allmodelpredict: Real-time predictions of the final size and the turning point...

In DengueParmEst: Parameter estimates (and real-time prediction) of a single dengue outbreak

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.
model	The nonlinear model to be used for fitting the data. Built-in models are "Richards"", "Logistic3P", "SigmEmax", "Gompertz", "Weibull" and "Logistic5P". 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

Author(s)

Carlos Sebrango, Lizet Sanchez, Ziv Shkedy

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

require(nlme)
require(NRAIA)
require(drc)


#Incidence Data example 1
inc1 <-c(1, 0, 2, 5, 2, 2, 3, 5, 7, 14, 17, 18, 20, 38, 50, 45,
57, 44, 12, 11, 10, 2, 3, 0, 3, 2, 2, 0, 2, 0, 0, 0, 1, 2, 2, 1)
#Incidence Data example 2
inc2<-c(5, 4, 4, 3, 7, 1, 4, 4, 9, 21,19, 33, 49, 45, 46, 28, 
25, 10, 5, 6, 2, 1, 4, 5, 0, 3, 2, 2, 1, 0, 3, 0, 2, 2)
# time vectors
tii<- c(1:36)
ti2<- c(1:34)

## Using only the information until time point 20 (data example 1)
## 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 

p1<-allmodelpredict(inc1[1:20],tii[1:20],36, model="Richards")
summary(p1)

## 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 

p2<-allmodelpredict(inc1[1:22],tii[1:22],30, model="all")
summary(p2)

## (data example 2)
## 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

p3<-allmodelpredict(inc2[1:18],ti2[1:18],31, model="logistic3P")
summary(p3)

## 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 

p4<-allmodelpredict(inc2[1:20],ti2[1:20],34, model="all")
summary(p4)

DengueParmEst documentation built on May 2, 2019, 5:17 p.m.