Question

此Q关于从lmer软件包中为每个线性关系获取正确的方程式。让我们考虑以下具有不相关随机效应的模型

fit1 <-lmer(log_age_1 ~ log_recruits + OW_P2 +  (1 + OW_P2|Bank2) + (1 + log_recruits|Bank2),
            data = sub)
> summary(fit1)
Linear mixed model fit by REML 
t-tests use  Satterthwaite approximations to degrees of freedom ['lmerMod']
Formula: log_age_1 ~ log_recruits + OW_P2 + (1 + OW_P2 | Bank2) + (1 +      log_recruits | Bank2)
   Data: sub

REML criterion at convergence: 270.5

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.01579 -0.71391 -0.02338  0.54065  2.03553 

Random effects:
 Groups   Name         Variance Std.Dev. Corr 
 Bank2    (Intercept)  7.94090  2.8180        
          OW_P2        0.14820  0.3850   -1.00
 Bank2.1  (Intercept)  7.94797  2.8192        
          log_recruits 0.03295  0.1815   -1.00
 Residual              0.80904  0.8995        
Number of obs: 90, groups:  Bank2, 9

Fixed effects:
             Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)    4.2703     1.7878 12.3750   2.389   0.0337 *  
log_recruits   0.6257     0.1095 14.4380   5.714 4.75e-05 ***
OW_P2         -0.4628     0.1922  8.5130  -2.408   0.0408 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) lg_rcr
log_recruts -0.652       
OW_P2       -0.713 -0.027
> coef(fit1)
$Bank2
  (Intercept) log_recruits       OW_P2
1    8.448863    0.3859443 -0.74818801
2   -2.708164    0.8123164  0.01390098
3    2.633726    0.4846902 -0.35098075
4    2.428635    0.5239703 -0.33697190
5    4.778026    0.6053660 -0.49744877
6    1.650567    0.4554392 -0.28382537
7   10.443363    0.7419798 -0.88442383
8    5.580971    0.8323623 -0.55229450
9    5.176478    0.7889412 -0.52466533
> ranef(fit1)
$Bank2
  (Intercept)       OW_P2 (Intercept) log_recruits
1   2.0892945 -0.28542162   3.7231490  -0.23972341
2  -3.4892188  0.47666737  -2.8988438   0.18664865
3  -0.8182740  0.11178563   2.1895257  -0.14097759
4  -0.9208193  0.12579449   1.5794658  -0.10169749
5   0.2538761 -0.03468239   0.3153068  -0.02030174
6  -1.3098534  0.17894102   2.6438223  -0.17022851
7   3.0865446 -0.42165744  -1.8064452   0.11631208
8   0.6553484 -0.08952811  -3.2101767   0.20669452
9   0.4531020 -0.06189894  -2.5358038   0.16327349
> fixef(fit1)
 (Intercept) log_recruits        OW_P2 
   4.2702740    0.6256677   -0.4627664

然后让我们基于模型进行预测，并绘制Bank2的预测值和线性关系

ggplot(sub,aes(x=OW_P2,y=log_age_1,colour=Bank2))+  geom_point() +
geom_point(aes(y = predict(fit1)), col = "black") +
geom_smooth(aes(y = predict(fit1), colour = Bank2), method = "lm") + facet_wrap(~Bank2)

问题：如何根据上述模型输出获取每种线性关系的alpha和beta参数估计？

lmer中每个线性关系的随机截距/斜率方程

0 个答案: