Description Usage Arguments Details Value Author(s) See Also Examples
Draws a palaeoenvironmental reconstruction of predicted environmental values for sub-fossil assemblages.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | reconPlot(x, ...)
## Default S3 method:
reconPlot(x, depths, errors,
display.error = c("none", "bars", "lines"),
rev.x = TRUE,
col.error = "grey", lty.error = "dashed",
type = "l",
xlim, ylim,
xlab = "", ylab = "", main = "",
...)
## S3 method for class 'predict.mat'
reconPlot(x, depths, use.labels = FALSE,
predictions = c("model", "bootstrap"),
display.error = c("none", "bars", "lines"),
sample.specific = TRUE, ...)
## S3 method for class 'predict.wa'
reconPlot(x, depths, use.labels = FALSE,
display.error = c("none", "bars", "lines"),
sample.specific = TRUE, ...)
|
x |
An R object. |
depths |
numeric; a vector of depths for which predicted values
exist or will be generated. Can be missing, in which case,
if |
errors |
numeric; a vector of errors for plotting error bars or lines. |
display.error |
character; hown should error bars be drawn on the
plot? One of |
rev.x |
logical; should the depth/age axis be reversed (drawn from high to low)? |
col.error, lty.error |
the colour and type of line drawn. See
|
type |
type of line drawn. See |
xlab, ylab |
character; the x- and y-axis labels respectively. |
main |
character; main title for the plot. |
xlim, ylim |
numeric, length 2; the x- and y-limits for the
plotted axes. If not provided, the function will calculate
appropriate values to cover the range of plotted values and any
error bars (if requested via |
use.labels |
logical; should |
predictions |
character; one of |
sample.specific |
logical; should sample specific errors be used?
Only for |
... |
arguments passed to other graphics functions. |
Conventionally, these plots are drawn on a depth or an age
scale. Argument depths
is used to provide the depth or age
axis, against which the predicted values are plotted.
If depths
is not provided, then the function will try to
derive the appropriate values from the labels of the predictions if
use.labels = TRUE
. You must provide depths
or set
use.labels = TRUE
otherwise an error will result. The derived
labels will be coerced to numerics. If your labels are not coercible,
then you'll either get nonsense on the plot or an error from R. If so,
provide suitable values for depths
.
A plot on the currently active device.
Gavin L. Simpson
mat
, and predict.mat
for MAT
transfer functions and wa
and predict.wa
for WA models.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ## Imbrie and Kipp example
## load the example data
data(ImbrieKipp)
data(SumSST)
data(V12.122)
## merge training and test set on columns
dat <- join(ImbrieKipp, V12.122, verbose = TRUE)
## extract the merged data sets and convert to proportions
ImbrieKipp <- dat[[1]] / 100
V12.122 <- dat[[2]] / 100
## Fit a MAT model
(ik.mat <- mat(ImbrieKipp, SumSST, method = "chord"))
## Reconstruct pH for the RLGH core
v12.pH <- predict(ik.mat, V12.122)
## draw the reconstruction
reconPlot(v12.pH, use.labels = TRUE, display.error = "bars",
xlab = "Depth", ylab = "Summer Seas-surface Temperature")
|
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-4
analogue version 0.17-1
Summary:
Rows Cols
Data set 1: 61 27
Data set 2: 110 30
Merged: 171 30
Modern Analogue Technique
Call:
mat(x = ImbrieKipp, y = SumSST, method = "chord")
Percentiles of the dissimilarities for the training set:
1% 2% 5% 10% 20%
0.220 0.280 0.341 0.414 0.501
Inferences based on the mean of k-closest analogues:
k RMSEP R2 Avg Bias Max Bias
1 2.501 0.880 0.321 9.000
2 1.875 0.931 0.284 6.000
3 1.713 0.941 0.133 5.167
4 1.796 0.935 0.177 5.125
5 1.748 0.939 0.209 5.100
6 1.716 0.943 0.284 5.667
7 1.763 0.943 0.381 6.429
8 1.831 0.941 0.390 6.625
9 1.913 0.940 0.449 7.222
10 2.040 0.935 0.577 7.500
Inferences based on the weighted mean of k-closest analogues:
k RMSEP R2 Avg Bias Max Bias
1 2.501 0.880 0.321 9.000
2 1.894 0.929 0.263 6.183
3 1.733 0.940 0.138 5.470
4 1.773 0.937 0.173 5.384
5 1.750 0.939 0.187 5.366
6 1.709 0.942 0.218 5.493
7 1.712 0.942 0.254 5.635
8 1.758 0.940 0.253 5.693
9 1.777 0.939 0.274 5.838
10 1.857 0.935 0.362 5.927
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.