| Type: | Package |
| Title: | Calculate Total Points and Probabilities for Nomogram |
| Version: | 1.2.0.0 |
| Description: | A nomogram, which can be carried out in 'rms' package, provides a graphical explanation of a prediction process. However, it is not very easy to draw straight lines, read points and probabilities accurately. Even, it is hard for users to calculate total points and probabilities for all subjects. This package provides formula_rd() and formula_lp() functions to fit the formula of total points with raw data and linear predictors respectively by polynomial regression. Function points_cal() will help you calculate the total points. prob_cal() can be used to calculate the probabilities after lrm(), cph() or psm() regression. For more complex condition, interaction or restricted cubic spine, TotalPoints.rms() can be used. |
| Author: | Jing Zhang, Zhi Jin |
| Maintainer: | Jing Zhang<zj391120@163.com> |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | rms, do, Hmisc |
| RoxygenNote: | 6.1.1 |
| URL: | https://github.com/yikeshu0611/nomogramFormula |
| BugReports: | https://github.com/yikeshu0611/nomogramFormula/issues |
| NeedsCompilation: | no |
| Packaged: | 2020-01-28 12:36:48 UTC; asus |
| Repository: | CRAN |
| Date/Publication: | 2020-01-28 13:10:02 UTC |
Caculate Total Points for nomogram Picture
Description
Compared with points_cal() command, TotalPoints.rms() is suit for more complexed condition. Since this command is based on formula from 'rms' package, it may be also more accurate. However, formula for each variable can not be caculated.
Usage
TotalPoints.rms(rd, fit, nom, kint = NULL)
Arguments
rd |
raw data |
fit |
regression result in 'rma' package |
nom |
nomoram() command result |
kint |
number of intercept. Default is to use fit$interceptRef if it exists, or 1. |
Value
a dataframe contains rawdata and total points
Examples
library(rms)
n <- 1000
set.seed(17)
d <- data.frame(age = rnorm(n, 50, 10),
blood.pressure = rnorm(n, 120, 15),
cholesterol = rnorm(n, 200, 25),
sex = factor(sample(c('female','male'), n,TRUE)))
d <- upData(d,
L = .4*(sex=='male') + .045*(age-50) +
(log(cholesterol - 10)-5.2)*(-2*(sex=='female') + 2*(sex=='male')),
y = ifelse(runif(n) < plogis(L), 1, 0))
ddist <- datadist(d); options(datadist='ddist')
f <- lrm(y ~ lsp(age,50) + sex * rcs(cholesterol, 4) + blood.pressure,
data=d)
nom <- nomogram(f)
TotalPoints.rms(rd = d,fit = f,nom = nom)
Explore the Formula of Total Points and Linear Predictors
Description
Explore the formula of total points and linear predictors by the best power.
Usage
formula_lp(nomogram, power, digits = 6)
Arguments
nomogram |
results of nomogram() function in 'rms' package |
power |
power can be automatically selected based on all R2 equal 1 |
digits |
default is 6 |
Value
formula is the formula of total points and linear predictors. test is the R2 and RMSE which are used to test the fitted points. diff is difference between nomogram points and fitted points
Examples
library(rms) # needed for nomogram
set.seed(2018)
n <-2019
age <- rnorm(n,60,20)
sex <- factor(sample(c('female','male'),n,TRUE))
sex <- as.numeric(sex)
weight <- sample(50:100,n,replace = TRUE)
time <- sample(50:800,n,replace = TRUE)
units(time)="day"
death <- sample(c(1,0,0),n,replace = TRUE)
df <- data.frame(time,death,age,sex,weight)
ddist <- datadist(df)
oldoption <- options(datadist='ddist')
f <- cph(formula(Surv(time,death)~sex+age+weight),data=df,
x=TRUE,y=TRUE,surv=TRUE,time.inc=3)
surv <- Survival(f)
nomo <- nomogram(f,
lp=TRUE,
fun=list(function(x) surv(365,x),
function(x) surv(365*2,x)),
funlabel=c("1-Year Survival Prob",
"2-Year Survival Prob"))
options(oldoption)
formula_lp(nomogram = nomo)
formula_lp(nomogram = nomo,power = 1)
formula_lp(nomogram = nomo,power = 3,digits=6)
Explore the Formula of Total Points and Raw Data
Description
Explore the formula of total points and raw data by the best power.
