Formatting data for use in mvgam

Nicholas J Clark

2025-03-14

This vignette gives an example of how to take raw data and format it for use in mvgam. This is not an exhaustive example, as data can be recorded and stored in a variety of ways, which requires different approaches to wrangle the data into the necessary format for mvgam. For full details on the basic mvgam functionality, please see the introductory vignette and the growing set of walk through video tutorials on mvgam applications.

Required tidy data format

Manipulating the data into a ‘long’ format (i.e. tidy format) is necessary for modelling in mvgam. By ‘long’ format, we mean that each series x time observation needs to have its own entry in the dataframe or list object that we wish to pass as data for to the two primary modelling functions, mvgam() and jsdgam(). A simple example can be viewed by simulating data using the sim_mvgam() function. See ?sim_mvgam for more details

simdat <- sim_mvgam(
  n_series = 4, 
  T = 24, 
  prop_missing = 0.2
)
head(simdat$data_train, 16)
#>     y season year   series time
#> 1   1      1    1 series_1    1
#> 2   0      1    1 series_2    1
#> 3  NA      1    1 series_3    1
#> 4   0      1    1 series_4    1
#> 5   0      2    1 series_1    2
#> 6   0      2    1 series_2    2
#> 7   0      2    1 series_3    2
#> 8   0      2    1 series_4    2
#> 9   0      3    1 series_1    3
#> 10  0      3    1 series_2    3
#> 11  1      3    1 series_3    3
#> 12  0      3    1 series_4    3
#> 13  2      4    1 series_1    4
#> 14 NA      4    1 series_2    4
#> 15  1      4    1 series_3    4
#> 16 NA      4    1 series_4    4

series as a factor variable

Notice how we have four different time series in these simulated data, and we have identified the series-level indicator as a factor variable.

class(simdat$data_train$series)
#> [1] "factor"
levels(simdat$data_train$series)
#> [1] "series_1" "series_2" "series_3" "series_4"

It is important that the number of levels matches the number of unique series in the data to ensure indexing across series works properly in the underlying modelling functions. Several of the main workhorse functions in the package (including mvgam() and get_mvgam_priors()) will give an error if this is not the case, but it may be worth checking anyway:

all(levels(simdat$data_train$series) %in% 
      unique(simdat$data_train$series))
#> [1] TRUE

Note that you can technically supply data that does not have a series indicator, and the package will generally assume that you are only using a single time series. There are exceptions to this, for example if you have grouped data and would like to estimate hierarchical dependencies (see an example of hierarchical process error correlations in the ?AR documentation) or if you would like to set up a Joint Species Distribution Model (JSDM) using a Zero-Mean Multivariate Gaussian distribution for the latent residuals (see examples in the ?ZMVN documentation).

A single outcome variable

You may also have notices that we do not spread the numeric / integer-classed outcome variable into different columns. Rather, there is only a single column for the outcome variable, labelled y in these simulated data (though the outcome does not have to be labelled y). This is another important requirement in mvgam, but it shouldn’t be too unfamiliar to R users who frequently use modelling packages such as lme4, mgcv, brms or the many other regression modelling packages out there. The advantage of this format is that it is now very easy to specify effects that vary among time series:

summary(glm(
  y ~ series + time,
  data = simdat$data_train,
  family = poisson()
))
#> 
#> Call:
#> glm(formula = y ~ series + time, family = poisson(), data = simdat$data_train)
#> 
#> Coefficients:
#>                Estimate Std. Error z value Pr(>|z|)  
#> (Intercept)    -0.88484    0.43147  -2.051   0.0403 *
#> seriesseries_2  0.46760    0.42275   1.106   0.2687  
#> seriesseries_3  0.23701    0.44988   0.527   0.5983  
#> seriesseries_4  0.40418    0.42796   0.944   0.3449  
#> time            0.04151    0.02865   1.449   0.1473  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for poisson family taken to be 1)
#> 
#>     Null deviance: 79.023  on 58  degrees of freedom
#> Residual deviance: 75.133  on 54  degrees of freedom
#>   (13 observations deleted due to missingness)
#> AIC: 155.11
#> 
#> Number of Fisher Scoring iterations: 6
summary(mgcv::gam(
  y ~ series + s(time, by = series),
  data = simdat$data_train,
  family = poisson()
))
#> 
#> Family: poisson 
#> Link function: log 
#> 
#> Formula:
#> y ~ series + s(time, by = series)
#> 
#> Parametric coefficients:
#>                Estimate Std. Error z value Pr(>|z|)
#> (Intercept)     -0.5400     0.3465  -1.558    0.119
#> seriesseries_2   0.2529     0.4828   0.524    0.600
#> seriesseries_3   0.2963     0.4600   0.644    0.519
#> seriesseries_4   0.2388     0.4769   0.501    0.617
#> 
#> Approximate significance of smooth terms:
#>                          edf Ref.df Chi.sq p-value  
#> s(time):seriesseries_1 1.000  1.000  0.166  0.6834  
#> s(time):seriesseries_2 1.294  1.531  6.063  0.0429 *
#> s(time):seriesseries_3 1.000  1.000  0.018  0.8923  
#> s(time):seriesseries_4 2.337  2.946  5.615  0.1285  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> R-sq.(adj) =  0.0916   Deviance explained = 22.3%
#> UBRE = 0.36673  Scale est. = 1         n = 59

