Description Usage Arguments Details Value Author(s) References Examples

Spacom provides a set of aggregation functions which can be used with
the functions `SpawAggregate`

and
`ExploreMLSpawAggregate`

. See **Details** for
descriptions of each function. Note that you are not restricted
to these.

User supplied aggregation functions can be used if they have the form

*x_w = f(x, w, ...)*

where *x* is the data to be aggregated,
*w* the weights to be applied. Any number of additional arguments
may also be used (see argument `additional.args`

in
`SpawAggregate`

for details on additional arguments. The
return value *x_w* must be a scalar value (i.e. not a vector).

1 2 3 4 5 6 7 8 | ```
wt.sd(data, weights=rep(1, length(data)))
wt.var(data, weights=rep(1, length(data)))
wt.gini(data, weights=rep(1, length(data)))
wt.gini.categ(data, weights=rep(1, length(data)))
wt.Theil(data, weights=rep(1, length(data)))
wt.RS(data, weights=rep(1, length(data)))
wt.Atkinson(data, weights=rep(1, length(data)))
wt.gini.group(data, weights=rep(1, length(data)), groups)
``` |

`data` |
A |

`weights` |
A |

`groups` |
name of the column used to group data (only |

`wt.var(data, weights)`

Computes the weighted variance according to*Var(x) = sum(w*(data-mu))/sum(w)*where the weighted mean is

*mu**mu = weighted.mean(data, weights)*`wt.sd(data, weights)`

Computes the weighted standard deviation according to*sd = sqrt(wt.var(data, weights))*`wt.gini`

The weighted Gini coefficient is given bywhere

*mu*is*mu = sum(w*x)*`wt.gini.categ`

Spacom also allows for the computation of inequality indicators for categorical variables. In this case, one has no*x_i*value but only the weighted frequencies*f_i*for each category. For that case, the Gini becomes*G = 1-sum(f^2)*The weighted frequencies are computed from

`data`

and`weights`

`wt.gini.group`

get from guy

A scalar value of type `numeric`

. For user-supplied aggregation
functions, this can potentially be of a differenet type.

Mathieu Cossuta, 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 | ```
## Creating indicators of spatially weighted inequalities
## load individual level data and remove missings
data(traces_ind)
traces_ind <- na.exclude(traces_ind)
## create variable "simulated income" in the dataset
## the variable is created only in demonstrative purposes,
## and it is not part of the TRACES dataset
traces_ind$sim_inc <- rnorm(nrow(traces_ind), 1000, 100)
## Step 1: Load distance matrix and create weights
data(d_geo)
geow.50 <- WeightMatrix(d_geo, bandwidth=50)
## Step 2: Spatially weighted gini for simulated income
si.gini <- SpawAggregate(contextual.data=traces_ind,
context.id="area.name",
contextual.names="sim_inc",
contextual.weight.matrices=geow.50,
aggregation.functions="wt.gini",
design.weight.names=NULL,
nb.resamples=5)
## Step 2: Spatially weighted gini for groups (Spatially weighted inequalities
## in simulated income for men and women)
si.gini.gr <- SpawAggregate(contextual.data=traces_ind,
context.id="area.name",
contextual.names="sim_inc",
contextual.weight.matrices=geow.50,
aggregation.functions="wt.gini.group",
additional.args="male",
design.weight.names=NULL,
nb.resamples=5)
``` |

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