at = list() does not find variable in the model fit
Ellesaere opened this issue · 0 comments
Ellesaere commented
- a possible bug
## load package
library("margins")
library("MASS")
## The following example does not work. But somehow the reproducible example I tried to make does work
## See https://stackoverflow.com/questions/67138433/how-to-use-margins-package-to-evaluate-marginal-affects-at-different-values-of-t
fit.polr2 <- polr(ordinal_dep_var ~ Dummy + Continuous + Dummy2 + as.factor(industry) + Urbanisation_Dummy + Size_Dummy, data = df2, method = "probit", Hess=TRUE)
summary(margins(fit.polr2))
polr_1st_margins <- summary(margins(fit.polr2, at = list(ordinal_dep_var= 0:3)))
Error in dat[, not_numeric, drop = FALSE] :
incorrect number of dimensions
df2 <- structure(list(ordinal_dep_var = structure(c(4L, 3L, 4L, 1L,
3L, 1L, 1L, 3L, 1L, 4L, 4L, 4L, 1L, 3L, 4L, 2L, 4L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 3L, 4L, 3L, 2L, 2L, 1L, 1L, 2L, 4L, 2L, 1L, 4L,
3L, 1L, 2L, 3L, 4L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 2L, 4L, 2L, 1L,
4L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 3L,
2L, 3L, 3L, 1L, 4L, 4L, 4L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 2L, 4L, 3L, 3L, 1L, 1L, 1L, 3L, 2L, 3L, 2L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 4L, 3L, 2L, 2L, 3L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("0",
"1", "2", "3"), class = c("ordered", "factor")), Dummy = c(0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1,
0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,
1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0), Continuous = c(15.6862745098039,
41.6666666666667, 26.9230769230769, 14.8514851485149, 32.1428571428571,
20, 43.75, 0, 0, 0, 80, 100, 21.4285714285714, 23.8095238095238,
28.125, 0, 30.3030303030303, 25, 100, 100, 100, 13.3333333333333,
66.6666666666667, 33.3333333333333, 55.5555555555556, 72.2222222222222,
57.3033707865169, 17.7777777777778, 47.6190476190476, 17.7777777777778,
41.6666666666667, 40, 20, 8.33333333333333, 16.6666666666667,
40, 100, 0, 50, 0, 0, 7.69230769230769, 0, 0, 0, 0, 0, 0, 0,
33.3333333333333, 20, 1.84089414858646, 1.84089414858646, 1.84089414858646,
30, 20, 0, 33.3333333333333, 33.3333333333333, 0, 0, 0, 100,
50, 22.2222222222222, 0, 0, 50, 50, 46.1538461538462, 44.4444444444444,
0, 5.55555555555556, 0, 0, 47.3684210526316, 43.75, 18.1818181818182,
0, 42.8571428571429, 14.2857142857143, 0, 50, 0, 0, 50, 20, 50,
100, 0, 42.8571428571429, 20, 25, 33.3333333333333, 0, 0, 0,
66.6666666666667, 0, 25.9259259259259, 0, 33.3333333333333, 0,
100, 25, 0, 0, 10, 100, 50, 33.3333333333333, 75, 0, 0, 40, 0,
33.3333333333333, 28.5714285714286, 0, 0, 28.5714285714286, 0,
28.5714285714286, 0, 0, 9.09090909090909, 30, 66.6666666666667,
50, 0, 20, 50, 0, 33.3333333333333, 0, 66.6666666666667, 18.1818181818182,
28.5714285714286, 36.9230769230769, 14.2857142857143, 36.3636363636364,
0, 0, 7.69230769230769), Dummy2 = structure(c(0, 0, 0, 0, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0,
0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1,
1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,
0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0,
1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0), label = "Gift/informal payment requested: tax inspectorate?", format.stata = "%14.0f", class = c("haven_labelled",
"vctrs_vctr", "double"), labels = c(Yes = 1, No = 2)), industry = structure(c(3,
7, 2, 7, 19, 17, 9, 7, 9, 11, 12, 10, 5, 5, 3, 12, 3, 5, 4, 1,
1, 11, 5, 7, 9, 9, 7, 9, 5, 9, 4, 7, 9, 11, 11, 22, 12, 21, 19,
11, 10, 7, 19, 23, 20, 20, 21, 24, 19, 1, 21, 21, 21, 21, 21,
21, 6, 6, 21, 3, 17, 19, 20, 12, 21, 20, 12, 10, 10, 21, 21,
19, 3, 21, 21, 10, 10, 21, 5, 20, 21, 19, 22, 15, 6, 21, 21,
10, 19, 21, 20, 3, 6, 10, 24, 24, 21, 23, 21, 21, 11, 21, 3,
3, 21, 24, 21, 1, 10, 22, 19, 17, 3, 20, 23, 1, 22, 21, 21, 23,
21, 23, 21, 22, 22, 21, 10, 13, 10, 15, 10, 24, 24, 7, 10, 10,
22, 21, 21, 23, 21, 22, 7, 21), label = "Industry", format.stata = "%34.0g", class = c("haven_labelled",
"vctrs_vctr", "double"), labels = c(Textiles = 1, Leather = 2,
Garments = 3, Agroindustry = 4, Food = 5, Beverages = 6, `Metals and machinery` = 7,
Electronics = 8, `Chemicals and pharmaceutics` = 9, Construction = 10,
`Wood and furniture` = 11, `Non-metallic and plastic materials` = 12,
Paper = 13, `Sport goods` = 14, `IT services` = 15, `Other manufacturing` = 16,
Telecommunications = 17, `Accounting and finance` = 18, `Advertising and marketing` = 19,
`Other services` = 20, `Retail and wholesale trade` = 21, `Hotels and restaurants` = 22,
Transport = 23, `Real estate and rental services` = 24, `Mining and quarrying` = 25,
`Auto and auto components` = 26, `Other transport equipment` = 27,
`Other unclassified` = 99)), Urbanisation_Dummy = structure(c(2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 2L, 3L,
3L, 3L, 1L, 1L, 1L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, 1L, 2L, 3L, 2L,
3L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 3L, 1L, 3L, 2L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 1L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 1L, 1L), .Label = c("City > 250",
"50k-250k", "< 50k"), class = "factor"), Size_Dummy = structure(c(3L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 3L, 3L,
3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 2L, 3L, 3L, 2L, 1L, 1L, 1L, 3L,
3L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 3L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 3L, 1L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 1L, 3L,
1L, 1L, 1L, 3L, 3L, 2L, 3L, 1L, 2L, 1L, 3L, 1L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 3L, 3L,
2L, 3L, 3L, 1L, 3L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L), .Label = c("Employees: < 10",
"Employees: 10-19", "Employees: 20+"), class = "factor")), row.names = c(NA,
-144L), class = c("data.table", "data.frame"))
## session info for your system
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)
Matrix products: default
Random number generation:
RNG: Mersenne-Twister
Normal: Inversion
Sample: Rounding
locale:
[1] LC_COLLATE=English_United Kingdom.1252 LC_CTYPE=English_United Kingdom.1252 LC_MONETARY=English_United Kingdom.1252 LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252
attached base packages:
[1] splines stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] margins_0.3.26 forcats_0.5.1 purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.0 tidyverse_1.3.0 dplyr_1.0.5
[9] data.table_1.14.0 lavaan_0.6-8 plm_2.4-1 modelr_0.1.8 magrittr_2.0.1 bbmle_1.0.23.1 openxlsx_4.2.3 AER_1.2-9
[17] sandwich_3.0-0 lmtest_0.9-38 car_3.0-10 carData_3.0-4 ff_4.0.4 bit_4.0.4 pryr_0.1.4 jomo_2.7-2
[25] mice_3.13.0 GGally_2.1.1 visdat_0.5.3 dummies_1.5.6 checkmate_2.0.0 randomForest_4.6-14 zoo_1.8-9 sjlabelled_1.1.7
[33] glmnet_4.1-1 Matrix_1.2-17 stringr_1.4.0 foreign_0.8-71 readxl_1.3.1 countrycode_1.2.0 logspline_2.1.16 fitdistrplus_1.1-3
[41] MASS_7.3-51.4 doBy_4.6.9 ivmodel_1.9.0 censReg_0.5-32 maxLik_1.4-6 miscTools_0.6-26 VGAM_1.1-5 stargazer_5.2.2
[49] growthrates_0.8.2 deSolve_1.28 rms_6.2-0 SparseM_1.81 Hmisc_4.5-0 ggplot2_3.3.3 Formula_1.2-4 survival_3.2-7
[57] lattice_0.20-38 sure_0.2.0 haven_2.3.1 session_1.0.3
loaded via a namespace (and not attached):
[1] backports_1.2.1 plyr_1.8.6 TH.data_1.0-10 digest_0.6.27 foreach_1.5.1 htmltools_0.5.1.1 fansi_0.4.2 cluster_2.1.0
[9] matrixStats_0.58.0 bdsmatrix_1.3-4 jpeg_0.1-8.1 colorspace_2.0-0 rvest_1.0.0 rbibutils_2.0 xfun_0.22 jsonlite_1.7.2
[17] crayon_1.4.1 lme4_1.1-26 iterators_1.0.13 glue_1.4.2 FME_1.3.6.1 gtable_0.3.0 MatrixModels_0.5-0 shape_1.4.5
[25] abind_1.4-5 scales_1.1.1 mvtnorm_1.1-1 DBI_1.1.1 Rcpp_1.0.6 htmlTable_2.1.0 tmvnsim_1.0-2 prediction_0.3.14
[33] httr_1.4.2 htmlwidgets_1.5.3 RColorBrewer_1.1-2 ellipsis_0.3.1 farver_2.1.0 pkgconfig_2.0.3 reshape_0.8.8 dbplyr_2.1.0
[41] nnet_7.3-12 utf8_1.2.1 labeling_0.4.2 tidyselect_1.1.0 rlang_0.4.10 reshape2_1.4.4 munsell_0.5.0 cellranger_1.1.0
[49] tools_3.6.1 cli_2.3.1 generics_0.1.0 broom_0.7.5 glmmML_1.1.1 fs_1.5.0 knitr_1.31 zip_2.1.1
[57] rootSolve_1.8.2.1 nlme_3.1-140 quantreg_5.85 xml2_1.3.2 compiler_3.6.1 rstudioapi_0.13 curl_4.3 png_0.1-7
[65] reprex_1.0.0 statmod_1.4.35 pbivnorm_0.6.0 stringi_1.5.3 nloptr_1.2.2.2 vctrs_0.3.6 pillar_1.5.1 lifecycle_1.0.0
[73] Rdpack_2.1.1 insight_0.13.1 conquer_1.0.2 R6_2.5.0 latticeExtra_0.6-29 gridExtra_2.3 rio_0.5.26 codetools_0.2-16
[81] polspline_1.1.19 boot_1.3-22 assertthat_0.2.1 minpack.lm_1.2-1 withr_2.4.1 mnormt_2.0.2 Deriv_4.1.3 multcomp_1.4-16
[89] parallel_3.6.1 hms_1.0.0 grid_3.6.1 rpart_4.1-15 coda_0.19-4 minqa_1.2.4 lubridate_1.7.10 numDeriv_2016.8-1.1
[97] base64enc_0.1-3