, linear = FALSE,
                    xo=seq(xmin, xmax, length=gridint), 
                    yo=seq(ymin, ymax, length=gridint) ))
df2 <- melt(fld2$z, na.rm = TRUE) #1000 obs

df.combined <- left_join(df2, df1, by = c("Var1", "Var2"))
df.combined %>% 
  filter(!is.na(value.y)) %>%        # compare for the overlapping range
  mutate(diff = value.x - value.y) %>%
  select(diff) %>% 
  summary()

      diff         
 Min.   :-303.360  
 1st Qu.: -42.399  
 Median :   8.763  
 Mean   :  -7.552  
 3rd Qu.:  36.132  
 Max.   : 238.647

Now add linear = FALSE to the first fld:

fld3 <- with(CBLo2, 
            interp(x = MD1, y = MD2, z = Abundance, 
                   linear = FALSE,
                   xo=seq(xmin, xmax, length=gridint), 
                   yo=seq(ymin, ymax, length=gridint) ))
df3 <- melt(fld3$z, na.rm = TRUE) # 6426 obs

df.combined <- left_join(df2, df3, by = c("Var1", "Var2"))
df.combined %>% 
  filter(!is.na(value.y)) %>%
  mutate(diff = value.x - value.y) %>%
  select(diff) %>% 
  summary()

      diff  
 Min.   :0  
 1st Qu.:0  
 Median :0  
 Mean   :0  
 3rd Qu.:0  
 Max.   :0

Issue 2: The range of interpolated values are very different.

# define column names
names(df2) <- c("MD1", "MD2", "Abundance")
names(df3) <- c("MD1", "MD2", "Abundance")

> range(df2$Abundance)
[1] -1136.341   420.369
> range(df3$Abundance)
[1] -297.9161  241.6618

We can see that even when values match at the same MD1/MD2 coordinates, the range of values in the expanded df2 far exceeds the range for df3. In order to ensure the same mapping between Abundance values and colours, we have to specify the fill limits based on the combined range of the two.

I'll use an ugly but visually distinct gradient to illustrate this:

library(gridExtra)

p <- ggplot() + 
  scale_fill_gradientn(name = "Rain (mm)", colours = rainbow(15),
                       limits = range(c(df2$Abundance, df3$Abundance)))

grid.arrange(p + geom_tile(data = df3, aes(x = MD1, y = MD2, fill = Abundance)),
             p + geom_tile(data = df2, aes(x = MD1, y = MD2, fill = Abundance)),
             nrow = 1)

If we overlay the plots, they overlap exactly (transparency tweaked to show the edges for df3):

p + 
  geom_tile(data = df3, aes(x = MD1, y = MD2, fill = Abundance), alpha = 0.5) +
  geom_tile(data = df2, aes(x = MD1, y = MD2, fill = Abundance), alpha = 0.5)