R中的多重插补（solve.default中的错误（xtx + diag（pen））：系统在计算上是奇异的：倒数条件数=）

Question

I want to analyze data about Covid-19. I have done some part of the data cleaning and I have end up with this dataset (160260 rows and 34 columns). I have converted the variables continent,location,tests_units into factors. I wanted to check about missing values so I calculated the percentages of missing values and the results were:

> (colMeans(is.na(dataset1)))*100
          continent                location                    date             total_cases 
          0.0000000               0.0000000               0.0000000               1.9699239 
          new_cases            total_deaths              new_deaths       reproduction_rate 
          2.0366904               8.0094846               8.1130663              14.0078622 
       icu_patients           hosp_patients   weekly_icu_admissions  weekly_hosp_admissions 
         84.7747410              83.7021091              96.2386123              92.5851741 
        total_tests               new_tests           positive_rate          tests_per_case 
         54.4465244              56.6966180              43.9292400              44.7154624 
        tests_units people_fully_vaccinated        new_vaccinations        stringency_index 
         38.0974666              73.6390865              76.2298765              15.7138400 
         population      population_density              median_age           aged_70_older 
          0.0000000               4.3073755              10.5291401              11.0077374 
     gdp_per_capita         extreme_poverty   cardiovasc_death_rate     diabetes_prevalence 
         11.9381006              42.0897292              11.0077374               6.7003619 
     female_smokers            male_smokers  handwashing_facilities         life_expectancy 
         32.9963809              33.9535754              55.9690503               0.4785973 
        human_development_index        excess_mortality
         13.3738924                    96.1225509 

I didn't want to analyze a dataset with missing values and as a result I searched a lot in order to find a way to fill these NAs.I found that I can use mice function in order to fill these NAs.My goals are:

1. Use the mice function in a way that the variable date is not used as a predictor.
2. Not to impute values in the variables: continent,location,date,population because they don't have NAs.
3. To impute values in the variables: total_cases,new_cases,total_deaths,new_deaths,reproduction_rate,icu_patients,hosp_patients,weekly_icu_admissions,weekly_hosp_admissions,total_tests,new_tests,positive_rate,tests_per_case,people_fully_vaccinated,new_vaccinations,stringency_index,population_density,median_age,aged_70_older,gdp_per_capita,extreme_poverty,cardiovasc_death_rate,diabetes_prevalence,female_smokers,male_smokers,handwashing_facilities,life_expectancy,human_development_index,excess_mortality with the method pmm (Predictive mean matching) because these variables are numeric.
4. To impute values in the variable tests_units with the method polyreg (Polytomous logistic regression) because this variable is a factor with 4 levels.

I followed every step from this link and I run this code:

library(mice)

init = mice(dataset1,maxit = 0)
meth = init$method
predM = init$predictorMatrix

predM[, c("date")] = 0 #goal number 1

meth[c("continent","location","date","population")] = "" #goal number 2

meth[c("total_cases","new_cases","total_deaths","new_deaths","reproduction_rate",
   "icu_patients","hosp_patients","weekly_icu_admissions",
   "weekly_hosp_admissions","total_tests","new_tests","positive_rate",
   "tests_per_case","people_fully_vaccinated",
   "new_vaccinations","stringency_index","population_density","median_age",
   "aged_70_older","gdp_per_capita","extreme_poverty",
   "cardiovasc_death_rate","diabetes_prevalence","female_smokers",
   "male_smokers","handwashing_facilities","life_expectancy",
   "human_development_index","excess_mortality")]="pmm" #goal number 3

meth[c("tests_units")] = "polyreg" #goal number 4

set.seed(103)

imputed = mice(dataset1, method=meth, predictorMatrix=predM, m=5)

The result I got was

> library(mice)
> init = mice(dataset1,maxit = 0)
Warning message:
Number of logged events: 1 
> meth = init$method
> predM = init$predictorMatrix
> predM[, c("date")] = 0
> meth[c("continent","location","date","population")] = ""
> meth[c("total_cases","new_cases","total_deaths","new_deaths","reproduction_rate",
+        "icu_patients","hosp_patients","weekly_icu_admissions",
+        "weekly_hosp_admissions","total_tests","new_tests","positive_rate",
+        "tests_per_case","people_fully_vaccinated",
+        "new_vaccinations","stringency_index","population_density","median_age",
+        "aged_70_older","gdp_per_capita","extreme_poverty",
+        "cardiovasc_death_rate","diabetes_prevalence","female_smokers",
+        "male_smokers","handwashing_facilities","life_expectancy",
+        "human_development_index","excess_mortality")]="pmm"
> meth[c("tests_units")] = "polyreg"
> 
> set.seed(103)
> imputed = mice(dataset1, method=meth, predictorMatrix=predM, m=5)

 iter imp variable
  1   1  total_casesError in solve.default(xtx + diag(pen)) : 
  system is computationally singular: reciprocal condition number = 2.80783e-24

which was not very pleasant. What should I change or which code should I run?

Thanks in advance!

Answer 1

have you checked your logged events:

view(init$loggedEvents)

Maybe it's because of some imputationmethods you used ("polyreg"). Have you tried to use more robust one's only (pmm)

Peace, M.