Issues performing Hosmer-Lemeshow test

Question

Please help!

I am trying to perform a HL test to assess the goodness of fit for my model but I keep getting the above error!

I have these packages installed: library(jtools) library(skimr) library(epiR) library(rms) library(epimisc) library(DescTools) library(car) library(readxl) library(summarytools) library(survival) library(ggplot2) library(survminer) library(PredictABEL)

This is my code:

# Final model
    l_final <- glm(pro$treatment ~ pro$age,family = binomial(link="logit"),data = pro, x=TRUE)
    summ(l_final)
  
# Assessing the fit of the model
    
    #predict proabailities
    pro$l_final_pred <- predict(l_final, type = "response")

    # Hosmer-Lemeshow test
    HosmerLemeshowTest(pro$l_final_pred, pro$treatment, X = l_final_pred$x)   

This is the output:

> # Assessing the fit of the model
>     pro$l_final_pred <- predict(l_final, type = "response")
Error:
! Assigned data `predict(l_final, type = "response")` must be compatible with existing data.
✖ Existing data has 866 rows.
✖ Assigned data has 864 rows.
ℹ Only vectors of size 1 are recycled.
Backtrace:
  1. base::`$<-`(`*tmp*`, l_final_pred, value = `<dbl>`)
 12. tibble (local) `<fn>`(`<vctrs___>`)
>     # Hosmer-Lemeshow test
>     HosmerLemeshowTest(pro$l_final_pred, pro$treatment, X = l_final_pred$x)   
Error in cut.default(fit, breaks = brks, include.lowest = TRUE) : 
  'x' must be numeric
In addition: Warning message:
Unknown or uninitialised column: `l_final_pred`.

Thankyou!!

I know it has something to do with the lengths of the data sets (there are two missing in treatment = 864, compared to age - 866) but can't quite work out for to fix it

Answer 1

Try to check the data

1. Does NULL/NA exist?

If yes, understand why it exists and if necessary --- remove those lines

(using, for example: 1 , 2 or 3).

2. Is the type “adequate” for all data?

For example, the number may be symbolic, which is not adequate

(using, for example: 4 , 5 or 6).

As I would do

if (!require("pacman"))
  install.packages("pacman")

# install/load nedded library
pacman::p_load(
  hablar)


# create data frame
df <- data.frame(
  x = c(1, 3, NA,-9.5, "NULL"),
  y = c("2", "5", "-0.1", "-3", "100"),
  text1 = letters[1:5],
  text2 = LETTERS[1:5],
  z = c(78, 10,-99, NA, 99)
)


# check data type of data frame
str(df)


# replace NULL to NA, because they are the same thing
df[df == "NULL"] <- NA

# filter rows with NA
df[!complete.cases(df), ]
# (replace NA to values or remove rows whit them)


# remove rows with NA
df <- na.omit(df)


# fix "inadequate" data
df <- retype(df) 
str(df)

Output

str(df)
'data.frame':   5 obs. of  5 variables:
 $ x    : chr  "1" "3" NA "-9.5" ...
 $ y    : chr  "2" "5" "-0.1" "-3" ...
 $ text1: chr  "a" "b" "c" "d" ...
 $ text2: chr  "A" "B" "C" "D" ...
 $ z    : num  78 10 -99 NA 99

filter rows with NA
     x    y text1 text2   z
3 <NA> -0.1     c     C -99
4 -9.5   -3     d     D  NA
5 <NA>  100     e     E  99

remove rows with NA
  x y text1 text2  z
1 1 2     a     A 78
2 3 5     b     B 10

str(df) after fix "inadequate" data
'data.frame':   2 obs. of  5 variables:
 $ x    : int  1 3
 $ y    : int  2 5
 $ text1: chr  "a" "b"
 $ text2: chr  "A" "B"
 $ z    : int  78 10
 - attr(*, "na.action")= 'omit' Named int [1:3] 3 4 5
  ..- attr(*, "names")= chr [1:3] "3" "4" "5"