Description Usage Arguments Value Author(s) References Examples
Integrates spatially weighted context data as higher-level predictors in multilevel analysis and produces conventional statistical estimates.
1 2 3 4 5 6 | MLSpawExact(individual.level.data,
context.id,
formula,
precise.data=NULL,
verbose=TRUE,
...)
|
individual.level.data |
A |
context.id |
The name of the context ID variable. 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 |
verbose |
if |
... |
All additional named arguments are handed through to the function
|
An object of class MLSpawExactOutput-class
Till Junge, Sandra Penic, Mathieu Cossuta, 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 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | ## Spatially weighted multilevel analysis, with standard estimates of
## standard errors.
## It is step-3 function
## Model with two contextual predictors, treated as precise,
## predicting collective guilt acceptance.
## 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 contextual indicator for aggregation
data(traces_event)
## 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 contextual indicators
## from micro-level survey data (treated as precise)
aggregate <- SpawAggregate(contextual.data=traces_event,
context.id="area.name",
contextual.names='w_all',
contextual.weight.matrices=geow.100,
aggregation.functions="weighted.mean",
design.weight.names="weight",
nb.resamples=0)
## from precise indicator
weighted.homog.census <- SpawExact(precise.data=homog_census,
context.id="area.name",
contextual.names="Homog_00",
contextual.weight.matrices=geow.100)
## merge aggregated and weighted data
context.data <- merge(aggregate, weighted.homog.census, by="area.name")
## Step 3: Perform MLSpawExact with two spatially weighted indicators
acc_w_homog_100 <-
MLSpawExact(individual.level.data=traces_ind,
context.id="area.name",
formula=cg_acc ~ victim_d + comb_d + male + age_1990 +
high_school + higher_edu + (1|area.name) + w_all.1 + Homog_00.1,
precise.data=context.data)
|
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