An implementation of a Poisson Naive Bayes classifier.
cl_poisson_naive_bayes(
ndr_container_or_object = NULL,
return_decision_values = TRUE
)The purpose of this argument is to make the constructor of the cl_poisson_naive_bayes classifier work with magrittr pipe (|>) operator. This argument should almost never be directly set by the user to anything other than NULL. If this is set to the default value of NULL, then the constructor will return a cl_poisson_naive_bayes object. If this is set to an ndr container, then a cl_poisson_naive_bayes object will be added to the container and the container will be returned. If this argument is set to another ndr object, then both that ndr object as well as a new cl_poisson_naive_bayes object will be added to a new container and the container will be returned.
A Boolean specifying whether the prediction function should return columns that have the decision values. Setting this to FALSE will save memory so can be useful when analyzing very large high temporal resolution data sets. However if this is set to FALSE< metrics won't be able to compute decoding accuracy measures that are based on the decision values; e.g., the rm_main_results object won't be able to calculate normalized rank decision values.
This constructor creates an NDR classifier object with the class
cl_poisson_naive_bayes. Like all NDR classifier objects, this classifier
will be used by a cross-validator to learn the relationship between neural
activity and experimental conditions on a training set of data, and then it
will be used to make predictions on a test set of data.
This classifier object implements a Poisson Naive Bayes classifier. The classifier works by learning the expected number of occurrences (denoted lambda) for each feature and each class by taking the average of the training data over all trials (separately for each feature and each class). To evaluate whether a given test point belongs to class i, the log of the likelihood function is calculated using the lambda values as parameters of Poisson distributions (i.e., there is a separate Poisson distribution for each feature, that is based on the lambda value for that feature). The overall likelihood value is calculated by multiplying the probabilities for each neuron together (i.e,. Naive Bayes classifiers assume that each feature is independent), or equivalently, adding the log of the probabilities for each feature together. The class with the highest likelihood value is chosen as the predicted label, and the decision values are the log likelihood values.
Note: this classifier uses spike counts, so the binned data must be converted to use this classifier, for example, if you are using the basic_DS data source, then use_count_data = TRUE should be set in the constructor. Also, preprocessors that convert the data into values that are not integers should not be used, for example, the fp_zscore should not be used with this classifier.
Like all classifiers, this classifier learning a model based on training data and then makes predictions on new test data.
Other classifier:
cl_max_correlation(),
cl_svm()
# running a basic decoding analysis using the cl_max_correlation
data_file <- system.file(file.path("extdata", "ZD_150bins_50sampled.Rda"),
package = "NeuroDecodeR")
ds <- ds_basic(data_file, "stimulus_ID", 18, use_count_data = TRUE)
#> Automatically selecting sites_IDs_to_use. Since num_cv_splits = 18 and num_label_repeats_per_cv_split = 1, all sites that have 18 repetitions have been selected. This yields 132 sites that will be used for decoding (out of 132 total).
fps <- list()
cl <- cl_poisson_naive_bayes()
cv <- cv_standard(datasource = ds,
classifier = cl,
feature_preprocessors = fps,
num_resample_runs = 2) # better to use more resample runs (default is 50)
#> Warning: The result_metrics argument is NULL in the cv_standard constructor. Setting the result_metrics to default values of rm_main_results and rm_confusion_matrix.
# \donttest{
DECODING_RESULTS <- run_decoding(cv)
#>
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# }