## Options

`keyATM`

takes various options. You can set options
through a list.

```
my_options <- list(seed = NULL, # automatically generate random seed
iterations = 1500,
verbose = FALSE,
llk_per = 10,
use_weights = TRUE,
weights_type = "information-theory",
prune = TRUE,
thinning = 5,
store_theta = FALSE,
store_pi = FALSE,
parallel_init = FALSE)
out <- keyATM(docs = keyATM_docs, # text input
regular_k = 3, # number of regular topics
keywords = bills_keywords, # keywords
model = "basic", # select the model
options = my_options, # use your own option list
keep = c("Z") # keep a specific object in the output
)
```

####
`seed`

This is a seed used to generate random numbers. The same seed is used
for initialization and fitting the model (`set.seed()`

is
executed before both initialization and fitting). If you do not provide
`seed`

, **keyATM** randomly selects a seed for
you.

####
`iterations`

The default value is `1500`

.

####
`verbose`

Default is `FALSE`

. If it is `TRUE`

, it shows
values of log-likelihood and perplexity.

####
`llk_per`

**keyATM** calculates and stores the log-likelihood and
perplexity. The default value is `10`

.

####
`use_weights`

The default value is `TRUE`

(use weights). We follow the
weighting Scheme in Wilson & Chew (2010). If you do not want to use
weights, please set it to `FALSE`

. Please check our paper for
details.

####
`weights_type`

You can select one of four weights implemented in
**keyATM**. The default is `information-theory`

.
**keyATM** can construct weights from the inverse frequency
of the words, `inv-frequency`

. There are normalized version
of two: `information-theory-normalized`

and
`inv-freq-normalized`

.

####
`prune`

Prune keywords that do not appear in the documents.

####
`thinning`

The default value is `5`

and **keyATM** keeps
every \(5\)th draw from the
sampling.

####
`store_theta`

The default value is `FALSE`

. Storing the value of thetas
allows the calculation of credible intervals.

####
`store_pi`

The default value is `FALSE`

. Storing the value of \(\pi_k\) for all \(k\) (the probability that the topic \(k\) use keyword topic-word
distribution).

####
`parallel_init`

Parallelize processes to speed up initialization. Default is
`FALSE`

. Note that even if you use the same
`seed`

, the initialization will become different between with
and without parallelization.

## Priors

You can manually set priors, but we **do not** recommend
doing it unless you understandd the consequences.

####
`alpha`

Prior for the document-topic distribution. This option only works for
`base`

model.

####
`beta`

Prior for the topic-word distribution.

####
`beta_s`

Prior for the keyword topic-word distribution.

####
`gamma`

Prior for the probability of using keywords in a topic.