如何解决计算具有相互作用类别和度量协变量之间的混合模型中边坡的边际效应
我目前正在研究一个混合模型，该模型包括分类变量和度量成本变量的多次交互以预测度量输出。由于种类繁多的分类变量，我对成本与产出（坡度）之间的线性关系的变化非常感兴趣。
因为我有多个与成本相互作用的分类协变量（并且还有随机效应），所以我寻求一种简单的方法来计算这些斜率的边际效应。我不是要对输出进行预测或期望，而是希望看到“边际斜率”。
我在下面包含了一些示例代码
library(housingData)
library(tidyverse)
library(lme4)
#> Loading required package: Matrix
#>
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#>
#> expand,pack,unpack
set.seed(12345)
df <- housing %>%
filter(complete.cases(.)) %>%
mutate(house_type = factor(sample(c("A","B","C"),nrow(.),replace = TRUE)),timeperiod = as.factor(if_else(lubridate::year(time) < 2012,"before_2012","2012_and_after")))
# model categorical effects on slope,after main effect has been fitted
lm1 <- lm(medSoldPriceSqft ~ 0 + medListPriceSqft,data = df)
coef(lm1)
#> medListPriceSqft
#> 0.9137324
df2 <- df %>%
modelr::add_residuals(lm1,"medSoldPriceSqft_after_list_price")
mod2 <- lmer(medSoldPriceSqft_after_list_price ~ 0 + medListPriceSqft:house_type
+ medListPriceSqft:timeperiod +
(0 + medListPriceSqft:house_type + medListPriceSqft:timeperiod| state),data = df2)
fixef(mod2)
#> medListPriceSqft:house_typeA medListPriceSqft:house_typeB
#> 0.02819789 0.01725657
#> medListPriceSqft:house_typeC medListPriceSqft:timeperiodbefore_2012
#> 0.01733967 0.01526881
ranef(mod2)$state
#> medListPriceSqft:house_typeA medListPriceSqft:house_typeB
#> AL -0.0332371439 -0.0342472679
#> AR -0.0161834913 -0.0197485094
#> AZ -0.0455633279 -0.0540328340
#> CA -0.0073736100 -0.0069451237
#> CO -0.0998324113 -0.2144934915
#> CT -0.0068844784 -0.0064218278
#> DC 0.1323688987 0.1625294726
#> DE 0.0194554114 0.0240963193
#> FL -0.0937513315 -0.1128399131
#> GA -0.0404177163 -0.0447775564
#> IA -0.0018353661 -0.0065783305
#> ID 0.0715311714 0.0827129047
#> IL 0.0364040836 0.0519384804
#> IN 0.3815562061 0.5127737560
#> KS 0.3195998033 0.3735174115
#> KY 0.0180534996 0.0284589475
#> LA -0.0034512710 0.0002398266
#> MA 0.0350797588 0.0495612406
#> MD -0.0061851180 0.0030358059
#> ME -0.1205435796 -0.1434406842
#> MI -0.0104773953 -0.0097225674
#> MN 0.0658291099 0.0815773829
#> MO 0.1658471055 0.2092785032
#> MS 0.0757674725 0.0957259331
#> MT -0.2226956530 -0.2848157760
#> NC -0.0801849629 -0.0930100202
#> ND 0.1471279546 0.1632516890
#> NE -0.0067148446 -0.0091668294
#> NH -0.0288545182 -0.0318590020
#> NJ 0.0419945037 0.0452393238
#> NM -0.3402646109 -0.4196645043
#> NV -0.0319233561 -0.0379518075
#> NY -0.0458677598 -0.0460468454
#> OH 0.0053817946 0.0082642996
#> OK -0.0197739286 -0.0218087721
#> OR -0.0312838479 -0.0325376041
#> PA 0.0548995292 0.0837696293
#> RI -0.0301300132 -0.0360929264
#> SC -0.0627597227 -0.0749119310
#> TN 0.0327156573 0.0397181302
#> TX 0.0813191504 0.0826967005
#> UT -0.0078590502 -0.0157365323
#> VA -0.0007437153 0.0095222182
#> VT -0.1811751876 -0.2204967507
#> WA -0.0090309685 -0.0112995414
#> WI -0.0244199376 -0.0283874222
#> WV -0.0755127962 -0.0908736075
#> medListPriceSqft:house_typeC medListPriceSqft:timeperiodbefore_2012
#> AL -0.0161539511 -0.033273312
#> AR -0.0143981558 -0.002542357
#> AZ -0.0354905346 -0.022865789
#> CA -0.0023188159 -0.016206762
#> CO -0.1891388131 -0.044702463
#> CT -0.0025396683 -0.007847727
#> DC 0.0915280219 0.147291653
#> DE 0.0138827021 0.019724991
#> FL -0.0701567701 -0.084079270
#> GA -0.0260708779 -0.036974280
#> IA -0.0102049733 0.015404515
#> ID 0.0420144801 0.079569678
#> IL 0.0595382970 -0.072987720
#> IN 0.4171418424 -0.035008222
#> KS 0.1945214096 0.349026709
#> KY 0.0298359996 -0.024003880
#> LA 0.0102208245 -0.036621369
#> MA 0.0494285509 -0.034373164
#> MD 0.0074462978 -0.007041631
#> ME -0.0886152719 -0.076925785
#> MI -0.0009559257 -0.024871313
#> MN 0.0334029227 0.132315832
#> MO 0.1447632248 0.074557543
#> MS 0.0624490008 0.054095381
#> MT -0.2229587595 0.002527347
#> NC -0.0581766037 -0.062570993
#> ND 0.0566945904 0.254531126
#> NE -0.0065563090 -0.003424448
#> NH -0.0121567614 -0.046952309
#> NJ 0.0457664635 -0.067748718
#> NM -0.2712390810 -0.211903077
#> NV -0.0242007544 -0.018678370
#> NY -0.0430582366 0.108457835
#> OH 0.0151824401 -0.038200489
#> OK -0.0100685546 -0.023278317
#> OR -0.0151429366 -0.036220790
#> PA 0.0781025694 -0.026947879
#> RI -0.0300959629 0.012527652
#> SC -0.0372963600 -0.092552679
#> TN 0.0308868734 -0.001932841
#> TX 0.0381596134 0.076102496
#> UT -0.0305654079 0.070222766
#> VA 0.0037802116 0.046899465
#> VT -0.1400341335 -0.118380902
#> WA -0.0022661974 -0.027577840
#> WI -0.0106677175 -0.046255988
#> WV -0.0542188035 -0.060304313
# make marginal effects accessible
## Average increase in medListPriceSqft-Slope for house_typeA and timeperiodbefore_2012
fixef(mod2)["medListPriceSqft:house_typeA"] + fixef(mod2)["medListPriceSqft:timeperiodbefore_2012"]
#> medListPriceSqft:house_typeA
#> 0.0434667
## Average increase in medListPriceSqft-Slope for house_typeA ()
prop_before_2012 <- table(df2$timeperiod)["before_2012"] / nrow(df2)
fixef(mod2)["medListPriceSqft:house_typeA"] +
fixef(mod2)["medListPriceSqft:timeperiodbefore_2012"] * prop_before_2012
#> medListPriceSqft:house_typeA
#> 0.03564273
## Average increase in medListPriceSqft-Slope for timeperiodbefore_2012
# ...
## Average increase in medListPriceSqft-Slope for house_typeA () for state == "WA"
# ...
如您所见，这很快就变得很乏味，我该如何以编程方式执行此操作？软件包{margins}或{ggeffects}似乎仅涵盖输出的边际效应？
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