lmer模型拟合的R-平方
lmer模型拟合的R-平方
提问于 2017-07-26 20:37:09
我有一个混合效果模型，我希望看到R²和p值。我认为这是可以通过summary()实现的，但事实并非如此。至少我没有意识到这一点。
> summary(fit1.lme <- lmer(log(log(Amplification)) ~ poly(Voltage, 3) + (1 | Serial_number), data = bdf))
Linear mixed model fit by REML ['lmerMod']
Formula: log(log(Amplification)) ~ poly(Voltage, 3) + (1 | Serial_number)
Data: bdf
REML criterion at convergence: -253237.6
Scaled residuals:
Min 1Q Median 3Q Max
-14.8183 -0.4863 -0.0681 0.2941 9.3292
Random effects:
Groups Name Variance Std.Dev.
Serial_number (Intercept) 0.008435 0.09184
Residual 0.001985 0.04456
Number of obs: 76914, groups: Serial_number, 1270
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.826745 0.002582 320
poly(Voltage, 3)1 286.978430 0.045248 6342
poly(Voltage, 3)2 -74.061993 0.045846 -1615
poly(Voltage, 3)3 39.605454 0.045505 870
Correlation of Fixed Effects:
(Intr) p(V,3)1 p(V,3)2
ply(Vlt,3)1 0.001
ply(Vlt,3)2 0.002 0.021
ply(Vlt,3)3 0.001 0.032 0.028 回答 3
Stack Overflow用户
回答已采纳
发布于 2017-07-26 20:44:52
对于R²，您可以使用‘MuMIn软件包中的r.squaredGLMM(fit1.lme)。它将返回边际和条件R²。
对于p值，可以通过在lmerTest包中使用summary来找到它们。
有关混合模型的p值的更多信息，请访问：http://mindingthebrain.blogspot.ch/2014/02/three-ways-to-get-parameter-specific-p.html
Stack Overflow用户
发布于 2018-08-25 20:37:27
我添加了一个非常小的演示，对臭氧分层建模，其中建模承认它是每月变化的。你可以在下面找到比较。我只能在MuMIn包中找到R squared术语。
MuMIn package
> data(airquality)
> MuMIn::r.squaredGLMM(lme4::lmer(data=airquality, Ozone ~ 1 + (1|Month)))
R2m R2c
[1,] 0 0.2390012
> summary(lm(data=airquality, Ozone ~ 1 + (1|Month)))$r.squared
[1] 0在这里我们比较线性回归和混合效应模型，也就是分层回归模型。
线性回归
> summary(lm(data=airquality, Ozone ~ 1 + (1|Month)))
Call:
lm(formula = Ozone ~ 1 + (1 | Month), data = airquality)
Residuals:
Min 1Q Median 3Q Max
-41.13 -24.13 -10.63 21.12 125.87
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 42.129 3.063 13.76 <2e-16 ***
1 | MonthTRUE NA NA NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 32.99 on 115 degrees of freedom
(37 observations deleted due to missingness)lmer4
> summary(lme4::lmer(data=airquality, Ozone ~ 1 + (1|Month)))
Linear mixed model fit by REML ['lmerMod']
Formula: Ozone ~ 1 + (1 | Month)
Data: airquality
REML criterion at convergence: 1116.5
Scaled residuals:
Min 1Q Median 3Q Max
-1.7084 -0.6269 -0.2669 0.4121 3.7507
Random effects:
Groups Name Variance Std.Dev.
Month (Intercept) 270.6 16.45
Residual 861.6 29.35
Number of obs: 116, groups: Month, 5
Fixed effects:
Estimate Std. Error t value
(Intercept) 41.093 7.922 5.187lmerTest
library(lmerTest)
> lmerTest::lmer(data=airquality, Ozone ~ 1 + (1|Month))
Linear mixed model fit by REML ['lmerModLmerTest']
Formula: Ozone ~ 1 + (1 | Month)
Data: airquality
REML criterion at convergence: 1116.544
Random effects:
Groups Name Std.Dev.
Month (Intercept) 16.45
Residual 29.35
Number of obs: 116, groups: Month, 5
Fixed Effects:
(Intercept)
41.09
> summary(lmerTest::lmer(data=airquality, Ozone ~ 1 + (1|Month)))
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Ozone ~ 1 + (1 | Month)
Data: airquality
REML criterion at convergence: 1116.5
Scaled residuals:
Min 1Q Median 3Q Max
-1.7084 -0.6269 -0.2669 0.4121 3.7507
Random effects:
Groups Name Variance Std.Dev.
Month (Intercept) 270.6 16.45
Residual 861.6 29.35
Number of obs: 116, groups: Month, 5
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 41.093 7.922 4.096 5.187 0.00616 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Stack Overflow用户
发布于 2021-11-06 21:46:05
您可以尝试软件包sjPlot或sjstats。第一个包帮助从lme4分析创建APA样式的表，第二个包用于提取拟合统计信息。
您只需简单地编写代码：
tab_model(fit1.lme)它将输出一个APA表，包括估计斜率、截距、CI、p值、方差、残差、观察值、ICC、边际和条件R方等。
看起来像这样：
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原文链接：
https://stackoverflow.com/questions/45327217
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