evaluatr.init | R Documentation |
Initialize analysis
evaluatr.init(country, data, pre_period_start = "start", post_period_start, post_period_end = eval_period_end, eval_period_start, eval_period_end, n_seasons = 12, year_def = "cal_year", group_name, date_name, outcome_name, set.burnN = 5000, set.sampleN = 10000, denom_name, log.covars = TRUE, ridge = F, error_dist = "iid", sparse_threshold = 5)
country |
A one-word label for output (eg country or state name). |
data |
Input dataframe. There should be a row for each time point (e.g., month) and category (e.g., age group). There should be variables for the date, for the category (or a column of 1s if only 1 category), for the outcome variable (a count), for the denominator (or a column of 1s if no denominator), and columns for each control variable. |
pre_period_start |
Date when analysis starts, YYYY-MM-01. defaults to first date in dataset. |
post_period_start |
Month when intervention introduced. YYY-MM-01 |
post_period_end |
Date when analysis ends, YYYY-MM-01. defaults to first date in dataset. Defaults to end of evaluation period. |
eval_period_start |
First month of the period when the effect of intervention is evaluated. YYYY-MM-01. typically 12-24 months after post_period_start. |
eval_period_end |
Last month of the period when the effect of intervention is evaluated. YYYY-MM-01. |
n_seasons |
How many observations per year? Defaults to 12 (monthly data) Change to 4 for quarterly |
year_def |
Should results be aggregated by calendar year ('cal_year': the default) or epidemiological year ('epi_year'; July-June) |
group_name |
Name of the stratification variable (e.g., age group). If only one age group present, add a column of 1s to the dataset |
date_name |
Name of the variable with the date for the time series |
outcome_name |
Name of the outcome (y) variable in the 'data' dataframe. Should be a count |
set.burnN |
Number of MCMC iterations for burn in (default 5000), |
set.sampleN |
Number of MCMC iterations post-burn-in to use for inference (default 10000), |
denom_name |
Name of the denominator variable in the 'data' dataframe. if there is no denominator, include a column of 1s. |
log.covars |
Should the covariate be log transformed? (default: TRUE) |
ridge |
Run ridge regression with AR(1) random intercepts (faster) or spike and slab (with iid random intercept) for variable selection. Logical, Default TRUE. |
error_dist |
For the INLA models: use an 'iid' or 'ar1' error on the random intercept. Defaults to iid. Use caution with AR(1) as it can introduce bias in some situations |
sparse_threshold |
Threshold for filtering out control variables based on sparsity (mean number of cases per time period). Defaults to 5. |
Initialized analysis object, 'analysis' as described below
'analysis$country' as passed to 'country'
'analysis$input_data' as passed to 'data'
'analysis$n_seasons' as passed to 'n_seasons'
'analysis$year_def' as passed to 'year_def'
'analysis$pre_period' Range of dates in the pre-intervention period
'analysis$post_period' Range of dates in the post-intervention period
'analysis$eval_period' Range of dates in the evaluation period
'analysis$start_date' First date of the pre-intervention period
'analysis$intervention_date' Last time point before the start of the post-period
'analysis$end_date' Last date in the evaluation period
'analysis$group_name' as passed to 'group_name'
'analysis$date_name' as passed in in 'date_name'
'analysis$outcome_name' as passed in in 'outcome_name'
'analysis$denom_name' as passed in in 'denom_name'
'analysis$time_points' Vector of time points in the dataset
'analysis$set.burnN' as passed in in 'set.burnN'
'analysis$set.sampleN' as passed in in 'set.sampleN'
'analysis$log.covars' as passed in in 'log.covars'
'analysis$groups' Vector of groups analyzed
'analysis$sparse_groups' Vector indicating which groups were too sparse to analyze
'analysis$model_size' Average number of covariates included in the synthetic control model
'analysis$covars' Matrix of covariates used for analysis
'analysis$outcome' as passeed to 'outcome_name'
'analysis$ridge' as passeed to 'ridge' 'analysis$error_dist' as passeed to 'error_dist' 'analysis$sparse_threshold' as passed to 'sparse_threshold'
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