R( solve.default(xtx +diag(Pen)中的误差)：系统是计算奇异的:倒数条件数=)
R( solve.default(xtx +diag(Pen)中的误差)：系统是计算奇异的:倒数条件数=)
提问于 2022-08-21 20:47:08
我想分析新冠肺炎的数据。我完成了部分数据清理，最后得到了这数据集(160260行和34列)。我把大陆、位置、tests_units等变量转化为因子。我想检查丢失的值，所以我计算了丢失值的百分比，结果如下：
> (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 我不想分析缺少值的数据集，因此我进行了大量搜索，以找到一种填充这些can的方法。我发现，我可以使用鼠标函数来填充这些NAs.My目标：
- 以不使用可变日期作为预测的方式使用鼠标函数。
- 不要在变量中计算值:大陆、位置、日期、人口，因为它们没有NAs。
- 在变量中计算值: 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、new_vaccinations、en19#、，human_development_index，excess_mortality与方法pmm (预测平均匹配)，因为这些变量是数值的。
- 用polyreg (Polytomous logistic回归)方法计算变量tests_units中的值，因为该变量是4个水平的因子。
我跟踪了这链接的每一步，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，或者应该运行哪些代码？
提前感谢！
回答 1
Stack Overflow用户
发布于 2022-09-24 11:55:21
你检查过你记录的事件了吗？
view(init$loggedEvents)也许是因为你所用的估算方法("polyreg")。您是否尝试过使用像pmm这样更健壮的方法？
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原文链接：
https://stackoverflow.com/questions/73438111
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