FitMultipleGrowthMCMC  R Documentation 
The class FitMultipleGrowthMCMC has been superseded by the toplevel class GlobalGrowthFit, which provides a unified approach for growth modelling.
Still, it is still returned if the superseded fit_multiple_growth_MCMC()
is called.
It is a subclass of list with the items:
fit_results: the object returned by modFit
.
best_prediction: a list with the models predictions for each condition.
data: a list with the 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 'FitMultipleGrowthMCMC'
print(x, ...)
## S3 method for class 'FitMultipleGrowthMCMC'
plot(
x,
y = NULL,
...,
add_factor = NULL,
ylims = NULL,
label_x = "time",
label_y1 = "logN",
label_y2 = add_factor,
line_col = "black",
line_size = 1,
line_type = "solid",
line_col2 = "black",
line_size2 = 1,
line_type2 = "dashed",
point_size = 3,
point_shape = 16,
subplot_labels = "AUTO"
)
## S3 method for class 'FitMultipleGrowthMCMC'
summary(object, ...)
## S3 method for class 'FitMultipleGrowthMCMC'
residuals(object, ...)
## S3 method for class 'FitMultipleGrowthMCMC'
coef(object, ...)
## S3 method for class 'FitMultipleGrowthMCMC'
vcov(object, ...)
## S3 method for class 'FitMultipleGrowthMCMC'
deviance(object, ...)
## S3 method for class 'FitMultipleGrowthMCMC'
fitted(object, ...)
## S3 method for class 'FitMultipleGrowthMCMC'
predict(object, env_conditions, times = NULL, ...)
## S3 method for class 'FitMultipleGrowthMCMC'
logLik(object, ...)
## S3 method for class 'FitMultipleGrowthMCMC'
AIC(object, ..., k = 2)
## S3 method for class 'FitMultipleGrowthMCMC'
predictMCMC(
model,
times,
env_conditions,
niter,
newpars = NULL,
formula = . ~ time
)
x 
an instance of FitMultipleGrowthMCMC. 
... 
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_x 
label of the xaxis 
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: 
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. 
point_size 
Size of the data points 
point_shape 
shape of the data points 
subplot_labels 
labels of the subplots according to 
object 
an instance of FitMultipleGrowthMCMC 
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. 
times 
Numeric vector of storage times for the predictions. 
k 
penalty for the parameters (k=2 by default) 
model 
An instance of FitMultipleGrowthMCMC 
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(FitMultipleGrowthMCMC)
: print of the model
plot(FitMultipleGrowthMCMC)
: comparison between the model fitted and the
data.
summary(FitMultipleGrowthMCMC)
: statistical summary of the fit.
residuals(FitMultipleGrowthMCMC)
: model residuals. They are returned as a tibble
with 4 columns: time (storage time), logN (observed count),
exp (name of the experiment) and res (residual).
coef(FitMultipleGrowthMCMC)
: vector of fitted model parameters.
vcov(FitMultipleGrowthMCMC)
: variancecovariance matrix of the model,
estimated as the variance of the samples from the Markov chain.
deviance(FitMultipleGrowthMCMC)
: deviance of the model, calculated as the sum of
squared residuals of the prediction with the lowest standard error.
fitted(FitMultipleGrowthMCMC)
: fitted values of the model. They are returned
as a tibble with 3 columns: time (storage time), exp (experiment
identifier) and fitted (fitted value).
predict(FitMultipleGrowthMCMC)
: model predictions. They are returned as a tibble
with 3 columns: time (storage time), logN (observed count),
and exp (name of the experiment).
logLik(FitMultipleGrowthMCMC)
: loglikelihood of the model
AIC(FitMultipleGrowthMCMC)
: Akaike Information Criterion
predictMCMC(FitMultipleGrowthMCMC)
: prediction including parameter uncertainty
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