Merge branch 'dev' of https://github.com/EarthSystemDiagnostics/hamstr …

…into dev # Conflicts: # README.md # inst/stan/hamstr.stan # man/figures/README-unnamed-chunk-14-1.svg # man/figures/README-unnamed-chunk-16-1.svg # man/figures/README-unnamed-chunk-17-1.svg # man/figures/README-unnamed-chunk-18-1.svg # man/figures/README-unnamed-chunk-19-1.svg # man/figures/README-unnamed-chunk-4-1.svg # man/figures/README-unnamed-chunk-5-1.svg # man/figures/README-unnamed-chunk-7-1.svg # man/figures/README-unnamed-chunk-8-1.png # man/figures/README-unnamed-chunk-9-1.svg
EarthSystemDiagnostics · Mar 6, 2024 · 9c0a136 · 9c0a136
2 parents 132c915 + 6c965a7
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diff --git a/NEWS.md b/NEWS.md
@@ -1,6 +1,7 @@
 # hamstr 0.8.1
 
 * Allow flat structure by setting K_factor > K_fine
+* Update stan code to reflect new array syntax
 
 # hamstr 0.8.0
 

diff --git a/R/stanmodels.R b/R/stanmodels.R
@@ -1,11 +1,10 @@
 # Generated by rstantools.  Do not edit by hand.
 
 # names of stan models
-stanmodels <- c("hamstr", "hamstr_newArray")
+stanmodels <- c("hamstr")
 
 # load each stan module
 Rcpp::loadModule("stan_fit4hamstr_mod", what = TRUE)
-Rcpp::loadModule("stan_fit4hamstr_newArray_mod", what = TRUE)
 
