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使用 rms package 预测 r 中的生存概率时出错

[英]Error using rms package for predict survival probability in r

 原文  2020-07-06 02:13:13  2   1    r/ predict/ survival-analysis

问题描述

I have a dataset of 5 variables: CANT_ANU, RETIRE, SEX, PROF and AGE_INI.我有一个包含 5 个变量的数据集：CANT_ANU、RETIRE、SEX、PROF 和 AGE_INI。 Like this:像这样：

library(data.table)
library(rms)

dt <- structure(list(CANT_ANU = c(32.1671232876712, 12.1671232876712, 
                                    45, 28.2465753424658, 33, 3, 34.2493150684932, 31.3287671232877, 
                                    40.413698630137, 31, 19.0849315068493, 31.0846994535519, 20.586301369863, 
                                    34, 36.0849315068493, 32.9150684931507, 24.0849315068493, 3.24657534246575, 
                                    17.0792349726776, 15.0849315068493, 25.7486338797814, 19, 31.3287671232877, 
                                    17.1616438356164, 31.0849315068493), RETIRE = c(1, 1, 1, 1, 1, 
                                                                                    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1), 
                 SEX = c(1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 
                          1, 1, 1, 1, 1, 0, 1, 0, 1), PROF = c(0, 1, 0, 0, 0, 
                                                                      0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 
                                                                      0), AGE_INI = c(49.2383561643836, 64.0575342465753, 32.9945205479452, 
                                                                                       44.6767123287671, 40.4383561643836, 68.7890410958904, 38.2459016393443, 
                                                                                       38.7479452054795, 30.5561643835616, 41.0684931506849, 51.1260273972603, 
                                                                                       41.6219178082192, 54.9479452054794, 36.0846994535519, 32.0109589041096, 
                                                                                       36.1698630136986, 44.6794520547945, 66.4630136986301, 54.8493150684931, 
                                                                                       54.4246575342466, 50.8191780821918, 48.6657534246575, 37.0493150684932, 
                                                                                       53.2712328767123, 37.6684931506849)), row.names = c(NA, -25L
                                                                                       ), class = c("data.table", "data.frame"))

I want to get survival probabilities for each subject on the entire dataset from a estimated exponential model, so I tried this:我想从估计的指数 model 中获得整个数据集上每个主题的生存概率，所以我尝试了这个：

mod.par.exp <- psm(formula = Surv(CANT_ANU, RETIRE) ~ SEX + PROF + AGE_INI,data = dt,dist = "exponential")
probs.exp.rms <- survest(mod.par.exp,dt,times = seq(10,50,10), conf.int=.9)

But I get the following error:但我收到以下错误：

Error in x %*% fit$var[-last, last, drop = FALSE] : 
  argumentos no compatibles

What I am doing wrong?我做错了什么？ I previously tried the examples indicated in survest.psm in the package manual and they worked.我之前尝试过 package 手册中survest.psm中指示的示例，并且它们有效。

1 个解决方案

解决方案1
 1 已采纳  2020-07-06 06:33:58

the error is called because the x is a 4x4 matrix and fit$var[-last, last, drop = FALSE] is a 3x1 column vector.之所以调用该错误，是因为x是一个 4x4 矩阵，而fit$var[-last, last, drop = FALSE]是一个 3x1 列向量。

Here is the part of the code: survest.psm这是代码的一部分： survest.psm

Attached a reprex that shows you why the error occurs.附上一个代表，向您展示错误发生的原因。

For conf.int = 0 or conf.int = FALSE it works.对于conf.int = 0或conf.int = FALSE它有效。

