Description Usage Arguments Details Value Author(s) References See Also Examples
This function performs the Matched Wake Analysis (mwa), which consists of two steps: counts for previous and posterior events are established for different spatial and temporal offsets from treatment and control events. After that, the treatment effect is estimated in a differenceindifferences regression design. For performance reasons, the iterative counting is done in Java using the rJava interface.
IMPORTANT: The size of the Java heap space has to be set before first calling the package via library(mwa)
since JVM size cannot change once it has been initialized. This also implies that R has to be restarted if another library was already using a JVM in order for the heap space option to have any effect. To set the heap space to 1 GB, for example, use options(java.parameters = "Xmx1g")
(512 MB is the default size).
1 2 3 4 5 6  matchedwake(data, t_window, spat_window, treatment, control,
dependent, matchColumns, t_unit = "days", estimation = "lm",
formula = "dependent_post ~ dependent_pre + treatment",
weighted = FALSE, estimationControls = c(), TCM = FALSE,
deleteSUTVA = FALSE, alpha1 = 0.05, alpha2 = 0.1,
match.default = TRUE, ...)

data 

t_window 
specification of temporal windows in 
spat_window 
specification of spatial windows in kilometers. See Details. 
treatment 
vector of Strings identifying which type of events serve as treatments. See Details. 
control 
vector of Strings identifying which type of events serve as controls. See Details. 
dependent 
vector of Strings identifying which type of events are affected by treatment. See Details. 
matchColumns 
vector of Strings indicating the columns to match on. See Details. 
t_unit 
String specifying the temporal units to be used, either "days", "hours", "mins" or "secs". Default = "days". See Details. 
estimation 
String specifying method used for estimation, "lm", "att" or "nb". Default = "lm". See Details. 
formula 
String specifying the model used for estimation. Default = "dependent_post ~ dependent_pre + treatment". See Details. 
weighted 
Boolean specifying whether regression is weighted (only affects estimations using "lm" or "att"). Default = FALSE. 
estimationControls 
vector of Strings indicating additional control dimensions to be included in the estimation. See Details. 
TCM 
Boolean to select whether the method should match on counts of previous treatment and control instances. Default = FALSE. 
deleteSUTVA 
Boolean to select whether overlapping treatment and control episodes are deleted. Default = FALSE. 
alpha1 
first significance level used for the analysis and plots. Default = 0.05. 
alpha2 
second significance level used for the analysis and plots. Default = 0.1. 
match.default 
Boolean to select whether observations are matched using 
... 
optional parameters that can be passed to the methods used for matching and estimation. See Details. 
The method expects data
to be a data.frame
. Dates must be given in column timestamp and formatted as a date string with format "YYYYMMDD hh:mm:ss". Alternatively, a POSIX Date can be specified using the same format. data
must also contain two entries called lat
and lon
for the geo location of each entry.
t_window
specifies the minimal and maximal temporal window sizes and corresponding steps used in the iteration. Required syntax is c(min_window, max_window, step_size) with step_size
in units of t_unit
. The spatial window spat_window
is specified in the same way with kilometers as units.
treatment
, control
and dependent
define which category of events is considered to be treatment, control and dependent cases respectively. The required syntax is c(column_name, value) where column_name
must be entered as String and value
can be Numeric, Boolean, or a String.
matchColumns
selects the columns in data
used for matching. Matching variables are expected to be coded together with every treatment and control type event and are assumed to reflect a set of suitable matching variables (what is suitable will, of course, vary from case to case).
The optional argument t_unit
specifies the temporal resolution for which the analysis is to be conducted, one of either "days", "hours", "mins" or "secs". If the time stamps provided in data
are more precise than the resolution they are truncated accordingly.
mwa estimates treatment effects using a diffindiffs regression design. By default this is specified as "dependent_post ~ dependent_pre + treatment" (where “pre” and “post” refer to pre and post intervention). Alternatively, "dependent_post  dependent_pre ~ treatment" is accepted. Only those two input specifications are allowed, any other input will result in an error.
Three different estimation approaches can be chosen using estimation
: a linear model ("lm", stats), all models available through ("att", cem) or a count dependent model ("glm.nb", MASS). For regressions using "lm" or "att" weighted
sets whether or not the regression is weighted by the number of treatment vs. control cases. Additional control variables can be specified via estimationControls
. For example, if estimationControls = c("covariate1"), the package automatically modifies the default estimation formula to "dependent_post ~ dependent_pre + covariate1 + treatment" (analogously for the other specification). In this case the output then also not only returns the estimate and p value for treatment but further returns the coefficients and p values for all additional control variables.
The package supports full inheritance for optional arguments of the following methods: cem
and att
(cem), lm
(stats), glm.nb
(MASS). To guarantee unique inputs for each method, options have to entered into matchedwake()
using a prefix that consists of the method name separated by “.”. For example, in order for cem
to return an exactly balanced dataset simply add cem.k2k = TRUE
as optional argument.
Returns an object of class matchedwake
, which is a list of objects with the following slots:
estimates 

matching 

SUTVA 

wakes 

parameters 

call 
the call. 
Sebastian Schutte and Karsten Donnay.
Schutte, S., Donnay, K. (2014). “Matched wake analysis: Finding causal relationships in spatiotemporal event data.” Political Geography 41:110.
mwapackage
, slidingWake
, slideWakeMatch
, print.matchedwake
, summary.matchedwake
, plot.matchedwake
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  # Loading sample data
data(mwa_data)
# Specify required parameters:
#  2 to 10 days in steps of 2
t_window < c(2,10,2)
#  2 to 10 kilometers in steps of 2
spat_window < c(2,10,2)
#  column and entries that indicate treatment events
treatment < c("type","treatment")
#  column and entries that indicate control events
control < c("type","control")
#  column and entries that indicate dependent events
dependent < c("type","dependent")
#  columns to match on
matchColumns < c("match1","match2")
# Specify optional parameters:
#  use weighted regression (default estimation method is "lm")
weighted < TRUE
#  temporal units
t_unit < "days"
#  match on counts of previous treatment and control events
TCM < TRUE
# Execute method:
results < matchedwake(mwa_data, t_window, spat_window, treatment, control, dependent,
matchColumns, weighted = weighted, t_unit = t_unit, TCM = TCM)
# Plot results:
plot(results)
# Return detailed summary of results:
summary(results, detailed = TRUE)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.