library(sspm)
#> Loading required package: sf
#> Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1; sf_use_s2() is TRUE
#> Loading required package: mgcv
#> Loading required package: nlme
#> This is mgcv 1.8-40. For overview type 'help("mgcv-package")'.
library(mgcv)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:nlme':
#> 
#>     collapse
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

The following example shows the typical sspm workflow. We will use simulated data.

sfa_boundaries
#> Simple feature collection with 4 features and 1 field
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -64.18658 ymin: 46.00004 xmax: -46.6269 ymax: 60.84333
#> Geodetic CRS:  WGS 84
#>   sfa                       geometry
#> 1   4 MULTIPOLYGON (((-59.36453 5...
#> 2   5 MULTIPOLYGON (((-55 53.75, ...
#> 3   6 MULTIPOLYGON (((-49.9269 49...
#> 4   7 MULTIPOLYGON (((-54.48779 4...
borealis_simulated
#> # A tibble: 1,800 × 8
#>    year_f sfa   weight_per_km2 temp_at_bottom lon_dec lat_dec   row uniqueID  
#>    <fct>  <chr>          <dbl>          <dbl>   <dbl>   <dbl> <int> <chr>     
#>  1 1995   7               14.7          1.31    -62.8    59.2     1 y1995s7r1 
#>  2 1995   7              366.           0       -56.7    55.2     2 y1995s7r2 
#>  3 1995   7              108.           0.982   -60.6    56.9     3 y1995s7r3 
#>  4 1995   7               62.3          0       -58.0    56.4     4 y1995s7r4 
#>  5 1995   7                0            0.698   -59.0    57.1     5 y1995s7r5 
#>  6 1995   7                0            1.45    -58.8    56.3     6 y1995s7r6 
#>  7 1995   7              236.           1.05    -51.1    50.7     7 y1995s7r7 
#>  8 1995   7               68.2          0       -54.1    54.5     8 y1995s7r8 
#>  9 1995   7               93.6          1.86    -54.9    53.7     9 y1995s7r9 
#> 10 1995   7               25.4          1.74    -54.7    50.5    10 y1995s7r10
#> # … with 1,790 more rows
#> # ℹ Use `print(n = ...)` to see more rows
predator_simulated
#> # A tibble: 10,200 × 7
#>    year_f sfa   weight_per_km2 lon_dec lat_dec   row uniqueID  
#>    <fct>  <chr>          <dbl>   <dbl>   <dbl> <int> <chr>     
#>  1 1995   7               9.90   -61.0    60.0     1 y1995s7r1 
#>  2 1995   7             128.     -60.0    57.6     2 y1995s7r2 
#>  3 1995   7             358.     -58.0    55.6     3 y1995s7r3 
#>  4 1995   7               0      -58.0    55.9     4 y1995s7r4 
#>  5 1995   7             103.     -60.5    57.2     5 y1995s7r5 
#>  6 1995   7              57.3    -53.7    51.8     6 y1995s7r6 
#>  7 1995   7              58.6    -53.6    50.7     7 y1995s7r7 
#>  8 1995   7               0      -50.2    50.1     8 y1995s7r8 
#>  9 1995   7               0      -54.6    54.0     9 y1995s7r9 
#> 10 1995   7             131.     -52.8    51.0    10 y1995s7r10
#> # … with 10,190 more rows
#> # ℹ Use `print(n = ...)` to see more rows
catch_simulated
#> # A tibble: 2,020 × 7
#>    year_f sfa   catch lon_dec lat_dec   row uniqueID  
#>    <fct>  <chr> <dbl>   <dbl>   <dbl> <int> <chr>     
#>  1 1991   4     2527.   -62.2    59.5     1 y1991s4r1 
#>  2 1991   4     4194.   -61.0    58.0     2 y1991s4r2 
#>  3 1991   4     7438.   -62.4    60.3     3 y1991s4r3 
#>  4 1991   4        0    -58.2    55.2     4 y1991s4r4 
#>  5 1991   4     3837.   -57.8    55.5     5 y1991s4r5 
#>  6 1991   4     3196.   -58.6    56.3     6 y1991s4r6 
#>  7 1991   4     3214.   -57.9    55.7     7 y1991s4r7 
#>  8 1991   4        0    -56.4    54.4     8 y1991s4r8 
#>  9 1991   4      539.   -58.9    55.6     9 y1991s4r9 
#> 10 1991   4     4234.   -55.2    53.2    10 y1991s4r10
#> # … with 2,010 more rows
#> # ℹ Use `print(n = ...)` to see more rows
  1. The first step of the sspm workflow is to create a sspm_boundary from an sf object, providing the boundary that delineates the boundary regions. The object can then be plotted with spm_plot (as can most sspm objects).
bounds <- spm_as_boundary(boundaries = sfa_boundaries, 
                          boundary = "sfa")

