Description Usage Arguments Value Author(s) References Examples
This step3 function performs multilevel analyses with spatially weighted context data based on precise macrolevel measures. The context data created in step 2 are combined with additional individual outcome and (optional) predictor variables, to test a userdefined 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 reuse.
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
SpawAggregateOutputclass
.
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 step2 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 + (1area.name), precise.data=homog.100,
nb.resamples=10)

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