Prize-package: Prize: an R package for prioritization estimation based on...

In Prize: Prize: an R package for prioritization estimation based on analytic hierarchy process

Description

The Prize package is a decision analysis tool based on Analytic Hierarchy Process (AHP), which provides a logical framework to rank and prioritize a group of alternatives. Once the decision makers have their list of available options (alternatives) and defined their criteria for prioritization, the Prize package allows aggregation of individual judgements against each criterion into a group judgement, then use these judgements to calculate final ahp weight for ranking. The Prize package also provides visualizarion tools to illustrate the problem hierarchy, the criteria weights and final ranking.

Author(s)

Daryanaz Dargahi <[e-mail]>

References

Saaty, T. L. (1977) A scaling methods for priorities in hierarchical structure. Journal of Mathematical Psychology, 15, 234-281.

T.L. Saaty. The Analytic Hierarchy Process, Planning, Piority Setting, Re- source Allocation. McGraw-Hill, New york, 1980.

E. Forman and K. Peniwati. Aggregating individual judgments and priorities with the analytic hierarchy process. European Journal of Operational Research, 108(1):165-169, 1998.

J.C. Gower. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53(3/4):pp. 325-338, 1966.

Examples

########################################
## aggregation of individual opinions
########################################
mat = matrix(nrow = 4, ncol = 1, data = NA)
mat[,1] = c(system.file('extdata','ind1.tsv',package = 'Prize'), 
            system.file('extdata','ind2.tsv',package = 'Prize'), 
            system.file('extdata','ind3.tsv',package = 'Prize'),
            system.file('extdata','ind4.tsv',package = 'Prize'))
rownames(mat) = c('ind1','ind2','ind3', 'ind4')
colnames(mat) = c('individual_judgement') 

# non-weighted AIJ 
res = gaggregate(srcfile = mat, method = 'geometric', simulation = 500)

########################################
## Estimating idealised prioritise
## Rating AHP
########################################
category_pcm = read.delim(system.file('extdata','number.tsv', package = 'Prize')
                          , sep = '\t', header = TRUE, row.names = 1)
alt_mat = read.delim(system.file('extdata','numEpitope_alternative_category.tsv', 
                    package = 'Prize'), sep = '\t', header = FALSE)
rate = rating(category_pcm, alt_mat, simulation = 500)

########################################
## Prioritization estimation using AHP
########################################
mat <- matrix(nrow = 7, ncol = 3, data = NA)
mat[,1] <- c('0', '1','2','3','4','4.1','4.2')
mat[,2] <- c('Prioritization_of_DE_genes','Tumor_expression','Normal_expression',
             'Frequency', 'Epitopes', 'Number_of_epitopes', 'Size_of_epitopes')
mat[,3] <- c(system.file('extdata','aggreg.judgement.tsv',package = 'Prize'), 
             system.file('extdata','tumor.PCM.tsv',package = 'Prize'), 
             system.file('extdata','normal.PCM.tsv',package = 'Prize'), 
             system.file('extdata','freq.PCM.tsv',package = 'Prize'), 
             system.file('extdata','epitope.PCM.tsv',package = 'Prize'), 
             system.file('extdata','epitopeNum.PCM.tsv',package = 'Prize'), 
             system.file('extdata','epitopeLength.PCM.tsv',package = 'Prize'))

# prioritization pipeline
prioritization <- pipeline(mat, model = 'relative', simulation = 500)

########################################
## visualizaion 
########################################
# visualizing individal opinion consistency ratio
crplot(ICR(res), angle = 45)

# visualizing the distance among individual opinion and aggregated group judgment
dplot(IP(res))

# visualizing problem hierarchy
ahplot(ahp_plot(prioritization), fontsize = 0.7, cradx = 0.11 ,sradx = 0.12, 
       cirx= 0.18, ciry = 0.07, dist = 0.06)

# visualizing prioritized alternatives
rainbowplot(rainbow_plot(prioritization)$criteria_rainbowplot, xcex = 3)
rainbowplot(rainbow_plot(prioritization)$subcriteria_rainbowplot, xcex = 3)

# visualizing criteria/subcriteria score
wplot(weight_plot(prioritization)$criteria_wplot, type = 'pie', 
      fontsize = 7, pcex = 3)
wplot(weight_plot(prioritization)$criteria_wplot, type = 'bar', 
      fontsize = 7, pcex = 3, xlab = 'Weight', ylab = 'Criteria')
          

Example output

Reading individual judgements.
Aggregating individual judgements with geometric mean (AIJ).
Computing group consistency ratio (GCR).
Computing consensus index (CI).
0 Prioritization_of_DE_genes is processed.
1 Tumor_expression is processed.
2 Normal_expression is processed.
3 Frequency is processed.
4 Epitopes is processed.
4.1 Number_of_epitopes is processed.
4.2 Size_of_epitopes is processed.

Prize documentation built on April 28, 2020, 7:48 p.m.