Package 'nomogramFormula' reference manual

Title:	Calculate Total Points and Probabilities for Nomogram
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 complexed condition, interaction or restricted cubic spine, TotalPoints.rms() can be used.
Authors:	Jing Zhang, Zhi Jin
Maintainer:	Jing Zhang<[email protected]>
License:	GPL-3
Version:	1.2.0.0
Built:	2025-03-03 03:18:00 UTC
Source:	https://github.com/yikeshu0611/nomogramformula

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)
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)
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 formula of points to each variable

Description

explore the points formula to each variable and get best power.

Usage

formula_points(nomogram, power, digits = 6)
formula_points(nomogram, power, digits = 6)

Arguments

`nomogram`	nomogram, after nomogram command in rms package
`power`	if missing, power will be choose automatically up to 100 based on all R2 equealling to 1
`digits`	default is 6

Value

a global variable Formula_points, the formula of points and each variable

Examples


library(rms)
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)
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"))
library(nomogramFormula)
formula_points(nomogram = nomo)
formula_points(nomogram = nomo,power = 1)
formula_points(nomogram = nomo,power = 2)
formula_points(nomogram = nomo,power = 3,digits=6)


library(rms)
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)
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"))
library(nomogramFormula)
formula_points(nomogram = nomo)
formula_points(nomogram = nomo,power = 1)
formula_points(nomogram = nomo,power = 2)
formula_points(nomogram = nomo,power = 3,digits=6)

explore formula for probability and total points formula

Description

explore the probability formula to total points and get the best power.

Usage

formula_probability(nomogram, power, digits = 6)
formula_probability(nomogram, power, digits = 6)

Arguments

`nomogram`	nomogram after nomogram command in rms package
`power`	if missing, power will be choose automatically up to 100 based on all R2 equealling to 1
`digits`	default is 6

Value

a global variable Formula_probability, the formula of probability and total points

Examples


library(rms)
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)
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"))
library(nomogramFormula)
formula_probability(nomogram = nomo)
formula_probability(nomogram = nomo,power = 2)
formula_probability(nomogram = nomo,power = 3)

library(rms)
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)
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"))
library(nomogramFormula)
formula_probability(nomogram = nomo)
formula_probability(nomogram = nomo,power = 2)
formula_probability(nomogram = nomo,power = 3)

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)
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)
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)
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$formula,rd=df)
library(rms)  # needed for nomogram
set.seed(2018)
n <-2019
age <- rnorm(n,60,20)
sex <- factor(sample(c('female','male'),n,TRUE))
sex <- as.numeric(sex)
weight <- sample(50:100,n,replace = TRUE)
time <- sample(50:800,n,replace = TRUE)
units(time)="day"
death <- sample(c(1,0,0),n,replace = TRUE)
df <- data.frame(time,death,age,sex,weight)
ddist <- datadist(df)
oldoption <- options(datadist='ddist')
f <- cph(formula(Surv(time,death)~sex+age+weight),data=df,
         x=TRUE,y=TRUE,surv=TRUE,time.inc=3)
surv <- Survival(f)
nomo <- nomogram(f,
                 lp=TRUE,
                 fun=list(function(x) surv(365,x),
                          function(x) surv(365*2,x)),
                 funlabel=c("1-Year Survival Prob",
                            "2-Year Survival Prob"))
options(oldoption)
#get the formula by the best power using formula_lp
results <- formula_lp(nomo)
points_cal(formula = results$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)

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)
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)))
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)))

Caculate nomogram total points

Description

Caculate nomogram total points

Usage

total_points(data, lp, digits = 6)
total_points(data, lp, digits = 6)

Arguments

`data`	data must be with no NA
`lp`	linear predictors
`digits`	default is 6

Value

total Points

Examples


library(rms)
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)
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"))
library(nomogramFormula)
#useing raw data to caculate total points
formula_points(nomo)
total_points(data=df)
#using linear predictors to caculate total points
f <- cph(formula(Surv(time,death)~sex+age+weight),data=df,
         linear.predictors=TRUE,
         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"))
formula_lp(nomo)
total_points(lp=f$linear.predictors)

library(rms)
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)
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"))
library(nomogramFormula)
#useing raw data to caculate total points
formula_points(nomo)
total_points(data=df)
#using linear predictors to caculate total points
f <- cph(formula(Surv(time,death)~sex+age+weight),data=df,
         linear.predictors=TRUE,
         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"))
formula_lp(nomo)
total_points(lp=f$linear.predictors)

Caculate nomogram total points

Description

caculate total probability

Usage

total_probability(totalpoints, digits = 6)
total_probability(totalpoints, digits = 6)

Arguments

`totalpoints`	totalpoints after function nomoFormu_total_points
`digits`	default is 6

Value

dataframe

Examples


library(rms)
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)
options(datadist='ddist')
f <- cph(formula(Surv(time,death)~sex+age+weight),data=df,
         linear.predictors=TRUE,
         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"))
library(nomogramFormula)
formula_lp(nomo)
totalpoints <- total_points(lp=f$linear.predictors)
formula_probability(nomo) 
total_probability(totalpoints = totalpoints)

library(rms)
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)
options(datadist='ddist')
f <- cph(formula(Surv(time,death)~sex+age+weight),data=df,
         linear.predictors=TRUE,
         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"))
library(nomogramFormula)
formula_lp(nomo)
totalpoints <- total_points(lp=f$linear.predictors)
formula_probability(nomo) 
total_probability(totalpoints = totalpoints)

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)
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)
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)

Package 'nomogramFormula'

Help Index

Explore the Formula of Total Points and Linear Predictors

Description

Usage

Arguments

Value

Examples

explore formula of points to each variable

Description

Usage

Arguments

Value

Examples

explore formula for probability and total points formula

Description

Usage

Arguments

Value

Examples

Explore the Formula of Total Points and Raw Data

Description

Usage

Arguments

Value

Examples

Calculate Total Points

Description

Usage

Arguments

Value

Examples

Calculate Probabilities

Description

Usage

Arguments

Value

Examples

Caculate nomogram total points

Description

Usage

Arguments

Value

Examples

Caculate nomogram total points

Description

Usage

Arguments

Value

Examples

Caculate Total Points for nomogram Picture

Description

Usage

Arguments

Value

Examples