Question

我正在使用lmer4程序包[lmer（）函数]估计几个平均模型，我想绘制它们的估计系数。我找到了该文档“ DanielLüdecke绘制回归模型的估计值（固定效果）”，该文档解释了如何绘制估计值，它适用于平均值模型，但是使用条件平均值而不是完全平均值。

脚本示例：

library(lme4)
options(na.action = "na.omit")
PA_model_clima1_Om_ST <- lmer(O.matt ~ mes_N + Temperatura_Ar_PM_ST + RH_PM_ST + Vento_V_PM_ST + Evapotranspiracao_PM_ST + Preci_total_PM_ST + (1|ID), data=Abund)

library(MuMIn)
options(na.action = "na.fail")
PA_clima1_Om_ST<-dredge(PA_model_clima1_Om_ST)

sort.PA_clima1_Om_ST<- PA_clima1_Om_ST[order(PA_clima1_Om_ST$AICc),]
top.models_PA_clima1_Om_ST<-get.models(sort.PA_clima1_Om_ST, subset = delta < 2) 

model.sel(top.models_PA_clima1_Om_ST)
Avg_PA_clima1_Om_ST<-model.avg(top.models_PA_clima1_Om_ST, fit = TRUE) 
summary(Avg_PA_clima1_Om_ST)

此脚本的结果：

Term codes: 
Evapotranspiracao_PM_ST       Preci_total_PM_ST                RH_PM_ST    Temperatura_Ar_PM_ST 
                      1                       2                       3                       4 
          Vento_V_PM_ST 
                      5 

Model-averaged coefficients:  
(full average) 
                        Estimate Std. Error Adjusted SE z value Pr(>|z|)    
(Intercept)               5.4199     1.4094      1.4124   3.837 0.000124 ***
Preci_total_PM_ST        -0.8679     1.0300      1.0313   0.842 0.400045    
RH_PM_ST                  0.6116     0.8184      0.8193   0.746 0.455397    
Temperatura_Ar_PM_ST     -1.9635     0.7710      0.7725   2.542 0.011026 *  
Vento_V_PM_ST            -0.6214     0.7043      0.7052   0.881 0.378289    
Evapotranspiracao_PM_ST  -0.1202     0.5174      0.5183   0.232 0.816654    
 
(conditional average) 
                        Estimate Std. Error Adjusted SE z value Pr(>|z|)    
(Intercept)               5.4199     1.4094      1.4124   3.837 0.000124 ***
Preci_total_PM_ST        -1.2200     1.0304      1.0322   1.182 0.237249    
RH_PM_ST                  1.0067     0.8396      0.8410   1.197 0.231317    
Temperatura_Ar_PM_ST     -1.9635     0.7710      0.7725   2.542 0.011026 *  
Vento_V_PM_ST            -0.8607     0.6936      0.6949   1.238 0.215546    
Evapotranspiracao_PM_ST  -0.3053     0.7897      0.7912   0.386 0.699619    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

情节记录：

library(sjPlot)
library(sjlabelled)
library(sjmisc)
library(ggplot2)
data(efc)
theme_set(theme_sjplot())

plot_model(Avg_PA_clima1_Om_ST, type="est", vline.color="black", sort.est = TRUE, show.values = TRUE, value.offset = .3, title= "O. mattogrossae")

图：

如您所见，它使用条件平均值的值，而不是完全平均值。如何使用完全平均值绘制平均模型的估计值？

Answer 1

我认为这是有条件的..因此，除非您破解该函数或与作者联系以选择该选项，否则一种方法是自己绘制系数：

library(lme4)
library(MuMIn)
options(na.action = "na.fail")
set.seed(888)
dat= data.frame(y = rnorm(100),
var1 = rnorm(100),var2 = rnorm(100),
var3=rnorm(100),rvar = sample(1:2,replace=TRUE,100))

lme_mod <- lmer(y ~ var1+ var2+ var3 + (1|rvar), dat)
dre_mod <- dredge(lme_mod)
avg_mod = model.avg(dre_mod,fit=TRUE) 

summary(avg_mod)

Model-averaged coefficients:  
(full average) 
            Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) -0.02988    0.18699     0.18936   0.158    0.875
var2        -0.03791    0.08817     0.08858   0.428    0.669
var1        -0.02999    0.07740     0.07778   0.386    0.700
var3         0.01521    0.05371     0.05404   0.281    0.778
 
