对于R²，可以使用‘MuMIn包中的r.squaredGLMM(fit1.lme)，它将返回边际和条件R²。
对于p值，您可以使用summary和lmerTest包来查找它们。
有关混合模型p值的详细信息：http://mindingthebrain.blogspot.ch/2014/02/three-ways-to-get-parameter-specific-p.html

我添加了一个非常小的臭氧层层次模型演示，其中模型承认它随月份变化。您可以在下面找到比较。我只能在MuMIn包中找到R squared术语。

MuMin封装

> 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

其中我们比较了线性回归和混合效应模型，即分层回归模型。

• 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)

• 第四次 *

> 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   5.187

聚合物测试

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 ' ' 1

您可以尝试sjPlot或sjstats包，第一个包帮助从lme4分析中创建APA样式的表，第二个包用于提取拟合统计数据。
您只需要简单地编写代码：

tab_model(fit1.lme)

它将输出APA表，包括估计值斜率、截距、CI、p值、方差、残差、观察数、ICC、边际和条件R平方等。
看起来像这样：