I hope the answer helps you to narrow down the problem.我希望答案可以帮助您缩小问题范围。 It might also be useful to contact the package author or post an issue on the github site.联系 package 作者或在 github 站点上发布问题也可能很有用。

library(data.table)
library(rms)
#> Lade nötiges Paket: Hmisc
#> Lade nötiges Paket: lattice
#> Lade nötiges Paket: survival
#> Lade nötiges Paket: Formula
#> Lade nötiges Paket: ggplot2
#> 
#> Attache Paket: 'Hmisc'
#> The following objects are masked from 'package:base':
#> 
#>     format.pval, units
#> Lade nötiges Paket: SparseM
#> 
#> Attache Paket: 'SparseM'
#> The following object is masked from 'package:base':
#> 
#>     backsolve

dt <- structure(list(CANT_ANU = c(32.1671232876712, 12.1671232876712, 
                                  45, 28.2465753424658, 33, 3, 34.2493150684932, 31.3287671232877, 
                                  40.413698630137, 31, 19.0849315068493, 31.0846994535519, 20.586301369863, 
                                  34, 36.0849315068493, 32.9150684931507, 24.0849315068493, 3.24657534246575, 
                                  17.0792349726776, 15.0849315068493, 25.7486338797814, 19, 31.3287671232877, 
                                  17.1616438356164, 31.0849315068493), RETIRE = c(1, 1, 1, 1, 1, 
                                                                                  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1), 
                     SEX = c(1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 
                             1, 1, 1, 1, 1, 0, 1, 0, 1), PROF = c(0, 1, 0, 0, 0, 
                                                                  0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 
                                                                  0), AGE_INI = c(49.2383561643836, 64.0575342465753, 32.9945205479452, 
                                                                                  44.6767123287671, 40.4383561643836, 68.7890410958904, 38.2459016393443, 
                                                                                  38.7479452054795, 30.5561643835616, 41.0684931506849, 51.1260273972603, 
                                                                                  41.6219178082192, 54.9479452054794, 36.0846994535519, 32.0109589041096, 
                                                                                  36.1698630136986, 44.6794520547945, 66.4630136986301, 54.8493150684931, 
                                                                                  54.4246575342466, 50.8191780821918, 48.6657534246575, 37.0493150684932, 
                                                                                  53.2712328767123, 37.6684931506849)), row.names = c(NA, -25L
                                                                                  ), class = c("data.table", "data.frame"))

mod.par.exp <- psm(formula = Surv(CANT_ANU, RETIRE) ~ SEX + PROF + AGE_INI,data = dt,dist = "exponential")

# Throws the error: 
probs.exp.rms <- survest(mod.par.exp,dt,times = seq(10,50,10), conf.int = .9)
#> Error in x %*% fit$var[-last, last, drop = FALSE]: nicht passende Argumente

# reason for the error: 
## setup of nvar Line 38 of linked github site: 
## nvar <- length(fit$coef) - num.intercepts(fit)

## in your case nvar is: 
nvar_test <- length(mod.par.exp$coef) - num.intercepts(mod.par.exp); nvar_test
#> [1] 3

## setup of x since linear.predictors are missing (first if)... and some more conditions: 
## x is defined in Line 52 of linked github site. 
##  if(missing(x)) x <- cbind(Intercept=1, predict(fit, newdata, type="x"))

## in your case x is: 
x_test <- cbind(Intercept=1, predict(mod.par.exp, dt, type="x")); x_test
#>    Intercept SEX PROF  AGE_INI
#> 1          1   1    0 49.23836
#> 2          1   1    1 64.05753
#> 3          1   1    0 32.99452
#> 4          1   1    0 44.67671
#> 5          1   0    0 40.43836
#> 6          1   0    0 68.78904
#> 7          1   0    1 38.24590
#> 8          1   0    1 38.74795
#> 9          1   1    0 30.55616
#> 10         1   1    0 41.06849
#> 11         1   0    0 51.12603
#> 12         1   1    0 41.62192
#> 13         1   1    0 54.94795
#> 14         1   0    0 36.08470
#> 15         1   1    0 32.01096
#> 16         1   1    0 36.16986
#> 17         1   1    0 44.67945
#> 18         1   1    0 66.46301
#> 19         1   1    0 54.84932
#> 20         1   1    0 54.42466
#> 21         1   1    1 50.81918
#> 22         1   0    0 48.66575
#> 23         1   1    0 37.04932
#> 24         1   0    1 53.27123
#> 25         1   1    0 37.66849

