GraphicsPredictionBPBM {CoDaLoMic} | R Documentation |
Plots the time series
Description
This function takes into account the data used to estimate and the data used to predict. We use this function when we want to observe the results obtained with the BPBM model.
Usage
GraphicsPredictionBPBM(
especie.All,
names.especie,
ExpectedValue.All,
VarianceValue.All,
Pred,
Plot.Tipe,
Varmas,
Varmenos,
Detail
)
Arguments
especie.All |
Matrix that contains at row i the bacterial taxa of bacteria i at all time points. |
names.especie |
Vector with the names of the bacteria in the same order that are placed in the |
ExpectedValue.All |
Matrix that contains at row i the expected value of the bacterial taxa i at all time points. The bacteria must be placed in the same order than in |
VarianceValue.All |
Matrix that contains at row i the variance of the bacterial taxa i at all time points. The bacteria must be placed in the same order than in |
Pred |
Number. Indicates the time point in which we start predicting. |
Plot.Tipe |
Character. If |
Varmas |
Matrix. Output of "PredictionBPBM" adding "$ExpVarmas". Matrix that contains at row i the expected value plus two times the sqrt(variance) of the bacteria i at all time points t= |
Varmenos |
Matrix. Output of "PredictionBPBM" adding "$ExpVarmenos". Matrix that contains at row i the expected value minus two times the sqrt(variance) of the bacteria i at all time points t= |
Detail |
Character. If |
Value
Returns the indicated plots with a vertical line when the time point is equal to Tt
=Pred-1, in Pred=Tt
+1 the predicction has started.
References
Creus-MartÃ, I., Moya, A., Santonja, F. J. (2022). Bayesian hierarchical compositional models for analysing longitudinal abundance data from microbiome studies. Complexity, 2022.
Examples
names.especie=c("Bact1", "Bact2", "Bact3")
especie.All=cbind(c(0.5,0.3,0.2),
c(0.6,0.3,0.1),
c(0.4,0.1,0.5),
c(0.4,0.1,0.5),
c(0.4,0.1,0.5),
c(0.4,0.1,0.5))
ExpectedValue.All=especie.All[,-1]+0.1
VarianceValue.All=matrix(c(runif(15,0.001,0.004)), 3,5)
Pred=4
Varmas=cbind(matrix(0,3,2),matrix(c(runif(9,0.001,0.004)) ,3 ,3 ))
Varmenos=cbind(matrix(0,3,2),matrix(c(runif(9,0.001,0.004)) ,3 ,3 ))
GraphicsPredictionBPBM(especie.All,
names.especie,
ExpectedValue.All,
VarianceValue.All ,
Pred ,
"Data",
Varmas,
Varmenos,
"no")
GraphicsPredictionBPBM(especie.All,
names.especie,
ExpectedValue.All,
VarianceValue.All ,
Pred ,
"DataExpected",
Varmas,
Varmenos,
"no")
GraphicsPredictionBPBM(especie.All,
names.especie,
ExpectedValue.All,
VarianceValue.All,
Pred ,
"All",
Varmas,
Varmenos,
"no")
GraphicsPredictionBPBM(especie.All,
names.especie,
ExpectedValue.All,
VarianceValue.All ,
Pred ,
"Var",
Varmas,
Varmenos,
"no")
GraphicsPredictionBPBM(especie.All,
names.especie,
ExpectedValue.All,
VarianceValue.All ,
Pred ,
"OnlyVar",
Varmas,
Varmenos,
"no")