Depending on the observation families you plan to use when building models, there may be some restrictions that need to be satisfied within the outcome variable. For example, a Beta regression can only handle proportional data, so values >= 1 or <= 0 are not allowed. Likewise, a Poisson regression can only handle non-negative integers. Most regression functions in R will assume the user knows all of this and so will not issue any warnings or errors if you choose the wrong distribution, but often this ends up leading to some unhelpful error from an optimizer that is difficult to interpret and diagnose. mvgam will attempt to provide some errors if you do something that is simply not allowed. For example, we can simulate data from a zero-centred Gaussian distribution (ensuring that some of our values will be < 1) and attempt a Beta regression in mvgam using the betar family:

gauss_dat <- data.frame(
  outcome = rnorm(10),
  series = factor("series1",
    levels = "series1"
  ),
  time = 1:10
)
gauss_dat
#>       outcome  series time
#> 1  -1.3118865 series1    1
#> 2   1.2438997 series1    2
#> 3   0.0677111 series1    3
#> 4   0.9289780 series1    4
#> 5  -0.1655907 series1    5
#> 6  -0.3463894 series1    6
#> 7   1.9459625 series1    7
#> 8  -0.8785205 series1    8
#> 9   1.6375181 series1    9
#> 10 -0.4529390 series1   10

A call to gam() using the mgcv package leads to a model that actually fits (though it does give an unhelpful warning message):

mgcv::gam(outcome ~ time,
  family = betar(),
  data = gauss_dat
)
#> 
#> Family: Beta regression(0.08) 
#> Link function: logit 
#> 
#> Formula:
#> outcome ~ time
#> Total model degrees of freedom 2 
#> 
#> REML score: -206.9263

But the same call to mvgam() gives us something more useful:

mvgam(outcome ~ time,
  family = betar(),
  data = gauss_dat
)
#> Error: Values <= 0 not allowed for beta responses

Please see ?mvgam_families for more information on the types of responses that the package can handle and their restrictions

A time variable

The other requirement for most models that can be fit in mvgam is a numeric / integer-classed variable labelled time. This ensures the modelling software knows how to arrange the time series when building models. This setup still allows us to formulate multivariate time series models. If you plan to use any of the autoregressive dynamic trend functions available in mvgam (see ?mvgam_trends for details of available dynamic processes), you will need to ensure your time series are entered with a fixed sampling interval (i.e. the time between timesteps 1 and 2 should be the same as the time between timesteps 2 and 3, etc…). But note that you can have missing observations for some (or all) series. mvgam() will check this for you, but again it is useful to ensure you have no missing timepoint x series combinations in your data. You can generally do this with a simple dplyr call:

# A function to ensure all timepoints within a sequence are identical
all_times_avail <- function(time, min_time, max_time) {
  identical(
    as.numeric(sort(time)),
    as.numeric(seq.int(from = min_time, to = max_time))
  )
}

# Get min and max times from the data
min_time <- min(simdat$data_train$time)
max_time <- max(simdat$data_train$time)

# Check that all times are recorded for each series
data.frame(
  series = simdat$data_train$series,
  time = simdat$data_train$time
) %>%
  dplyr::group_by(series) %>%
  dplyr::summarise(all_there = all_times_avail(
    time,
    min_time,
    max_time
  )) -> checked_times
if (any(checked_times$all_there == FALSE)) {
  warning("One or more series in is missing observations for one or more timepoints")
} else {
  cat("All series have observations at all timepoints :)")
}
#> All series have observations at all timepoints :)

Note that models which use dynamic components will assume that smaller values of time are older (i.e. time = 1 came before time = 2, etc…)

Irregular sampling intervals?

Most mvgam dynamic trend models expect time to be measured in discrete, evenly-spaced intervals (i.e. one measurement per week, or one per year, for example; though missing values are allowed). But please note that irregularly sampled time intervals are allowed, in which case the CAR() trend model (continuous time autoregressive) is appropriate. You can see an example of this kind of model in the Examples section in ?CAR. You can also use trend_model = 'None' (the default in mvgam()) and instead use a Gaussian Process to model temporal variation for irregularly-sampled time series. See the ?brms::gp for details. But to reiterate the point from above, if you do not have time series data (or don’t want to estimate latent temporal dynamics) but you would like to estimate correlated latent residuals among multivariate outcomes, you can set up models that use trend_model = ZMVN(...) without the need for a time variable (see ?ZMVN for details).

