testing/inst/scripts/example_aegis_indicators.R
In jae0/emei: Spatiotemporal models of variability
# exploratory / alternate methods .. examples using aegis idnicators
# -----------------------------
# speciescomposition
year.assessment=lubridate::year(Sys.Date())
# year.assessment=lubridate::year(Sys.Date()) -1
p = aegis.speciescomposition::speciescomposition_parameters( yrs=1999:year.assessment )
# varname="pca1"
# varname="pca2"
p$varstomodel = c("pca1", "pca2")
for ( varname in p$varstomodel) {
print(varname)
# ~ 5GB / process for spate
p = aegis.speciescomposition::speciescomposition_parameters(
yrs=1999:year.assessment,
dimensionality="space-time",
project_class = "stmv",
stmv_variables=list(Y=varname),
libs = c("mgcv", "spate", "stmvdev"),
stmv_local_modelengine = "userdefined",
stmv_local_modelengine_userdefined = stmvdev::stmv__spate,
stmv_spate_method = "mcmc_fast",
stmv_distance_statsgrid = 5, # resolution (km) of data aggregation (i.e. generation of the ** statistics ** )
stmv_distance_scale = c(75, 90, 110),
stmv_spate_boost_timeseries = TRUE, # use simple GAM spectral contraint to structure timeseries as spate's fft in time seems to cause overfitting ?
stmv_gam_optimizer=c("outer", "bfgs") # .. spate uses local gam-based boost of ts, as in 'twostep'
stmv_local_modelformula = formula( paste(
varname, '~ s(yr, k=5, bs="ts") + s(cos.w, k=3, bs="ts") + s(sin.w, k=3, bs="ts") ',
' + s(cos.w, sin.w, yr, bs="ts", k=16) ',
' + s(plon, k=3, bs="ts") + s(plat, k=3, bs="ts") + s(plon, plat, k=16, bs="ts") ' ) ),
# similar to GAM model .. this is the seed model
stmv_local_model_distanceweighted = TRUE,
clusters=rep("localhost", 4),
stmv_spate_nburnin = 1000,
stmv_spate_nposteriors = 1000,
stmv_spate_nCovUpdates = 20 # no of times to update cov matrix during simulations
)
# stmv_local_modelengine = "twostep",
# stmv_local_modelformula = formula( paste(
# ' snowcrab.large.males_abundance', '~ s(yr, k=10, bs="ts") + s(cos.w, k=3, bs="ts") + s(sin.w, k=3, bs="ts") ',
# ' + s(cos.w, sin.w, yr, bs="ts", k=20) ',
# ' + s(plon, k=3, bs="ts") + s(plat, k=3, bs="ts") + s(plon, plat, k=20, bs="ts") ' ) ),
# # stmv_twostep_space = "krige", # other possibilities: "fft", "tps"
# stmv_twostep_space = "gam", # fft, krige (very slow), lowpass, lowpass_fft
# stmv_local_modelformula_space = formula( paste(
# 'snowcrab.large.males_abundance', '~ s(log(z), k=3, bs="ts") + s(plon, k=3, bs="ts") + s(plat, k=3, bs="ts") + s( log(z), plon, plat, k=27, bs="ts") ') ),
# stmv_twostep_time = "gam",
# stmv_local_modelformula_time = formula( paste(
# 'snowcrab.large.males_abundance', ' ~ s(yr, k=10, bs="ts") + s(cos.w, k=3, bs="ts") + s(sin.w, k=3, bs="ts") ',
# '+ s( cos.w, sin.w, yr, k=45, bs="ts")') ),
p = stmv( p=p, runmode=c("initialize", "globalmodel" ) ) # no global_model and force a clean restart
currentstatus = stmv_statistics_status( p=p )
parallel_run( stmv_interpolate, p=p,
runindex=list( locs=currentstatus$todo[sample.int(length( currentstatus$todo ))] ),
local.n.complete=currentstatus["n.complete"]
)
stmv_db( p=p, DS="save_current_state" ) # saved current state (internal format)
