Analyze group differences in a single dMRI tract profile using GAMs

Usage

tractable_single_bundle(
  df_afq,
  tract,
  dwi_metric,
  participant_id = "subjectID",
  group_by = "group",
  covariates = c(group_by),
  smooth_terms = NULL,
  k = "auto",
  family = "auto",
  ...
)

Arguments

df_afq: Input AFQ dataframe. If NULL, this function will load data using read_afq_files and the additional arguments in ...
tract: Abbreviated tract name, e.g., "CST_L" or "OR"
dwi_metric: The diffusion metric to model (e.g. "FA", "MD")
participant_id: The name of the column that encodes participant ID
group_by: The grouping variable used to group nodeID smoothing terms
covariates: List of strings of GAM covariates, not including the smoothing terms over nodes and the random effect due to subjectID.
smooth_terms: Smoothing terms, not including the smoothing terms over nodes and the random effect due to subjectID.
k: Dimension of the basis used to represent the node smoothing term, If k = 'auto', this function will attempt to find the best value
family: Distribution to use for the gam. Must be either 'gamma', 'beta', or 'auto'. If 'auto', this function will select the best fit between beta and gamma distributions.
...: Arguments to pass to fit_gam

Examples

sarica <- read_afq_sarica()
sarica$group <- factor(sarica$class)
sarica$subjectID <- unclass(factor(sarica$subjectID))
tractable_single_bundle(df_afq = sarica,
                     tract = "Right Corticospinal",
                     participant_id = "subjectID",
                     group_by = "group",
                     covariates = c("age","group"),
                     dwi_metric = "fa")
#> 
#> Family: Beta regression(61.146) 
#> Link function: logit 
#> 
#> Formula:
#> fa ~ age + group + s(nodeID, by = group, k = 16) + s(subjectID, 
#>     bs = "re")
#> 
#> Estimated degrees of freedom:
#> 14.5 14.4 37.9  total = 69.76 
#> 
#> fREML score: -6443.644