Checking data with get_mvgam_priors()

The get_mvgam_priors() function is designed to return information about the parameters in a model whose prior distributions can be modified by the user. But in doing so, it will perform a series of checks to ensure the data are formatted properly. It can therefore be very useful to new users for ensuring there isn’t anything strange going on in the data setup. For example, we can replicate the steps taken above (to check factor levels and timepoint x series combinations) with a single call to get_mvgam_priors(). Here we first simulate some data in which some of the timepoints in the time variable are not included in the data:

bad_times <- data.frame(
  time = seq(1, 16, by = 2),
  series = factor("series_1"),
  outcome = rnorm(8)
)
bad_times
#>   time   series     outcome
#> 1    1 series_1 -0.34136647
#> 2    3 series_1  0.91462991
#> 3    5 series_1 -1.19635373
#> 4    7 series_1  0.05270635
#> 5    9 series_1 -0.05586121
#> 6   11 series_1  1.22344733
#> 7   13 series_1 -0.14425579
#> 8   15 series_1 -0.32964559

Next we call get_mvgam_priors() by simply specifying an intercept-only model, which is enough to trigger all the checks:

get_mvgam_priors(outcome ~ 1,
  data = bad_times,
  family = gaussian()
)
#> Error: One or more series in data is missing observations for one or more timepoints

This error is useful as it tells us where the problem is. There are many ways to fill in missing timepoints, so the correct way will have to be left up to the user. But if you don’t have any covariates, it should be pretty easy using expand.grid():

bad_times %>%
  dplyr::right_join(expand.grid(
    time = seq(
      min(bad_times$time),
      max(bad_times$time)
    ),
    series = factor(unique(bad_times$series),
      levels = levels(bad_times$series)
    )
  )) %>%
  dplyr::arrange(time) -> good_times
good_times
#>    time   series     outcome
#> 1     1 series_1 -0.34136647
#> 2     2 series_1          NA
#> 3     3 series_1  0.91462991
#> 4     4 series_1          NA
#> 5     5 series_1 -1.19635373
#> 6     6 series_1          NA
#> 7     7 series_1  0.05270635
#> 8     8 series_1          NA
#> 9     9 series_1 -0.05586121
#> 10   10 series_1          NA
#> 11   11 series_1  1.22344733
#> 12   12 series_1          NA
#> 13   13 series_1 -0.14425579
#> 14   14 series_1          NA
#> 15   15 series_1 -0.32964559

Now the call to get_mvgam_priors(), using our filled in data, should work:

get_mvgam_priors(outcome ~ 1,
  data = good_times,
  family = gaussian()
)
#>                             param_name param_length           param_info
#> 1                          (Intercept)            1          (Intercept)
#> 2 vector<lower=0>[n_series] sigma_obs;            1 observation error sd
#>                                    prior                  example_change
#> 1 (Intercept) ~ student_t(3, -0.1, 2.5);     (Intercept) ~ normal(0, 1);
#> 2   sigma_obs ~ inv_gamma(1.418, 0.452); sigma_obs ~ normal(0.22, 0.69);
#>   new_lowerbound new_upperbound
#> 1             NA             NA
#> 2             NA             NA

This function should also pick up on misaligned factor levels for the series variable. We can check this by again simulating, this time adding an additional factor level that is not included in the data:

bad_levels <- data.frame(
  time = 1:8,
  series = factor("series_1",
    levels = c(
      "series_1",
      "series_2"
    )
  ),
  outcome = rnorm(8)
)

levels(bad_levels$series)
#> [1] "series_1" "series_2"

Another call to get_mvgam_priors() brings up a useful error:

get_mvgam_priors(outcome ~ 1,
  data = bad_levels,
  family = gaussian()
)
#> Error: Mismatch between factor levels of "series" and unique values of "series"
#> Use
#>   `setdiff(levels(data$series), unique(data$series))` 
#> and
#>   `intersect(levels(data$series), unique(data$series))`
#> for guidance

Following the message’s advice tells us there is a level for series_2 in the series variable, but there are no observations for this series in the data:

setdiff(levels(bad_levels$series), 
        unique(bad_levels$series))
#> [1] "series_2"

Re-assigning the levels fixes the issue:

bad_levels %>%
  dplyr::mutate(series = droplevels(series)) -> good_levels
levels(good_levels$series)
#> [1] "series_1"
get_mvgam_priors(
  outcome ~ 1,
  data = good_levels,
  family = gaussian()
)
#>                             param_name param_length           param_info
#> 1                          (Intercept)            1          (Intercept)
#> 2 vector<lower=0>[n_series] sigma_obs;            1 observation error sd
#>                                  prior                  example_change
#> 1  (Intercept) ~ student_t(3, 0, 2.5);     (Intercept) ~ normal(0, 1);
#> 2 sigma_obs ~ inv_gamma(1.418, 0.452); sigma_obs ~ normal(-0.85, 0.6);
#>   new_lowerbound new_upperbound
#> 1             NA             NA
#> 2             NA             NA