Usage
formula_rd(nomogram, power, digits = 6)
Arguments
nomogram |
results of nomogram() function in 'rms' package |
power |
power can be automatically selected based on all R2 equal 1 |
digits |
default is 6 |
Value
formula is the formula of total points and raw data. test is the R2 and RMSE which are used to test the fitted points. diff is difference between nomogram points and fitted points
Examples
library(rms) # needed for nomogram
set.seed(2018)
n <-2019
age <- rnorm(n,60,20)
sex <- factor(sample(c('female','male'),n,TRUE))
sex <- as.numeric(sex)
weight <- sample(50:100,n,replace = TRUE)
time <- sample(50:800,n,replace = TRUE)
units(time)="day"
death <- sample(c(1,0,0),n,replace = TRUE)
df <- data.frame(time,death,age,sex,weight)
ddist <- datadist(df)
oldoption <- options(datadist='ddist')
f <- cph(formula(Surv(time,death)~sex+age+weight),data=df,
x=TRUE,y=TRUE,surv=TRUE,time.inc=3)
surv <- Survival(f)
nomo <- nomogram(f,
lp=TRUE,
fun=list(function(x) surv(365,x),
function(x) surv(365*2,x)),
funlabel=c("1-Year Survival Prob",
"2-Year Survival Prob"))
options(oldoption)
formula_rd(nomogram = nomo)
formula_rd(nomogram = nomo,power = 1)
formula_rd(nomogram = nomo,power = 3,digits=6)
Calculate Total Points
Description
Calculate total points.
Usage
points_cal(formula, rd, lp, digits = 6)
Arguments
formula |
the formula of total points with raw data or linear predictors |
rd |
raw data, which cannot have missing values |
lp |
linear predictors |
digits |
default is 6 |
Value
total Points
Examples
library(rms) # needed for nomogram
set.seed(2018)
n <-2019
age <- rnorm(n,60,20)
sex <- factor(sample(c('female','male'),n,TRUE))
sex <- as.numeric(sex)
weight <- sample(50:100,n,replace = TRUE)
time <- sample(50:800,n,replace = TRUE)
units(time)="day"
death <- sample(c(1,0,0),n,replace = TRUE)
df <- data.frame(time,death,age,sex,weight)
ddist <- datadist(df)
oldoption <- options(datadist='ddist')
f <- cph(formula(Surv(time,death)~sex+age+weight),data=df,
x=TRUE,y=TRUE,surv=TRUE,time.inc=3)
surv <- Survival(f)
nomo <- nomogram(f,
lp=TRUE,
fun=list(function(x) surv(365,x),
function(x) surv(365*2,x)),
funlabel=c("1-Year Survival Prob",
"2-Year Survival Prob"))
options(oldoption)
#get the formula by the best power using formula_lp
results <- formula_lp(nomo)
points_cal(formula = results$formula,lp=f$linear.predictors)
#get the formula by the best power using formula_rd
results <- formula_rd(nomogram = nomo)
points_cal(formula = results$formula,rd=df)
Calculate Probabilities
Description
Use Survival() function from 'rms' pacakge to calculate probabilities after lrm(), cph() or psm() regression. If you want to calculate lrm() probabilities, please leave linear.predictors be TRUE and times be missing. If you want to calculate cph() probabilites, please leave both linear.predictors and surv be TRUE.
Usage
prob_cal(reg, times, q, lp)
Arguments
reg |
regression results after lrm(), cph() or psm() in 'rms' package. |
times |
if you want to calculate probabilities for lrm() function, please left times missing. |
q |
quantile, for example 0.5 |
lp |
linear predictors |
Value
lieaner predictors and probabilities as a dataframe
Examples
set.seed(2018)
n <-2019
age <- rnorm(n,60,20)
sex <- factor(sample(c('female','male'),n,TRUE))
sex <- as.numeric(sex)
weight <- sample(50:100,n,replace = TRUE)
time <- sample(50:800,n,replace = TRUE)
units(time)="day"
death <- sample(c(1,0,0),n,replace = TRUE)
df <- data.frame(time,death,age,sex,weight)
library(rms) #needed for lrm(), cph() and psm()
ddist <- datadist(df)
oldoption <- options(datadist='ddist')
# lrm() function
f <- lrm(death~sex+age+weight,data=df,
linear.predictors = TRUE)
head(prob_cal(reg = f))
# cph() function
f <- cph(Surv(time,death)~sex+age+weight,data=df,
linear.predictors=TRUE,surv=TRUE)
head(prob_cal(reg = f,times = c(365,365*2)))
# psm() function
f <- psm(Surv(time,death)~sex+age+weight,data=df)
head(prob_cal(reg = f,times = c(365,365*2)))