formula_lp: Explore the Formula of Total Points and Linear Predictors

In nomogramFormula: Calculate Total Points and Probabilities for Nomogram

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)

Example output

Loading required package: Hmisc
Loading required package: lattice
Loading required package: survival
Loading required package: Formula
Loading required package: ggplot2

Attaching package: ‘Hmisc’

The following objects are masked from ‘package:base’:

    format.pval, units

Loading required package: SparseM

Attaching package: ‘SparseM’

The following object is masked from ‘package:base’:

    backsolve

$formula
                       b0      x^1
linear predictor 131.9182 821.7001

$test
                 R2 RMSE
linear predictor  1    0

$diff
              -0.2    -0.15     -0.1   -0.05        0     0.05      0.1
nomogram -32.42182 8.663185 49.74819 90.8332 131.9182 173.0032 214.0882
fit      -32.42182 8.663185 49.74819 90.8332 131.9182 173.0032 214.0882
diff       0.00000 0.000000  0.00000  0.0000   0.0000   0.0000   0.0000
             0.15
nomogram 255.1732
fit      255.1732
diff       0.0000

$formula
                       b0      x^1
linear predictor 131.9182 821.7001

$test
                 R2 RMSE
linear predictor  1    0

$diff
              -0.2    -0.15     -0.1   -0.05        0     0.05      0.1
nomogram -32.42182 8.663185 49.74819 90.8332 131.9182 173.0032 214.0882
fit      -32.42182 8.663185 49.74819 90.8332 131.9182 173.0032 214.0882
diff       0.00000 0.000000  0.00000  0.0000   0.0000   0.0000   0.0000
             0.15
nomogram 255.1732
fit      255.1732
diff       0.0000

$formula
                       b0      x^1 x^2 x^3
linear predictor 131.9182 821.7001   0   0

$test
                 R2 RMSE
linear predictor  1    0

$diff
              -0.2    -0.15     -0.1   -0.05        0     0.05      0.1
nomogram -32.42182 8.663185 49.74819 90.8332 131.9182 173.0032 214.0882
fit      -32.42182 8.663185 49.74819 90.8332 131.9182 173.0032 214.0882
diff       0.00000 0.000000  0.00000  0.0000   0.0000   0.0000   0.0000
             0.15
nomogram 255.1732
fit      255.1732
diff       0.0000

nomogramFormula documentation built on Jan. 28, 2020, 5:07 p.m.