Covariates with no NAs

Covariates can be used in models just as you would when using mgcv (see ?formula.gam for details of the formula syntax). But although the outcome variable can have NAs, covariates cannot. Most regression software will silently drop any raws in the model matrix that have NAs, which is not helpful when debugging. Both the mvgam() and get_mvgam_priors() functions will run some simple checks for you, and hopefully will return useful errors if it finds in missing values:

miss_dat <- data.frame(
  outcome = rnorm(10),
  cov = c(NA, rnorm(9)),
  series = factor("series1",
    levels = "series1"
  ),
  time = 1:10
)
miss_dat
#>       outcome        cov  series time
#> 1   0.6932572         NA series1    1
#> 2   0.2439107  0.5637476 series1    2
#> 3   0.2023784 -1.7441241 series1    3
#> 4   2.0214616 -0.8088920 series1    4
#> 5   0.2373302 -1.1971944 series1    5
#> 6  -0.6387349 -1.9779087 series1    6
#> 7  -0.3811196 -1.4809676 series1    7
#> 8   2.5308185 -0.4013272 series1    8
#> 9   2.1037136 -0.4598667 series1    9
#> 10 -0.5123787  0.1100437 series1   10
get_mvgam_priors(
  outcome ~ cov,
  data = miss_dat,
  family = gaussian()
)
#>                             param_name param_length           param_info
#> 1                          (Intercept)            1          (Intercept)
#> 2                                  cov            1     cov fixed effect
#> 3 vector<lower=0>[n_series] sigma_obs;            1 observation error sd
#>                                   prior                  example_change
#> 1 (Intercept) ~ student_t(3, 0.2, 2.5);     (Intercept) ~ normal(0, 1);
#> 2             cov ~ student_t(3, 0, 2);             cov ~ normal(0, 1);
#> 3  sigma_obs ~ inv_gamma(1.418, 0.452); sigma_obs ~ normal(0.71, 0.55);
#>   new_lowerbound new_upperbound
#> 1             NA             NA
#> 2             NA             NA
#> 3             NA             NA

Just like with the mgcv package, mvgam can also accept data as a list object. This is useful if you want to set up linear functional predictors or even distributed lag predictors. The checks run by mvgam should still work on these data. Here we change the cov predictor to be a matrix:

miss_dat <- list(
  outcome = rnorm(10),
  series = factor("series1",
    levels = "series1"
  ),
  time = 1:10
)
miss_dat$cov <- matrix(rnorm(50), ncol = 5, nrow = 10)
miss_dat$cov[2, 3] <- NA

A call to get_mvgam_priors() returns the same error:

get_mvgam_priors(
  outcome ~ cov,
  data = miss_dat,
  family = gaussian()
)
#>                             param_name param_length           param_info
#> 1                          (Intercept)            1          (Intercept)
#> 2                                 cov1            1    cov1 fixed effect
#> 3                                 cov2            1    cov2 fixed effect
#> 4                                 cov3            1    cov3 fixed effect
#> 5                                 cov4            1    cov4 fixed effect
#> 6                                 cov5            1    cov5 fixed effect
#> 7 vector<lower=0>[n_series] sigma_obs;            1 observation error sd
#>                                    prior                   example_change
#> 1 (Intercept) ~ student_t(3, -0.2, 2.5);      (Intercept) ~ normal(0, 1);
#> 2             cov1 ~ student_t(3, 0, 2);             cov1 ~ normal(0, 1);
#> 3             cov2 ~ student_t(3, 0, 2);             cov2 ~ normal(0, 1);
#> 4             cov3 ~ student_t(3, 0, 2);             cov3 ~ normal(0, 1);
#> 5             cov4 ~ student_t(3, 0, 2);             cov4 ~ normal(0, 1);
#> 6             cov5 ~ student_t(3, 0, 2);             cov5 ~ normal(0, 1);
#> 7   sigma_obs ~ inv_gamma(1.418, 0.452); sigma_obs ~ normal(-0.52, 0.62);
#>   new_lowerbound new_upperbound
#> 1             NA             NA
#> 2             NA             NA
#> 3             NA             NA
#> 4             NA             NA
#> 5             NA             NA
#> 6             NA             NA
#> 7             NA             NA

Plotting with plot_mvgam_series()

