将执行以下代码：
asp.net core，但以下操作失败：
> mod1b <- lrm(dependent_variable ~ merged_dataset$Lag4_CPI, data = merged_dataset, na.action="na.delete")
> print(mod1b)
Logistic Regression Model
lrm(formula = dependent_variable ~ merged_dataset$Lag4_CPI, data = merged_dataset,
na.action = "na.delete")
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 19209 LR chi2 1305.57 R2 0.105 C 0.685
0 15465 d.f. 1 g 0.690 Dxy 0.369
1 3744 Pr(> chi2) <0.0001 gr 1.994 gamma 0.378
max |deriv| 4e-11 gp 0.112 tau-a 0.116
Brier 0.146
Coef S.E. Wald Z Pr(>|Z|)
Intercept -1.6305 0.0210 -77.82 <0.0001
merged_dataset 0.2809 0.0079 35.77 <0.0001
同时执行此模型
> mod1b <- lrm(dependent_variable ~ merged_dataset$Lag4_CPI + quarterYear.x, data = merged_dataset, na.action="na.delete")
singular information matrix in lrm.fit (rank= 39 ). Offending variable(s):
quarterYear.x=2017 4
Warning message:
In lrm(dependent_variable ~ merged_dataset$Lag4_CPI + quarterYear.x, :
Unable to fit model using “lrm.fit”
> print(mod1b)
Error in 1:ns : argument of length 0
并使用glm执行：
> mod1b <- lrm(dependent_variable ~ quarterYear.x, data = merged_dataset, na.action="na.delete")
> print(mod1b)
Logistic Regression Model
lrm(formula = dependent_variable ~ quarterYear.x, data = merged_dataset,
na.action = "na.delete")
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 19209 LR chi2 2821.65 R2 0.218 C 0.746
0 15465 d.f. 38 g 0.947 Dxy 0.492
1 3744 Pr(> chi2) <0.0001 gr 2.579 gamma 0.503
max |deriv| 3e-10 gp 0.154 tau-a 0.154
Brier 0.130
Coef S.E. Wald Z Pr(>|Z|)
Intercept -1.4663 0.1601 -9.16 <0.0001
quarterYear.x=2008 3 -0.0657 0.2225 -0.30 0.7678
quarterYear.x=2008 4 0.1500 0.2068 0.73 0.4683
quarterYear.x=2009 1 -0.0208 0.2506 -0.08 0.9340
quarterYear.x=2009 2 -0.0681 0.2437 -0.28 0.7798
quarterYear.x=2009 3 -0.2177 0.2528 -0.86 0.3891
quarterYear.x=2009 4 -0.1368 0.2514 -0.54 0.5862
quarterYear.x=2010 1 1.7921 0.1906 9.40 <0.0001
quarterYear.x=2010 2 1.7773 0.1893 9.39 <0.0001
quarterYear.x=2010 3 1.5728 0.1906 8.25 <0.0001
quarterYear.x=2010 4 1.6011 0.1861 8.60 <0.0001
quarterYear.x=2011 1 1.8365 0.1979 9.28 <0.0001
quarterYear.x=2011 2 1.6809 0.1916 8.77 <0.0001
quarterYear.x=2011 3 1.5499 0.1884 8.23 <0.0001
quarterYear.x=2011 4 1.5944 0.1932 8.25 <0.0001
quarterYear.x=2012 1 0.7949 0.2047 3.88 0.0001
quarterYear.x=2012 2 0.6866 0.2029 3.38 0.0007
quarterYear.x=2012 3 0.4700 0.2113 2.22 0.0261
quarterYear.x=2012 4 0.1778 0.2251 0.79 0.4295
quarterYear.x=2013 1 -0.5261 0.2296 -2.29 0.0219
quarterYear.x=2013 2 -0.5155 0.2283 -2.26 0.0239
quarterYear.x=2013 3 -0.3346 0.2173 -1.54 0.1236
quarterYear.x=2013 4 0.0757 0.1908 0.40 0.6914
quarterYear.x=2014 1 -0.3843 0.1970 -1.95 0.0511
quarterYear.x=2014 2 -0.2848 0.1960 -1.45 0.1462
quarterYear.x=2014 3 -0.3608 0.1971 -1.83 0.0671
quarterYear.x=2014 4 -0.5849 0.2033 -2.88 0.0040
quarterYear.x=2015 1 -0.3867 0.1945 -1.99 0.0468
quarterYear.x=2015 2 -0.7632 0.2031 -3.76 0.0002
quarterYear.x=2015 3 -0.7042 0.2007 -3.51 0.0005
quarterYear.x=2015 4 -0.6335 0.1971 -3.21 0.0013
quarterYear.x=2016 1 -0.5354 0.1949 -2.75 0.0060
quarterYear.x=2016 2 -0.6899 0.1977 -3.49 0.0005
quarterYear.x=2016 3 -1.2847 0.2197 -5.85 <0.0001
quarterYear.x=2016 4 -0.9774 0.2065 -4.73 <0.0001
quarterYear.x=2017 1 -0.9550 0.2035 -4.69 <0.0001
quarterYear.x=2017 2 -1.1674 0.2133 -5.47 <0.0001
quarterYear.x=2017 3 -0.9355 0.2036 -4.60 <0.0001
quarterYear.x=2017 4 -0.8023 0.2098 -3.82 0.0001
有任何想法为什么会发生这种情况以及如何解决？