rmsb
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20 hours ago •
harrelfe
harrelfe
Problem with several rmsb errors
I found several possible bugs while using the functions of the new rmsb package. I report the first one, summary(fit) or plot(summary(fit) does not seem to work in this example. Please check and see what you can do. Thank you.
library(rms)
#> 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
library(Hmisc)
library(rmsb)
#> Warning: package 'rmsb' was built under R version 4.1.0
DAT<-readRDS("C:/Users/Alberto/Desktop/caterina bayes/datos", refhook = NULL)
DAT$RR_PAC5<-DAT$RR_PAC4
DAT$RR_PAC5[DAT$RR_PAC5==1]<-0
DAT$RR_PAC5[DAT$RR_PAC5==2]<-1
DAT$RR_PAC5[DAT$RR_PAC5==3]<-2
DAT$RR_PAC5<-factor(DAT$RR_PAC5)
dd<-datadist(DAT)
options(datadist='dd')
bsix2 <- blrm(HE6 ~ cir * rcs(Age,3)+ linf+ RR_PAC5 + Esquema2+Estadio3 + pol(EORTC),
~ pol(EORTC), cppo=function(y) y, data=DAT, file='C:/Users/Alberto/Desktop/caterina bayes/mod_finalint.rds')
bsix2
#> Frequencies of Missing Values Due to Each Variable
#> HE6 cir Age linf RR_PAC5 Esquema2 Estadio3 EORTC
#> 0 0 0 0 0 0 0 2
#>
#> Constrained Partial Proportional Odds Ordinal Logistic Model
#>
#> blrm(formula = HE6 ~ cir * rcs(Age, 3) + linf + RR_PAC5 + Esquema2 +
#> Estadio3 + pol(EORTC), ppo = ~pol(EORTC), cppo = function(y) y,
#> data = DAT, file = "C:/Users/Alberto/Desktop/caterina bayes/mod_finalint.rds")
#>
#>
#> Frequencies of Responses
#>
#> 0 8.3 16.7 25 33.3 41.7 50 58.3 66.7 75 83.3 91.7 100
#> 5 1 4 1 9 2 43 18 35 20 48 8 23
#>
#>
#> Mixed Calibration/ Discrimination Rank Discrim.
#> Discrimination Indexes Indexes Indexes
#> Obs217 LOO log L-458.6+/-12.05 g 1.15 [0.896, 1.414] C 0.651 [0.629, 0.671]
#> Draws4000 LOO IC 917.2+/-24.1 gp 0.228 [0.188, 0.269] Dxy 0.301 [0.258, 0.342]
#> Chains4 Effective p27.62+/-2.08 EV 0.177 [0.119, 0.23]
#> p 13 B 0.214 [0.203, 0.225] v 1.18 [0.634, 1.712]
#> vp 0.042 [0.029, 0.055]
#>
#> Mode Beta Mean Beta Median Beta S.E. Lower Upper
#> cir=1 -0.3048 -0.2117 -0.2311 2.5772 -5.4525 4.6297
#> Age 0.0781 0.0814 0.0810 0.0422 -0.0042 0.1628
#> Age' -0.1145 -0.1195 -0.1186 0.0529 -0.2267 -0.0194
#> linf 0.2816 0.2868 0.2916 0.3336 -0.3699 0.9325
#> RR_PAC5=1 -0.5909 -0.5951 -0.5896 0.3487 -1.2717 0.0925
#> RR_PAC5=2 -0.7137 -0.7289 -0.7364 0.3644 -1.4355 -0.0187
#> Esquema2 -0.0156 -0.0222 -0.0305 0.3128 -0.6002 0.6123
#> Estadio3=1 0.0299 0.0286 0.0284 0.3145 -0.5739 0.6349
#> Estadio3=2 -0.3872 -0.4075 -0.4141 0.5537 -1.5124 0.6412
#> EORTC 0.2385 0.2528 0.2524 0.0980 0.0573 0.4420
#> EORTC^2 -0.0013 -0.0014 -0.0014 0.0007 -0.0028 -0.0001
#> cir=1 * Age -0.0077 -0.0100 -0.0097 0.0550 -0.1200 0.0943
#> cir=1 * Age' 0.0103 0.0139 0.0117 0.0723 -0.1251 0.1555
#> EORTC x f(y) 0.0686 0.0800 0.0776 0.0558 -0.0266 0.1880
#> EORTC^2 x f(y) -0.0003 -0.0004 -0.0004 0.0004 -0.0012 0.0003
#> Pr(Beta>0) Symmetry
#> cir=1 0.4655 1.02
#> Age 0.9755 1.08
#> Age' 0.0092 0.96
#> linf 0.8090 0.98
#> RR_PAC5=1 0.0460 1.01
#> RR_PAC5=2 0.0217 1.00
#> Esquema2 0.4622 0.99
#> Estadio3=1 0.5370 1.04
#> Estadio3=2 0.2260 1.01
#> EORTC 0.9955 1.00
#> EORTC^2 0.0190 1.02
#> cir=1 * Age 0.4298 0.97
#> cir=1 * Age' 0.5700 1.03
#> EORTC x f(y) 0.9290 1.12
#> EORTC^2 x f(y) 0.1668 0.88
summary(bsix2) # not working
#> Error in xd %*% beta: argumentos no compatibles
plot(summary(bsix2)) # not working
#> Error in xd %*% beta: argumentos no compatibles
´´´
Since you do not wish to follow guidelines for posting minimal working examples I don't know how to help.
I'm so sorry I did it wrong. I have reduced the code to the essentials, added a simulated database, and after the model I include the functions that I can't make work. Thank you very much for your time and valuable support, Professor Harrell.
library(rms)
library(Hmisc)
library(rmsb)
data<- data.frame(HE6=as.integer(cut2(sample(1:100, 200, replace = TRUE),g=10)), Age = sample(1:85, 200, replace = TRUE), EORTC = sample(1:100, 200, replace = TRUE), cir=rbinom(200, 1,.5))
head(data)
dd<-datadist(data)
options(datadist='dd')
bsx <- blrm(HE6 ~ cir*rcs(Age,3)+ pol(EORTC),
~ cir*rcs(Age,3)+ pol(EORTC), cppo=function(y) y, data=data)
summary (bsx)
plot(summary(bsx))
newdata <- data.frame(cir=c(0,1), Age=53, EORTC= 75)
predict(bsx, newdata, type='fitted') #
ggplot (Predict (bsx), abbrev =TRUE , ylab=NULL) # ci out of range
exprob <- ExProb (bsx)
fun5<-function(x) exprob (x, y=5)
fun6<-function(x) exprob (x, y=6)
fun9<-function(x) exprob (x, y=9)
M <- Mean (bsx)
qu <-Quantile (bsx)
med <- function (lp) qu(.5 , lp)
nom.ord<- nomogram(bsx, fun=list(Mean=M, "Prob y>=5"=fun5,"Prob y >=6"=fun6, "Prob y>=9"=fun9), lp=F)
plot(nom.ord)
I downloaded the new version of rms/rmsb. The plot for partial effects now works fine. The function 'summary' works for the previous example, but not if more variables are added. For example:
data<- data.frame(HE6=as.integer(cut2(sample(1:100, 200, replace = TRUE),g=10)),
Age = sample(1:85, 200, replace = TRUE), EORTC = sample(1:100, 200, replace = TRUE),
linf=rbinom(200, 1,.5),
cir=rbinom(200, 1,.5))
bsx <- blrm(HE6 ~ cir*rcs(Age,3)+ pol(EORTC)+linf,
~ cir*rcs(Age,3)+ pol(EORTC), cppo=function(y) y, data=data)
summary (bsx)
plot(summary(bsx))
plot(summary (bsx, ycut=5))
The nomogram doesn't work either.
exprob <- ExProb (bsx)
fun5<-function(x) exprob (x, y=5)
fun6<-function(x) exprob (x, y=6)
fun9<-function(x) exprob (x, y=9)
M <- Mean (bsx)
qu <-Quantile (bsx)
med <- function (lp) qu(.5 , lp)
nom.ord<- nomogram(bsx, fun=list(Mean=M, "Prob y>=5"=fun5,"Prob y >=6"=fun6, "Prob y>=9"=fun9), lp=F)
plot(nom.ord)