Print out a quick overview of a remstimate object

A function that prints out the estimates returned by a 'remstimate' object.

Usage

# S3 method for remstimate
print(x, ...)

Arguments

x

is a remstimate object.

...

further arguments to be passed to the print method.

Value

no return value. Prints out the main characteristics of a 'remstimate' object.

Examples

# ------------------------------------ #
#       method 'print' for the         #
#       tie-oriented model: "BSIR"     #
# ------------------------------------ #

# loading data
data(tie_data)

# processing event sequence with remify
tie_reh <- remify::remify(edgelist = tie_data$edgelist, model = "tie")
  
# specifying linear predictor
tie_model <- ~ 1 + 
               remstats::indegreeSender()+
               remstats::inertia()+
               remstats::reciprocity() 

# calculating statistics
tie_reh_stats <- remstats::remstats(reh = tie_reh, 
                                    tie_effects = tie_model)

# running estimation
tie_mle <- remstimate::remstimate(reh = tie_reh,
                                  stats = tie_reh_stats,
                                  method = "BSIR",
                                  nsim = 100,
                                  ncores = 1)

# print
tie_mle
#> Relational Event Model (tie oriented) 
#> 
#> Posterior Modes:
#> 
#>       baseline indegreeSender        inertia    reciprocity 
#>    -4.94474683     0.06081207    -0.24980370    -0.07232806 

# ------------------------------------ #
#      method 'print' for the          #
#      actor-oriented model: "BSIR"    #
# ------------------------------------ #

# loading data
data(ao_data)

# processing event sequence with remify
ao_reh <- remify::remify(edgelist = ao_data$edgelist, model = "actor")
  
# specifying linear predictor (for sender rate and receiver choice model)
rate_model <- ~ 1 + remstats::indegreeSender()
choice_model <- ~ remstats::inertia() + remstats::reciprocity()

# calculating statistics
ao_reh_stats <- remstats::remstats(reh = ao_reh, 
                                   sender_effects = rate_model, 
                                   receiver_effects = choice_model)

# running estimation
ao_mle <- remstimate::remstimate(reh = ao_reh,
                                 stats = ao_reh_stats,
                                 method = "BSIR",
                                 nsim = 100,
                                 ncores = 1)

# print
ao_mle
#> Relational Event Model (actor oriented) 
#> 
#> Posterior Modes rate model **for sender**:
#> 
#>       baseline indegreeSender 
#>   -4.821751052   -0.007672502 
#> 
#> --------------------------------------------------------------------------------
#> 
#> Posterior Modes choice model **for sender**:
#> 
#>     inertia reciprocity 
#>  -0.0329211   0.0155800 

# ------------------------------------ #
#   for more examples check vignettes  #
# ------------------------------------ #