Description Usage Arguments Value Author(s) References Examples
This step-3 function performs multilevel analyses with spatially weighted context data based on precise macro-level measures. The context data created in step 2 are combined with additional individual outcome and (optional) predictor variables, to test a user-defined model. An ad hoc stratified bootstrap resampling procedure generates robust point estimates for regression coefficients and model fit indicators, and computes confidence intervals adjusted for measurement dependency. For each tested model, contextual residuals can be stored for later re-use.
1 2 3 4 5 6 7 8 9 |
individual.level.data |
A |
context.id |
This variable allows matching
contextual units from different data sets ( |
formula |
Formula description of the model.The formula is handed down to |
precise.data |
A |
confidence.intervals |
|
nb.resamples |
number of resamples to be evaluated. By default set to 1000. |
individual.sample.seed |
Seed used to generate the random sampling of the individual data Is one of three things
Defaults to |
verbose |
if |
... |
All additional named arguments are handed through to the function
|
An object of class
SpawAggregateOutput-class
.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | # Spatially weighted multilevel analysis, with resampled individual
# level indicators and precise contextual indicator.
## It is step-2 function
## Data preparation
## 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)
## Step 1: Create spatial weights
geow.100 <- WeightMatrix(d_geo, bandwidth=100)
## Step 2: Create spatially weighted precise contextual indicator
homog.100 <- SpawExact(precise.data=homog_census,
context.id="area.name",
contextual.names="Homog_00",
contextual.weight.matrices=geow.100)
## rename weighted variable names so they reflect the used weighting
## matrix
names(homog.100)[2] <- "Homog.100"
## Step 3: Perform ResampleMLSpawExact
acc_homog100 <-
ResampleMLSpawExact(
individual.level.data=traces_ind,
context.id="area.name",
formula=cg_acc ~ victim_d + comb_d + male + age_1990 + high_school +
higher_edu + Homog.100 + (1|area.name), precise.data=homog.100,
nb.resamples=10)
|
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