DynNom包下在线列线图绘制报错

zhuyu_0610

各位老师好，我在使用DynNom进行绘制在线列线图时，最后一步报错：警告: Error in findInterval: .Internal(findInterval)收到了5个参数，但它最多只能用6个参数
224: findInterval
223: findrow
222: summary.survfit
220: survest.cph
216: getpred.DN
199: <reactive>
197: .func
194: contextFunc
193: env$runWith
182: ctx$run
181: self$.updateValue
179: data2
178: renderPlot
176: func
136: drawPlot
122: <reactive:plotObj>
102: drawReactive
89: renderFunc
88: output$plot
3: runApp
2: DynNom.surv
1: DynNom

这个数据是我之前按照别人视频教授方法做的，在今年1月份时候还可以跑出结果，但前几天更新R后就不能用了。
这是我的代码
`install.packages('rms')
install.packages('DynNom')
##0.1 调用R包
#清除R中的所有数据（归零）
rm(list = ls())
library(rms)
library(DynNom)
library(shiny)
library(plotly)
library(compare)
library(stargazer)
##0.2 设置数据存放的文件夹目录
data_dir <- choose.dir(default = "D:\\data_dir", caption = "选择数据存放的文件夹目录")
##0.3 设置导出图片的文件夹目录
output_dir <- choose.dir(default = "D:\\output_dir", caption = "选择导出图片的文件夹目录")
#选择文件路径
training_dataset_path <- choose.files(default = data_dir, caption = "请选训练集数据文件的txt文件。",
multi = TRUE, filters = Filters,
index = nrow(Filters))
#读取数据
training_dataset<- read.csv(training_dataset_path, header = TRUE,sep="\t", stringsAsFactors=FALSE)
#查看数据
print(paste0("该训练集有 ",dim(training_dataset)[1]," 个样本； ",dim(training_dataset)[2]," 个变量"))
ddist <- datadist(training_dataset)
options(datadist='ddist')
#查看数据集中的变量名
colnames(training_dataset)
#构建多因素回归模型
f_cph <- cph(Surv(OS,Censor) ~ Age+Gender+Blood_Glucose+Gender+WHO_class,
x=T, y=T, surv=T,
data=training_dataset)
#查看多因素Cox分析结果，最下方可见其对应的Coef、p值
print(f_cph)

DynNom(f_cph,training_dataset,
clevel = 0.95) #生成动态列线图`
只有最后一步错误，前面用Cox分析都是可以出结果的。
请麻烦各位老师帮忙指导以下。我刚入门，都是模仿别人做的，很多地方确实不懂，谢谢

liang-qijie

有测试数据吗？

zhuyu_0610

谢谢，这是数据链接。这是数据的百度云链接：[https://pan.baidu.com/s/11S-DNj6lo9GAMnHLHmKfYw?pwd=1234]

liang-qijie

没测试出你说的问题，下面是环境配置、代码、运行代码的方法、输出结果和系统环境

注：我把你给的代码写在名为 R-test.R 的文件里

环境配置

conda create -n r-env
conda activate r-env
conda install r-base
conda install r-rms
conda install r-dynnom

代码

library(rms)
library(DynNom)
library(shiny)
library(plotly)
library(compare)
library(stargazer)

training_dataset_path = '/home/liangqijie/cox_all_dataset_origin.txt'

training_dataset<- read.csv(training_dataset_path, header = TRUE,sep="\t", stringsAsFactors=FALSE)

print(paste0("该训练集有 ",dim(training_dataset)[1]," 个样本； ",dim(training_dataset)[2]," 个变量"))


ddist <- datadist(training_dataset)
options(datadist='ddist')

#查看数据集中的变量名
colnames(training_dataset)

#构建多因素回归模型
f_cph <- cph(Surv(OS,Censor) ~ Age+Gender+Blood_Glucose+Gender+WHO_class,
x=T, y=T, surv=T,
data=training_dataset)
#查看多因素Cox分析结果，最下方可见其对应的Coef、p值
print(f_cph)

DynNom(f_cph,training_dataset,
clevel = 0.95) #生成动态列线图

运行代码的方法

执行 Rscript R-test.R 就可以得到下面的输出结果

输出结果

载入需要的程辑包：Hmisc

载入程辑包：‘Hmisc’

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

    format.pval, units

载入需要的程辑包：magrittr

载入程辑包：‘shiny’

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

    validate

载入需要的程辑包：ggplot2

载入程辑包：‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

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

    subplot

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

    filter

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

    layout


载入程辑包：‘compare’

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

    isTRUE


Please cite as:

 Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
 R package version 5.2.3. https://CRAN.R-project.org/package=stargazer

[1] "该训练集有 228 个样本； 13 个变量"
Frequencies of Missing Values Due to Each Variable
Surv(OS, Censor)              Age           Gender    Blood_Glucose
               0                0                0                1
       WHO_class
               0

Cox Proportional Hazards Model

cph(formula = Surv(OS, Censor) ~ Age + Gender + Blood_Glucose +
    Gender + WHO_class, data = training_dataset, x = T, y = T,
    surv = T)


                       Model Tests    Discrimination
                                             Indexes
Obs       227    LR chi2     20.67    R2       0.087
Events    164    d.f.            5    R2(5,227)0.067
Center -0.531    Pr(> chi2) 0.0009    R2(5,164)0.091
                 Score chi2  21.39    Dxy      0.284
                 Pr(> chi2) 0.0007

                        Coef    S.E.   Wald Z Pr(>|Z|)
Age                      0.0114 0.0094  1.21  0.2264
Gender=M                -0.5130 0.1685 -3.05  0.0023
Blood_Glucose           -0.0149 0.0060 -2.48  0.0132
WHO_class=WHO_class_II  -0.0363 0.3957 -0.09  0.9269
WHO_class=WHO_class_III  0.2398 0.3550  0.68  0.4993


Listening on http://127.0.0.1:7907

浏览器访问 http://127.0.0.1:7907 正常，并能够绘制出图（其他的还没研究）

系统环境

> sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Debian GNU/Linux 12 (bookworm)

Matrix products: default
BLAS/LAPACK: /home/liangqijie/anaconda3/envs/r-env/lib/libopenblasp-r0.3.21.so;  LAPACK version 3.9.0

locale:
 [1] LC_CTYPE=zh_CN.UTF-8       LC_NUMERIC=C
 [3] LC_TIME=zh_CN.UTF-8        LC_COLLATE=zh_CN.UTF-8
 [5] LC_MONETARY=zh_CN.UTF-8    LC_MESSAGES=zh_CN.UTF-8
 [7] LC_PAPER=zh_CN.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=zh_CN.UTF-8 LC_IDENTIFICATION=C

time zone: Asia/Shanghai
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base    

other attached packages:
[1] stargazer_5.2.3 compare_0.2-6   plotly_4.10.2   ggplot2_3.4.4
[5] shiny_1.7.5.1   DynNom_5.0.2    magrittr_2.0.3  rms_6.7-1
[9] Hmisc_5.1-1

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0   viridisLite_0.4.2  dplyr_1.1.3        fastmap_1.1.1
 [5] lazyeval_0.2.2     TH.data_1.1-2      prediction_0.3.14  promises_1.2.1
 [9] digest_0.6.33      rpart_4.1.21       mime_0.12          lifecycle_1.0.3
[13] cluster_2.1.4      ellipsis_0.3.2     survival_3.5-7     compiler_4.3.1
[17] rlang_1.1.1        sass_0.4.7         tools_4.3.1        yaml_2.3.7
[21] utf8_1.2.4         data.table_1.14.8  knitr_1.44         htmlwidgets_1.6.2
[25] multcomp_1.4-25    polspline_1.1.23   foreign_0.8-85     withr_2.5.1
[29] purrr_1.0.2        nnet_7.3-19        grid_4.3.1         fansi_1.0.5
[33] xtable_1.8-4       colorspace_2.1-0   scales_1.2.1       MASS_7.3-60
[37] BBmisc_1.13        cli_3.6.1          mvtnorm_1.2-3      rmarkdown_2.25
[41] generics_0.1.3     rstudioapi_0.15.0  httr_1.4.7         cachem_1.0.8
[45] stringr_1.5.0      splines_4.3.1      base64enc_0.1-3    vctrs_0.6.4
[49] Matrix_1.6-1.1     sandwich_3.0-2     jsonlite_1.8.7     SparseM_1.81
[53] Formula_1.2-5      htmlTable_2.4.1    tidyr_1.3.0        jquerylib_0.1.4
[57] glue_1.6.2         codetools_0.2-19   stringi_1.7.12     gtable_0.3.4
[61] later_1.3.1        munsell_0.5.0      tibble_3.2.1       pillar_1.9.0
[65] htmltools_0.5.6.1  quantreg_5.97      R6_2.5.1           evaluate_0.22
[69] lattice_0.22-5     backports_1.4.1    httpuv_1.6.11      bslib_0.5.1
[73] MatrixModels_0.5-2 Rcpp_1.0.11        gridExtra_2.3      nlme_3.1-163
[77] checkmate_2.3.0    xfun_0.40          zoo_1.8-12         pkgconfig_2.0.3

能在 other attached packages 这一列看到对应的包

zhuyu_0610

liang-qijie 太感谢您了！我再学习一下，再次感谢！

liang-qijie

zhuyu_0610 环境配置那部分内容增加了虚拟环境 r-env 的 activate，之前忘记加了，抱歉（其他的没变）