allmodels: Parameter estimate for each built-in model and model averaged...

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

Description

This function gives the parameter estimate for each built-in model and model averaged estimate for final size and turning point of outbreak. 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.this function is used retrospectively, that is, when the outbreak occurred.

Usage

## Object of the S3 class dengue
allmodels(inc,time,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).
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
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)
## Parameter estimate for Richards model (for Incidence data example 1)

allmodels(inc1,time=tii, start=list(alpha=375,k=2.38,gamma=0.76,eta=16),model="Richards")

## or
allmodels(inc1,time=tii, model="Richards")

## Parameter estimate for each built-in model and model averaged 
##estimate for final size and turning point of outbreak 
##(for Incidence data example 1)

allmodels(inc1,time=tii,start=list(list(alpha=375,k=2.38,gamma=0.76,eta=16),
list(alpha=375,gamma=1,eta=16),list(alpha=380,eta=13,beta=7,n=5),
list(alpha=380,eta=20,beta=0,gamma=1),list(alpha=410,eta=12,beta=11,k=3),
list(alpha=380,beta=4,g=1,eta=13,k=15)),model="all")

## or
allmodels(inc1,time=tii,model="all")

# Parameter estimate for 3P Logistic model (for Incidence data example 2)

allmodels(inc2,ti2, start=list(alpha=375,gamma=1,eta=16), model="logistic3P")

## or
allmodels(inc2,ti2,model="logistic3P")

## Parameter estimate for each built-in model and model averaged estimate 
##for final size and turning point of outbreak 
##for Incidence data example 2
allmodels(inc2,ti2,start=list(list(alpha=355,k=1,gamma=1,eta=14),
list(alpha=355,gamma=1,eta=14), list(alpha=355,eta=13,beta=10,n=6),
list(alpha=355,eta=11,beta=20,gamma=1),list(alpha=355,eta=12,beta=22,k=3),
list(alpha=355,beta=15,g=1,eta=13,k=10)),model="all")

## or
allmodels(inc2,ti2,model="all")

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