scidca {ggscidca}

scidca

Description

You can use it to generate a decision curve with coloured bars.

Usage

scidca(
  fit,
  newdata = NULL,
  timepoint = "median",
  cmprsk = FALSE,
  modelnames = NULL,
  merge = FALSE,
  y.min = NULL,
  xstop = NULL,
  y.max = NULL,
  pyh = NULL,
  relcol = "#c01e35",
  irrelcol = "#0151a2",
  relabel = "Nomogram relevant",
  irrellabel = "Nomogram irrelevant",
  text.size = 4.5,
  text.col = "green",
  colbar = TRUE,
  threshold.text = FALSE,
  threshold.line = FALSE,
  nudge_x = 0,
  nudge_y = 0,
  threshold.linetype = 2,
  threshold.linewidth = 1.2,
  threshold.linecol = "black",
  po.text.size = 4,
  po.text.col = "black",
  po.text.fill = "white",
  liftpec = NULL,
  rightpec = NULL,
  legend.position = c(0.85, 0.75)
)

Arguments

fit

Fill in the model you want to analyze. Support survival analysis and logistic regression.

newdata

If the decision curve of the validation set is to be analysed. Fill in the validation set data here.

timepoint

If it is a survival analysis, fill in the point in time you need to study. The default is the median time.

cmprsk

If it is a competitive risk model, select TRUE here.

modelnames

Defines the name of the generated image model.

merge

If true is selected it will merge the two long zones.

y.min

The maximum value of the negative part of the picture. Generally defaults to positive values multiplied by 0.4.

xstop

The maximum value of the X-axis of the picture.

y.max

The maximum value of the Y-axis. The default value is the maximum net benefit.

pyh

The height at which the bars are plotted cannot exceed y.min.

relcol

The colour of the relevant part of the bar. The default is red.

irrelcol

The colour of the irrelevant part of the bar. The default is blue.

relabel

Relevance Tags.

irrellabel

No relevant tags.

text.size

Font size.

text.col

The colour of the font.

colbar

The default is true, and if false is selected, bar plotting is cancelled.

threshold.text

The default is FALSE, if TRUE is selected, a text message for the threshold will be added.

threshold.line

The default is FALSE, and if TRUE is selected, lines for the threshold will be added.

nudge_x

Used to adjust the x-axis position of the point where the threshold is located.

nudge_y

Used to adjust the y-axis position of the point where the threshold is located.

threshold.linetype

The line shape of the threshold line.

threshold.linewidth

The line width of the threshold line.

threshold.linecol

The colour of the threshold line.

po.text.size

The size of the threshold point text.

po.text.col

The colour of the threshold point text.

po.text.fill

The background of the threshold point text.

liftpec

Threshold point left displacement.

rightpec

Threshold point right displacement.

legend.position

Set the position of the legend.

Details

Table 1 represents the relationship between the baseline values of the data. This function can be easily done.Creates 'Table 1', i.e., description of baseline patient characteristics, which is essential in every medical research. Supports both continuous and categorical variables, as well as p-values and standardized mean differences.

Value

A picture.

Examples

library(survival)
library(reshape2)
library(ggplot2)
##Import the internal data of the R package
bc<-Breastcancer
##Categorical variables converted to factors
bc$histgrad<-as.factor(bc$histgrad)
bc$er<-as.factor(bc$er)
bc$pr<-as.factor(bc$pr)
bc$ln_yesno<-as.factor(bc$ln_yesno)
##Generate Survival Analysis Model
f1<-coxph(Surv(time,status)~er+histgrad+pr+age+ln_yesno,bc)
##Draw decision curve
scidca(f1)
scidca(f1,threshold.line = TRUE,threshold.text = TRUE)
##logistic regression model
fit<-glm(status~er+histgrad+pr+age+ln_yesno,family = binomial(link = "logit"),data=bc)
##Draw decision curve
scidca(f1)
scidca(f1,threshold.line = TRUE,threshold.text = TRUE)
##random forest model
library(randomForest)
LIRI<-LIRI
set.seed(1)
index <- sample(2,nrow(LIRI),replace = TRUE,prob=c(0.7,0.3))
traindata <- LIRI[index==1,]
testdata <- LIRI[index==2,]
traindata$status<-as.factor(traindata$status)
#Modelling random forests
fit<-randomForest(status ~ANLN+CENPA+GPR182+BCO2 ,data=traindata,ntree=500,
important=TRUE,proximity=TRUE)
scidca(fit,newdata = traindata)
scidca(fit,newdata = testdata )
scidca(fit,newdata = testdata ,threshold.line = TRUE,threshold.text = TRUE)