ecmgis: Package that interacts with stmv.

  # -----------------------------
  # estimate metabolic demand, given size structure

  if (!exists("year.assessment")) {
    year.assessment=lubridate::year(Sys.Date())
    year.assessment=lubridate::year(Sys.Date()) - 1
  }

  p = aegis::aegis_parameters( DS="metabolism", yrs=1970:year.assessment )

  metabolism.db( DS="metabolism.redo", p=p )
# o = metabolism.db( DS="metabolism", p=p )

  if (0) {
    # about 700 MB/process ..
    ram_required_per_process = 1  # about 600 MB per process in 2017 GB
    ncpus = min( parallel::detectCores(), floor( ram_local() / ram_required_per_process ) )
    ncpus.covars = min( parallel::detectCores(), floor( ram_local() / .7 ) )  # 700 GB in 2018 .. for prediction of global covars
  }

 # -----------------------------
  for ( vn in p$varstomodel) {

    #  vn="smr"
    print(vn)

    p = aegis::aegis_parameters(
      DS="metabolism",
        variables=list(Y=vn),
        yrs = c(1999:year.assessment),  # years for modelling and interpolation
        stmv_dimensionality="space-year",
        stmv_global_modelengine = "gam",
        stmv_global_family = gaussian(link="identity"),
        stmv_global_modelformula = formula( paste(
          vn, '~ s(t, k=3, bs="ts") + s(tsd.climatology, k=3, bs="ts") + s(tmean.climatology, k=3, bs="ts") ',
          ' + s(t.range, k=3, bs="ts") + s( 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") ' )),
        stmv_local_modelengine ="twostep",
        stmv_twostep_space = "fft",  # other possibilities:  "fft", "tps"
        stmv_twostep_time = "gam",
        stmv_local_modelformula_time = formula( paste(
          vn, '~ 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=30, bs="ts")') ),

        stmv_local_model_distanceweighted = TRUE,

        stmv_rsquared_threshold = 0.2, # lower threshold
        stmv_distance_statsgrid = 4, # resolution (km) of data aggregation (i.e. generation of the ** statistics ** )
        stmv_distance_scale = c(40, 60, 80), # km ... approx guess of 95% AC range .. data tends to be sprse realtive to pure space models
        # stmv_distance_prediction_fraction = 1, # stmv_distance_prediction = stmv_distance_statsgrid * XX ..this is a half window km (default is 0.75)
        depth.filter = 0, # the depth covariate is input as log(depth) so, choose stats locations with elevation > log(1 m) as being on land
        stmv_nmin = 8*(year.assessment-1999),# floor( 7 * p$ny ) # min number of data points req before attempting to model timeseries in a localized space
        stmv_nmax = 8*(year.assessment-1999)*11, # max( floor( 7 * p$ny ) * 11, 8000), # no real upper bound
        stmv_clusters = list( scale=rep("localhost", ncpus), interpolate=rep("localhost", ncpus) )  # ncpus for each runmode

      )
    )

    stmv( p=p, runmode=c("globalmodel", "interpolate" )  ) # no global_model and force a clean restart

    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_db( p=p, DS="global_model")
      summary( global_model )
      plot(global_model)
    }
  }



---
Family: gaussian
Link function: identity

Formula:
mr ~ 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)  15253.7      202.8   75.23   <2e-16 ***
---
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)                       1.5477      2 129.275  < 2e-16 ***
s(tsd.climatology)         1.9837      2  36.564  < 2e-16 ***
s(tmean.climatology)       1.8999      2  10.677 9.44e-06 ***
s(log(t.range))            1.9377      2  15.969 6.41e-08 ***
s(log(b.range))            1.4403      2   2.834 0.024740 *
s(log(z))                  0.9176      2   4.735 0.000536 ***
s(log(dZ))                 1.8186      2  13.770 2.17e-07 ***
s(log(ddZ))                1.7520      2   2.385 0.064152 .
s(log(substrate.grainsize)) 1.8101      2  42.411  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.0494   Deviance explained = 5.01%
GCV = 9.1465e+08  Scale est. = 9.1399e+08  n = 22230
---



