R 如何解释原始尺度上混合模型的对数转换模型的系数?
植物生长的完整模型如下所示:R 如何解释原始尺度上混合模型的对数转换模型的系数?,r,logarithm,mixed-models,exponent,R,Logarithm,Mixed Models,Exponent,植物生长的完整模型如下所示: lmer(log(growth) ~ nutrition + fertilizer + season + (1|block) REML criterion at convergence: 71.9 Scaled residuals: Min 1Q Median 3Q Max -1.82579 -0.59620 0.04897 0.62629 1.54639 Random effects: Group
lmer(log(growth) ~ nutrition + fertilizer + season + (1|block)
REML criterion at convergence: 71.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.82579 -0.59620 0.04897 0.62629 1.54639
Random effects:
Groups Name Variance Std.Dev.
block (Intercept) 0.06008 0.2451
Residual 0.48633 0.6974
Number of obs: 32, groups: tank, 16
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 3.5522 0.2684 19.6610 13.233 3.02e-11 ***
nutritionP 0.2871 0.2753 13.0000 1.043 0.31601
fertlizeradded -0.3513 0.2753 13.0000 -1.276 0.22436
seasonwet 1.0026 0.2466 15.0000 4.066 0.00101 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
营养(氮/磷)、肥料(无/添加)、季节(干/湿)
模型概述如下:
lmer(log(growth) ~ nutrition + fertilizer + season + (1|block)
REML criterion at convergence: 71.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.82579 -0.59620 0.04897 0.62629 1.54639
Random effects:
Groups Name Variance Std.Dev.
block (Intercept) 0.06008 0.2451
Residual 0.48633 0.6974
Number of obs: 32, groups: tank, 16
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 3.5522 0.2684 19.6610 13.233 3.02e-11 ***
nutritionP 0.2871 0.2753 13.0000 1.043 0.31601
fertlizeradded -0.3513 0.2753 13.0000 -1.276 0.22436
seasonwet 1.0026 0.2466 15.0000 4.066 0.00101 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
这里的植物生长仅取决于季节,生长量的增加在对数尺度上为1.0026。如果我想知道实际植物高度的增加是什么,我如何在原始数据的尺度上解释这一点?它仅仅是e(1.0026)~3cms,还是有其他的解释方法?exp(1.0026)
确实大约是3(2.72),但这个值表示成比例的变化。在其他条件相同的情况下,雨季的生长速度是旱季的三倍