library("iCare")
data("bc_data", package="iCare")

### Example 1.A: 
# Fitting a SNP-only model, with no specific genotypes supplied for estimation
# Population disease rates from SEER
res_snps_miss = compute.absolute.risk(model.snp.info = bc_15_snps, 
                      model.disease.incidence.rates = bc_inc,
                      model.competing.incidence.rates = mort_inc, 
                      apply.age.start = 50, 
                      apply.age.interval.length = 30,
                      return.refs.risk = T) # requesting that referent dataset risks be included in results
# Visualizing the Results
summary(res_snps_miss$risk)
summary(res_snps_miss$refs.risk)
plot(density(res_snps_miss$risk), lwd=2, main="SNP-only Risk Stratification: Ages 50-80", xlab="Absolute Risk of Breast Cancer")

### Example 1.B

# Estimating risk in ages 50-80
# Fitting a SNP-only model, with three specific genotypes supplied for estimation (with some missing data)
res_snps_dat = compute.absolute.risk(model.snp.info = bc_15_snps, 
                                model.disease.incidence.rates = bc_inc,
                                model.competing.incidence.rates = mort_inc, 
                                apply.age.start = 50, 
                                apply.age.interval.length = 30, 
                                apply.snp.profile = new_snp_prof, 
                                return.refs.risk = T) # requesting that referent dataset risks be included in results
# Visualizing the Results
names(res_snps_dat)
plot(density(res_snps_dat$refs.risk), lwd=2, main="Referent SNP-only Risk Distribution: Ages 50-80", xlab="Absolute Risk of Breast Cancer")
abline(v=res_snps_dat$risk, col="red")
legend("topright", legend="New Profiles", col="red", lwd=1)

### Example 2

# example of creating the model.cov.info input in the proper format
v1=list()
v1$name = "famhist"
v1$type = "continuous"
v2 = list()
v2$name = "parity"
v2$type = "factor"
v2$levels = c(0,1,2,3,4)
v2$ref = 0
bc_model_cov_info <- list(v1, v2)

# Estimating risk in ages 50-80
# Fitting a model with two risk factors and 15 SNPs, with
# three specific covariate profiles supplied for estimation (with some missing data)
res_covs_snps = compute.absolute.risk(model.formula     = caco ~ famhist + as.factor(parity), 
                                         model.cov.info    = bc_model_cov_info,
                                         model.snp.info    = bc_15_snps,
                                         model.log.RR      = bc_model_log_or,
                                         model.ref.dataset = ref_cov_dat,
                                         model.disease.incidence.rates   = bc_inc,
                                         model.competing.incidence.rates = mort_inc, 
                                         model.bin.fh.name = "famhist",
                                         apply.age.start    = 50, 
                                         apply.age.interval.length = 30,
                                         apply.cov.profile  = new_cov_prof,
                                         apply.snp.profile  = new_snp_prof, 
                                         return.refs.risk   = T)
# Viewing detailed output
print(res_covs_snps$details)