currentstatus = stmv_statistics_status( p=p, reset="incomplete" )
parallel_run( stmv_interpolate, p=p,
runindex=list( locs=currentstatus$todo[sample.int(length( currentstatus$todo ))] ),
local.n.complete=currentstatus["n.complete"]
)
stmv_db( p=p, DS="save_current_state" ) # saved current state
currentstatus = stmv_statistics_status( p=p, reset="incomplete" )
parallel_run( stmv_interpolate, p=p,
runindex=list( locs= currentstatus$todo[sample.int(length( currentstatus$todo ))] ),
local.n.complete=currentstatus["n.complete"],
stmv_local_modelengine = "tps"
)
stmv_db( p=p, DS="save_current_state" )
stmv_db( p=p, DS="stmv.results" ) # save to disk for use outside stmv*, returning to user scale
# if (really.finished) stmv_db( p=p, DS="cleanup.all" )
aegis_db( p=p, DS="predictions.redo" ) # warp predictions to other grids
aegis_db( p=p, DS="stmv.stats.redo" ) # warp stats to other grids
aegis_db( p=p, DS="complete.redo" )
aegis_db( p=p, DS="baseline.redo" )
aegis_db_map( p=p )
if (0) {
global_model = stmv_global_model( p=p, DS="global_model")
summary( global_model )
plot(global_model)
}
}
# ----------------
# ca1 :
Family: gaussian
Link function: identity
Formula:
ca1 ~ s(t, k = 3, bs = "ts") + s(tsd.climatology, k = 3, bs = "ts") +
s(tmean.climatology, k = 3, bs = "ts") + s(log(t.range),
k = 3, bs = "ts") + s(log(b.range), k = 3, bs = "ts") + s(log(z),
k = 3, bs = "ts") + s(log(dZ), k = 3, bs = "ts") + s(log(ddZ),
k = 3, bs = "ts") + s(log(substrate.grainsize), k = 3, bs = "ts")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.025523 0.004243 6.015 1.83e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(t) 1.998 2 274.88 < 2e-16 ***
s(tsd.climatology) 1.976 2 77.31 < 2e-16 ***
s(tmean.climatology) 2.000 2 7016.86 < 2e-16 ***
s(log(t.range)) 1.396 2 68.72 < 2e-16 ***
s(log(b.range)) 1.969 2 21.13 4.59e-10 ***
s(log(z)) 1.983 2 112.45 < 2e-16 ***
s(log(dZ)) 1.888 2 14.47 1.98e-07 ***
s(log(ddZ)) 1.856 2 12.15 2.05e-06 ***
s(log(substrate.grainsize)) 1.752 2 274.56 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.642 Deviance explained = 64.2%
GCV = 0.37765 Scale est. = 0.37733 n = 20956
---
# -------------------
# varname ="ca2"
Family: gaussian
Link function: identity
Formula:
ca2 ~ s(t, k = 3, bs = "ts") + s(tsd.climatology, k = 3, bs = "ts") +
s(tmean.climatology, k = 3, bs = "ts") + s(log(t.range),
k = 3, bs = "ts") + s(log(b.range), k = 3, bs = "ts") + s(log(z),
k = 3, bs = "ts") + s(log(dZ), k = 3, bs = "ts") + s(log(ddZ),
k = 3, bs = "ts") + s(log(substrate.grainsize), k = 3, bs = "ts")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.11437 0.00408 -28.1 <2e-16
Approximate significance of smooth terms:
edf Ref.df F p-value
s(t) 1.58 2 313.49 < 2e-16
s(tsd.climatology) 1.99 2 280.46 < 2e-16
s(tmean.climatology) 2.00 2 81.55 < 2e-16
s(log(t.range)) 1.99 2 62.90 < 2e-16
s(log(b.range)) 1.50 2 12.85 2.7e-07
s(log(z)) 2.00 2 3337.43 < 2e-16
s(log(dZ)) 1.90 2 5.53 0.0029
s(log(ddZ)) 1.98 2 30.29 4.3e-14
s(log(substrate.grainsize)) 1.99 2 140.98 < 2e-16
R-sq.(adj) = 0.748 Deviance explained = 74.8%
GCV = 0.22366 Scale est. = 0.22336 n = 13444