plot(bounds)

  1. The second step consists in wrapping a data.frame, tibble or sf object into a sspm_data object, with a few other pieces of relevant information, such as the name, dataset type (biomass, predictor or catch, depending on the type of information contained), time column and coordinates column (i not sf) and unique row identifier. Here we wrap the borealis dataset that contains the biomass information.
biomass_dataset <- 
  spm_as_dataset(borealis_simulated, name = "borealis",
                 density = "weight_per_km2",
                 time = "year_f",
                 coords = c('lon_dec','lat_dec'), 
                 uniqueID = "uniqueID")
#>   Casting data matrix into simple feature collection using columns: lon_dec, lat_dec
#> !  Warning: sspm is assuming WGS 84 CRS is to be used for casting

biomass_dataset
#> 
#> ‒‒ Dataset borealis ‒‒
#> →  [1800 rows, 9 columns]
#> →  Density : weight_per_km2
#> →  Time : year_f
  1. We do the same with the predator data, which are of the predictor type.
predator_dataset <- 
  spm_as_dataset(predator_simulated, name = "all_predators", 
                 density = "weight_per_km2",
                 time = "year_f",
                 coords = c("lon_dec", "lat_dec"),
                 uniqueID = "uniqueID")
#>   Casting data matrix into simple feature collection using columns: lon_dec, lat_dec
#> !  Warning: sspm is assuming WGS 84 CRS is to be used for casting

predator_dataset
#> 
#> ‒‒ Dataset all_predators ‒‒
#> →  [10200 rows, 8 columns]
#> →  Density : weight_per_km2
#> →  Time : year_f
  1. The sspm workflow relies on the discretization of the boundary objects, the default method being voronoi tesselation.
bounds_voronoi <- bounds %>% 
  spm_discretize(method = "tesselate_voronoi",
                 with = biomass_dataset, 
                 nb_samples = 30)
#>   Discretizing using method tesselate_voronoi

bounds_voronoi
#> 
#> ‒‒ Boundaries (Discrete) ‒‒
#> →  [4 rows, 3 columns]
#>   ★ Points — [120 features, 10 columns]
#>   ★ Patches — [92 features, 4 columns]
#> →  Column : sfa
#> →  Area : area_sfa

The other available method is triangulate_delaunay for delaunay triangulation. Here the a argument is used to set the size of the mesh (see RTriangle::triangulate for more details).

## Not run
bounds_delaunay <- bounds %>%
  spm_discretize(method = "triangulate_delaunay", a = 1, q = 30)
bounds_delaunay
  1. Plotting the object shows the polygons that have been created.
plot(bounds_voronoi)

