FitDynamicGrowthMCMC  R Documentation 
The class FitDynamicGrowthMCMC has been superseded by the toplevel class GrowthFit, which provides a unified approach for growth modelling.
Still, it is returned if the superseded fit_MCMC_growth()
is called.
It is a subclass of list with the items:
fit_results: the object returned by modMCMC
.
best_prediction: the model prediction for the fitted parameters.
env_conditions: environmental conditions for the fit.
data: data used for the fit.
starting: starting values for model fitting
known: parameter values set as known.
sec_models: a named vector with the secondary model for each environmental factor
## S3 method for class 'FitDynamicGrowthMCMC'
print(x, ...)
## S3 method for class 'FitDynamicGrowthMCMC'
plot(
x,
y = NULL,
...,
add_factor = NULL,
ylims = NULL,
label_y1 = "logN",
label_y2 = add_factor,
line_col = "black",
line_size = 1,
line_type = 1,
point_col = "black",
point_size = 3,
point_shape = 16,
line_col2 = "black",
line_size2 = 1,
line_type2 = "dashed"
)
## S3 method for class 'FitDynamicGrowthMCMC'
summary(object, ...)
## S3 method for class 'FitDynamicGrowthMCMC'
residuals(object, ...)
## S3 method for class 'FitDynamicGrowthMCMC'
coef(object, ...)
## S3 method for class 'FitDynamicGrowthMCMC'
vcov(object, ...)
## S3 method for class 'FitDynamicGrowthMCMC'
deviance(object, ...)
## S3 method for class 'FitDynamicGrowthMCMC'
fitted(object, ...)
## S3 method for class 'FitDynamicGrowthMCMC'
predict(object, times = NULL, newdata = NULL, ...)
## S3 method for class 'FitDynamicGrowthMCMC'
logLik(object, ...)
## S3 method for class 'FitDynamicGrowthMCMC'
AIC(object, ..., k = 2)
## S3 method for class 'FitDynamicGrowthMCMC'
predictMCMC(
model,
times,
env_conditions,
niter,
newpars = NULL,
formula = . ~ time
)
x 
The object of class 
... 
ignored 
y 
ignored 
add_factor 
whether to plot also one environmental factor.
If 
ylims 
A two dimensional vector with the limits of the primary yaxis. 
label_y1 
Label of the primary yaxis. 
label_y2 
Label of the secondary yaxis. 
line_col 
Aesthetic parameter to change the colour of the line geom in the plot, see: 
line_size 
Aesthetic parameter to change the thickness of the line geom in the plot, see: 
line_type 
Aesthetic parameter to change the type of the line geom in the plot, takes numbers (16) or strings ("solid") see: 
point_col 
Aesthetic parameter to change the colour of the point geom, see: 
point_size 
Aesthetic parameter to change the size of the point geom, see: 
point_shape 
Aesthetic parameter to change the shape of the point geom, see: 
line_col2 
Same as lin_col, but for the environmental factor. 
line_size2 
Same as line_size, but for the environmental factor. 
line_type2 
Same as lin_type, but for the environmental factor. 
object 
an instance of FitDynamicGrowthMCMC 
times 
Numeric vector of storage times for the predictions. 
newdata 
a tibble describing the environmental conditions (as 
k 
penalty for the parameters (k=2 by default) 
model 
An instance of FitDynamicGrowthMCMC 
env_conditions 
Tibble with the (dynamic) environmental conditions during the experiment. It must have one column named 'time' with the storage time and as many columns as required with the environmental conditions. 
niter 
Number of iterations. 
newpars 
A named list defining new values for the some model parameters.
The name must be the identifier of a model already included in the model.
These parameters do not include variation, so defining a new value for a fitted
parameters "fixes" it. 
formula 
A formula stating the column named defining the elapsed time in

An instance of MCMCgrowth()
.
print(FitDynamicGrowthMCMC)
: print of the model
plot(FitDynamicGrowthMCMC)
: compares the model fitted against the data.
summary(FitDynamicGrowthMCMC)
: statistical summary of the fit.
residuals(FitDynamicGrowthMCMC)
: model residuals.
coef(FitDynamicGrowthMCMC)
: vector of fitted model parameters.
vcov(FitDynamicGrowthMCMC)
: variancecovariance matrix of the model,
estimated as the variance of the samples from the Markov chain.
deviance(FitDynamicGrowthMCMC)
: deviance of the model, calculated as the sum
of squared residuals for the parameter values resulting in the best fit.
fitted(FitDynamicGrowthMCMC)
: vector of fitted values.
predict(FitDynamicGrowthMCMC)
: vector of model predictions.
logLik(FitDynamicGrowthMCMC)
: loglikelihood of the model
AIC(FitDynamicGrowthMCMC)
: Akaike Information Criterion
predictMCMC(FitDynamicGrowthMCMC)
: prediction including parameter uncertainty
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.