# -------------------
# varname ="pca1" ; # July 2017 results follow
Family: gaussian
Link function: identity
Formula:
pca1 ~ s(t, k = 3, bs = "ts") + s(tsd.climatology, k = 3, bs = "ts") +
s(tmean.climatology, k = 3, bs = "ts") + s(log(t.range),
k = 3, bs = "ts") + s(log(b.range), k = 3, bs = "ts") + s(log(z),
k = 3, bs = "ts") + s(log(dZ), k = 3, bs = "ts") + s(log(ddZ),
k = 3, bs = "ts") + s(log(substrate.grainsize), k = 3, bs = "ts")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.124972 0.000809 154 <2e-16
Approximate significance of smooth terms:
edf Ref.df F p-value
s(t) 1.998 2 76.58 < 2e-16
s(tsd.climatology) 1.989 2 141.55 < 2e-16
s(tmean.climatology) 1.995 2 5021.31 < 2e-16
s(log(t.range)) 1.976 2 105.57 < 2e-16
s(log(b.range)) 1.974 2 40.42 < 2e-16
s(log(z)) 1.969 2 25.18 5.8e-12
s(log(dZ)) 0.764 2 1.49 0.041
s(log(ddZ)) 1.361 2 15.25 2.6e-09
s(log(substrate.grainsize)) 1.952 2 296.83 < 2e-16
R-sq.(adj) = 0.739 Deviance explained = 73.9%
GCV = 0.0088125 Scale est. = 0.0088014 n = 13444
# -------------------
# varname ="pca2"
Family: gaussian
Link function: identity
Formula:
pca2 ~ s(t, k = 3, bs = "ts") + s(tsd.climatology, k = 3, bs = "ts") +
s(tmean.climatology, k = 3, bs = "ts") + s(log(t.range),
k = 3, bs = "ts") + s(log(b.range), k = 3, bs = "ts") + s(log(z),
k = 3, bs = "ts") + s(log(dZ), k = 3, bs = "ts") + s(log(ddZ),
k = 3, bs = "ts") + s(log(substrate.grainsize), k = 3, bs = "ts")
---
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02133 0.00081 26.3 <2e-16
Approximate significance of smooth terms:
edf Ref.df F p-value
s(t) 1.79e+00 2 101.1 < 2e-16
s(tsd.climatology) 1.98e+00 2 194.5 < 2e-16
s(tmean.climatology) 1.93e+00 2 45.2 < 2e-16
s(log(t.range)) 1.93e+00 2 20.9 4.1e-10
s(log(b.range)) 1.91e+00 2 29.3 4.9e-14
s(log(z)) 1.70e+00 2 2350.7 < 2e-16
s(log(dZ)) 4.76e-08 2 0.0 0.77011
s(log(ddZ)) 1.93e+00 2 7.5 0.00041
s(log(substrate.grainsize)) 1.42e+00 2 413.4 < 2e-16
R-sq.(adj) = 0.699 Deviance explained = 69.9%
GCV = 0.0088397 Scale est. = 0.0088295 n = 13444
if (p$stmv_local_modelengine %in% c("gaussianprocess") ) p$libs = c( p$libs )
if (p$stmv_local_modelengine %in% c("splancs") ) p$libs = c( p$libs, "splancs" )
if (p$stmv_local_modelengine %in% c("LaplacesDemon") ) p$libs = c( p$libs, "LaplacesDemonCpp" )
if (p$stmv_local_modelengine %in% c("stan") ) p$libs = c( p$libs, "cmdstanr" )
# if (p$stmv_local_modelengine %in% c("spate") ) p$libs = c( p$libs, "spate" ) # now copied directly into stmv
if (p$stmv_local_modelengine %in% c("stan", "gaussianprocess")) {
message( "Precompiling stan program ... ")
p$stanmodel = gaussian_process_stanmodel() # precompile outside of the loop to speed the rest
}
habitat = stmv__habitat( p, dat, pa ), # TODO
gaussianprocess = stmv__gaussianprocess( p, dat, pa ), # TODO
LaplacesDemon = stmv__LaplacesDemon( p, dat, pa ),
stan = stmv__stan( p, dat, pa, stanmodel=p$stanmodel ), ## todo
splancs = stmv__splancs( p, dat, pa ), # TODO
spate = stmv__spate( p, dat, pa, sloc=Sloc[Si,], distance=stmv_distance_cur, nu=nu, phi=phi, varObs=varObs, varSpatial=varSpatial),
jae0/emei documentation built on Jan. 25, 2024, 10:57 p.m.
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# exploratory / alternate methods .. examples using aegis idnicators
# -----------------------------
# speciescomposition
year.assessment=lubridate::year(Sys.Date())
# year.assessment=lubridate::year(Sys.Date()) -1
p = aegis.speciescomposition::speciescomposition_parameters( yrs=1999:year.assessment )
# varname="pca1"
# varname="pca2"
p$varstomodel = c("pca1", "pca2")
for ( varname in p$varstomodel) {
print(varname)
# ~ 5GB / process for spate
p = aegis.speciescomposition::speciescomposition_parameters(
yrs=1999:year.assessment,
dimensionality="space-time",
project_class = "stmv",
stmv_variables=list(Y=varname),
libs = c("mgcv", "spate", "stmvdev"),
stmv_local_modelengine = "userdefined",
stmv_local_modelengine_userdefined = stmvdev::stmv__spate,
stmv_spate_method = "mcmc_fast",
stmv_distance_statsgrid = 5, # resolution (km) of data aggregation (i.e. generation of the ** statistics ** )
stmv_distance_scale = c(75, 90, 110),
stmv_spate_boost_timeseries = TRUE, # use simple GAM spectral contraint to structure timeseries as spate's fft in time seems to cause overfitting ?
stmv_gam_optimizer=c("outer", "bfgs") # .. spate uses local gam-based boost of ts, as in 'twostep'
stmv_local_modelformula = formula( paste(
varname, '~ s(yr, k=5, bs="ts") + s(cos.w, k=3, bs="ts") + s(sin.w, k=3, bs="ts") ',
' + s(cos.w, sin.w, yr, bs="ts", k=16) ',
' + s(plon, k=3, bs="ts") + s(plat, k=3, bs="ts") + s(plon, plat, k=16, bs="ts") ' ) ),
# similar to GAM model .. this is the seed model
stmv_local_model_distanceweighted = TRUE,
clusters=rep("localhost", 4),
stmv_spate_nburnin = 1000,
stmv_spate_nposteriors = 1000,
stmv_spate_nCovUpdates = 20 # no of times to update cov matrix during simulations
)
# stmv_local_modelengine = "twostep",
# stmv_local_modelformula = formula( paste(
# ' snowcrab.large.males_abundance', '~ s(yr, k=10, bs="ts") + s(cos.w, k=3, bs="ts") + s(sin.w, k=3, bs="ts") ',
# ' + s(cos.w, sin.w, yr, bs="ts", k=20) ',
# ' + s(plon, k=3, bs="ts") + s(plat, k=3, bs="ts") + s(plon, plat, k=20, bs="ts") ' ) ),
# # stmv_twostep_space = "krige", # other possibilities: "fft", "tps"
# stmv_twostep_space = "gam", # fft, krige (very slow), lowpass, lowpass_fft
# stmv_local_modelformula_space = formula( paste(
# 'snowcrab.large.males_abundance', '~ s(log(z), k=3, bs="ts") + s(plon, k=3, bs="ts") + s(plat, k=3, bs="ts") + s( log(z), plon, plat, k=27, bs="ts") ') ),
# stmv_twostep_time = "gam",
# stmv_local_modelformula_time = formula( paste(
# 'snowcrab.large.males_abundance', ' ~ s(yr, k=10, bs="ts") + s(cos.w, k=3, bs="ts") + s(sin.w, k=3, bs="ts") ',
# '+ s( cos.w, sin.w, yr, k=45, bs="ts")') ),
p = stmv( p=p, runmode=c("initialize", "globalmodel" ) ) # no global_model and force a clean restart
currentstatus = stmv_statistics_status( p=p )
parallel_run( stmv_interpolate, p=p,
runindex=list( locs=currentstatus$todo[sample.int(length( currentstatus$todo ))] ),
local.n.complete=currentstatus["n.complete"]
)
stmv_db( p=p, DS="save_current_state" ) # saved current state (internal format)