## Not run
plot(bounds_delaunay)
  1. The results of the discretization can also be explored with spm_patches() and spm_points().
spm_patches(bounds_voronoi)
#> Simple feature collection with 92 features and 3 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -64.18658 ymin: 46.00004 xmax: -46.6269 ymax: 60.84488
#> Geodetic CRS:  WGS 84
#> # A tibble: 92 × 4
#>    sfa   patch_id patch_area                                            geometry
#>  * <fct> <fct>        [km^2]                                       <POLYGON [°]>
#>  1 4     P1            5867. ((-62.93792 60.81457, -62.99813 60.82602, -62.9986…
#>  2 4     P2            6294. ((-61.47678 59.40916, -61.27552 59.36503, -60.8767…
#>  3 4     P3            1594. ((-61.98352 59.91756, -62.14032 59.90335, -62.3916…
#>  4 4     P4            1882. ((-61.60726 58.17744, -61.55968 58.21747, -61.5430…
#>  5 4     P5            1890. ((-61.54308 58.72455, -60.98005 58.90554, -60.9787…
#>  6 4     P6            2149. ((-62.81971 59.18792, -62.54312 59.42411, -62.5654…
#>  7 4     P7            2461. ((-61.55968 58.21747, -60.90082 58.30673, -60.7969…
#>  8 4     P8            1580. ((-60.85729 60.08226, -60.87677 60.07412, -61.2755…
#>  9 4     P9            7244. ((-60.55198 59.91351, -60.70791 59.55391, -61.2203…
#> 10 4     P10           1570. ((-59.93021 58.87649, -60.19091 58.62062, -60.0961…
#> # … with 82 more rows
#> # ℹ Use `print(n = ...)` to see more rows
spm_points(bounds_voronoi)
#> Simple feature collection with 120 features and 9 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -62.95014 ymin: 46.05739 xmax: -47.39935 ymax: 60.81292
#> Geodetic CRS:  WGS 84
#> # A tibble: 120 × 10
#> # Groups:   sfa [4]
#>    year_f weight_per_km2 temp_at_bottom lon_dec lat_dec   row uniqueID   
#>  * <fct>       [kg/km^2]          <dbl>   <dbl>   <dbl> <int> <chr>      
#>  1 1995            7117.          3.88    -61.7    60.0    21 y1995s6r21 
#>  2 1995               0           2.33    -59.2    57.3    23 y1995s6r23 
#>  3 1995               0           1.03    -54.0    46.1    40 y1995s6r40 
#>  4 1996             553.          2.97    -59.5    56.0    65 y1996s7r65 
#>  5 1996            6538.          4.42    -61.3    60.8   102 y1996s5r102
#>  6 1996            2427.          2.17    -55.4    54.4   105 y1996s5r105
#>  7 1996            1945.          0       -48.4    47.2   138 y1996s4r138
#>  8 1997               0           0.649   -55.3    54.7   144 y1997s7r144
#>  9 1997            1430.          0       -54.4    50.2   149 y1997s7r149
#> 10 1997               0           5.18    -60.9    59.6   182 y1997s4r182
#> # … with 110 more rows, and 3 more variables: geometry <POINT [°]>, sfa <fct>,
#> #   area_sfa [km^2]
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
  1. The next step in this workflow is to smooth the variables to be used in the final sspm model, by using spatial-temporal smoothers, by passing each dataset through spm_smooth. Here we first smooth weight_per_km2 as well as temp_at_bottom. Note that the boundary column sfa can be used in the formula as the data will be first joined to the provided boundaries.
biomass_smooth <- biomass_dataset %>%  
  spm_smooth(weight_per_km2 ~ sfa + smooth_time(by = sfa) + 
               smooth_space() + 
               smooth_space_time(),
             boundaries = bounds_voronoi, 
             family=tw)%>% 
  spm_smooth(temp_at_bottom ~ smooth_time(by=sfa, xt = NULL) +
               smooth_space() +
               smooth_space_time(xt = NULL),
             family=gaussian)
#>   Fitting formula: weight_per_km2 ~ sfa + smooth_time(by = sfa) + smooth_space() + smooth_space_time() for dataset 'borealis'
#>   Note:  response variable temp_at_bottom is NOT a biomass density variable
#>   Fitting formula: temp_at_bottom ~ smooth_time(by = sfa, xt = NULL) + smooth_space() + smooth_space_time(xt = NULL) for dataset 'borealis'

biomass_smooth
#> 
#> ‒‒ Dataset borealis (Mapped) ‒‒
#> →  [1801 rows, 12 columns]
#> →  Density : weight_per_km2
#> →  Time : year_f
#> →  Smoothed data : [2208 rows, 8 columns]
#>    ★ Smoothed vars: temp_at_bottomweight_per_km2
  1. The smoothed results for any smoothed variables (listed in “smoothed vars” above) can be easily plotted:
plot(biomass_smooth, var = "weight_per_km2", log = FALSE, interval = T)
#> Registered S3 method overwritten by 'ggforce':
#>   method           from 
#>   scale_type.units units