Plotting the data is a useful way to ensure everything looks ok, once you’ve gone throug the above checks on factor levels and timepoint x series combinations. The plot_mvgam_series() function will take supplied data and plot either a series of line plots (if you choose series = 'all') or a set of plots to describe the distribution for a single time series. For example, to plot all of the time series in our data, and highlight a single series in each plot, we can use:

plot_mvgam_series(
  data = simdat$data_train,
  y = "y",
  series = "all"
)

Plotting time series features for GAM models in mvgam

Or we can look more closely at the distribution for the first time series:

plot_mvgam_series(
  data = simdat$data_train,
  y = "y",
  series = 1
)

Plotting time series features for GAM models in mvgam

If you have split your data into training and testing folds (i.e. for forecast evaluation), you can include the test data in your plots:

plot_mvgam_series(
  data = simdat$data_train,
  newdata = simdat$data_test,
  y = "y",
  series = 1
)

Plotting time series features for GAM models in mvgam

Example with NEON tick data

To give one example of how data can be reformatted for mvgam modelling, we will use observations from the National Ecological Observatory Network (NEON) tick drag cloth samples. Ixodes scapularis is a widespread tick species capable of transmitting a diversity of parasites to animals and humans, many of which are zoonotic. Due to the medical and ecological importance of this tick species, a common goal is to understand factors that influence their abundances. The NEON field team carries out standardised long-term monitoring of tick abundances as well as other important indicators of ecological change. Nymphal abundance of I. scapularis is routinely recorded across NEON plots using a field sampling method called drag cloth sampling, which is a common method for sampling ticks in the landscape. Field researchers sample ticks by dragging a large cloth behind themselves through terrain that is suspected of harboring ticks, usually working in a grid-like pattern. The sites have been sampled since 2014, resulting in a rich dataset of nymph abundance time series. These tick time series show strong seasonality and incorporate many of the challenging features associated with ecological data including overdispersion, high proportions of missingness and irregular sampling in time, making them useful for exploring the utility of dynamic GAMs.

We begin by loading NEON tick data for the years 2014 - 2021, which were downloaded from NEON and prepared as described in Clark & Wells 2022. You can read a bit about the data using the call ?all_neon_tick_data

data("all_neon_tick_data")
str(dplyr::ungroup(all_neon_tick_data))
#> tibble [3,505 × 24] (S3: tbl_df/tbl/data.frame)
#>  $ Year                : num [1:3505] 2015 2015 2015 2015 2015 ...
#>  $ epiWeek             : chr [1:3505] "37" "38" "39" "40" ...
#>  $ yearWeek            : chr [1:3505] "201537" "201538" "201539" "201540" ...
#>  $ plotID              : chr [1:3505] "BLAN_005" "BLAN_005" "BLAN_005" "BLAN_005" ...
#>  $ siteID              : chr [1:3505] "BLAN" "BLAN" "BLAN" "BLAN" ...
#>  $ nlcdClass           : chr [1:3505] "deciduousForest" "deciduousForest" "deciduousForest" "deciduousForest" ...
#>  $ decimalLatitude     : num [1:3505] 39.1 39.1 39.1 39.1 39.1 ...
#>  $ decimalLongitude    : num [1:3505] -78 -78 -78 -78 -78 ...
#>  $ elevation           : num [1:3505] 168 168 168 168 168 ...
#>  $ totalSampledArea    : num [1:3505] 162 NA NA NA 162 NA NA NA NA 164 ...
#>  $ amblyomma_americanum: num [1:3505] NA NA NA NA NA NA NA NA NA NA ...
#>  $ ixodes_scapularis   : num [1:3505] 2 NA NA NA 0 NA NA NA NA 0 ...
#>  $ time                : Date[1:3505], format: "2015-09-13" "2015-09-20" ...
#>  $ RHMin_precent       : num [1:3505] NA NA NA NA NA NA NA NA NA NA ...
#>  $ RHMin_variance      : num [1:3505] NA NA NA NA NA NA NA NA NA NA ...
#>  $ RHMax_precent       : num [1:3505] NA NA NA NA NA NA NA NA NA NA ...
#>  $ RHMax_variance      : num [1:3505] NA NA NA NA NA NA NA NA NA NA ...
#>  $ airTempMin_degC     : num [1:3505] NA NA NA NA NA NA NA NA NA NA ...
#>  $ airTempMin_variance : num [1:3505] NA NA NA NA NA NA NA NA NA NA ...
#>  $ airTempMax_degC     : num [1:3505] NA NA NA NA NA NA NA NA NA NA ...
#>  $ airTempMax_variance : num [1:3505] NA NA NA NA NA NA NA NA NA NA ...
#>  $ soi                 : num [1:3505] -18.4 -17.9 -23.5 -28.4 -25.9 ...
#>  $ cum_sdd             : num [1:3505] 173 173 173 173 173 ...
#>  $ cum_gdd             : num [1:3505] 1129 1129 1129 1129 1129 ...

