ExploreMLSpawExact: Exploratory multilevel analysis with spatially weighted...
In spacom: Spatially Weighted Context Data for Multilevel Modelling

Description Usage Arguments Details Value Author(s) References Examples

These exploratory functions provide fast-track procedures, which condense steps 1 to 3. On the basis of precise contextual measures and/or micro-level survey data for estimating contextual indicators, a distance matrix between contextual units, and individual outcomes and (optional) predictor variables, it directly generates point estimates for regression coefficients of a user-defined multilevel model, for a range of different bandwidth values. This function can be used to first explore the scale of relevant contextual effects, before parametrising the model more precisely, calculating (computationally expensive) confidence intervals and estimating spatial dependency for specific models, on a step-by-step basis.

ExploreMLSpawExact(individual.level.data,
 contextual.name,
 context.id,
 formula,
 distance.matrix,
 multilevel.bandwidths,
 precise.data,
 kernel = NULL,
 verbose = TRUE)

ExploreMLSpawAggregate(individual.level.data,
 contextual.name,
 contextual.data,
 context.id, formula,
 distance.matrix,
 multilevel.bandwidths,
 design.weight.names = NULL,
 aggregation.function = "weighted.mean",
 kernel = NULL,
 additional.args = NULL,
 verbose = TRUE)

`individual.level.data`	A `data.frame` containing the individual level predictor and outcome variables. The data must be numeric (`logical`, `integer`, or plain `numeric`). There must not be any missing values, `NaN`s, `NULL`s or `NA`s.
`contextual.name`	A name of contextual variable to be weighted.
`contextual.data`	A `data.frame` consisting of micro-level data to generate contextual indicators by aggregation and containing a column named `context.id` with the context ID variable. This is an individual level dataframe. The data must be numeric (`logical`, `integer`, or plain `numeric`). There must not be any missing values, `NaN`s, `NULL`s or `NA`s. By default set to `NULL`.
`context.id`	The name of the context ID variable. This variable allows matching contextual units from different data sets (`contextual.data`, `individual.level.data`, `distance.matrix`). Values can by of any type but need to be defined consitently across data sets.
`formula`	Formula description of the model.The formula is handed down to `lmer` function of the package `lme4`. Refer to the documentation of `lmer` for details.
`distance.matrix`	A square matrix of dimension n by n, where n is the number of contextual units.
`multilevel.bandwidths`	A `vector` of scalar numeric values specifying the bandwidth h
`precise.data`	A `data.frame` containing the precise contextual data.There must not be any missing values, `NaN`s, `NULL`s or `NA`s. By default set to `NULL`.
`design.weight.names`	A name of optional design weight at the individual level used for aggregation (for example, for a weighted mean). By default set to `NULL`.
`aggregation.function`	A name of aggregation function. Function takes either 1 argument in which case the corresponding individual design weight is `NULL`, 2 arguments in which case the second argument is taken from the corresponding individual design weight. Defaults to `"mean"`.
`kernel`	A function applied to the distance matrix. By default `NULL`, in which case the kernel function w_ij = f(d, h) = (1/2)^((d_ij/h)^2) is used, where w_ij, d_ij, h are elements of the weight matrix W, of the distance matrix D and the bandwidth h. User-supplied kernel functions have to take 2 arguments and return a matrix of the same dimension as the first argument.
`additional.args`	For aggregation functions which take additional arguments (that is in addition to the data to aggregate and design weights), they can be specified here. `additional.args` defaults to `NULL`
`verbose`	if `TRUE`, progress information and an estimate of the remaining computing time is shown at the screen. Defaults to `TRUE`

ExploreMLSpawExact performs exploratory multilevel analysis with a precise spatially weighted contextual indicator. ExploreMLSpawAggregate performs analysis with an aggregated spatially weighted contextual indicator. Both functions provide only conventional statistical estimates and accept only one contextual predictor.

A list of MLSpawExactOutput-class objects

Till Junge, Sandra Penic, Guy Elcheroth

Elcheroth, G., Penic, S., Fasel, R., Giudici, F., Glaeser, S., Joye, D., Le Goff, J.-M., Morselli, D., & Spini, D. (2012). Spatially weighted context data: a new approach for modelling the impact of collective experiences. LIVES Working Papers, 19.

#### ExploreMLSpaw
# Exploratory spatially weighted multilevel with standard estimates of
# standard errors. Accepts only one contextual predictor. Predicting
# collective guilt acceptance. Precise contextual predictor (ethnic homogeneity)
# weighted with geographical proximity weights, h=50,100,200.

# load individual level data, remove collective guilt assignment from the
# data frame, and remove NA's
data(traces_ind)

traces_ind <- traces_ind[,-7]
traces_ind <- na.exclude(traces_ind)

# load precise contextual indicator
data(homog_census)

# load distance matrix
data(d_geo)

# perform ExploreMLSpawExact
acc_homog <- ExploreMLSpawExact(
  individual.level.data=traces_ind,
  contextual.name="Homog_00",
  context.id="area.name",
  formula=cg_acc ~ victim_d + comb_d + male + age_1990 + high_school +
  higher_edu + (1|area.name),
  precise.data=homog_census,
  distance.matrix=d_geo,
  multilevel.bandwidths=c(50, 100))