我已经针对各种结果进行了一系列逻辑回归，并且一直使用rms包来计算Nagelkerke R ^ 2到目前为止没有任何问题...
当我尝试将其用于肥胖（= 1）或'正常'体重（= 0）的二元结果回归分析中时，我收到一条消息，说我的配方有错误，无法拟合使用lrm.fit进行建模。我尝试将结果编码为数字和因数，但得到相同的错误。我还尝试使用数据的子集，但不包括既不肥胖也不“正常”体重的病例。那也不行。
我在下面粘贴了我的公式。 （由于我无权共享数据，因此无法包含数据。）我还粘贴了使用glm函数获得的回归输出。
我在做什么错？我确定已经发布了解决方案，但是在Stack Overflow或其他论坛上找不到。 （我的主管要求我在结果中包括伪R ^ 2。我还包括平均边际效应。）
在此先感谢您的帮助！
require(rms)
logit.obnorm.base <- lrm(obnorm13 ~ wgt9 + gender + birthweight + breastfed +
pcgweight + smoked + pcgeducation + income + welfare, data = gui)
print(logit.obnorm.base)
Error in lrm(obnorm13 ~ wgt9 + gender + birthweight + breastfed + pcgweight + :
Unable to fit model using “lrm.fit”
glm进行回归时的回归输出glm(formula = obnorm13 ~ wgt9 + gender + birthweight + breastfed +
pcgweight + smoked + pcgeducation + income + welfare, family = binomial(link = "logit"),
data = gui, weights = bdwg01, na.action = na.omit)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.4402 -0.1509 -0.0876 -0.0618 6.2779
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.73687 0.73321 -7.824 5.10e-15 ***
wgt92 3.25940 0.20237 16.106 < 2e-16 ***
wgt93 6.38153 0.28246 22.593 < 2e-16 ***
gender 0.26701 0.17444 1.531 0.125854
birthweight 0.14304 0.15234 0.939 0.347751
breastfed 0.24493 0.19173 1.277 0.201430
pcgweight2 0.35537 0.22073 1.610 0.107401
pcgweight3 1.11191 0.21671 5.131 2.89e-07 ***
smoked 0.50183 0.19956 2.515 0.011913 *
pcgeducation2 -0.71452 0.21294 -3.356 0.000792 ***
pcgeducation3 -0.81295 0.27853 -2.919 0.003515 **
pcgeducation4 -0.88217 0.32623 -2.704 0.006848 **
income -0.01348 0.07976 -0.169 0.865744
welfare 0.11014 0.06298 1.749 0.080290 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2410.1 on 4758 degrees of freedom
Residual deviance: 1037.4 on 4745 degrees of freedom
(2536 observations deleted due to missingness)
AIC: 1048.4
Number of Fisher Scoring iterations: 7
dput(head(mydata))的输出> dput(head(gui))
structure(list(zid01 = c(1000, 2000, 3000, 4000, 5000, 6000),
bdwg01 = c(0.222555072843037, 1.95115048656779, 1.08399948558217,
0.697544449190721, 0.910021907765603, 0.278315557267728),
gender = c(0, 0, 0, 0, 0, 0), wgt9 = structure(c(1L, 1L,
1L, 1L, 1L, 1L), .Label = c("1", "2", "3"), class = "factor"),
apcb01 = c(3.4, 3.1, 3.4, 3.3, 3.6, 2.8), smoked = c(1, 1,
0, 0, 1, 1), breastfed = c(0, 0, 0, 0, 1, 0), pcgweight = structure(c(2L,
1L, 1L, 1L, 2L, 2L), .Label = c("1", "2", "3"), class = "factor"),
pcgeducation = structure(c(4L, 1L, 4L, 4L, 4L, 2L), .Label = c("1",
"2", "3", "4"), class = "factor"), welfare = c(2, 7, 2, 3,
2, 3), adsd59b = c(5, 2, 4, 3, 5, 2), aded08a = c(51, 31,
35, 44, 52, 30), aded08b = c(11, 10, 4, 10, 11, 8), aded08c = c(15,
7, 8, 11, 14, 8), aded08d = c(10, 7, 7, 8, 8, 7), aded08e = c(12,
6, 9, 11, 13, 5), aded08f = c(11, 5, 9, 8, 10, 5), aded08g = c(8,
4, 7, 8, 10, 6), aded10a = c(0, 8, 4, 2, 1, 2), aded10b = c(1,
4, 1, 1, 0, 0), aded10c = c(4, 10, 4, 3, 2, 4), aded10d = c(1,
3, 1, 0, 2, 0), aded10e = c(9, 7, 9, 10, 5, 9), aded10f = c(6,
25, 10, 6, 5, 6), aded11a = c(1.8, 3, 1.2, 1.2, 2.8, 2),
aded11b = c(1.4, 5, 1.2, 1.4, 1.2, 1.8), aded11c = c(4.8,
5, 4.8, 5, 5, 4.6), aded11d = c(3.8, 3.4, 4, 4.2, 4.2, 3.6
