MLSpawExactOutput-class: Class '"MLSpawExactOutput"'

Description Objects from the Class Slots Methods Author(s) Examples

Description

Output object of the function MLSpawExact.

Objects from the Class

Objects can be created by calls of the form new("MLSpawExactOutput", ...).

Slots

lme:

Object of class "merMod"

beta:

Object of class "numeric"

Methods

AIC

signature(object = "MLSpawExactOutput"): merMod of lme4

BIC

signature(object = "MLSpawExactOutput"): merMod of lme4

fixef

signature(object = "MLSpawExactOutput"): merMod of lme4

print

signature(x = "MLSpawExactOutput")

ranef

signature(object = "MLSpawExactOutput"): merMod of lme4

show

signature(object = "MLSpawExactOutput")

summary

signature(object = "MLSpawExactOutput")

VarCorr

signature(x = "MLSpawExactOutput"): merMod of lme4

Author(s)

Till Junge

Examples

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## 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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