For this exercise, we will use the epiWeek variable as an index of seasonality, and we will only work with observations from a few sampling plots (labelled in the plotID column):

plotIDs <- c(
  "SCBI_013", "SCBI_002",
  "SERC_001", "SERC_005",
  "SERC_006", "SERC_012",
  "BLAN_012", "BLAN_005"
)

Now we can select the target species we want (I. scapularis), filter to the correct plot IDs and convert the epiWeek variable from character to numeric:

model_dat <- all_neon_tick_data %>%
  dplyr::ungroup() %>%
  dplyr::mutate(target = ixodes_scapularis) %>%
  dplyr::filter(plotID %in% plotIDs) %>%
  dplyr::select(Year, epiWeek, plotID, target) %>%
  dplyr::mutate(epiWeek = as.numeric(epiWeek))

Now is the tricky part: we need to fill in missing observations with NAs. The tick data are sparse in that field observers do not go out and sample in each possible epiWeek. So there are many particular weeks in which observations are not included in the data. But we can use expand.grid() again to take care of this:

model_dat %>%
  # Create all possible combos of plotID, Year and epiWeek;
  # missing outcomes will be filled in as NA
  dplyr::full_join(expand.grid(
    plotID = unique(model_dat$plotID),
    Year = unique(model_dat$Year),
    epiWeek = seq(1, 52)
  )) %>%
  # left_join back to original data so plotID and siteID will
  # match up, in case you need the siteID for anything else later on
  dplyr::left_join(all_neon_tick_data %>%
    dplyr::select(siteID, plotID) %>%
    dplyr::distinct()) -> model_dat

Create the series variable needed for mvgam modelling:

model_dat %>%
  dplyr::mutate(
    series = plotID,
    y = target
  ) %>%
  dplyr::mutate(
    siteID = factor(siteID),
    series = factor(series)
  ) %>%
  dplyr::select(-target, -plotID) %>%
  dplyr::arrange(Year, epiWeek, series) -> model_dat

Now create the time variable, which needs to track Year and epiWeek for each unique series. The n function from dplyr is often useful if generating a time index for grouped dataframes:

model_dat %>%
  dplyr::ungroup() %>%
  dplyr::group_by(series) %>%
  dplyr::arrange(Year, epiWeek) %>%
  dplyr::mutate(time = seq(1, dplyr::n())) %>%
  dplyr::ungroup() -> model_dat

Check factor levels for the series:

levels(model_dat$series)
#> [1] "BLAN_005" "BLAN_012" "SCBI_002" "SCBI_013" "SERC_001" "SERC_005" "SERC_006"
#> [8] "SERC_012"

This looks good, as does a more rigorous check using get_mvgam_priors():

get_mvgam_priors(
  y ~ 1,
  data = model_dat,
  family = poisson()
)
#>    param_name param_length  param_info                                  prior
#> 1 (Intercept)            1 (Intercept) (Intercept) ~ student_t(3, -2.3, 2.5);
#>                example_change new_lowerbound new_upperbound
#> 1 (Intercept) ~ normal(0, 1);             NA             NA

We can also set up a model in mvgam() but use run_model = FALSE to further ensure all of the necessary steps for creating the modelling code and objects will run. It is recommended that you use the cmdstanr backend if possible, as the auto-formatting options available in this package are very useful for checking the package-generated Stan code for any inefficiencies that can be fixed to lead to sampling performance improvements:

testmod <- mvgam(
  y ~ s(epiWeek, by = series, bs = "cc") +
    s(series, bs = "re"),
  trend_model = AR(),
  data = model_dat,
  backend = "cmdstanr",
  run_model = FALSE
)

This call runs without issue, and the resulting object now contains the model code and data objects that are needed to initiate sampling:

