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DengueRT (version 1.0.1)

allmodelpredict: Real-time predictions of the final size and the turning point at the end of the epidemic, the incidence and the cumulative number of cases in future observations.

Description

This function provides real-time predictions of the final size and the turning point at the end of the epidemic for each built-in model and model averaged, as well as the incidence and the cumulative number of cases in future observations. Also this function, when all the built-in models are used, gives the AIC of each model, the model averaged weights and predicted incidence and cumulative cases.

Usage

## Object of the S3 class dengue allmodelpredict(inc,time,pred,start=NULL,model)

Arguments

inc,time
Vector of equal length specifying incidence (number of reported cases per time unit) and time interval (from the start of outbreak).
pred
Number of observation in which the incidence and the cumulative number of cases will be predicted.
start
A list with the starting values of the model to be used for fitting the data. If model="all" the imput must be a list of a list with the starting values of Richards, 3P logistic, Sigmoid Emax, Gompertz, Weibull and 5P logistic model parameters. By default, the initial values are provided by self-starting functions (see argument start in allmodels function).
model
The nonlinear model to be used for fitting the data. Built-in models are "Richards"", "Logistic3P", "SigmEmax", "Gompertz", "Weibull" and "Logistic5P" (see argument model in allmodels function). If model = "all" the parameter estimate will be done taking into account all built-in models via model averaging.

Value

An object with the parameter estimate for each built-in model and model averaged estimate for final size and turning point of outbreak. It is a list:
Incidence
All the available incidences
Time
All the available time points
PredTime
Period of time for which the prediction is required
AIC
The AIC for each built-in model and model averaged
tTable
A table with parameter estimates and t test. It is not available when all the model are used.
Weights
Model averaged weights. It is not availabe when is used only one built-in model
FinalSize
95% confidence interval and point estimate of the final size of outbreak for each built-in model and model averaged estimate
TurningPoint
95% confidence interval and point estimate of the turning point of outbreak for each built-in model and model averaged estimate
Predict
Predicted cumulative cases for each built-in model
PredictMA
Predicted cumulative cases for model averaged
PredInc
Predicted incidence for each built-in model
PredMAInc
Predicted incidence for model averaged
function.type
Name of the function
model.type
models used to estimate
Generic functions such as plot and summary have methods to show the results of the fit

References

K. Burnham, D. R. Anderson, Model Selection and Multimodel Inference: A Practical Information - theoretic Approach, 2nd Edition, Springer-Verlag, New York, 2002. G. Claeskens, N. L. Hjort, Model selection and model averaging, Cambridge University Press, 2008.

Examples

Run this code
## Not run: 
# ## (data example 1)
# data("dengueoutbreak1")
# 
# ## Using only the information until time point 20
# ## Prediction of the final size and turning point at the end of epidemic,  
# ## the incidence and the cumulative number of cases in the observation  
# ## number 36 using only the Richards model
# allmodelpredict(dengueoutbreak1$Incidence[1:20],dengueoutbreak1$Time[1:20],36, 
# model = "Richards")
# 
# ## Using only the information until time point 22
# ## Now using all built-in model, Prediction of the final size and turning point  
# ## at the end of epidemic, the incidence and the cumulative number of cases 
# ## in the observation number 30 
# allmodelpredict(dengueoutbreak1$Incidence[1:22],dengueoutbreak1$Time[1:22],30, 
# model = "all")## End(Not run)

## (data example 2)
data("dengueoutbreak2")
## Using only the information until time point 18, Prediction of the final size and  
## turning point at the end of epidemic, the incidence and the cumulative number  
## of cases in the observation number 31 using only the 3P logistic model

allmodelpredict(dengueoutbreak2$Incidence[1:18],dengueoutbreak2$Time[1:18],31,
model = "logistic3P")

## Not run: 
# ## Using only the information until time point 20
# ## Now using all built-in model, Prediction of the final size and turning point 
# ## at the end of  epidemic, the incidence and the cumulative number of cases  
# ## in the observation number 30
# allmodelpredict(dengueoutbreak2$Incidence[1:20],dengueoutbreak2$Time[1:20],34, 
# model = "all")## End(Not run)

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