Description Usage Arguments Value Author(s) References Examples
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 all the data are available.
1 2 |
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.
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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.For all built-in model, C(t) represents the cumulative number of reported cases at time t and also the turning point (eta) and the final size of epidemic (alpha) are parameters in the models. The model expressions of each built-in model are given as follow:
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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
Carlos Sebrango, Lizet Sanchez, Ziv Shkedy
K. Burnham, D. R. Anderson, Model Selection and Multimodel Inference: A Practical Information-theoretic Approach,
2nd Edition, Springer-Verlag, New York, 2002.
J. MacDougall, Analysis of dose responses Studies: Emax model, in: N. Ting (Ed.), Dose Finding in Drug Development, Statistics for Biology and Health, Springer New York, pp. 127, 2006.
G. Claeskens, N. L. Hjort, Model selection and model averaging, Cambridge University Press, 2008.
D. Ratkowsky, Handbook of nonlinear regression models, Marcel Dekker, New York, 1990.
F. Richards, A flexible growth function for empirical use, Journal of Experimental Botany 10 (29), pp 290-301, 1959.
Y.H. Hsieh, Temporal trend and regional variability of 2001-2002 multiwave DENV-3 epidemic in Havana City: did Hurricane Michelle contribute to its severity?, Tropical Medicine and International Health, Vol. 18, no. 7, pp 830-838, 2013.
A. Tsoularis, J. Wallace, Analysis of logistic growth models, Mathematical Biosciences, Vol. 179, no. 1, pp 21-55, 2002.
J. Liao, R. Liu, Re-parameterization of five-parameter logistic function, Journal of Chemometrics 23 (5), pp 248-253, 2009.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | ## (data example 1)
data("dengueoutbreak1")
## Not run:
## Parameter estimate for Richards model (for Incidence data example 1)
allmodels(dengueoutbreak1$Incidence,dengueoutbreak1$Time,
start=list(alpha=375,k=2.38,gamma=0.76,eta=16), model="Richards")
## End(Not run)
## or
p1<-allmodels(dengueoutbreak1$Incidence,dengueoutbreak1$Time, model="Richards")
## summary function for a allmodels object
summary(p1)
## plot function for a allmodels object
plot(p1,which=c(1,2))
## Not run:
## Parameter estimate for each built-in model and model averaged
##estimate for final size and turning point of outbreak
allmodels(dengueoutbreak1$Incidence,dengueoutbreak1$Time,
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(dengueoutbreak1$Incidence,dengueoutbreak1$Time,model="all")
## (data example 2)
data("dengueoutbreak2")
# Parameter estimate for 3P Logistic model
allmodels(dengueoutbreak2$Incidence,dengueoutbreak2$Time,
start=list(alpha=375,gamma=1,eta=16), model="logistic3P")
## or
allmodels(dengueoutbreak2$Incidence,dengueoutbreak2$Time,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(dengueoutbreak2$Incidence,dengueoutbreak2$Time,
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(dengueoutbreak2$Incidence,dengueoutbreak2$Time,model="all")
## End(Not run)
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