从MuMIn提取平均模型以输出乳胶 [英] Extract average model from MuMIn for latex output
问题描述
我正在尝试从 MuMIn 中提取两个不同的平均模型，以通过 texreg 或 stargazer 输出到乳胶中.我想要一张表，可以比较两种物种对不同的非生物变量集的响应，该表看起来与使用两个模型对象创建的变量相同
I'm trying to extract two different averaged models from MuMIn for output to latex via texreg or stargazer. I'd like to have one table where I can compare two species' response to different sets of abiotic variables, that looks the same as one created from two model objects using
glmtable <- texreg(list(m1, m2).
以上代码将对glm对象起作用，但不适用于在 MuMIn 中创建的平均模型对象.
The above code will work on glm objects but not on averaged model objects created in MuMIn.
我尝试通过 https://sites.google.com/site/上的示例进行操作rforfishandwildlifegrads/home/mumin_usage_examples ，以输出可以输出到乳胶的文本表.
I tried following an example at https://sites.google.com/site/rforfishandwildlifegrads/home/mumin_usage_examples, to output a text table that can be output to latex.
以下是使用水泥数据的可重现示例:
Here's a reproducible example using the cement data:
library(MuMIn)
data(cement)
# full model
fm1 <- lm(y ~ ., data = Cement, na.action = na.fail)
# create and examine candidate models
(ms1 <- dredge(fm1))
# average models with delta AICc <5, include adjusted SE
MA.ests<-model.avg(ms1, subset= delta < 5, revised.var = TRUE)
这很好.但是当我打电话
This works fine. However when I call
MA.ests$avg.model
我得到> NULL.
是否已弃用avg.model?还是有其他方法可以做到这一点?
Has avg.model been deprecated? Or is there another way to do this?
我可以使用这三个电话中的任何一个来解决问题，但它们并不是我想要的.
I can do a workaround using any of these three calls, but they're not exactly what I want.
coefTable(MA.ests)
coef(MA.ests)
modavg.table <- as.data.frame(summary(MA.ests)$coefmat)
(也就是说，我不知道如何在没有更多代码的情况下将这些对象放入乳胶中.)
(that is, I don't know how to get these objects into latex without a lot more code.)
提前感谢您的任何建议.
Thanks in advance for any suggestions.
推荐答案
texreg 软件包的最新版本1.34.3 支持 model.selection 和 averaging 对象.
The latest version 1.34.3 of the texreg package supports both model.selection and averaging objects.
您的代码示例:
library("texreg")
library("MuMIn")
data(cement)
fm1 <- lm(y ~ ., data = Cement, na.action = na.fail)
ms1 <- dredge(fm1)
screenreg(ms1)
产量:
==========================================================================================================================================================================================================
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 Model 11 Model 12 Model 13 Model 14 Model 15 Model 16
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
(Intercept) 52.58 *** 71.65 *** 48.19 *** 103.10 *** 111.68 *** 203.64 *** 62.41 131.28 *** 72.07 *** 117.57 *** 57.42 *** 94.16 81.48 *** 72.35 *** 110.20 *** 95.42 ***
(2.29) (14.14) (3.91) (2.12) (4.56) (20.65) (70.07) (3.27) (7.38) (5.26) (8.49) (56.63) (4.93) (17.05) (7.95) (4.17)
X1 1.47 *** 1.45 *** 1.70 *** 1.44 *** 1.05 *** 1.55 * 1.87 *** 2.31 *
(0.12) (0.12) (0.20) (0.14) (0.22) (0.74) (0.53) (0.96)
X2 0.66 *** 0.42 * 0.66 *** -0.92 *** 0.51 0.73 *** 0.79 *** 0.31
(0.05) (0.19) (0.04) (0.26) (0.72) (0.12) (0.17) (0.75)
X4 -0.24 -0.61 *** -0.64 *** -1.56 *** -0.14 -0.72 *** -0.74 *** -0.46
(0.17) (0.05) (0.04) (0.24) (0.71) (0.07) (0.15) (0.70)
X3 0.25 -0.41 * -1.45 *** 0.10 -1.20 *** -1.01 *** 0.49 -1.26 *
(0.18) (0.20) (0.15) (0.75) (0.19) (0.29) (0.88) (0.60)
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Log Likelihood -28.16 -26.93 -26.95 -29.82 -27.31 -29.73 -26.92 -35.37 -40.96 -45.87 -46.04 -45.76 -48.21 -48.00 -50.98 -53.17
AICc 69.31 72.44 72.48 72.63 73.19 78.04 79.84 83.74 94.93 100.41 100.74 104.52 105.08 109.01 110.63 111.54
Delta 0.00 3.13 3.16 3.32 3.88 8.73 10.52 14.43 25.62 31.10 31.42 35.21 35.77 39.70 41.31 42.22
Weight 0.57 0.12 0.12 0.11 0.08 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Num. obs. 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
==========================================================================================================================================================================================================
*** p < 0.001, ** p < 0.01, * p < 0.05
模型平均:
MA.ests <- model.avg(ms1, subset = delta < 5, revised.var = TRUE)
screenreg(MA.ests)
产量:
=======================
Model 1
-----------------------
(Intercept) 64.69 **
(22.24)
X1 1.46 ***
(0.20)
X2 0.63 ***
(0.12)
X4 -0.48 *
(0.22)
X3 -0.02
(0.38)
-----------------------
Num. obs. 13
=======================
*** p < 0.001, ** p < 0.01, * p < 0.05
要进行微调，请参见帮助页面上的两个 extract 方法的参数:?extract
For finetuning, see also the arguments of the two extract methods on the help page: ?extract
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