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).
The above code will work on glm objects but not on averaged model objects created in MuMIn.
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.
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)
This works fine. However when I call
MA.ests$avg.model
I get >NULL.
Has avg.model been deprecated? Or is there another way to do this?
I can do a workaround using any of these three calls, but they're not exactly what I want.
coefTable(MA.ests)
coef(MA.ests)
modavg.table <- as.data.frame(summary(MA.ests)$coefmat)
(that is, I don't know how to get these objects into latex without a lot more code.)
Thanks in advance for any suggestions.
The latest version 1.34.3 of the texreg package supports both model.selection and averaging objects.
Your code example:
library("texreg")
library("MuMIn")
data(cement)
fm1 <- lm(y ~ ., data = Cement, na.action = na.fail)
ms1 <- dredge(fm1)
screenreg(ms1)
yields:
==========================================================================================================================================================================================================
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
And model averaging:
MA.ests <- model.avg(ms1, subset = delta < 5, revised.var = TRUE)
screenreg(MA.ests)
yields:
=======================
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
For finetuning, see also the arguments of the two extract methods on the help page: ?extract