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Habitat Sampler
HabitatSampler
Commits
38b12292
Commit
38b12292
authored
Jul 29, 2021
by
Daniela Rabe
Browse files
update documentation
parent
50d3244f
Changes
3
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R-package/man/model_opt_r.Rd
View file @
38b12292
...
...
@@ -10,16 +10,15 @@ model_opt_r(
sample_type,
buffer,
model,
area,
seed,
n,
sample_size,
n_channel,
seed2,
mtry,
mod.error,
pbtn1,
pbtn2,
ras_vx,
rast,
max_samples_per_class
)
}
...
...
@@ -34,8 +33,6 @@ model_opt_r(
\item{model}{which machine learning classifier to use c("rf", "svm") for random forest or support vector machine implementation}
\item{area}{extent where the the classification is happening}
\item{seed}{set seed for reproducible results}
\item{n}{number of iterations for model accuracy}
...
...
@@ -48,11 +45,11 @@ model_opt_r(
\item{mtry}{number of predictor used at random forest splitting nodes (mtry << n predictors)}
\item{
pbtn1}{matrix for points
}
\item{
mod.error}{threshold for model error until which iteration is being executed
}
\item{pbtn
2
}{matrix for points}
\item{pbtn
1
}{matrix for points}
\item{ras
_vx}{velox
raster}
\item{ras
t}{
raster}
\item{max_samples_per_class}{maximum number of samples per class}
}
...
...
R-package/man/multi_Class_Sampling.Rd
View file @
38b12292
...
...
@@ -14,6 +14,7 @@ multi_Class_Sampling(
reference
,
model
=
"rf"
,
mtry
=
10
,
mod
.
error
=
0.02
,
last
=
F
,
seed
=
3
,
init
.
seed
=
"sample"
,
...
...
@@ -23,6 +24,7 @@ multi_Class_Sampling(
n_classes
,
multiTest
=
1
,
RGB
=
c
(
19
,
20
,
21
),
in
.
memory
=
TRUE
,
color
=
c
(
"lightgrey"
,
"orange"
,
"yellow"
,
"limegreen"
,
"forestgreen"
),
overwrite
=
TRUE
,
save_runs
=
TRUE
,
...
...
@@ -50,6 +52,8 @@ multi_Class_Sampling(
\
item
{
mtry
}{
number
of
predictor
used
at
random
forest
splitting
nodes
(
mtry
<<
n
predictors
)}
\
item
{
mod
.
error
}{
threshold
for
model
error
until
which
iteration
is
being
executed
}
\
item
{
last
}{
only
true
for
one
class
classifier
c
(
"FALSE"
,
TRUE
")}
\item{seed}{set seed for reproducible results}
...
...
@@ -68,7 +72,9 @@ multi_Class_Sampling(
\item{RGB}{rgb channel numbers for image plot}
\item{color}{color pallet}
\item{in.memory}{boolean for raster processing (memory = "
TRUE
", from disk = "
FALSE
")}
\item{color}{single colors for continuous color palette interpolation}
\item{overwrite}{overwrite the KML and raster files from previous runs (default TRUE)}
...
...
@@ -85,7 +91,7 @@ multi_Class_Sampling(
\enumerate{
\item Habitat type probability map as geocoded *.kmz file (with a *.kml layer and *.png image output), and *.tif raster file
\item A Habitat object (only if save_runs is set to TRUE) consisting of 7 slots: \cr
run1@models - list of selcted classifiers \cr
run1@models - list of sel
e
cted classifiers \cr
run1@ref_samples - list of SpatialPointsDataFrames with same length as run1@models holding reference labels \link{1,2} for each selected model \cr
run1@switch - vector of lenght run1@models indicating if target class equals 2, if not NA the labels need to be switched \cr
run1@layer - raster map of habitat type probability \cr
...
...
R-package/man/sample_nb.Rd
View file @
38b12292
...
...
@@ -13,11 +13,12 @@ sample_nb(
buffer,
reference,
model,
area,
mtry,
mod.error,
last,
seed,
init.seed,
in.memory,
save_runs,
parallel_mode,
max_num_cores,
...
...
@@ -39,16 +40,18 @@ sample_nb(
\item{model}{which machine learning classifier to use c("rf", "svm") for random forest or suppurt vector machine implementation}
\item{area}{extent where the the classification is happening}
\item{mtry}{number of predictor used at random forest splitting nodes (mtry << n predictors)}
\item{mod.error}{threshold for model error until which iteration is being executed}
\item{last}{only true for one class classifier c("FALSE", TRUE")}
\item{seed}{set seed for reproducable results}
\item{init.seed}{"sample" for new or use run1@seeds to reproduce previous steps}
\item{in.memory}{boolean for raster processing (memory = "TRUE", from disk = "FALSE")}
\item{save_runs}{if the user wants to save the runs, if TRUE the complete Habitat Class object is returned}
\item{parallel_mode}{run loops in parallel}
...
...
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