toy <- data.frame(
age=c(55,72), sex_txt=c("male","female"),
eGFR=c(45,28), uACR=c(120,800),
dm=c(1,0), htn=c(1,1),
albumin=c(4.2,3.4), phosphorous=c(3.3,4.6),
bicarbonate=c(24,22), calcium=c(9.1,9.8)
)
rp <- kfre:::RiskPredictor$new(
df = toy,
columns = list(age="age", sex="sex_txt", eGFR="eGFR", uACR="uACR",
dm="dm", htn="htn", albumin="albumin", phosphorous="phosphorous",
bicarbonate="bicarbonate", calcium="calcium")
)
rp$predict_kfre(years=2, is_north_american=TRUE, num_vars=4)
#> [1] 0.01247073 0.09997874
toy2 <- kfre::add_kfre_risk_col(toy, "age","sex_txt","eGFR","uACR",
dm_col="dm", htn_col="htn",
albumin_col="albumin", phosphorous_col="phosphorous",
bicarbonate_col="bicarbonate", calcium_col="calcium",
num_vars=c(4,6,8), years=c(2,5), is_north_american=TRUE)
head(toy2)
#> age sex_txt eGFR uACR dm htn albumin phosphorous bicarbonate calcium
#> 1 55 male 45 120 1 1 4.2 3.3 24 9.1
#> 2 72 female 28 800 0 1 3.4 4.6 22 9.8
#> kfre_4var_2year kfre_4var_5year kfre_6var_2year kfre_6var_5year
#> 1 0.01247073 0.03842137 0.0119651 0.03688339
#> 2 0.09997874 0.28026055 0.1094176 0.30356514
#> kfre_8var_2year kfre_8var_5year
#> 1 0.01126961 0.03624505
#> 2 0.11930161 0.33888148