# multiScaleR Quick-Start Guide

#### Bill Peterman

# 1 What is multiScaleR?

`multiScaleR` estimates the spatial scale at which landscape variables influence ecological responses measured at field survey locations. Rather than testing a fixed set of buffer radii, the package fits a distance-weighted kernel function to the landscape data and estimates the kernel width, σ (sigma), directly as a parameter of your regression model. The result is a scale-of-effect estimate with a standard error and confidence interval, returned alongside the familiar regression coefficients.

This guide walks through a minimal working example to get you up and running. For conceptual background, full model-selection workflows, alternative kernel functions, and integration with `unmarked`, see `vignette("multiScaleR_Guide", package = "multiScaleR")`. For guidance on spatial projection and clamping, see `vignette("spatial_projection_clamping", package = "multiScaleR")`.

# 2 Installation

    install.packages("multiScaleR")

# 3 The Core Workflow

Every `multiScaleR` analysis follows four steps:

1. Prepare inputs with `kernel_prep()`: extracts distance-weighted raster values at each sample point and stores the information needed for optimization.
2. Fit an initial model: use any supported regression function (`glm`, `lme4`, `gls`, `pscl::zeroinfl`, `unmarked`, etc.) with the kernel-weighted covariate values from step 1.
3. Optimize scales of effect with `multiScale_optim()`: simultaneously estimates σ for each spatial covariate and updates regression coefficients by maximum likelihood.
4. Examine and visualize results: `summary()`, `plot()`, `plot_marginal_effects()`, `profile_sigma()`, and `kernel_scale.raster()`.

# 4 A Complete Example

## 4.1 Load the package and data

`multiScaleR` ships with a simulated data set of Poisson counts at 100 survey locations and three landscape raster layers.

    data("landscape_counts")
    dat <- landscape_counts

    data("surv_pts")
    pts <- vect(surv_pts)

    land_rast <- rast(pkg_extdata("landscape.tif"))

The `landscape_counts` data frame contains counts and a site-level covariate (`site`). The raster stack has three layers: `land1` (binary habitat), `land2` (continuous), and `land3` (continuous). Counts were simulated with `land1` (σ = 250 m, β = −0.5) and `land2` (σ = 500 m, β = 0.7) as true predictors; `land3` has no effect.

    plot(land_rast)

Landscape rasters with survey locations.

    plot(land_rast$land1, main = "land1 with survey points")
    points(pts, pch = 19, cex = 0.6, col = 'red')

Landscape rasters with survey locations.

## 4.2 Prepare inputs with `kernel_prep()`

`kernel_prep()` extracts raster values within `max_D` meters of each survey point and computes initial distance-weighted covariate values. Set `max_D` to a value that comfortably exceeds the scale of effect you expect. If optimization later warns that σ is near `max_D`, re-run `kernel_prep()` with a larger value.

    kernel_inputs <- kernel_prep(
      pts          = pts,
      raster_stack = land_rast,
      max_D        = 1700,
      kernel       = "gaussian",
      verbose      = FALSE
    )

Combine the kernel-weighted covariate values with the response data:

    df <- data.frame(dat, kernel_inputs$kernel_dat)

## 4.3 Fit an initial model

Fit the regression model you intend to optimize. The formula and family you specify here are inherited by `multiScale_optim()`, so get the model structure right before optimizing. The coefficient values at this stage are only starting values.

    mod <- glm(counts ~ site + land1 + land2,
               family = poisson(),
               data   = df)

## 4.4 Optimize scales of effect

`multiScale_optim()` searches over σ values for each raster layer while simultaneously updating regression coefficients to maximize the model likelihood. Use `n_cores` to parallelize and speed up the search.

    opt <- multiScaleR::multiScale_optim(fitted_mod    = mod,
                                         kernel_inputs = kernel_inputs)
    #>
    #>
    #> Optimization complete

## 4.5 Examine results

`summary()` reports the estimated σ and its standard error and 95% confidence interval for each spatial covariate, followed by the standard regression summary. By default, confidence intervals are computed using the delta method. For more reliable bounds when the likelihood surface is asymmetric (e.g., when σ is near `max_D` or sample size is small), pass `profile = TRUE`.

    summary(opt)
    #>
    #> Call:
    #> multiScaleR::multiScale_optim(fitted_mod = mod, kernel_inputs = kernel_inputs)
    #>
    #>
    #> Kernel used:
    #> gaussian
    #>
    #> ***** Optimized Scale of Effect -- Sigma *****
    #>
    #>           Mean       SE     2.5%    97.5%
    #> land1 238.4505 33.14563 172.6569 304.2441
    #> land2 499.5337 47.47022 405.3061 593.7614
    #>
    #>
    #>   ====================================
    #>
    #> ***** Optimized Scale of Effect -- Distance *****
    #> 90% Kernel Weight
    #>
    #>         Mean   2.5%  97.5%
    #> land1 392.22 284.00 500.44
    #> land2 821.66 666.67 976.65
    #>
    #>
    #>   ====================================
    #>
    #>  *****     Fitted Model Summary     *****
    #>
    #>
    #> Call:
    #> glm(formula = counts ~ site + land1 + land2, family = poisson(),
    #>     data = data)
    #>
    #> Coefficients:
    #>             Estimate Std. Error z value Pr(>|z|)
    #> (Intercept)  0.39293    0.09098   4.319 1.57e-05 ***
    #> site         0.17476    0.08246   2.119   0.0341 *
    #> land1       -0.48620    0.07922  -6.137 8.40e-10 ***
    #> land2        0.62567    0.07245   8.636  < 2e-16 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> (Dispersion parameter for poisson family taken to be 1)
    #>
    #>     Null deviance: 224.24  on 99  degrees of freedom
    #> Residual deviance: 109.57  on 96  degrees of freedom
    #> AIC: 314.24
    #>
    #> Number of Fisher Scoring iterations: 5

The true simulated values (σ = 250 m for `land1`, σ = 500 m for `land2`) should fall within the reported confidence intervals.

## 4.6 Visualize kernel decay and covariate effects

`plot()` shows how the kernel weight assigned to each landscape cell decreases with distance. The vertical line marks the distance enclosing 90% of the total kernel weight (the effective scale of effect).

    plot(opt)

`plot_marginal_effects()` shows the estimated effect of each covariate on the response, holding all other covariates at their mean values.

    plot_marginal_effects(opt)

## 4.7 Diagnose the likelihood surface

`profile_sigma()` evaluates AICc (or log-likelihood) across the sigma search range for each covariate, holding all other sigmas at their optimized values. A clear trough centered near the optimized sigma confirms a well-identified scale of effect; a flat or monotone profile indicates the data do not strongly constrain sigma for that variable.

    prof <- profile_sigma(opt, n_pts = 15, verbose = FALSE)
    plot(prof)

For a deeper explanation of how to interpret the profile and when to prefer profile-likelihood CIs over delta-method CIs, see `vignette("multiScaleR_Guide", package = "multiScaleR")`.

## 4.8 Project the fitted model to the landscape

`kernel_scale.raster()` applies the optimized kernel to the full raster extent and, with `scale_center = TRUE`, standardizes the result using the same centering and scaling used during model fitting. The output can be passed directly to `terra::predict()`.

    r_scaled <- kernel_scale.raster(raster_stack = land_rast,
                                    multiScaleR  = opt,
                                    scale_center = TRUE,
                                    clamp        = TRUE
                                    )
    #>
    #> Smoothing spatRaster 1 of 2: land1 at sigma = 238
    #>
    #> Smoothing spatRaster 2 of 2: land2 at sigma = 499
    plot(r_scaled)

Kernel-smoothed raster layers at optimized scales.

    pred <- terra::predict(r_scaled, opt$opt_mod, type = "response")
    plot(pred, main = "Predicted counts")
    plot(surv_pts, add = T, col = 'red')

Predicted counts across the landscape.

The nature of this toy example and simulated data is such that predicted counts go to zero at low levels of `land2`. In this example that is most of the border region. This results is simply an artifact of the small landscape and large scale of effect.

# 5 Quick-Reference: Full Workflow

    library(multiScaleR)
    library(terra)

    # 1. Load data
    data("landscape_counts"); data("surv_pts")
    pts      <- vect(surv_pts)
    land_rast <- rast(pkg_extdata("landscape.tif"))

    # 2. Prepare kernel inputs
    kernel_inputs <- kernel_prep(pts = pts, raster_stack = land_rast,
                                 max_D = 1700, kernel = "gaussian")

    # 3. Fit initial model
    df  <- data.frame(landscape_counts, kernel_inputs$kernel_dat)
    mod <- glm(counts ~ land1 + land2, family = poisson(), data = df)

    # 4. Optimize scales of effect
    opt <- multiScale_optim(fitted_mod = mod, kernel_inputs = kernel_inputs,
                            n_cores = 2)

    # 5. Summarize and visualize
    summary(opt)                        # sigma estimates + regression table
    summary(opt, profile = TRUE)        # profile-likelihood CIs (slower)
    plot(opt)                           # kernel decay curves
    plot_marginal_effects(opt)          # covariate effect plots
    prof <- profile_sigma(opt)          # AICc/LL surface across sigma space
    plot(prof)                          # plot profile with optimized sigma marked

    # 6. Project to landscape
    r_scaled <- kernel_scale.raster(land_rast, multiScaleR = opt,
                                    scale_center = TRUE, clamp = TRUE)
    terra::predict(r_scaled, opt$opt_mod, type = "response")

# 6 Learn More

- Full user guide: `vignette("multiScaleR_Guide", package = "multiScaleR")` covers kernel selection, model comparison with AIC/BIC, optimization with `unmarked`, zero-inflated models, and simulation tools.

- Spatial projection and clamping: `vignette("spatial_projection_clamping", package = "multiScaleR")` explains what happens when model predictions are extended beyond the sampled environmental range and how `clamp` and `pct_mx` control that behavior.