| summary.bootstrap.mat {analogue} | R Documentation |
Summarise bootstrap resampling for MAT models
Description
summary method for class "bootstrap.mat".
Usage
## S3 method for class 'bootstrap.mat':
summary(object, ...)
Arguments
object |
an object of class "bootstrap.mat", usually the
result of a call to bootstrap.mat. |
... |
arguments passed to or from other methods. |
Value
A data frame with the following components:
observed |
vector of observed environmental values. |
model |
a list containing the apparent or non-bootstrapped
estimates for the training set. With the following components:
estimated: - estimated values for the response
residuals: - model residuals
r.squared: - Apparent R^2 between observed and
estimated values of
y
avg.bias: - Average bias of the model residuals
max.bias: - Maximum bias of the model residuals
rmse: - Apparent error (RMSE) for the model
k: - numeric; indicating the size of model used in
estimates and predictions
|
bootstrap |
a list containing the bootstrap estimates for the
training set. With the following components:
estimated: - Bootstrap estimates for the response
residuals: - Bootstrap residuals for the response
r.squared: - Bootstrap derived R^2 between observed
and estimated values of the response
avg.bias: - Average bias of the bootstrap derived model
residuals
max.bias: - Maximum bias of the bootstrap derived model
residuals
rmsep: - Bootstrap derived RMSEP for the model
s1: - Bootstrap derived S1 error component for the
model
s2: - Bootstrap derived S2 error component for the
model
k: - numeric; indicating the size of model used in
estimates and predictions
|
sample.errors |
a list containing the bootstrap-derived sample
specific errors for the training set. With the following components:
rmsep: - Bootstrap derived RMSEP for the training set
samples
s1: - Bootstrap derived S1 error component for training
set samples
s2: - Bootstrap derived S2 error component for training
set samples
|
weighted |
logical; whether the weighted mean was used instead of
the mean of the environment for k-closest analogues |
auto |
logical; whether k was choosen automatically or
user-selected |
n.boot |
numeric; the number of bootstrap samples taken |
call |
the matched call |
call |
model type |
predictions |
a list containing the apparent and
bootstrap-derived estimates for the new data, with the following
components:
observed: - the observed values for the new samples —
only if
newenv is provided
model: - a list containing the apparent or
non-bootstrapped estimates for the new samples. A list with the
same components as
apparent, above
bootstrap: - a list containing the bootstrap estimates
for the new samples, with some or all of the same components as
bootstrap, above
sample.errors: - a list containing the bootstrap-derived
sample specific errors for the new samples, with some or all of
the same components as
sample.errors, above
|
Author(s)
Gavin L. Simpson
See Also
bootstrap.mat, mat,
summary.
Examples
## Not run:
## continue the RLGH example from ?join
example(join)
## fit the MAT model using the squared chord distance measure
swap.mat <- mat(swapdiat, swappH, method = "SQchord")
## bootstrap training set
swap.boot <- bootstrap(swap.mat, k = 10, n.boot = 100)
swap.boot
summary(swap.boot)
## End(Not run)
[Package
analogue version 0.6-8
Index]