str(testmod$model_data)
#> List of 25
#>  $ y           : num [1:416, 1:8] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
#>  $ n           : int 416
#>  $ X           : num [1:3328, 1:73] 1 1 1 1 1 1 1 1 1 1 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:3328] "1" "2" "3" "4" ...
#>   .. ..$ : chr [1:73] "X.Intercept." "V2" "V3" "V4" ...
#>  $ S1          : num [1:8, 1:8] 1.037 -0.416 0.419 0.117 0.188 ...
#>  $ zero        : num [1:73] 0 0 0 0 0 0 0 0 0 0 ...
#>  $ S2          : num [1:8, 1:8] 1.037 -0.416 0.419 0.117 0.188 ...
#>  $ S3          : num [1:8, 1:8] 1.037 -0.416 0.419 0.117 0.188 ...
#>  $ S4          : num [1:8, 1:8] 1.037 -0.416 0.419 0.117 0.188 ...
#>  $ S5          : num [1:8, 1:8] 1.037 -0.416 0.419 0.117 0.188 ...
#>  $ S6          : num [1:8, 1:8] 1.037 -0.416 0.419 0.117 0.188 ...
#>  $ S7          : num [1:8, 1:8] 1.037 -0.416 0.419 0.117 0.188 ...
#>  $ S8          : num [1:8, 1:8] 1.037 -0.416 0.419 0.117 0.188 ...
#>  $ p_coefs     : Named num 0
#>   ..- attr(*, "names")= chr "(Intercept)"
#>  $ p_taus      : num 0.845
#>  $ ytimes      : int [1:416, 1:8] 1 9 17 25 33 41 49 57 65 73 ...
#>  $ n_series    : int 8
#>  $ sp          : Named num [1:9] 0.368 0.368 0.368 0.368 0.368 ...
#>   ..- attr(*, "names")= chr [1:9] "s(epiWeek):seriesBLAN_005" "s(epiWeek):seriesBLAN_012" "s(epiWeek):seriesSCBI_002" "s(epiWeek):seriesSCBI_013" ...
#>  $ y_observed  : num [1:416, 1:8] 0 0 0 0 0 0 0 0 0 0 ...
#>  $ total_obs   : int 3328
#>  $ num_basis   : int 73
#>  $ n_sp        : num 9
#>  $ n_nonmissing: int 400
#>  $ obs_ind     : int [1:400] 89 93 98 101 115 118 121 124 127 130 ...
#>  $ flat_ys     : num [1:400] 2 0 0 0 0 0 0 25 36 14 ...
#>  $ flat_xs     : num [1:400, 1:73] 1 1 1 1 1 1 1 1 1 1 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:400] "705" "737" "777" "801" ...
#>   .. ..$ : chr [1:73] "X.Intercept." "V2" "V3" "V4" ...
#>  - attr(*, "trend_model")= chr "AR1"
stancode(testmod)
#> // Stan model code generated by package mvgam
#> data {
#>   int<lower=0> total_obs; // total number of observations
#>   int<lower=0> n; // number of timepoints per series
#>   int<lower=0> n_sp; // number of smoothing parameters
#>   int<lower=0> n_series; // number of series
#>   int<lower=0> num_basis; // total number of basis coefficients
#>   vector[num_basis] zero; // prior locations for basis coefficients
#>   matrix[total_obs, num_basis] X; // mgcv GAM design matrix
#>   array[n, n_series] int<lower=0> ytimes; // time-ordered matrix (which col in X belongs to each [time, series] observation?)
#>   matrix[8, 8] S1; // mgcv smooth penalty matrix S1
#>   matrix[8, 8] S2; // mgcv smooth penalty matrix S2
#>   matrix[8, 8] S3; // mgcv smooth penalty matrix S3
#>   matrix[8, 8] S4; // mgcv smooth penalty matrix S4
#>   matrix[8, 8] S5; // mgcv smooth penalty matrix S5
#>   matrix[8, 8] S6; // mgcv smooth penalty matrix S6
#>   matrix[8, 8] S7; // mgcv smooth penalty matrix S7
#>   matrix[8, 8] S8; // mgcv smooth penalty matrix S8
#>   int<lower=0> n_nonmissing; // number of nonmissing observations
#>   array[n_nonmissing] int<lower=0> flat_ys; // flattened nonmissing observations
#>   matrix[n_nonmissing, num_basis] flat_xs; // X values for nonmissing observations
#>   array[n_nonmissing] int<lower=0> obs_ind; // indices of nonmissing observations
#> }
#> parameters {
#>   // raw basis coefficients
#>   vector[num_basis] b_raw;
#>   
#>   // random effect variances
#>   vector<lower=0>[1] sigma_raw;
#>   
#>   // random effect means
#>   vector[1] mu_raw;
#>   
#>   // latent trend AR1 terms
#>   vector<lower=-1, upper=1>[n_series] ar1;
#>   
#>   // latent trend variance parameters
#>   vector<lower=0>[n_series] sigma;
#>   
#>   // latent trends
#>   matrix[n, n_series] trend;
#>   
#>   // smoothing parameters
#>   vector<lower=0>[n_sp] lambda;
#> }
#> transformed parameters {
#>   // basis coefficients
#>   vector[num_basis] b;
#>   b[1 : 65] = b_raw[1 : 65];
#>   b[66 : 73] = mu_raw[1] + b_raw[66 : 73] * sigma_raw[1];
#> }
#> model {
#>   // prior for random effect population variances
#>   sigma_raw ~ inv_gamma(1.418, 0.452);
#>   
#>   // prior for random effect population means
#>   mu_raw ~ std_normal();
#>   
#>   // prior for (Intercept)...
#>   b_raw[1] ~ student_t(3, -2.3, 2.5);
#>   
#>   // prior for s(epiWeek):seriesBLAN_005...
#>   b_raw[2 : 9] ~ multi_normal_prec(zero[2 : 9], S1[1 : 8, 1 : 8] * lambda[1]);
#>   
#>   // prior for s(epiWeek):seriesBLAN_012...
#>   b_raw[10 : 17] ~ multi_normal_prec(zero[10 : 17],
#>                                      S2[1 : 8, 1 : 8] * lambda[2]);
#>   
#>   // prior for s(epiWeek):seriesSCBI_002...
#>   b_raw[18 : 25] ~ multi_normal_prec(zero[18 : 25],
#>                                      S3[1 : 8, 1 : 8] * lambda[3]);
#>   
#>   // prior for s(epiWeek):seriesSCBI_013...
#>   b_raw[26 : 33] ~ multi_normal_prec(zero[26 : 33],
#>                                      S4[1 : 8, 1 : 8] * lambda[4]);
#>   
#>   // prior for s(epiWeek):seriesSERC_001...
#>   b_raw[34 : 41] ~ multi_normal_prec(zero[34 : 41],
#>                                      S5[1 : 8, 1 : 8] * lambda[5]);
#>   
#>   // prior for s(epiWeek):seriesSERC_005...
#>   b_raw[42 : 49] ~ multi_normal_prec(zero[42 : 49],
#>                                      S6[1 : 8, 1 : 8] * lambda[6]);
#>   
#>   // prior for s(epiWeek):seriesSERC_006...
#>   b_raw[50 : 57] ~ multi_normal_prec(zero[50 : 57],
#>                                      S7[1 : 8, 1 : 8] * lambda[7]);
#>   
#>   // prior for s(epiWeek):seriesSERC_012...
#>   b_raw[58 : 65] ~ multi_normal_prec(zero[58 : 65],
#>                                      S8[1 : 8, 1 : 8] * lambda[8]);
#>   
#>   // prior (non-centred) for s(series)...
#>   b_raw[66 : 73] ~ std_normal();
#>   
#>   // priors for AR parameters
#>   ar1 ~ std_normal();
#>   
#>   // priors for smoothing parameters
#>   lambda ~ normal(5, 30);
#>   
#>   // priors for latent trend variance parameters
#>   sigma ~ inv_gamma(1.418, 0.452);
#>   
#>   // trend estimates
#>   trend[1, 1 : n_series] ~ normal(0, sigma);
#>   for (s in 1 : n_series) {
#>     trend[2 : n, s] ~ normal(ar1[s] * trend[1 : (n - 1), s], sigma[s]);
#>   }
#>   {
#>     // likelihood functions
#>     vector[n_nonmissing] flat_trends;
#>     flat_trends = to_vector(trend)[obs_ind];
#>     flat_ys ~ poisson_log_glm(append_col(flat_xs, flat_trends), 0.0,
#>                               append_row(b, 1.0));
#>   }
#> }
#> generated quantities {
#>   vector[total_obs] eta;
#>   matrix[n, n_series] mus;
#>   vector[n_sp] rho;
#>   vector[n_series] tau;
#>   array[n, n_series] int ypred;
#>   rho = log(lambda);
#>   for (s in 1 : n_series) {
#>     tau[s] = pow(sigma[s], -2.0);
#>   }
#>   
#>   // posterior predictions
#>   eta = X * b;
#>   for (s in 1 : n_series) {
#>     mus[1 : n, s] = eta[ytimes[1 : n, s]] + trend[1 : n, s];
#>     ypred[1 : n, s] = poisson_log_rng(mus[1 : n, s]);
#>   }
#> }