analysis (hours): 0.239
Family: gaussian
Link function: identity

Formula:
smr ~ 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) 0.0055192  0.0000149   370.3   <2e-16 ***
---
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)                       1.983      2 586.368  < 2e-16 ***
s(tsd.climatology)         1.998      2  74.818  < 2e-16 ***
s(tmean.climatology)       1.463      2  76.785  < 2e-16 ***
s(log(t.range))            1.800      2   5.143   0.0034 **
s(log(b.range))            1.999      2  22.493 1.67e-10 ***
s(log(z))                  1.973      2  81.062  < 2e-16 ***
s(log(dZ))                 1.785      2   1.585   0.1684
s(log(ddZ))                1.976      2  27.487 5.79e-13 ***
s(log(substrate.grainsize)) 1.976      2  34.289 7.35e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.125   Deviance explained = 12.6%
GCV = 2.8606e-06  Scale est. = 2.8566e-06  n = 12860




Family: gaussian
Link function: identity

Formula:
Pr.Reaction ~ 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) 1.3178636  0.0001544    8534   <2e-16 ***
---
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)                       2.000      2 4.116e+05  < 2e-16 ***
s(tsd.climatology)         1.999      2 7.274e+01  < 2e-16 ***
s(tmean.climatology)       1.949      2 9.606e+01  < 2e-16 ***
s(log(t.range))            1.993      2 2.489e+01 1.39e-11 ***
s(log(b.range))            1.875      2 4.128e+00  0.01224 *
s(log(z))                  1.995      2 7.228e+01  < 2e-16 ***
s(log(dZ))                 1.469      2 9.550e-01  0.25856
s(log(ddZ))                1.861      2 5.331e+00  0.00287 **
s(log(substrate.grainsize)) 1.834      2 3.186e+00  0.03113 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.986   Deviance explained = 98.6%
GCV = 0.00055543  Scale est. = 0.000555  n = 23274
---






Family: gaussian
Link function: identity

Formula:
Ea ~ 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) -616.7698     0.1834   -3363   <2e-16 ***
---
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)                       2.000      2 8.236e+05  < 2e-16 ***
s(tsd.climatology)         1.997      2 7.086e+01  < 2e-16 ***
s(tmean.climatology)       1.915      2 9.120e+01  < 2e-16 ***
s(log(t.range))            1.979      2 1.915e+01 3.82e-09 ***
s(log(b.range))            1.656      2 1.469e+00  0.16791
s(log(z))                  1.978      2 5.256e+01  < 2e-16 ***
s(log(dZ))                 1.591      2 1.452e+00  0.15461
s(log(ddZ))                1.726      2 4.613e+00  0.00471 **
s(log(substrate.grainsize)) 1.827      2 2.821e+00  0.04527 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.994   Deviance explained = 99.4%
GCV = 783.38  Scale est. = 782.78    n = 23274
---




Family: gaussian
Link function: identity

Formula:
A ~ 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) 4.022e-03  8.277e-06   485.9   <2e-16 ***
---
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)                       1.845      2  20.925 1.73e-10 ***
s(tsd.climatology)         1.921      2  43.541  < 2e-16 ***
s(tmean.climatology)       1.981      2 223.441  < 2e-16 ***
s(log(t.range))            1.803      2   7.098 0.000413 ***
s(log(b.range))            1.999      2  14.331 5.82e-07 ***
s(log(z))                  1.974      2  42.884  < 2e-16 ***
s(log(dZ))                 1.789      2   2.620 0.053728 .
s(log(ddZ))                1.975      2  19.701 1.61e-09 ***
s(log(substrate.grainsize)) 1.901      2  93.460  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.0999   Deviance explained = 10.1%
GCV = 1.5244e-06  Scale est. = 1.5231e-06  n = 22230
---




Family: gaussian
Link function: identity

Formula:
zm ~ 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) 0.4998119  0.0009438   529.6   <2e-16 ***
---
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)                       1.990      2 388.578  < 2e-16 ***
s(tsd.climatology)         1.987      2 118.949  < 2e-16 ***
s(tmean.climatology)       1.954      2  52.391  < 2e-16 ***
s(log(t.range))            1.975      2  41.094  < 2e-16 ***
s(log(b.range))            1.956      2  47.027  < 2e-16 ***
s(log(z))                  1.945      2 146.267  < 2e-16 ***
s(log(dZ))                 1.843      2   5.737 0.001912 **
s(log(ddZ))                1.863      2   6.990 0.000527 ***
s(log(substrate.grainsize)) 1.922      2  58.181  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.103   Deviance explained = 10.4%
GCV = 0.020771  Scale est. = 0.020755  n = 23302
---