 # instantiate each stanmodel object
 stanmodels <- sapply(stanmodels, function(model_name) {

diff --git a/README.md b/README.md
@@ -225,16 +225,16 @@ predict(hamstr_fit_1)
 #> # A tibble: 396,000 × 3
 #>     iter depth   age
 #>    <int> <dbl> <dbl>
-#>  1     1   1.5 4488.
-#>  2     1   2.5 4497.
-#>  3     1   3.5 4505.
-#>  4     1   4.5 4517.
-#>  5     1   5.5 4527.
-#>  6     1   6.5 4534.
-#>  7     1   7.5 4542.
-#>  8     1   8.5 4550.
-#>  9     1   9.5 4559.
-#> 10     1  10.5 4567.
+#>  1     1   1.5 4517.
+#>  2     1   2.5 4525.
+#>  3     1   3.5 4534.
+#>  4     1   4.5 4548.
+#>  5     1   5.5 4559.
+#>  6     1   6.5 4566.
+#>  7     1   7.5 4574.
+#>  8     1   8.5 4578.
+#>  9     1   9.5 4585.
+#> 10     1  10.5 4601.
 #> # ℹ 395,990 more rows
 ```
 
@@ -245,16 +245,16 @@ summary(hamstr_fit_1)
 #> # A tibble: 99 × 15
 #>    depth   idx par         mean se_mean    sd `2.5%` `15.9%` `25%` `50%` `75%`
 #>    <dbl> <dbl> <chr>      <dbl>   <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl>
-#>  1   1.5     1 c_ages[1]  4510.   1.57   64.1  4367.   4449. 4472. 4516. 4554.
-#>  2   2.5     2 c_ages[2]  4521.   1.43   59.9  4391.   4463. 4485. 4527. 4563.
-#>  3   3.5     3 c_ages[3]  4533.   1.31   56.4  4414.   4477. 4497. 4537. 4571.
-#>  4   4.5     4 c_ages[4]  4544.   1.19   53.6  4434.   4491. 4511. 4549. 4580.
-#>  5   5.5     5 c_ages[5]  4556.   1.09   51.4  4450.   4505. 4523. 4559. 4590.
-#>  6   6.5     6 c_ages[6]  4567.   1.01   49.3  4466.   4518. 4535. 4570. 4600.
-#>  7   7.5     7 c_ages[7]  4578.   0.946  47.7  4482.   4530. 4547. 4581. 4610.
-#>  8   8.5     8 c_ages[8]  4590.   0.911  46.6  4497.   4543. 4560. 4593. 4621.
-#>  9   9.5     9 c_ages[9]  4603.   0.877  45.6  4511.   4558. 4573. 4605. 4633.
-#> 10  10.5    10 c_ages[10] 4617.   0.828  43.9  4531.   4574. 4588. 4619. 4647.
+#>  1   1.5     1 c_ages[1]  4512.   1.57   64.0  4374.   4449. 4472. 4517. 4557.
+#>  2   2.5     2 c_ages[2]  4523.   1.43   60.0  4396.   4464. 4485. 4527. 4565.
+#>  3   3.5     3 c_ages[3]  4534.   1.30   56.6  4415.   4478. 4497. 4537. 4574.
+#>  4   4.5     4 c_ages[4]  4545.   1.19   53.9  4432.   4493. 4511. 4548. 4583.
+#>  5   5.5     5 c_ages[5]  4556.   1.09   51.6  4448.   4506. 4523. 4558. 4592.
+#>  6   6.5     6 c_ages[6]  4567.   0.992  49.5  4465.   4519. 4536. 4569. 4602.
+#>  7   7.5     7 c_ages[7]  4578.   0.911  47.8  4479.   4532. 4548. 4580. 4612.
+#>  8   8.5     8 c_ages[8]  4590.   0.860  46.7  4494.   4544. 4559. 4591. 4622.
+#>  9   9.5     9 c_ages[9]  4602.   0.813  45.6  4510.   4558. 4572. 4604. 4634.
+#> 10  10.5    10 c_ages[10] 4617.   0.758  44.0  4527.   4573. 4588. 4618. 4647.
 #> # ℹ 89 more rows
 #> # ℹ 4 more variables: `84.1%` <dbl>, `97.5%` <dbl>, n_eff <dbl>, Rhat <dbl>
 ```
@@ -279,14 +279,14 @@ summary(age.mods.interp)
 #>    <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl>   <dbl>   <dbl>
 #>  1     0  NaN   NA      NA      NA    NA    NA    NA      NA      NA 
 #>  2     1  NaN   NA      NA      NA    NA    NA    NA      NA      NA 
-#>  3     2 4516.  61.9  4380.   4456. 4478. 4522. 4558.   4575.   4621.
-#>  4     3 4527.  58.0  4404.   4471. 4491. 4532. 4566.   4583.   4628.
-#>  5     4 4538.  54.9  4425.   4483. 4503. 4543. 4575.   4592.   4636.
-#>  6     5 4550.  52.4  4441.   4498. 4517. 4554. 4585.   4600.   4644.
-#>  7     6 4561.  50.3  4459.   4511. 4529. 4564. 4595.   4611.   4653.
-#>  8     7 4573.  48.4  4474.   4524. 4541. 4576. 4605.   4620.   4661.
-#>  9     8 4584.  47.0  4490.   4537. 4554. 4587. 4615.   4630.   4672.
-#> 10     9 4596.  46.0  4505.   4550. 4566. 4599. 4627.   4642.   4684.
+#>  3     2 4517.  61.9  4386.   4457. 4479. 4522. 4561.   4578.   4622.
+#>  4     3 4528.  58.2  4405.   4471. 4492. 4532. 4569.   4586.   4629.
+#>  5     4 4539.  55.1  4424.   4485. 4504. 4542. 4579.   4594.   4636.
+#>  6     5 4551.  52.7  4441.   4499. 4517. 4553. 4588.   4603.   4644.
+#>  7     6 4562.  50.5  4457.   4513. 4530. 4564. 4597.   4612.   4653.
+#>  8     7 4573.  48.6  4472.   4526. 4542. 4574. 4607.   4621.   4662.
+#>  9     8 4584.  47.2  4487.   4538. 4553. 4586. 4617.   4631.   4671.
+#> 10     9 4596.  46.1  4502.   4551. 4565. 4597. 4628.   4642.   4682.
 #> # ℹ 91 more rows
 ```
 
@@ -312,16 +312,16 @@ summary(hamstr_fit_1, type = "acc_rates")
 #> # A tibble: 196 × 15
 #>    depth c_depth_top c_depth_bottom acc_rate_unit   idx   tau  mean    sd `2.5%`
 #>    <dbl>       <dbl>          <dbl> <chr>         <dbl> <dbl> <dbl> <dbl>  <dbl>
-#>  1   1.5         1.5            2.5 depth_per_ti…     1     0 156.  160.    30.3
-#>  2   2.5         2.5            3.5 depth_per_ti…     2     0 138.  119.    32.1
-#>  3   3.5         3.5            4.5 depth_per_ti…     3     0 132.  109.    32.7
-#>  4   4.5         4.5            5.5 depth_per_ti…     4     0 121.   83.7   35.9
-#>  5   5.5         5.5            6.5 depth_per_ti…     5     0 119.   80.3   36.7
-#>  6   6.5         6.5            7.5 depth_per_ti…     6     0 122.   92.0   36.9
-#>  7   7.5         7.5            8.5 depth_per_ti…     7     0 124.   96.7   35.9
-#>  8   8.5         8.5            9.5 depth_per_ti…     8     0  97.1  52.1   35.6
-#>  9   9.5         9.5           10.5 depth_per_ti…     9     0  82.7  41.0   32.6
-#> 10  10.5        10.5           11.5 depth_per_ti…    10     0  78.3  40.7   30.2
+#>  1   1.5         1.5            2.5 depth_per_ti…     1     0 165.  180.    31.4
+#>  2   2.5         2.5            3.5 depth_per_ti…     2     0 146.  137.    32.8
+#>  3   3.5         3.5            4.5 depth_per_ti…     3     0 142.  130.    32.1
+#>  4   4.5         4.5            5.5 depth_per_ti…     4     0 130.  104.    35.7
+#>  5   5.5         5.5            6.5 depth_per_ti…     5     0 125.   94.6   37.7
+#>  6   6.5         6.5            7.5 depth_per_ti…     6     0 127.   90.7   36.6
+#>  7   7.5         7.5            8.5 depth_per_ti…     7     0 129.   96.6   35.7
+#>  8   8.5         8.5            9.5 depth_per_ti…     8     0 102.   56.0   36.0
+#>  9   9.5         9.5           10.5 depth_per_ti…     9     0  85.3  43.9   33.2
+#> 10  10.5        10.5           11.5 depth_per_ti…    10     0  80.4  42.7   30.4
 #> # ℹ 186 more rows
 #> # ℹ 6 more variables: `15.9%` <dbl>, `25%` <dbl>, `50%` <dbl>, `75%` <dbl>,
 #> #   `84.1%` <dbl>, `97.5%` <dbl>