Model an imputed dataset

Usage

model_impute(
  object,
  model_fun,
  rhs,
  model_args = list(),
  extractor,
  extractor_args = list(),
  filter = list(),
  mutate = list(),
  ...,
  timeout = 600
)

# S4 method for class 'ANY'
model_impute(
  object,
  model_fun,
  rhs,
  model_args = list(),
  extractor,
  extractor_args = list(),
  filter = list(),
  mutate = list(),
  ...,
  timeout = 600
)

# S4 method for class 'aggregatedImputed'
model_impute(
  object,
  model_fun,
  rhs,
  model_args = list(),
  extractor,
  extractor_args = list(),
  filter = list(),
  mutate = list(),
  ...,
  timeout = 600
)

Arguments

object: The imputed dataset.
model_fun: The function to apply on each imputation set. Or a string with the name of the function. Include the package name when the function is not in one of the base R packages. For example: "glm" or "INLA::inla".
rhs: The right hand side of the model.
model_args: An optional list of arguments to pass to the model function.
extractor: A function which return a matrix or data.frame. The first column should contain the estimate, the second the standard error of the estimate.
extractor_args: An optional list of arguments to pass to the extractor function.
filter: An optional argument to filter the aggregated dataset. Either a function which takes the Covariate slot as an argument. Or a list which will be passed to the .dots argument of dplyr::filter(). You can filter on the covariates in the aggregated dataset. Besides those you can also filter on Imputation_min and Imputation_max. These variables represent the lowest and highest value of the imputations per row in the data.
mutate: An optional argument to alter the aggregated dataset. Will be passed to the .dots argument of dplyr::mutate(). This is mainly useful for simple conversions, e.g. factors to numbers and vice versa.
...: currently ignored.
timeout: Maximum duration allowed for fitting a single imputation model in seconds. Defaults to 600 seconds (10 minutes).

Examples

dataset <- generate_data(n_year = 10, n_site = 50, n_run = 1)
dataset$Count[sample(nrow(dataset), 50)] <- NA
model <- lm(Count ~ Year + factor(Period) + factor(Site), data = dataset)
imputed <- impute(data = dataset, model = model)
aggr <- aggregate_impute(imputed, grouping = c("Year", "Period"), fun = sum)
extractor <- function(model) {
  summary(model)$coefficients[, c("Estimate", "Std. Error")]
}
model_impute(
  object = aggr,
  model_fun = lm,
  rhs = "0 + factor(Year)",
  extractor = extractor
)
#> # A tibble: 10 × 5
#>    Parameter      Estimate    SE   LCL   UCL
#>    <fct>             <dbl> <dbl> <dbl> <dbl>
#>  1 factor(Year)1     1460.  216. 1036. 1883.
#>  2 factor(Year)2     1460.  216. 1037. 1883.
#>  3 factor(Year)3     1569.  216. 1145. 1993.
#>  4 factor(Year)4     1808.  216. 1385. 2231.
#>  5 factor(Year)5     1671.  216. 1247. 2094.
#>  6 factor(Year)6     1546.  217. 1121. 1970.
#>  7 factor(Year)7     1681.  216. 1257. 2104.
#>  8 factor(Year)8     1633.  216. 1209. 2056.
#>  9 factor(Year)9     1798.  216. 1375. 2220.
#> 10 factor(Year)10    1426.  216. 1003. 1849.