## next condition from line 55 to 62: 
## g1 in line 56 can be calculated properly

## last is calculated from 57 - 60
# last <- {
#   nscale <- length(fit$icoef) - 1
#   ncol(fit$var) - (1 : nscale) + 1
# }

## in your case last is: 
last_test <- {
  nscale <- length(mod.par.exp$icoef) - 1
  ncol(mod.par.exp$var) - (1 : nscale) + 1
}
last_test
#> [1] 4

## computation of g2 in Line 62 throws the error: 

## g2 <- drop(x %*% fit$var[-last, last, drop=FALSE])

column_vec <- mod.par.exp$var[-last_test, last_test, drop = FALSE]; column_vec
#>               [,1]
#> [1,] -0.0222297822
#> [2,] -0.0003043264
#> [3,] -0.0022725899

## error in matrix multiplication: 4x4 %*% 3x1 does not work!! 
x_test %*% column_vec
#> Error in x_test %*% column_vec: nicht passende Argumente

# setting conf.int to FALSE or 0 works. 
probs.exp.rms_noconf <- survest(mod.par.exp, dt, times = seq(10,50,10), conf.int = FALSE)
probs.exp.rms_noconf
#>           10         20          30           40           50
#> 1  0.6109711 0.37328575 0.228066821 0.1393422481 8.513409e-02
#> 2  0.5169610 0.26724870 0.138157161 0.0714218675 3.692232e-02
#> 3  0.8116670 0.65880336 0.534728967 0.4340218715 3.522812e-01
#> 4  0.6790361 0.46109007 0.313096820 0.2126040542 1.443658e-01
#> 5  0.6610245 0.43695340 0.288836903 0.1909282713 1.262083e-01
#> 6  0.1565442 0.02450608 0.003836283 0.0006005478 9.401225e-05
#> 7  0.7981757 0.63708443 0.508505305 0.4058765720 3.239608e-01
#> 8  0.7933484 0.62940168 0.499334815 0.3961464753 3.142822e-01
#> 9  0.8324214 0.69292545 0.576806000 0.4801456797 3.996836e-01
#> 10 0.7262735 0.52747323 0.383089845 0.2782280123 2.020696e-01
#> 11 0.4825998 0.23290259 0.112398750 0.0542436172 2.617796e-02
#> 12 0.7194063 0.51754539 0.372325405 0.2678532341 1.926953e-01
#> 13 0.5135481 0.26373161 0.135438854 0.0695543604 3.571951e-02
#> 14 0.7197743 0.51807509 0.372897156 0.2684018024 1.931887e-01
#> 15 0.8202983 0.67288936 0.551970025 0.4527800935 3.714148e-01
#> 16 0.7812767 0.61039332 0.476886090 0.3725800018 2.910881e-01
#> 17 0.6789980 0.46103834 0.313044133 0.2125563541 1.443253e-01
#> 18 0.2936587 0.08623542 0.025323782 0.0074365485 2.183807e-03
#> 19 0.5153319 0.26556694 0.136855109 0.0705257999 3.634419e-02
#> 20 0.5229761 0.27350397 0.143036034 0.0748044237 3.912092e-02
#> 21 0.7206720 0.51936820 0.374294139 0.2697433227 1.943965e-01
#> 22 0.5274674 0.27822187 0.146752970 0.0774074094 4.082989e-02
#> 23 0.7721483 0.59621304 0.460364901 0.3554699878 2.744756e-01
#> 24 0.6071013 0.36857201 0.223760552 0.1358453261 8.247188e-02
#> 25 0.7655297 0.58603572 0.448627746 0.3434378625 2.629119e-01

Created on 2020-07-06 by the reprex package (v0.3.0)由reprex package (v0.3.0) 于 2020 年 7 月 6 日创建

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