Description Usage Arguments Details Value Examples

View source: R/plot_functions.R

A function for mapping hotspots according to user defined criteria.

1 2 3 4 5 6 7 8 9 10 11 12 |

`lg` |
Output from a call to |

`covariates` |
A |

`threshold.var` |
A vector of one or two strings specifying the variables to define the hotspots, see Details for how to specify. |

`threshold.value` |
A vector or one or two values indicating the threshold(s) for determining a hotspot. Given in the same order as threshold.var. |

`labels` |
A vector of two or four labels for the hotspots, see Details. |

`threshold.prob` |
A vector of one or two values specifying the exceedence probabilities. |

`relative` |
A logical value. If one or both of the variable is with respect to a previous time period, whether the comparison should be relative (TRUE) or absolute (FALSE) |

`osm` |
A logical value indicating Whether to include a Open Street Map map under the plot. |

`per.days` |
If one or both of the variables is incidence, the denominator number of person-days. |

`msq` |
The denominator for the population density in m^2. Default is hectares (per 10,000m^2) |

A “hotspot” is defined as an area that exceeds a user-defined criterion with probability of at least p. The criterion can be a function of one or two variables derived from the model; where two variables are used then there are four possible hotspot classifications, where only one is used then there are two classifications (above or below the threshold).

The log-linear model can be divided into a set of multiplicative components:

(A) population density x (B) size of the area x (C) average disease rate x (D) RR observed covariates x (E) RR latent process

A threshold can be any combination of these factors, or their difference over time.
The user can specify the combination using the labels
(A)x(C) `poppp`

(A)x(B)x(C) `pop`

(D) `obs`

(E) `latent`

in the argument to `threshold.var`

as an additive sum. For example, to specify
the incidence (in person-days) as the variable 'poppp+obs+latent', or to specify
the overall relative risk of an area 'obs+latent'. To difference the variable with
respect to t time periods prior, add '+lag(t)'. So to use the incidence rate ratio
relative to 7 days prior, we can specify 'poppp+obs+latent+lag(7)'. The 'hotspot' is
an area where Pr(variable > threshold) > p.

Hotspots are labelled in the following way. For a single variable definition, the labels are given
as `c(a,b)`

where

a = Pr(variable > threshold) <= p

b = Pr(variable > threshold) > p

For a two variable definition the labels are `c(a,b,c,d)`

where

a = Pr(variable 1 > threshold 1) <= p1 & Pr(variable 2 > threshold 2) <= p2

b = Pr(variable 1 > threshold 1) > p1 & Pr(variable 2 > threshold 2) <= p2

c = Pr(variable 1 > threshold 1) <= p1 & Pr(variable 2 > threshold 2) > p2

d = Pr(variable 1 > threshold 1) > p1 & Pr(variable 2 > threshold 2) > p2

The labels do not need to be unique.

An lgcpRealPlot object comprising a list of two ggplot objects.
The first is the hotspot classifications, the second the exceedence probabilities. An object
`outl`

is exported to the global environment to reduce needing to reload sampling
data on further calls to the same `lgcpReal`

object. This can be removed if needed as
it can be large.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
data(dat,square,square_pop)
lg1 <- lgcp(data=dat,
pop.var = c("popdens"),
boundary=square,
covariates=square_pop,
cellwidth=0.1,
laglength = 7,
mala.pars=c(200,100,1),
nchains=2)
plot_hotspot(lg1,
covariates = square_pop,
threshold.var = c("poppp+obs+latent",
"poppp+obs+latent+lag(3)"),
threshold.value = c(0.1,1),
threshold.prob=0.8,
labels=c('low','high incidence',
'rising incidence','both'))
``` |

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