我想分析有关新冠肺炎的数据。我已经完成了部分数据清理，最终得到了这个数据集(160260行34列(。我已经将变量大陆、地点、测试单位转换为因子。我想检查缺失值，所以我计算了缺失值的百分比，结果是：
> (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
我不想分析一个缺少值的数据集，因此我搜索了很多，以找到填充这些NA的方法。我发现我可以使用鼠标功能来填充这些NA。我的目标是：
- 以一种不将可变日期用作预测值的方式使用鼠标功能
- 不要在变量中估算值：大陆、地点、日期、人口，因为他们没有NA
- 在变量中估算值：total_case、new_case、total_death、new_death、reproduction_rate、icu_patients、hosp_patient、weekly_icu_admissions、weekly-hosts_admissions，total_tests，new_tests、positive_rate、tests_per_case、people_fully_vaccinated、new_vaccinations、strincy_index、population_density、median_age、aged_70_older、gdp_per_capita、extreme_police、cardiovasc_death_rate，糖尿病患病率、女性吸烟者、男性吸烟者、洗手能力、寿命预期、人类发展指数、卓越能力，因为这些变量都是数字
- 使用polyreg(Polytomous逻辑回归(方法估算变量tests_units中的值，因为该变量是一个具有4个水平的因子
我遵循了这个链接的每一步，并运行了以下代码：
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)
我得到的结果是
> 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
这不是很愉快。我应该更改什么或运行哪些代码？
提前感谢！
您检查了记录的事件吗？
view(init$loggedEvents)
也许是因为你使用的插补方法("polyreg"(。您是否尝试过使用像pmm这样更健壮的方法？