currentstatus = stmv_statistics_status( p=p, reset="incomplete" )
parallel_run( stmv_interpolate, p=p,
runindex=list( locs=currentstatus$todo[sample.int(length( currentstatus$todo ))] ),
local.n.complete=currentstatus["n.complete"]
)
stmv_db( p=p, DS="save_current_state" ) # saved current state
currentstatus = stmv_statistics_status( p=p, reset="incomplete" )
parallel_run( stmv_interpolate, p=p,
runindex=list( locs= currentstatus$todo[sample.int(length( currentstatus$todo ))] ),
local.n.complete=currentstatus["n.complete"],
stmv_local_modelengine = "tps"
)
stmv_db( p=p, DS="save_current_state" )
stmv_db( p=p, DS="stmv.results" ) # save to disk for use outside stmv*, returning to user scale
# if (really.finished) stmv_db( p=p, DS="cleanup.all" )
aegis_db( p=p, DS="predictions.redo" ) # warp predictions to other grids
aegis_db( p=p, DS="stmv.stats.redo" ) # warp stats to other grids
aegis_db( p=p, DS="complete.redo" )
aegis_db( p=p, DS="baseline.redo" )
aegis_db_map( p=p )
if (0) {
global_model = stmv_global_model( p=p, DS="global_model")
summary( global_model )
plot(global_model)
}
}
# ----------------
# ca1 :
Family: gaussian
Link function: identity
Formula:
ca1 ~ s(t, k = 3, bs = "ts") + s(tsd.climatology, k = 3, bs = "ts") +
s(tmean.climatology, k = 3, bs = "ts") + s(log(t.range),
k = 3, bs = "ts") + s(log(b.range), k = 3, bs = "ts") + s(log(z),
k = 3, bs = "ts") + s(log(dZ), k = 3, bs = "ts") + s(log(ddZ),
k = 3, bs = "ts") + s(log(substrate.grainsize), k = 3, bs = "ts")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.025523 0.004243 6.015 1.83e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(t) 1.998 2 274.88 < 2e-16 ***
s(tsd.climatology) 1.976 2 77.31 < 2e-16 ***
s(tmean.climatology) 2.000 2 7016.86 < 2e-16 ***
s(log(t.range)) 1.396 2 68.72 < 2e-16 ***
s(log(b.range)) 1.969 2 21.13 4.59e-10 ***
s(log(z)) 1.983 2 112.45 < 2e-16 ***
s(log(dZ)) 1.888 2 14.47 1.98e-07 ***
s(log(ddZ)) 1.856 2 12.15 2.05e-06 ***
s(log(substrate.grainsize)) 1.752 2 274.56 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.642 Deviance explained = 64.2%
GCV = 0.37765 Scale est. = 0.37733 n = 20956
---
# -------------------
# varname ="ca2"
Family: gaussian
Link function: identity
Formula:
ca2 ~ s(t, k = 3, bs = "ts") + s(tsd.climatology, k = 3, bs = "ts") +
s(tmean.climatology, k = 3, bs = "ts") + s(log(t.range),
k = 3, bs = "ts") + s(log(b.range), k = 3, bs = "ts") + s(log(z),
k = 3, bs = "ts") + s(log(dZ), k = 3, bs = "ts") + s(log(ddZ),
k = 3, bs = "ts") + s(log(substrate.grainsize), k = 3, bs = "ts")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.11437 0.00408 -28.1 <2e-16
Approximate significance of smooth terms:
edf Ref.df F p-value
s(t) 1.58 2 313.49 < 2e-16
s(tsd.climatology) 1.99 2 280.46 < 2e-16
s(tmean.climatology) 2.00 2 81.55 < 2e-16
s(log(t.range)) 1.99 2 62.90 < 2e-16
s(log(b.range)) 1.50 2 12.85 2.7e-07
s(log(z)) 2.00 2 3337.43 < 2e-16
s(log(dZ)) 1.90 2 5.53 0.0029
s(log(ddZ)) 1.98 2 30.29 4.3e-14
s(log(substrate.grainsize)) 1.99 2 140.98 < 2e-16
R-sq.(adj) = 0.748 Deviance explained = 74.8%
GCV = 0.22366 Scale est. = 0.22336 n = 13444