You can also make a spatial plot

plot(biomass_smooth, var = "weight_per_km2", use_sf = TRUE)

  1. We also smooth the weight_per_km2 variable in the predator data.
predator_smooth <- predator_dataset %>%  
  spm_smooth(weight_per_km2 ~ smooth_time() + smooth_space(),
             boundaries = bounds_voronoi,
             drop.unused.levels = F, family=tw, method= "fREML")
#>   Fitting formula: weight_per_km2 ~ smooth_time() + smooth_space() for dataset 'all_predators'

predator_smooth
#> 
#> ‒‒ Dataset all_predators (Mapped) ‒‒
#> →  [10201 rows, 11 columns]
#> →  Density : weight_per_km2
#> →  Time : year_f
#> →  Smoothed data : [3680 rows, 7 columns]
#>    ★ Smoothed vars: weight_per_km2
  1. Before we assemble the full model with our newly smoothed data, we need to deal with the catch data. We first load the dataset.
catch_dataset <- 
  spm_as_dataset(catch_simulated, name = "catch_data", 
                 biomass = "catch",
                 time = "year_f", 
                 uniqueID = "uniqueID", 
                 coords = c("lon_dec", "lat_dec"))
#>   Casting data matrix into simple feature collection using columns: lon_dec, lat_dec
#> !  Warning: sspm is assuming WGS 84 CRS is to be used for casting

catch_dataset
#> 
#> ‒‒ Dataset catch_data ‒‒
#> →  [2020 rows, 8 columns]
#> →  Biomass : catch
#> →  Time : year_f
  1. We then need to aggregate this data. This illustrate using the spm_aggregate functions. Here we use spm_aggregate_catch:
biomass_smooth_w_catch <- 
  spm_aggregate_catch(biomass = biomass_smooth, 
                      catch = catch_dataset, 
                      biomass_variable = "weight_per_km2",
                      catch_variable = "catch",
                      fill = mean)
#>   Offsetting biomass with catch data using columns: weight_per_km2, catch

biomass_smooth_w_catch
#> 
#> ‒‒ Dataset borealis (Mapped) ‒‒
#> →  [1801 rows, 12 columns]
#> →  Density : weight_per_km2
#> →  Time : year_f
#> →  Smoothed data : [2208 rows, 13 columns]
#>    ★ Smoothed vars: temp_at_bottomweight_per_km2
#>    ★ Vars with catch: weight_per_km2_borealis_with_catch
  1. Once data has been smoothed, we can assemble a sspm model object, using one dataset of type biomass, one dataset of type predictor and (optionnaly) a dataset of type catch.
sspm_model <- sspm(biomass = biomass_smooth_w_catch, 
                   predictors = predator_smooth)
#>   Joining smoothed data from all datasets

sspm_model
#> 
#> ‒‒ Model (2 datasets) ‒‒
#> →  Smoothed data : [2208 rows, 14 columns]
#>    ★ Smoothed vars: temp_at_bottomweight_per_km2_all_predatorsweight_per_km2_borealis
#>    ★ Vars with catch: weight_per_km2_borealis_with_catch
  1. Before fitting the model, we must split data into test/train with spm_split.
sspm_model <- sspm_model %>% 
  spm_split(year_f %in% c(1990:2017))

sspm_model
#> 
#> ‒‒ Model (2 datasets) ‒‒
#> →  Smoothed data : [2208 rows, 15 columns] / [2116 train, 92 test]
#>    ★ Smoothed vars: temp_at_bottomweight_per_km2_all_predatorsweight_per_km2_borealis
#>    ★ Vars with catch: weight_per_km2_borealis_with_catch
  1. To fit the model, we might be interested in including lagged values. This is done with spm_lag.
sspm_model <- sspm_model %>% 
  spm_lag(vars = c("weight_per_km2_borealis", 
                   "weight_per_km2_all_predators"), 
          n = 1)