Further reading

The following papers and resources offer useful material about Dynamic GAMs and how they can be applied in practice:

Clark, Nicholas J. and Wells, K. Dynamic Generalized Additive Models (DGAMs) for forecasting discrete ecological time series. Methods in Ecology and Evolution. (2023): 14, 771-784.

Clark, Nicholas J., et al. Beyond single-species models: leveraging multispecies forecasts to navigate the dynamics of ecological predictability. PeerJ. (2025): 13:e18929

de Sousa, Heitor C., et al. Severe fire regimes decrease resilience of ectothermic populations. Journal of Animal Ecology (2024): 93(11), 1656-1669.

Hannaford, Naomi E., et al. A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant. Computational Statistics & Data Analysis (2023): 179, 107659.

Karunarathna, K.A.N.K., et al. Modelling nonlinear responses of a desert rodent species to environmental change with hierarchical dynamic generalized additive models. Ecological Modelling (2024): 490, 110648.

Zhu, L., et al. Responses of a widespread pest insect to extreme high temperatures are stage-dependent and divergent among seasonal cohorts. Functional Ecology (2025): 39, 165–180. https://doi.org/10.1111/1365-2435.14711

Interested in contributing?

I’m actively seeking PhD students and other researchers to work in the areas of ecological forecasting, multivariate model evaluation and development of mvgam. Please see this small list of opportunities on my website and do reach out if you are interested (n.clark’at’uq.edu.au)