# -------------------
# varname ="pca1" ; # July 2017 results follow
Family: gaussian
Link function: identity
Formula:
pca1 ~ s(t, k = 3, bs = "ts") + s(tsd.climatology, k = 3, bs = "ts") +
s(tmean.climatology, k = 3, bs = "ts") + s(log(t.range),
k = 3, bs = "ts") + s(log(b.range), k = 3, bs = "ts") + s(log(z),
k = 3, bs = "ts") + s(log(dZ), k = 3, bs = "ts") + s(log(ddZ),
k = 3, bs = "ts") + s(log(substrate.grainsize), k = 3, bs = "ts")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.124972 0.000809 154 <2e-16
Approximate significance of smooth terms:
edf Ref.df F p-value
s(t) 1.998 2 76.58 < 2e-16
s(tsd.climatology) 1.989 2 141.55 < 2e-16
s(tmean.climatology) 1.995 2 5021.31 < 2e-16
s(log(t.range)) 1.976 2 105.57 < 2e-16
s(log(b.range)) 1.974 2 40.42 < 2e-16
s(log(z)) 1.969 2 25.18 5.8e-12
s(log(dZ)) 0.764 2 1.49 0.041
s(log(ddZ)) 1.361 2 15.25 2.6e-09
s(log(substrate.grainsize)) 1.952 2 296.83 < 2e-16
R-sq.(adj) = 0.739 Deviance explained = 73.9%
GCV = 0.0088125 Scale est. = 0.0088014 n = 13444
# -------------------
# varname ="pca2"
Family: gaussian
Link function: identity
Formula:
pca2 ~ s(t, k = 3, bs = "ts") + s(tsd.climatology, k = 3, bs = "ts") +
s(tmean.climatology, k = 3, bs = "ts") + s(log(t.range),
k = 3, bs = "ts") + s(log(b.range), k = 3, bs = "ts") + s(log(z),
k = 3, bs = "ts") + s(log(dZ), k = 3, bs = "ts") + s(log(ddZ),
k = 3, bs = "ts") + s(log(substrate.grainsize), k = 3, bs = "ts")
---
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02133 0.00081 26.3 <2e-16
Approximate significance of smooth terms:
edf Ref.df F p-value
s(t) 1.79e+00 2 101.1 < 2e-16
s(tsd.climatology) 1.98e+00 2 194.5 < 2e-16
s(tmean.climatology) 1.93e+00 2 45.2 < 2e-16
s(log(t.range)) 1.93e+00 2 20.9 4.1e-10
s(log(b.range)) 1.91e+00 2 29.3 4.9e-14
s(log(z)) 1.70e+00 2 2350.7 < 2e-16
s(log(dZ)) 4.76e-08 2 0.0 0.77011
s(log(ddZ)) 1.93e+00 2 7.5 0.00041
s(log(substrate.grainsize)) 1.42e+00 2 413.4 < 2e-16
R-sq.(adj) = 0.699 Deviance explained = 69.9%
GCV = 0.0088397 Scale est. = 0.0088295 n = 13444
if (p$stmv_local_modelengine %in% c("gaussianprocess") ) p$libs = c( p$libs )
if (p$stmv_local_modelengine %in% c("splancs") ) p$libs = c( p$libs, "splancs" )
if (p$stmv_local_modelengine %in% c("LaplacesDemon") ) p$libs = c( p$libs, "LaplacesDemonCpp" )
if (p$stmv_local_modelengine %in% c("stan") ) p$libs = c( p$libs, "cmdstanr" )
# if (p$stmv_local_modelengine %in% c("spate") ) p$libs = c( p$libs, "spate" ) # now copied directly into stmv
if (p$stmv_local_modelengine %in% c("stan", "gaussianprocess")) {
message( "Precompiling stan program ... ")
p$stanmodel = gaussian_process_stanmodel() # precompile outside of the loop to speed the rest
}
habitat = stmv__habitat( p, dat, pa ), # TODO
gaussianprocess = stmv__gaussianprocess( p, dat, pa ), # TODO
LaplacesDemon = stmv__LaplacesDemon( p, dat, pa ),
stan = stmv__stan( p, dat, pa, stanmodel=p$stanmodel ), ## todo
splancs = stmv__splancs( p, dat, pa ), # TODO
spate = stmv__spate( p, dat, pa, sloc=Sloc[Si,], distance=stmv_distance_cur, nu=nu, phi=phi, varObs=varObs, varSpatial=varSpatial),
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