FluMoDL (version 0.0.3)

summary.FluMoDL: Summary method for FluMoDL objects

Description

This function creates a summarized version of a 'FluMoDL' object. It contains the sets of coefficients and variance-covariance matrices for the incidence proxy terms (for influenza, and for RSV if provided), and the predictions for these terms.

Usage

# S3 method for FluMoDL
summary(object, ...)

Arguments

object

An object of class 'FluMoDL'

...

Further arguments passed to or from other methods.

Value

An object of class 'summary.FluMoDL'. This is a list containing the following elements:

$type

A string describing the meaning of the coefficients. Defaults to "summary", meaning a first-stage model summary. Alternatively, "blup" means Best Unbiased Linear Predictor (BLUP) coefficients, and "pooled" refers to coefficients pooled in the course of a multivariate meta-analysis. See metaFluMoDL.

$description

A string with an additional description. For objects created with summary.FluMoDL() it is an empty string, but see metaFluMoDL.

$coef

A list of numeric vectors, with names 'proxyH1', 'proxyH3' and 'proxyB' (and 'proxyRSV' if provided in the function arguments), containing the model coefficients for these terms.

$vcov

A list of variance-covariance matrices, with names 'proxyH1', 'proxyH3' and 'proxyB' (and 'proxyRSV' if provided in the function arguments), for the respective model coefficients.

$pred

A list with names 'proxyH1', 'proxyH3' and 'proxyB' (and 'proxyRSV' if provided in the function arguments), containing predictions (in the form of crosspred objects) for each exposure. These can be plotted in both the exposure-response and lag-response dimensions, see crosspred, plot.crosspred and the example below.

Details

These summaries can be used to run a multivariate meta-analysis and calculate pooled effect estimates and BLUP (Best Unbiased Linear Predictor) estimates for influenza (and RSV if provided).

Examples

# NOT RUN {
data(greece) # Use example surveillance data from Greece
m <- with(greece, fitFluMoDL(deaths = daily$deaths,
    temp = daily$temp, dates = daily$date,
    proxyH1 = weekly$ILI * weekly$ppH1,
    proxyH3 = weekly$ILI * weekly$ppH3,
    proxyB = weekly$ILI * weekly$ppB,
    yearweek = weekly$yearweek))
summ <- summary(m)
summ

# Plot the association between A(H1N1)pdm09 activity and mortality:
plot(summ$pred$proxyH1, "overall")

# }