(conditional average) 
            Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) -0.02988    0.18699     0.18936   0.158    0.875
var2        -0.16862    0.11197     0.11339   1.487    0.137
var1        -0.15293    0.10841     0.10978   1.393    0.164
var3         0.11227    0.10200     0.10327   1.087    0.277

The matrix is under:

    summary(avg_mod)$coefmat.full
                    Estimate Std. Error Adjusted SE   z value  Pr(>|z|)
(Intercept) -0.02988418 0.18698720  0.18935677 0.1578194 0.8745991
var2        -0.03791016 0.08816936  0.08857788 0.4279867 0.6686608
var1        -0.02998709 0.07740247  0.07778028 0.3855360 0.6998404
var3         0.01520633 0.05371407  0.05404100 0.2813850 0.7784151

我们将其取出，旋转并绘图：

library(ggplot2)

df = data.frame(summary(avg_mod)$coefmat.full)
df$variable = rownames(df)
colnames(df)[2] = "std_error"
df = df[df$variable !="(Intercept)",]
df$type = ifelse(df$Estimate>0,"pos","neg")

ggplot(df,aes(x=variable,y=Estimate))+
geom_point(aes(col=type),size=3) + 
geom_errorbar(aes(col=type,ymin=Estimate-1.96*std_error,ymax=Estimate+1.96*std_error),width=0,size=1) + 
geom_text(aes(label=round(Estimate,digits=2)),nudge_x =0.1) +
geom_hline(yintercept=0,col="black")+ theme_bw()+coord_flip()+
scale_color_manual(values=c("#c70039","#111d5e")) +
theme(legend.position="none")

Answer 2

您还可以使用sjPlot::plot_model()内部使用的parameters::model_parameters()。 model_parameters()有一个component参数来决定要返回哪个组件。但是，plot_model()尚未将其他参数传递给model_parameters()。我将在sjPlot中解决这个问题。同时，至少使用model_parameters()提供了一种快速的plot()方法。

library(lme4)
library(MuMIn)
options(na.action = "na.fail")
set.seed(888)
dat= data.frame(y = rnorm(100),
                var1 = rnorm(100),var2 = rnorm(100),
                var3=rnorm(100),rvar = sample(1:2,replace=TRUE,100))

lme_mod <- lmer(y ~ var1+ var2+ var3 + (1|rvar), dat)
dre_mod <- dredge(lme_mod)
xavg_mod = model.avg(dre_mod,fit=TRUE) 

library(parameters)
model_parameters(avg_mod)
#> Parameter   | Coefficient |   SE |        95% CI |    z | df |     p
#> --------------------------------------------------------------------
#> (Intercept) |       -0.03 | 0.19 | [-0.40, 0.34] | 0.16 | 96 | 0.875
#> var2        |       -0.17 | 0.11 | [-0.39, 0.05] | 1.49 | 96 | 0.137
#> var1        |       -0.15 | 0.11 | [-0.37, 0.06] | 1.39 | 96 | 0.164
#> var3        |        0.11 | 0.10 | [-0.09, 0.31] | 1.09 | 96 | 0.277

model_parameters(avg_mod, component = "full")
#> Parameter   | Coefficient |   SE |        95% CI |    z | df |     p
#> --------------------------------------------------------------------
#> (Intercept) |       -0.03 | 0.19 | [-0.40, 0.34] | 0.16 | 96 | 0.875
#> var2        |       -0.04 | 0.09 | [-0.21, 0.14] | 0.43 | 96 | 0.669
#> var1        |       -0.03 | 0.08 | [-0.18, 0.12] | 0.39 | 96 | 0.700
#> var3        |        0.02 | 0.05 | [-0.09, 0.12] | 0.28 | 96 | 0.778

plot(model_parameters(avg_mod, component = "full"))

您可以对该图进行一些小的修改：

library(ggplot2)
plot(model_parameters(avg_mod, component = "full")) +
  geom_text(aes(label = round(Coefficient, 2)), nudge_x = .2)

^{由reprex package（v0.3.0）于2020-06-27创建}

绘制回归模型的估计值（固定效应）

2 个答案: