Description Objects from the Class Slots Methods Author(s) Examples
Output object of the function MLSpawExact
.
Objects can be created by calls of the form new("MLSpawExactOutput", ...)
.
lme
:Object of class "merMod"
beta
:Object of class "numeric"
signature(object = "MLSpawExactOutput")
:
merMod
of lme4
signature(object = "MLSpawExactOutput")
:
merMod
of lme4
signature(object = "MLSpawExactOutput")
:
merMod
of lme4
signature(x = "MLSpawExactOutput")
signature(object = "MLSpawExactOutput")
:
merMod
of lme4
signature(object = "MLSpawExactOutput")
signature(object = "MLSpawExactOutput")
signature(x = "MLSpawExactOutput")
:
merMod
of lme4
Till Junge
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 | ## Perform MLSpawExact
## Import and prepare data (see details in MLSpawExact)
data(traces_ind)
traces_ind <- traces_ind[,-7]
traces_ind <- na.exclude(traces_ind)
data(traces_event)
data(homog_census)
data(d_geo)
## Step 1: Create spatial weights
geow.100 <- WeightMatrix(d_geo, bandwidth=100)
## Step 2: Create spatially weighted contextual indicators
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)
## Step 3: Perform MLSpawExact with the spatially weighted indicator
acc_w100 <- 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,
precise.data=aggregate)
## acc_w100 is an object of class MLSpawExactOutput
class(acc_w100)
## to assess standardized coefficients
acc_w100@beta
## look at the fitted mixed-effect model :
acc_w100@lme
## to assess non-standardized fixed coefficients
fixef(acc_w100@lme)
## to assess the deviance of the fitted model
deviance(acc_w100@lme)
## for details about slots within @lme see ?merMod-class{lme4}
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