如果我重新调整了回归量，如何使用 visreg 函数绘制预测响应和 95%CI？

Question

当我缩放回归量时，有什么方法可以使用 visreg 函数制作条件图吗？ 我想绘制重新调整后的响应以及 95% CI、预测曲线和部分残差。

install.packages("lme4")
library("lme4")    
y <- c(18, 0, 2, 0,  0,  0,  2,  0,  0,  1,  7,  0,  0,  0,  0,  0,  0,  0,  0)
x1 <- c(501, 1597, 1156, 1134, 1924,  507, 1022,  0,  92, 1729, 85, 963, 544, 1315, 2250, 1366,  458,  385,  930)
x2 <- c(0,  92,  959, 1146,  900,  0,  276, 210,  980, 8, 0, 473, 0, 255, 1194, 542, 983, 331,  923)
x3 <- c("site1", "site1", "site2","site2","site3","site3","site4","site4","site5","site5","site6","site7","site8","site9","site10","site11","site12","site13","site14")

offset_1 <- c(59, 34, 33, 35, 60, 58, 59, 33, 34, 61, 58, 58, 55, 26, 26, 18, 26, 26, 26)
data_1 <- data.frame(y,x1,x2,offset_1)

m1 <- glmer.nb(y ~ -1 + scale(x1) + scale(x2) + + (1|x3) + offset(log(offset_1)), data=data_1)
summary(m1)
visreg(m1, "x1", scale="response", cond=list(offset_1=1), partial=TRUE,
       rug=2,line=list(lwd=0.5, col="black"), points=list(cex=1.4, lwd=0.1, col="black", pch=21))

Answer 1

正如我在上面的评论中提到的，

visreg

不会做你想做的事。您可以使用

ggeffects

包来做到这一点。

library("lme4")    
#> Loading required package: Matrix
y <- c(18, 0, 2, 0,  0,  0,  2,  0,  0,  1,  7,  0,  0,  0,  0,  0,  0,  0,  0)
x1 <- c(501, 1597, 1156, 1134, 1924,  507, 1022,  0,  92, 1729, 85, 963, 544, 1315, 2250, 1366,  458,  385,  930)
x2 <- c(0,  92,  959, 1146,  900,  0,  276, 210,  980, 8, 0, 473, 0, 255, 1194, 542, 983, 331,  923)
x3 <- c("site1", "site1", "site2","site2","site3","site3","site4","site4","site5","site5","site6","site7","site8","site9","site10","site11","site12","site13","site14")

offset_1 <- c(59, 34, 33, 35, 60, 58, 59, 33, 34, 61, 58, 58, 55, 26, 26, 18, 26, 26, 26)
data_1 <- data.frame(y,x1,x2,offset_1)

m1 <- glmer.nb(y ~ -1 + scale(x1) + scale(x2) + + (1|x3) + offset(log(offset_1)), data=data_1)
#> Warning in theta.ml(Y, mu, weights = object@resp$weights, limit = limit, :
#> iteration limit reached
summary(m1)
#> Generalized linear mixed model fit by maximum likelihood (Laplace
#>   Approximation) [glmerMod]
#>  Family: Negative Binomial(260.1065)  ( log )
#> Formula: y ~ -1 + scale(x1) + scale(x2) + +(1 | x3) + offset(log(offset_1))
#>    Data: data_1
#> 
#>      AIC      BIC   logLik deviance df.resid 
#>     86.0     89.8    -39.0     78.0       15 
#> 
#> Scaled residuals: 
#>     Min      1Q  Median      3Q     Max 
#> -1.2281 -0.2982 -0.1695 -0.0254  1.9513 
#> 
#> Random effects:
#>  Groups Name        Variance Std.Dev.
#>  x3     (Intercept) 104.6    10.23   
#> Number of obs: 19, groups:  x3, 14
#> 
#> Fixed effects:
#>           Estimate Std. Error z value Pr(>|z|)
#> scale(x1)  -0.6437     0.6776  -0.950    0.342
#> scale(x2)  -3.8961     4.1772  -0.933    0.351
#> 
#> Correlation of Fixed Effects:
#>           scl(1)
#> scale(x2) -0.740

library(ggeffects)
## confidence interval for slope, assuming
## 0 random effect variance. 
g1 <- ggpredict(m1, "x1 [all]", type="fixed")
plot(g1)

## prediction intervals including 
## random effect variance
g2 <- ggpredict(m1, "x1 [all]", type="random")
plot(g2)

^{创建于 2023-08-12，使用 reprex v2.0.2}