), obese13 = c(0, 0, 0, 0, 0, 0), obnorm13 = c(0, 0, 0, 0,
0, 0), xswgt13 = c(0, 0, 0, 0, 0, 0), up = c("0", "0", "0",
"0", "0", "0"), down = c(NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_), obese9 = c(0,
0, 0, 0, 0, 0), obnorm9 = c("0", "0", "0", "0", "0", "0"),
xswgt9 = c(0, 0, 0, 0, 0, 0), wgt9.1 = c("1", "1", "1", "1",
"1", "1"), income = c(5, 2, 4, 3, 5, 2), birthweight = c(3.4,
3.1, 3.4, 3.3, 3.6, 2.8), PHtotal = c(51, 31, 35, 44, 52,
30), PHbehaviour = c(11, 10, 4, 10, 11, 8), PHintel_school = c(15,
7, 8, 11, 14, 8), PHphys_app = c(10, 7, 7, 8, 8, 7), PHfreedom_anx = c(12,
6, 9, 11, 13, 5), PHpopularity = c(11, 5, 9, 8, 10, 5), PHhappiness = c(8,
4, 7, 8, 10, 6), SDQEmotional = c(0, 8, 4, 2, 1, 2), SDQConduct = c(1,
4, 1, 1, 0, 0), SDQHyperactivity = c(4, 10, 4, 3, 2, 4),
SDQPeer_Rel = c(1, 3, 1, 0, 2, 0), SDQProsocial = c(9, 7,
9, 10, 5, 9), SDQTotalDiff = c(6, 25, 10, 6, 5, 6), EASShyness = c(1.8,
3, 1.2, 1.2, 2.8, 2), EASEmotionality = c(1.4, 5, 1.2, 1.4,
1.2, 1.8), EASActivity = c(4.8, 5, 4.8, 5, 5, 4.6), EASSociability = c(3.8,
3.4, 4, 4.2, 4.2, 3.6), factor.wgt9 = structure(c(1L, 1L,
1L, 1L, 1L, 1L), .Label = c("1", "2", "3"), class = "factor"),
factor.pcged = structure(c(4L, 1L, 4L, 4L, 4L, 2L), .Label = c("1",
"2", "3", "4"), class = "factor"), factor.pcgweight = structure(c(2L,
1L, 1L, 1L, 2L, 2L), .Label = c("1", "2", "3"), class = "factor"),
orig.wgt9 = c("1", "1", "1", "1", "1", "1"), orig.pcgweight = c(2,
1, 1, 1, 2, 2), orig.pcgeducation = c(4, 1, 4, 4, 4, 2),
PHintelschool = c(15, 7, 8, 11, 14, 8)), .Names = c("zid01",
"bdwg01", "gender", "wgt9", "apcb01", "smoked", "breastfed",
"pcgweight", "pcgeducation", "welfare", "adsd59b", "aded08a",
"aded08b", "aded08c", "aded08d", "aded08e", "aded08f", "aded08g",
"aded10a", "aded10b", "aded10c", "aded10d", "aded10e", "aded10f",
"aded11a", "aded11b", "aded11c", "aded11d", "obese13", "obnorm13",
"xswgt13", "up", "down", "obese9", "obnorm9", "xswgt9", "wgt9.1",
"income", "birthweight", "PHtotal", "PHbehaviour", "PHintel_school",
"PHphys_app", "PHfreedom_anx", "PHpopularity", "PHhappiness",
"SDQEmotional", "SDQConduct", "SDQHyperactivity", "SDQPeer_Rel",
"SDQProsocial", "SDQTotalDiff", "EASShyness", "EASEmotionality",
"EASActivity", "EASSociability", "factor.wgt9", "factor.pcged",
"factor.pcgweight", "orig.wgt9", "orig.pcgweight", "orig.pcgeducation",
"PHintelschool"), row.names = c(NA, 6L), class = "data.frame")
答案 0 :(得分：1)
我遇到了类似的问题，并且找到了解决方案here。问题可能是MLE没有在多个步骤中收敛。我在参数中添加了maxit=1000，它起作用了。
答案 1 :(得分：0)
tmfmnk建议的另一种计算R ^ 2的方法可以正常工作。
我将其粘贴在这里以防其他人使用。谢谢大家的答复。
# Install the DescTools package and open the library
install.packages("DescTools")
library(DescTools)
# Run the baseline logistic regression for obese / no excess weight at 13, including weights
summary(logit.obnorm.base <- glm(obnorm13 ~ wgt9 + gender + birthweight + breastfed +
pcgweight + smoked + pcgeducation + income + welfare, data = gui, weights = bdwg01,
family = binomial(link = "logit"), na.action=na.omit))
# Calculate Nagelkerke R^2
PseudoR2(logit.obnorm.base, which = "Nagelkerke")
>Nagelkerke
0.7309776