Description Usage Arguments Value Examples
View source: R/zinb_analysis_tool.R
Perform ZINB analysis on real data.
1 2 3 4 5 6 7 8 9 10  zinb_analysis_tool(
grp_data,
pair_data,
merge_list,
min_freq = 15,
min_size = 20,
n_perm = 20,
n_copies = round(detectCores()/2),
seed = 1234
)

grp_data 
a data.frame containing the following 2 columns:

pair_data 
a data.frame containing the following 3 columns:

merge_list, 
a list with nesting lists, indicating which vaccines are of interest and which vaccines are to be merged. For example, if you are interested in vaccines FLU3 and FLU4, and you want to merge them into one vaccine FLU, you would like to add list(c('FLU3','FLU4'),'FLU') in this list 
min_freq 
a nonnegative integer indicating the criteria for scarce AEs. If an AE appears less than 'min_freq' times in the data.frame pair_data, it will be labeled as scarce and be ignored in the study. It can be set to 0. 
min_size 
a positive integer indicating the criteria for small AE groups. If an AE group contains less than 'min_size' AEs, it will be label as a small AE group, and be ignored from the study. 
n_perm 
a positive integer that decide the number of permutations to determine the p value for observed RRs. 
n_copies 
a positive integer indicating the number of permutations can be done in parallel. This number is default to half of the computer's number of cores. 
seed 
to obtain reproducible result, you may want to set this seed. 
A big data.frame containing the following columns:
y: The weighted count of the vaccineAE cell
E: The baseline frequency of the vaccineAE cell
vaccine: The vaccine type
AE_grp: The AE group
AE: The Adverse Event (AE)
s: The group level RR for this vaccine
r: The over dispersion parameter for this AE group
p: The zero component probability of this vaccineAE_grp combination
beta: The log mean parameter for this vaccineAE_grp combination
lambda_hat: The individual AE RR (Bayes Estimator)
s_pval: The p value for s
lambda_pval: The p value for lambda_hat (individual AE RR)
1 2 3 4 5 6 7 8  data(vaers_data)
data("dd.meddra")
data("merge_list")
big_data = zinb_analysis_tool(dd.meddra[1:10000,],
vaers_data[1:10000,],
merge_list,
n_perm = 2,
n_copies = 2)

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