Merge pull request #376 from aappling-usgs/err-proc-day

implement daytime process error bayesian models (err_proc_dayiid=T)

.travis.yml

@@ -4,9 +4,30 @@
 
 language: r
 sudo: required
-dist: trusty
 warnings_are_errors: true
 
+matrix:
+  include:
+    - os: linux
+      dist: trusty
+      r: release
+      r_binary_packages:
+        - devtools
+      addons:
+        apt:
+          sources:
+            - ubuntu-toolchain-r-test
+          packages:
+            - g++-7
+      env:
+        - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7"
+
+before_install:
+  - eval "${MATRIX_EVAL}"
+  - mkdir -p ~/.R/
+  - echo "CXX14 = g++-7 -fPIC" >> ~/.R/Makevars
+  - echo "CXX14FLAGS = -mtune=native -march=native -Wno-unused-variable -Wno-unused-function -Wno-unused-local-typedefs -Wno-ignored-attributes -Wno-deprecated-declarations -Wno-attributes -O3" >> ~/.R/Makevars
+
 env:
    global:
      - R_BUILD_ARGS="--no-build-vignettes --no-manual"
@@ -23,7 +44,6 @@ before_script:
   - Rscript -e "install.packages('rmarkdown')"
 
 r_packages:
- - modeest
  - deSolve
  - rstan
 

DESCRIPTION

@@ -1,9 +1,9 @@
 Package: streamMetabolizer
 Type: Package
 Title: Models for Estimating Aquatic Photosynthesis and Respiration
-Version: 0.11.0
-Date: 2018-10-10
-Author: Alison P. Appling, Robert O. Hall, Maite Arroita, and Charles B.
+Version: 0.11.3
+Date: 2019-02-02
+Author: Alison P. Appling, Robert O. Hall, Jr., Maite Arroita, and Charles B.
     Yackulic
 Authors@R: c(
     person("Alison P.", "Appling", , "[email protected]", c("aut", "cre")),
@@ -124,5 +124,5 @@ Collate:
     'streamMetabolizer-deprecated.R'
     'streamMetabolizer.R'
     'zz_build_docs.R'
-RoxygenNote: 6.1.0
-Encoding: UTF-8 
+RoxygenNote: 6.1.1
+Encoding: UTF-8

NEWS.md

@@ -1,3 +1,12 @@
+# 0.11.2
+
+* changed `err_proc_dayiid` to `err_proc_GPP` and switched to a more GPP-oriented
+process error for those models
+
+# 0.11.1
+
+* added `err_proc_dayiid` option for Bayesian models
+
 # 0.11.0
 
 * added `GPP_fun='satlight'` option for Bayesian models

R/metab_bayes.R

@@ -535,7 +535,9 @@ prepdata_bayes <- function(
     switch(
       features$GPP_fun,
       linlight = list(
-        frac_GPP = {
+        # X_mult_Y syntax: X = process reflected by multiplier, Y = quantity
+        # modified by multiplier
+        light_mult_GPP = {
           mat_light <- time_by_date_matrix(data$light)
           # normalize light by the sum of light in the first 24 hours of the time window
           in_solar_day <- apply(obs_times, MARGIN=2, FUN=function(timevec) {timevec - timevec[1] <= 1} )
@@ -551,8 +553,9 @@ prepdata_bayes <- function(
     ),
 
     list(
-      frac_ER  = time_by_date_matrix(1),
-      frac_D   = time_by_date_matrix(timestep_days), # the yackulic shortcut models rely on this being constant over time
+      # X_mult_Y syntax: X = process reflected by multiplier, Y = quantity
+      # modified by multiplier
+      const_mult_ER  = time_by_date_matrix(1),
       KO2_conv = {
         KO2_conv_vec <- suppressWarnings(convert_k600_to_kGAS(k600=1, temperature=data$temp.water, gas="O2"))
         if(any(is.nan(KO2_conv_vec))) {
@@ -783,15 +786,16 @@ format_mcmc_mat_nosplit <- function(mcmc_mat, data_list_d, data_list_n, model_na
       'lnK600_lnQ_nodes'),
     daily = c(
       'GPP', 'ER',
-      'GPP_daily', 'Pmax', 'alpha', 'ER_daily', 'K600_daily', 
-      if(features$pool_K600_type %in% c('linear','binned')) 'K600_daily_predlog'),
+      'GPP_daily', 'Pmax', 'alpha', 'ER_daily', 'K600_daily', 'DO_R2',
+      if(features$pool_K600_type %in% c('linear','binned')) 'K600_daily_predlog',
+      if(features$err_proc_GPP) 'GPP_pseudo_R2'),
     inst = c(
       'DO_mod', 'DO_mod_partial', # d*n
       'DO_mod_partial_sigma', # d*n
       'GPP_inst', 'ER_inst', 'KO2_inst', # d*n
+      'GPP_inst_partial', 'err_proc_GPP', # d*n for GPP process error
       'err_proc_acor_inc', 'err_proc_acor', # can be d*n (trapezoid) or d*(n-1) (euler), same timestamp indexing as GPP_inst
-      'err_obs_iid', 'err_proc_iid', # d*(n-1), timestamp[i+1] relates to var[i:i+1]
-      'coef_GPP'
+      'err_obs_iid', 'err_proc_iid' # d*(n-1), timestamp[i+1] relates to var[i:i+1]
     )
   )