sspm_model
#> 
#> ‒‒ Model (2 datasets) ‒‒
#> →  Smoothed data : [2208 rows, 17 columns] / [2116 train, 92 test]
#>    ★ Smoothed vars: temp_at_bottomweight_per_km2_all_predatorsweight_per_km2_borealis
#>    ★ Vars with catch: weight_per_km2_borealis_with_catch
#>    ★ lagged vars: weight_per_km2_all_predators_lag_1weight_per_km2_borealis_lag_1
  1. We can now fit the final spm model with spm.
sspm_model_fit <- sspm_model %>% 
  spm(log_productivity ~ sfa +
        weight_per_km2_all_predators_lag_1 +
        smooth_space(by = weight_per_km2_borealis_lag_1) +
        smooth_space(), 
      family = mgcv::scat)
#>   Fitting SPM formula: log_productivity ~ sfa + weight_per_km2_all_predators_lag_1 + smooth_space(by = weight_per_km2_borealis_lag_1) + smooth_space()

sspm_model_fit
#> 
#> ‒‒ Model fit ‒‒
#> →  Smoothed data : [2208 rows, 17 columns] / [2116 train, 92 test]
#> →  Fit summary : 
#> 
#> Family: Scaled t(6.899,0.191) 
#> Link function: identity 
#> 
#> Formula:
#> log_productivity ~ sfa + weight_per_km2_all_predators_lag_1 + 
#>     s(patch_id, k = 30, bs = "mrf", xt = list(penalty = pen_mat_space), 
#>         by = weight_per_km2_borealis_lag_1) + s(patch_id, k = 30, 
#>     bs = "mrf", xt = list(penalty = pen_mat_space))
#> 
#> Parametric coefficients:
#>                                      Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)                         6.543e-01  9.082e-02   7.205 8.22e-13 ***
#> sfa5                                1.232e-01  9.212e-02   1.337   0.1813    
#> sfa6                                8.236e-02  1.113e-01   0.740   0.4594    
#> sfa7                                1.897e-01  1.219e-01   1.557   0.1197    
#> weight_per_km2_all_predators_lag_1 -2.324e-05  9.952e-06  -2.335   0.0196 *  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Approximate significance of smooth terms:
#>                                             edf Ref.df      F p-value    
#> s(patch_id):weight_per_km2_borealis_lag_1 20.79     30 48.238  <2e-16 ***
#> s(patch_id)                               14.90     29  6.683  <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> R-sq.(adj) =  0.608   Deviance explained =   42%
#> -REML =   2967  Scale est. = 1         n = 2024
  1. Plotting the object produces a actual vs predicted plot (with TEST/TRAIN data highlighted.
plot(sspm_model_fit, train_test = TRUE, scales = "free")
#> Warning: Removed 92 rows containing missing values (geom_point).

  1. We can also extract the predictions.
preds <- predict(sspm_model_fit)
head(preds)
#> Simple feature collection with 6 features and 6 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -64.18658 ymin: 59.47292 xmax: -62.26589 ymax: 60.84335
#> Geodetic CRS:  WGS 84
#>      pred_log      pred patch_id year_f sfa      patch_area
#> 1 -0.08672783 0.9169266       P1   1995   4 5866.769 [km^2]
#> 2 -0.22531868 0.7982618       P1   1996   4 5866.769 [km^2]
#> 3  0.01525631 1.0153733       P1   1997   4 5866.769 [km^2]
#> 4  0.06903202 1.0714705       P1   1998   4 5866.769 [km^2]
#> 5  0.07017219 1.0726929       P1   1999   4 5866.769 [km^2]
#> 6  0.10157292 1.1069106       P1   2000   4 5866.769 [km^2]
#>                         geometry
#> 1 POLYGON ((-62.93792 60.8145...
#> 2 POLYGON ((-62.93792 60.8145...
#> 3 POLYGON ((-62.93792 60.8145...
#> 4 POLYGON ((-62.93792 60.8145...
#> 5 POLYGON ((-62.93792 60.8145...
#> 6 POLYGON ((-62.93792 60.8145...

We can also get the predictions for biomass by passing the biomass variable name.

biomass_preds <- predict(sspm_model_fit, biomass = "weight_per_km2_borealis")
head(biomass_preds)
#> Simple feature collection with 6 features and 8 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -64.18658 ymin: 59.47292 xmax: -62.26589 ymax: 60.84335
#> Geodetic CRS:  WGS 84
#> # A tibble: 6 × 9
#>   year_f patch_id sfa   patch_area biomass_with_catch   biomass biomas…¹ bioma…²
#>    <dbl> <fct>    <fct>     [km^2]               [kg]      [kg] [kg/km^[kg/km
#> 1   1995 P1       4          5867.                NA        NA       NA      NA 
#> 2   1996 P1       4          5867.          17447783. 17443366.    2974.   2973.
#> 3   1997 P1       4          5867.          16951564. 16949023.    2889.   2889.
#> 4   1998 P1       4          5867.          16556674. 16555294.    2822.   2822.
#> 5   1999 P1       4          5867.          16578042. 16572881.    2826.   2825.
#> 6   2000 P1       4          5867.          16331012. 16326595.    2784.   2783.
#> # … with 1 more variable: geometry <POLYGON [°]>, and abbreviated variable
#> #   names ¹​biomass_density_with_catch, ²​biomass_density
#> # ℹ Use `colnames()` to see all variable names

We can also predict the biomass one step ahead.

biomass_one_step <- predict(sspm_model_fit, biomass = "weight_per_km2_borealis", 
                            next_ts = TRUE)
head(biomass_one_step)
#> Simple feature collection with 6 features and 5 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -64.18658 ymin: 58.17744 xmax: -60.85729 ymax: 60.84488
#> Geodetic CRS:  WGS 84
#> # A tibble: 6 × 6
#>   patch_id year_f sfa     biomass patch_area                            geometry
#>   <fct>     <dbl> <fct>      [kg]     [km^2]                       <POLYGON [°]>
#> 1 P1         2019 4     15847797.      5867. ((-62.93792 60.81457, -62.99813 60…
#> 2 P2         2019 4     17016407.      6294. ((-61.47678 59.40916, -61.27552 59…
#> 3 P3         2019 4      4305195.      1594. ((-61.98352 59.91756, -62.14032 59…
#> 4 P4         2019 4      5116919.      1882. ((-61.60726 58.17744, -61.55968 58…
#> 5 P5         2019 4      5125076.      1890. ((-61.54308 58.72455, -60.98005 58…
#> 6 P6         2019 4      5804063.      2149. ((-62.81971 59.18792, -62.54312 59…
  1. We can produce an array of plots, as timeseries or as spatial plots
plot(sspm_model_fit, log = T, scales = 'free')
#> Warning: Removed 1 row(s) containing missing values (geom_path).
#> Warning: Removed 92 rows containing missing values (geom_point).

plot(sspm_model_fit, log = T, use_sf = TRUE)

plot(sspm_model_fit, biomass = "weight_per_km2_borealis",  scales = "free")
#> Warning: Removed 1 row(s) containing missing values (geom_path).
#> Warning: Removed 92 rows containing missing values (geom_point).

plot(sspm_model_fit, biomass = "weight_per_km2_borealis", use_sf = TRUE)

plot(sspm_model_fit, biomass = "weight_per_km2_borealis",
     next_ts = TRUE, aggregate = TRUE, scales = "free", 
     smoothed_biomass = TRUE, interval = T)
#> Warning: Removed 1 row(s) containing missing values (geom_path).
#> Warning: Removed 4 rows containing missing values (geom_point).
#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf

#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf

#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf

#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf

#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf

#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf

#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf

#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf