Trying to run a simple logistic regression without luck - r

I am trying to run a logistic regression on a small data set, but after going at it for three weeks I am about to give up. I have been getting a range of error messages and I am now even having doubts whether my R engine is working as it should. DPUT of my data looks like this:
structure(list(SEASON = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L
), .Label = c("1", "2", "3"), class = "factor"), OO_OBS = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1,
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1), HW_OBS = c(1, 1, 1, 0,
0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1), F_OO = structure(c(2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 1L, 1L, 2L), .Label = c("0", "1"), class = "factor"), F_HW = c(1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1,
0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1), INIT_OO = structure(c(2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 3L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
3L, 1L, 1L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 2L, 2L, 3L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 3L), .Label = c("0", "1", "NA"), class = "factor"),
INIT_HW = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L), .Label = c("0", "NA"), class = "factor"),
INIT_BOTH = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L), .Label = c("0", "1", "NA"), class = "factor"),
as.is = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE)), .Names = c("SEASON", "OO_OBS", "HW_OBS", "F_OO",
"F_HW", "INIT_OO", "INIT_HW", "INIT_BOTH", "as.is"), row.names = c(NA,
-119L), class = "data.frame")
These variables are all factors with two levels, except SEASON, that has three levels.
I want to run a logistic regression where "F_HW" is the response variable and "SEASON" and "F_OO" are dependent variables. This is the script I have been trying to run, which should be really simple, but it doesn't work (my data object is called feed.df):
feed.df$SEASON <- as.factor(feed.df$SEASON)
feed.df$F_OO <- as.factor(feed.df$F_OO)
feed.df$FEED_HW <- as.factor(feed.df$FEED_HW)
attach(feed.df)
ml <- glm(F_HW ~ SEASON + F_OO, family=binomial)
lm.out = glm(F_HW ~ SEASON + F_OO, family=binomial)
summary (lm.out)

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I've got clustered patient data of 700 patients (clustered into two groups for two different hospitals). I'm trying to figure out if a certain cardiovacsular risk factor exposure (factor with 3 levels: fall, stable [reference], and rise) is related to my binary outcome_pres (numeric with 0's for no outcome and 1's for yes outcome), adjusted for age and sex. To do this I've used a GEE model using the geepack package in R:
library(tidyverse)
library(magrittr)
library(geepack)
data <- structure(list(id = c(23, 30, 92, 122, 132, 141, 157, 158, 167,
175, 200, 230, 237, 257, 283, 297, 336, 339, 357, 376, 379, 421,
425, 431, 436, 437, 443, 449, 458, 505, 518, 521, 546, 547, 573,
613, 618, 644, 655, 672, 697, 730, 750, 755, 780, 786, 798, 853,
862, 874, 882, 916, 945, 948, 979, 982, 1002, 1003, 1006, 1022,
1059, 1069, 1092, 1095, 1116, 1127, 1133, 1162, 1178, 1188, 1201,
1210, 1239, 1242, 1258, 1280, 1281, 1297, 1307, 1318, 1331, 1353,
1369, 1383, 1407, 1463, 1473, 1477, 1485, 1519, 1555, 1567, 1573,
1611, 1636, 1659, 1686, 1700, 1712, 1744, 1766, 1767, 1771, 1778,
1797, 1806, 1810, 1821, 1822, 1875, 1879, 1890, 1903, 1917, 1964,
2007, 2010, 2018, 2028, 2067, 2071, 2077, 2078, 2086, 2090, 2103,
2128, 2148, 2185, 2223, 2239, 2257, 2267, 2283, 2300, 2304, 2332,
2395, 2399, 2407, 2427, 2431, 2434, 2440, 2445, 2449, 2480, 2515,
2533, 2536, 2546, 2552, 2635, 2660, 2697, 2705, 2724, 2725, 2728,
2748, 2778, 2794, 2798, 2830, 2843, 2895, 2902, 2907, 2915, 2924,
2929, 2952, 2955, 2963, 2982, 3016, 3018, 3036, 3065, 3083, 3087,
3177, 3178, 3186, 3196, 3199, 3230, 3272, 3278, 3292, 3302, 3304,
3311, 3312, 3351, 3378, 3399, 3401, 3403, 3445, 3447, 3449, 3457,
3472, 3477, 3486, 3490, 3515, 3516, 3519, 3521, 17001, 64001,
128001, 136001, 177001, 177002, 240001, 240001, 248002, 352001,
370002, 410001, 426001, 443002, 443002, 466002, 466002, 469002,
470001, 489002, 520002, 542001, 595001, 615001, 651001, 651002,
657001, 658002, 665001, 665001, 687002, 698002, 745001, 754001,
800002, 804001, 811002, 818001, 881001, 881001, 920001, 927001,
927001, 943001, 974001, 1015002, 1037001, 1081001, 1081002, 1133001,
1136001, 1141001, 1157001, 1175002, 1206001, 1247001, 1247002,
1283001, 1290002, 1296001, 1307001, 1344002, 1346001, 1346001,
1379001, 1419002, 1427001, 1438001, 1592002, 1652002, 1711001,
1754001, 1763001, 1811001, 1833001, 1878002, 1892001, 1915001,
1915002, 1921001, 1949002, 1961001, 1995001, 2014001, 2022002,
2102001, 2102002, 2138002, 2141001, 2141002, 2193002, 2240001,
2281001, 2281002, 2432001, 2493001, 2517001, 2558001, 2558002,
2588001, 2588001, 2590001, 2601001, 2620002, 2653001, 2678001,
2714001, 2721001, 2721002, 2721002, 2731001, 2777001, 2806001,
2813001, 2862001, 2862002, 2872001, 2872002, 2902001, 2959001,
2967001, 3026002, 3026002, 3037001, 3063002, 3094002, 3126002,
3139001, 3196001, 3233001, 3252001, 3282002, 3290002, 3314001,
3334001, 3334001, 3377002, 3420001, 3427001, 3458001, 3532002,
3558001, 3653001, 3663002, 3675002, 3678002, 3692001, 3757002,
3764002, 3764002, 3774001, 3780001, 3823002, 3823002, 3836001,
3862001, 3916001, 3953001, 4032001, 4042001, 4063001, 4077001,
4084001, 4094002, 4118002, 4140002, 4140002, 4149001, 4168004,
4171001, 4241002, 4254001, 4257002, 4269001, 4270001, 4291001,
4298001, 4302002, 4302002, 4325001, 4325001, 4343002, 4353001,
4353001, 4377001, 4422002, 4447002, 4563002, 4568002, 4578001,
4578002, 4601001, 4620002, 4641002, 4673002, 4687001, 4687002,
4695002, 4695002, 4700002, 4707001, 4707001, 4768002, 4802001,
4821001, 4833001, 4839001, 4839001, 4848001, 4854002, 4893002,
4895002, 4918001, 4969001, 4972001, 4985001, 4985001, 4985002,
4991001, 5045001, 5072002, 5181001, 5234002, 5240001, 5361001,
5361002, 5388002, 5400001, 5467002, 5469001, 5528001, 5588001,
5593001, 5643002, 5676001, 5683002, 5690001, 5708002, 5710002,
5722002, 5722002, 5725002, 5770001, 5826001, 5866001, 5869001,
5982002, 5993001, 6039002, 6054002, 6095001, 6095002, 6121003,
6195002, 6258002, 6260002, 6391001, 6391002, 6414001, 6489002,
6552002, 6555002, 6573001, 6578002, 6582002, 6583002, 6583002,
6588001, 6588002, 6625001, 6625002, 6642001, 6660001, 6806002,
6887002, 6888001, 6893002, 6916001, 6916001, 6953002, 6960001,
6960001, 6991002, 7010001, 7010002, 7021001, 7037001, 7037001,
7038001, 7038002, 7089001, 7121001, 7141001, 7150002, 7173002,
7192001, 7220001, 7301002, 7318002, 7339002, 7344002, 7421001,
7436002, 7522002, 7606002, 7653002, 7681001, 7686001, 7759002,
7781001, 7805001, 7816001, 7837002, 7846001, 7856001, 7856002,
7884002, 7934001, 7971002, 7984001, 7995001, 8016002, 8052001,
8064002, 8064003, 8076001, 8109001, 8125001, 8143001, 8144001,
8194001, 8194002, 8218001, 8247001, 8333001, 8360001, 8401001,
8480001, 8545002, 8556001, 8556002, 8596002, 8599001, 8638001,
8751002, 8943002, 9042001, 9075001, 9112002, 9195001, 9404001,
9437001, 9452002, 9456001, 9528001, 9528002, 9535001, 9556001,
9591001), group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
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1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L), .Label = c("1", "2", "3"), class = "factor"), age = c(61.6700889801506,
63.3182751540041, 66.611909650924, 64.435318275154, 63.6796714579055,
64.6351813826146, 60.6351813826146, 63.9780971937029, 70.4394250513347,
63.7700205338809, 62.8583162217659, 64.5859000684463, 70.9733059548255,
63.7125256673511, 63.8466803559206, 91.1813826146475, 86.0342231348392,
75.9069130732375, 63.4496919917864, 63.8056125941136, 67.2388774811773,
64.104038329911, 65.284052019165, 64.4188911704312, 82.2203969883641,
65.0814510609172, 71.4277891854894, 62.2395619438741, 77.7385352498289,
61.5030800821355, 82.3244353182751, 69.5687885010267, 78.0752908966461,
65.3826146475017, 62.9267624914442, 63.6988364134155, 63.2553045859001,
62.1409993155373, 64.6351813826146, 63.1512662559891, 66.3764544832307,
71.5947980835044, 63.2826830937714, 65.9657768651608, 63.6194387405886,
63.4414784394251, 61.5359342915811, 78.031485284052, 63.2772073921971,
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71.7618069815195, 62.9733059548255, 67.5208761122519, 61.3388090349076,
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), class = "factor"), outcome_pres = c(1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1,
1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,
1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)), row.names = c(NA,
-570L), class = c("tbl_df", "tbl", "data.frame"))
And the model
model <- geeglm(formula=outcome_pres~exposure+sex+age, data=data, id=id, family=binomial("logit"), corstr="ar1")
How do I check if R actually used the 0's of outcome_pres as the reference category in this analysis?

I think you are misunderstanding something here. outcome_pres is your outcome, which you define as a binomial distribution, and has the values 0 and 1:
str(data)
tibble [570 x 6] (S3: tbl_df/tbl/data.frame)
$ id : num [1:570] 23 30 92 122 132 141 157 158 167 175 ...
$ group : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
$ age : num [1:570] 61.7 63.3 66.6 64.4 63.7 ...
$ sex : Factor w/ 2 levels "Women","Men": 1 1 1 1 1 2 1 2 1 1 ...
$ exposure : Factor w/ 3 levels "Stable","Fall",..: 1 1 1 3 1 3 1 1 1 1 ...
$ outcome_pres: num [1:570] 1 1 1 1 1 0 1 1 1 1 ...
There are no reference category in this case.
If your question is how do I know what is the reference category of my exposure, then you can see it directly in the summary of your model:
summary(model)
Call:
geeglm(formula = outcome_pres ~ exposure + sex + age, family = binomial("logit"),
data = data, id = id, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -3.652686 1.826395 4.00 0.0455 *
exposureFall 0.718930 0.444144 2.62 0.1055
exposureRise -0.854571 0.325662 6.89 0.0087 **
sexMen 0.000538 0.242922 0.00 0.9982
age 0.080847 0.028230 8.20 0.0042 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
You have exposureFall and exposureRise, meaning that your reference is the category not present in the summary, that is stable:
data$exposure %>% unique()
[1] Stable Rise Fall
Levels: Stable Fall Rise
Rise has thus a protective effect (your Odd ratio is exp(-0.854571)) compared to stable

"Error in Contrasts" message in glm model after confirming that all Factor variables are longer than 2 unique values [R]:

I am not sure why I am still receiving this message when running a base model with all variables in my dataset:
My data, with anonymized variables:
set.seed(1234)
#dput(df)
structure(list(outcome_1= structure(c(2L, 1L, 1L, 2L,
1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 1L, NA, 2L, 1L), .Label = c("0", "1"), class = "factor"),
outcome_2= structure(c(2L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, NA, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, NA, 2L, 1L), .Label = c("0", "1"), class = "factor"),
outcome_3= structure(c(2L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, NA, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, NA, 1L, 1L), .Label = c("0", "1"), class = "factor"),
bl_ep = c(16, 92, 10, 40, 19, 1, 16, 10, 22, 28, 8, 11, 6,
47, 12, 1, 9, 20, 2, 14, 72, 28, 5, 16, 61, 12, 24, 22, 44,
44, 16, 36, 62, 10, 16, 10, 89, 22, 5, 38, 8, 11), bl_days = c(12,
28, 10, 25, 19, 1, 10, 9, 13, 28, 4, 11, 6, 20, 12, 1, 8,
16, 2, 12, 27, 28, 5, 13, 24, 10, 18, 18, 16, 16, 10, 28,
22, 5, 15, 8, 28, 15, 5, 22, 7, 11), score_1 = c(11,
19, 17, 17, 12, 14, 8, 12, 14, 15, 14, 13, 12, 14, 15, 5,
11, 14, 14, 13, 16, 11, 11, 14, 20, 14, 12, 11, 17, 15, 14,
18, 15, 14, 12, 10, 17, 16, 11, 13, 18, 17), score_2 = c(1.1,
1.6, 1.6, 2.8, 1.9, 3.3, 4, 3.8, 1.8, 1.4, 2, 3.55, 1.6,
1.8, 2.4, 3.7, 1.4, 2.9, 3.55, 2.5, 1.6, 3.2, 3.5, 2.4, 3.1,
2.3, 3.8, 3.9, 1.1, 1.7, 2.3, 1.5, 1.9, 3.3, 3, 2.9, 1.6,
3.1, 3.7, 2.8, 1.2, 1.9), score_3 = c(1,
1.22222222222222, 1.11111111111111, 1.88888888888889, 1.44444444444444,
1.44444444444444, 3.22222222222222, 2.77777777777778, 1.11111111111111,
1, 1, 2.83333333333333, 1.22222222222222, 1.875, 1.55555555555556,
2.66666666666667, 1, 2.25, 1.72222222222222, 2.05555555555556,
1.22222222222222, 2, 2, 1.77777777777778, 1.33333333333333,
1.11111111111111, 2.5, 2.55555555555556, 1, 1.22222222222222,
1.77777777777778, 1.22222222222222, 2.44444444444444, 1.55555555555556,
1.77777777777778, 1.66666666666667, 1.11111111111111, 2.33333333333333,
2.88888888888889, 1.55555555555556, 1, 1.25), score_4 = c(1.31428571428571,
1.37142857142857, 1.08571428571429, 1.83809523809524, 1.37142857142857,
1.8952380952381, 4, 3.88571428571429, 3.02857142857143, 2.12222222222222,
1.43333333333333, 3.39047619047619, 1.74285714285714, 1.67619047619048,
2.02857142857143, 3.48571428571429, 1.24761904761905, 3.73333333333333,
3.08571428571429, 2.56666666666667, 1.74285714285714, 2.6952380952381,
3.45714285714286, 2.27619047619048, 1.9047619047619, 2.62857142857143,
3.74285714285714, 3.74285714285714, 1.24761904761905, 1.39047619047619,
1.83809523809524, 2.74285714285714, 4, 1.77142857142857,
3.42857142857143, 3.2, 1.65714285714286, 2.55238095238095,
2.38095238095238, 2.40952380952381, 2.07619047619048, 2.56666666666667
), score_5 = c(1, 1, 1, 1, 1.33333333333333,
1, 3.33333333333333, 3.66666666666667, 1.66666666666667,
1.66666666666667, 2, 2.5, 1.66666666666667, 1, 1.33333333333333,
3, 1, 1.66666666666667, 2.16666666666667, 2.16666666666667,
1.33333333333333, 2.66666666666667, 3, 2.66666666666667,
1.33333333333333, 2.66666666666667, 3, 1.33333333333333,
1, 1, 1, 1, 1, 1.33333333333333, 3, 3.66666666666667, 1.66666666666667,
1.33333333333333, 2.33333333333333, 1.66666666666667, 2,
2), sex = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = c("F", "M"), class = "factor"), age = c(64,
66, 51, 69, 60, 65, 65, 69, 50, 78, 75, 78, 35, 77, 69, 48,
65, 72, 60, 64, 78, 71, 58, 55, 55, 57, 81, 76, 56, 71, 56,
73, 69, 51, 43, 77, 31, 64, 69, 63, 38, 71), childbirth = structure(c(2L,
2L, 2L, 1L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, NA, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L), .Label = c("N",
"Y"), class = "factor"), x1= c(3, 2, 2, NA,
3, 2, 3, NA, 3, 3, 2, 2, NA, 2, 5, 2, 2, 2, 4, 3, 2, 2, 3,
NA, 2, 3, NA, NA, 2, 2, 2, 2, 2, 2, 3, 2, 1, NA, 2, 2, 1,
3), x2= c(0, 0, 0, NA, 1, 0, 0, NA, 0, 0,
0, 0, NA, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, NA, NA,
0, 0, 0, 0, 0, 0, 0, 0, 1, NA, 0, 0, 0, 0), x3= structure(c(4L,
1L, 1L, 2L, 1L, 1L, 1L, NA, 4L, 1L, 1L, 4L, NA, 4L, 1L, 4L,
4L, 4L, 4L, 3L, 1L, 1L, 1L, 2L, 4L, 1L, NA, 2L, 1L, 4L, 1L,
1L, 4L, 4L, 1L, 4L, 4L, 2L, 4L, 4L, 4L, 1L), .Label = c("N",
"NA", "UNK", "Y"), class = "factor"), x4= structure(c(4L,
1L, 1L, 2L, 1L, 1L, 1L, NA, 1L, 1L, 4L, 1L, NA, 1L, 1L, 4L,
3L, 1L, 4L, 4L, 1L, 4L, 4L, 2L, 1L, 4L, NA, 2L, 4L, 1L, 4L,
1L, 1L, 4L, 4L, 1L, 4L, 2L, 4L, 1L, 4L, 4L), .Label = c("N",
"NA", "UNK", "Y"), class = "factor"), x5= structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, NA, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L), .Label = c("N",
"Y"), class = "factor"), x6= structure(c(2L, 2L, 2L, 1L,
1L, 2L, 2L, NA, 1L, 1L, 1L, 2L, NA, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 1L, 2L, 2L, NA, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L), .Label = c("N", "Y"), class = "factor"),
x7= structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, NA, 1L, 1L, 1L, 1L, NA, 1L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 1L, NA, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
3L, 1L, 1L, 1L, 1L, 2L, 3L), .Label = c("N", "NA", "Y"), class = "factor"),
x8= structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L,
2L, 2L, 2L, NA, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, NA, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L), .Label = c("N", "Y"), class = "factor"), x9= structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("N",
"Y"), class = "factor"), x10= structure(c(1L, 2L, 2L,
1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L), .Label = c("N", "Y"), class = "factor"),
x11= structure(c(1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), .Label = c("N", "Y"), class = "factor"),
x12= structure(c(1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L), .Label = c("N", "Y"), class = "factor"),
x13= structure(c(2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L), .Label = c("N", "Y"), class = "factor"),
x14= c(41, 7, 8, 9, 7, 2, 1, 5, 9, 6, 6, 8,
14, 2, 4, NA, 11, 9, 31, 13, 8, 2, 11, 20, 8, 7, 6, 8, 2,
12, 32, 1, 2, 38, 10, 17, 5, 28, 31, 10, 3, 6), x15= structure(c(3L,
4L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 5L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 1L, 2L, 2L, 3L, 3L, 3L,
2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L), .Label = c("IATRO",
"IDIO", "OBST", "OBST/IDIO", "TRAUM"), class = "factor"),
x16= structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L), .Label = c("N", "Y"), class = "factor"),
x17= structure(c(2L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L), .Label = c("N", "Y"), class = "factor"),
x18= c(31.8, 20, 30.9, 23.3, 22.5, 23.1, 23.6, 25.9, 22.8,
25.2, 30.2, 23.4, 22.2, 29, 24.8, 32.7, 20.8, 28.5, 24.6,
23, 23.4, 21.1, 24.9, 18, 21.7, 27.6, 27, 29, 32.9, 26, 29.3,
27.1, 22.7, 19.7, 25, 22.3, 21.3, 17.5, 20.9, 20.1, 25.1,
22.1), x19= structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L), .Label = c("No", "Yes"), class = "factor"),
x20 = structure(c(2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 2L), .Label = c("NO", "YES"), class = "factor"),
x21= structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 1L), .Label = c("NO", "YES"), class = "factor")), row.names = c(NA,
-42L), class = c("tbl_df", "tbl", "data.frame"))
logit1 <-glm(outcome_1~., data = df, family = "binomial")
Which yielded the classic error message for a logit model:
#Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
# contrasts can be applied only to factors with 2 or more levels
Ok, so I went to double check that all factor variables indeed have more than 1 unique value, and can verify:
sapply(lapply(df, unique), length)
returned all variables showing 2 or more unique values. Still same error message when I ran the model again.
I even attempted to run one solution I found online:
values_count <- sapply(lapply(df, unique), length)
logit1 <-
lm(outcome_1~ ., df[ , values_count > 1])
What's going on? Am I blind in seeing some variable that is secretly saying it has more than one unique value and does not?
Thank you!

The regression works on the supplied data for simple models, such as
logit1 <-glm(outcome_1~ sex + age, data = df, family = "binomial")
It's a small data set with lots of variables, the computer is not going to be able to pull out the meaningful relationships even if they are there. Start with some exploratory data plots, and think about how the (biological) relationship between your outcomes and other variables in order to come up with hypotheses you can test with you data. Realistically, which measurements do you think actually affect patient outcomes?

How to set the color range of scatter plot so it is consistent with histogram

I have the following data set:
dat <- structure(list(
cell_name = structure(c(
1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L
), .Label = c("Px", "Cx", "Mx", "Ox", "OC"), class = "factor"),
gexp = c(
0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.078053491967664,
0.0787946080465952, 0.0849179303351091, 0.0893393503333397,
0.0904401481651504, 0.108991747968639, 0.109472235592895,
0.120876521863314, 0.121633996276386, 0.133260178961047,
0.141422491346724, 0.151765761772331, 0.163039227361379,
0.181821496314555, 0.183023962970076, 0.185012779171506,
0.190674320101334, 0.191500130355834, 0.245151812914058,
0.251786197407558, 0.268528061492397, 0.303601828212538,
0.33030785071184, 0.380051212059645, 0.409937261758804, 0.413185421525087
), sample.category = structure(c(
2L, 1L, 1L, 1L, 1L, 1L,
3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
1L, 1L, 3L, 1L, 3L, 2L, 1L, 1L, 1L, 3L, 2L, 3L, 1L, 1L, 3L,
1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L,
1L, 2L, 3L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 3L, 3L, 1L, 3L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 1L, 3L, 3L, 2L, 1L, 1L, 3L, 1L, 1L, 3L,
1L, 2L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L
), .Label = c(
"Xt.NT.0hr",
"Xt.Saline.16hr", "Xt.Compound.16hr"
), class = "factor"),
x = c(
-6.12836877150557, -7.88484374327681, -6.18700496001265,
-6.45607224745772, -6.91398421568892, -5.17557040495894,
-5.00434676451704, -5.90220013899824, -5.52279416365645,
-7.23571482939741, -4.00645772261641, -7.60095492644331,
-6.57969895644209, -5.71780339522383, -7.29465762419722,
-6.09494725508711, -6.92634764952681, -7.31916800780318,
-7.69346801085493, -4.80783835692427, -5.9156226281645, -6.23338071150801,
-6.39048472685835, -3.98181144042036, -7.35286227507613,
-6.37823573393843, -5.96767512602827, -4.74095240874312,
-7.12100688262007, -7.69579879088423, -7.40592185301802,
-5.54702035231612, -7.33453170104048, -6.12831488890669,
-7.86401644988081, -5.20023671431563, -6.26484719557783,
-7.81010619444868, -6.60071936888716, -7.31798640532515,
-4.35606614394209, -6.38609496397993, -7.18059436125777,
-6.27779713912031, -7.20054999632857, -7.76712313933394,
-5.52495375914595, -6.24379435820601, -5.23566857619307,
-6.05110780043623, -6.87949982924483, -7.27079001708052,
-6.85096398634932, -5.3437461022856, -3.93442956252119, -7.59850207610152,
-7.65125361723921, -6.25943747801802, -7.33143512053511,
-6.33743230147383, -6.08643952651045, -7.55096713347456,
-7.11144343657515, -5.95002309126875, -6.10922948164961,
-7.18890372557661, -7.12671843810103, -6.24059716506026,
-4.30699292464278, -5.66289655013106, -4.80185262007735,
-7.13948622984907, -6.67150870604536, -7.37687579436323,
-7.78391352934859, -7.2490023736479, -5.74496260924361, -6.03136102004073,
-7.06212893767378, -6.37314883513472, -5.33852473540327,
-6.11003104491255, -5.68365517897627, -7.04923526091597,
-5.93282214446089, -6.32528439803145, -4.86897603316328,
-7.29054347319624, -7.63038436217329, -5.71889964385054,
-6.09542743010542, -4.82401458067915, -5.97893325133345,
-6.71384087843916, -7.20524493498823, -4.3980297212126, -4.11487237257979,
-6.85030833525679, -6.87816754622481, -7.87402716918013,
-5.62621775908491, -4.99655858321211, -4.66852847380659,
-7.57268325133345, -5.39896384520552, -6.60474101347945,
-7.77267066283247, -7.69671145720503, -5.77326957030318,
-7.80957309050581, -4.55219546599409, -6.01630631728194,
-5.50212136549971, -7.76106826109907, -4.21713153166792,
-7.63483706755659, -7.89539233489058, -4.19935838027022,
-5.78868190093062, -5.27231732649824, -6.6918529634001, -7.19847861571333,
-6.77350703520796, -7.29259482665083, -7.66503230376265,
-5.92225924773238, -5.94090358061812, -4.94412461562178,
-5.27848092360518, -6.46139279646895, -4.23630992217085,
-6.28692427916548, -5.00668660445235, -5.03211299223921,
-7.29572287840864, -5.33259049696944
), y = c(
-5.02424657839496,
-6.71462500590045, -4.64553797739703, -4.8909190942641, -5.71065485972125,
-4.82234514254291, -5.28217733401019, -3.866351013362, -4.37375534075458,
-6.48378050821979, -1.45741885650117, -5.84999812144, -5.37730658549029,
-5.34863889712054, -5.33161938685138, -4.89835823076923,
-5.95062935847003, -4.7071119596358, -6.26194823282916, -5.22036922472674,
-4.32524025934894, -3.79248035448749, -4.39562714594562,
-5.28746831911761, -4.56550610560138, -5.81744492548663,
-1.6384685088988, -2.85430014628131, -6.03716719645221, -7.30025113123614,
-7.1568714429732, -6.01424372690875, -6.00170434015948, -3.03584480780322,
-6.57955277460773, -4.41522968310077, -5.37504447001178,
-6.52249014872272, -4.75782311457355, -4.62974846857745,
-4.80379808443744, -4.52536237734515, -5.20433223742206,
-4.70545566576678, -6.43369257944781, -6.41709864634235,
-4.82305062311847, -2.91744268435199, -4.4250496675368, -5.37218845385272,
-4.68633187311847, -2.56733632582385, -2.32696414488513,
-2.86756802099902, -5.36454570788104, -6.49232972162921,
-7.18896258372027, -5.87897027033527, -5.03146756190021,
-3.6963902761336, -4.67556036013324, -7.27969754236896, -4.89728296297748,
-4.84503138560016, -3.55614126223285, -2.56781030195911,
-6.00860703486163, -2.6597498704787, -5.33996284502704, -3.27229035395343,
-5.52028096216876, -6.94654047983844, -5.05352461832721,
-4.85841691988666, -6.13735354441363, -2.54840064543445,
-2.09675896662433, -4.46512854593951, -6.61105263727862,
-6.10234320658404, -4.03706944483478, -4.91794002550799,
-2.51595926779468, -4.77913272875506, -2.05771953362186,
-5.00882280367572, -5.52451956766803, -6.13459790247638,
-6.88176024454791, -5.43877637881, -4.64195335406024, -2.93488967913348,
-4.90864980715472, -3.13988101977069, -2.98691988486011,
-2.36754042404849, -1.61479792493541, -2.42353100079257,
-5.22080147761066, -6.9881349851485, -2.50280356901843, -2.4409405042525,
-5.19058645266254, -6.65987217920978, -5.1250020315047, -4.80788171786029,
-6.56144059199054, -7.22644150751788, -5.39587510126788,
-6.68008864420611, -3.0989873458739, -3.61012685793597, -3.17221487063129,
-7.18178618448932, -2.10658872622211, -7.30663311976153,
-7.09194481867511, -1.59743498015363, -2.7611458351012, -5.10656679171283,
-2.5872366477843, -3.18813729780871, -6.07152092951495, -4.62400186556537,
-6.3747350026961, -2.67246747511584, -6.22303259867389, -2.53317165869433,
-2.45842046040256, -3.20620191591937, -1.96011829870898,
-2.98910189169604, -2.67913473147113, -1.90748242038447,
-6.58255875605304, -2.57597566145618
), cluster = c(
1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1
)
), row.names = c(NA, -136L), class = c("tbl_df", "tbl","data.frame"))
I am making the histogram and scatter plot with this code:
library(tidyverse)
library(ggpubr)
## Making Barplot (histogram)
nbp <- ggpubr::ggbarplot(dat, x = "sample.category", y = "gexp", facet.by = "cell_name", add = "mean_se", scales = "fixed") +
theme(strip.text.x = element_text(size = 20, colour = "black", face = "bold")) +
theme(legend.position = "none") +
xlab("") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
nbp
## Making scatter plot (histogram)
pge <- ggplot(dat, aes(x, y, color = gexp)) +
geom_point(alpha = 0.9, size = 5) +
scale_color_gradient(low = "#ededed", high = "#67000d", na.value = "#f0f0f0") +
facet_wrap(cell_name ~ sample.category, scales = "free", ncol = 3) +
theme_bw() +
xlab("UMAP 1") +
ylab("UMAP 2")
pge
The bar plot looks like this:
And scatter plot with color scale looks like this:
As you can see in the histogram clearly Xt.Saline.16hr is stronger
than Xt.NT.0hr. But in the scatter plot color scale we get the vivid
impression that Xt.NT.0hr is stronger than Xt.Saline.16hr.
How can I adjust the color scale in scatter plot so that it
matches the histogram?

I post an answer to be able to show some plots.
As remarked in the comment, your plot seems correct. However, boxplot alone might be misleading in this case.
If you add the actual points you will see that many NT points are equal to 0 and some of them peaks just above 0.4. Please, see the plot below, I have used your color scale and geom_jitter to show distribution of your points for the gexp variable.
library(ggplot2)
ggplot(data = dat, aes(x = sample.category, y = gexp, color = gexp)) +
# geom_boxplot() +
facet_grid(.~sample.category, scale = "free_x") +
geom_jitter() +
scale_color_gradient(low = "#ededed", high = "#67000d", na.value = "#f0f0f0") +
theme_bw()

How to make a stacked barplot with nested grouping variables?

I am trying to make a stacked barplot with two variables. My desired outcome looks like this:
This is the first part of my data. There are 220 more rows:
Type Week Stage
<chr> <dbl> <dbl>
1 Captured 1 2
2 Captured 1 1
3 Captured 1 1
4 Captured 1 2
5 Captured 1 1
6 Captured 1 3
7 Captured 1 NA
8 Captured 1 3
9 Captured 1 2
10 Captured 1 1
So far I'm not getting anywhere, this is my code so far
library(data.table)
dat.m <- melt(newrstudio2, id.vars="Type")
dat.m
library(ggplot2)
ggplot(dat.m, aes(x=Type, y=value, fill=variable)) +
geom_bar(stat="identity")
I guess I need to calculate the number of observations of each stage in each week of each type? I've tried both long and wide data, but I somehow need to combine week with type? I don't know, I'm at a loss.

Alternative way:
set.seed(123)
# sample data
my_data <- data.frame(Type = sample(c("W", "C"), 220, replace = TRUE),
Week = sample(paste0("Week ", 1:4), 220, replace = TRUE),
Stage = sample(paste0('S', 1:4), 220, replace = TRUE))
head(my_data)
library(ggplot2)
ggplot(my_data, aes(x = Type, fill = Stage)) +
geom_bar(aes(y = (..count..)/sum(..count..)), position = "fill") +
facet_grid(. ~ Week, switch="both") +
scale_y_continuous(labels = scales::percent) +
ylab("Stage [%]") +
theme(strip.background = element_blank(),
strip.placement = "outside",
panel.spacing = unit(0, "lines"))

Alternatively we could use base graphics. First, what you're probably most interested in, we should reshape the data.
For this we could split the data per week and run a dcast() over it.
L <- lapply(split(d, d$week), function(x)
data.table::dcast(x, type ~ stage, value.var="stage", fun=length))
d2 <- do.call(rbind, L) # transform back into a data frame
Now – with credits to #alemol – we want the proportions.
d2[-1] <- t(apply(d2[-1], 1, prop.table))
Then we are able to plot relatively simply. Note, that barplot() additionally gives us a vector of bar coordinates which we can use later for the axis() labels.
cols <- c("#ed1c24", "#ff7f27", "#00a2e8", "#fff200") # define stage colors
par(mar=c(5, 5, 3, 5) + .1, xpd=TRUE) # set plot margins
p <- barplot(t(d2[-1]), col=cols, border="white", space=rep(c(.2, 0), 5),
font.axis=2, xaxt="n", yaxt="n", xlab="Week")
axis(1, at=p, labels=rep(c("C", "W"), 5), tick=FALSE, line=0)
axis(1, at=apply(matrix(p, , 2, byrow=TRUE), 1, mean), labels=1:5, tick=FALSE, line=1)
axis(2, at=0:10/10, labels=paste0(seq(0, 100, 10), "%"), line=0, las=2)
legend(12, .5, legend=rev(names(d2[-1])), col=rev(cols), pch=15, title="Stage")
Result:
Data:
d <- structure(list(type = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L), .Label = c("C", "W"), class = "factor"), week = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5), stage = c(3L,
1L, 1L, 2L, 2L, 2L, 1L, 3L, 2L, 4L, 1L, 1L, 2L, 2L, 3L, 4L, 3L,
2L, 4L, 1L, 1L, 3L, 1L, 2L, 3L, 1L, 4L, 1L, 2L, 4L, 2L, 3L, 4L,
4L, 2L, 4L, 4L, 2L, 3L, 1L, 1L, 4L, 4L, 1L, 4L, 3L, 3L, 3L, 2L,
1L, 3L, 4L, 2L, 4L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 1L, 3L, 2L,
1L, 1L, 1L, 4L, 2L, 4L, 1L, 4L, 3L, 4L, 4L, 4L, 2L, 2L, 2L, 2L,
2L, 1L, 3L, 4L, 2L, 4L, 4L, 2L, 2L, 3L, 4L, 4L, 3L, 3L, 1L, 1L,
1L, 2L, 4L, 3L, 1L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 4L, 2L, 1L,
2L, 1L, 3L, 3L, 2L, 4L, 3L, 1L, 1L, 4L, 1L, 4L, 4L, 1L, 2L, 2L,
2L, 1L, 3L, 4L, 3L, 4L, 3L, 4L, 4L, 3L, 1L, 1L, 2L, 1L, 2L, 3L,
2L, 2L, 1L, 4L, 3L, 4L, 2L, 2L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 2L,
1L, 2L, 2L, 1L, 1L, 3L, 4L, 3L, 4L, 2L, 4L, 1L, 1L, 2L, 1L, 3L,
2L, 1L, 3L, 3L, 2L, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 4L, 2L, 4L, 2L,
4L, 3L, 3L, 1L, 3L, 4L, 3L, 2L, 1L, 2L, 4L, 1L, 2L, 4L, 2L, 1L,
2L, 1L, 2L, 2L, 3L, 1L, 3L, 3L, 3L, 2L, 2L, 1L, 2L, 3L, 2L, 2L,
1L, 2L, 1L, 3L, 3L, 2L, 1L, 3L, 4L, 2L, 1L, 2L, 4L, 3L, 4L, 2L,
3L, 2L, 4L, 1L, 4L, 4L, 2L, 1L, 2L)), row.names = c(NA, -250L
), class = "data.frame")

Is this what you're looking for:
set.seed(123)
# sample data
my_data <- data.frame(Type = sample(paste0('T', 1:4), 220, replace = TRUE),
Week = sample(paste0('W', 1:4), 220, replace = TRUE),
Stage = sample(paste0('S', 1:4), 220, replace = TRUE))
ggplot(my_data, aes(x=Week:Type, fill = Stage)) + geom_bar()

Plotting 3-way interaction of factor variables using `lme4`

I'm looking for an elaboration on the amazing answer already provided about creating an interaction plot with a continuous and categorical variable using the predict function of the (development version) of the lme4 package.
I have run a model with an interaction between three categorical variables: discount_i (0/1), rank_i (0/1), and msg ("No norm","Provincial",and "Norm") including subject random effects (id). My outcome variable (choice) is dichotomous. Specifically, my command is:
m1 <- glmer(choice ~ msg*discount_i*rank_i + (1|id), data=df, family="binomial")
I then create a prediction frame:
predframe <- with(df,expand.grid(rank_i=levels(rank_i),msg=levels(msg),discount_i=levels(discount_i)))
And use the predict function (EDITED):
predframe$pred.logit <- predict(m1,newdata=predframe,REform=NA)
However, this is the point where I part ways with #mnel's instructions. How would I go about graphing the three way interaction between factor variables, rather than a two way interaction between a factor variable and a continuous variable?
Sample data below:
> dput(df[1:700,2:6])
structure(list(time = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), choice = c(1, 1,
1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0,
1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1,
1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0,
0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,
0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1,
1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1,
1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1,
1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1,
1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1,
1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,
1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1,
0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0,
0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1,
1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0,
1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1,
0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1,
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1,
1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,
0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,
1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1,
1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,
1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,
1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1,
0, 1, 0, 1, 0), msg = structure(c(3L, 1L, 1L, 2L, 3L, 1L, 3L,
3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 1L, 3L, 2L, 3L, 1L, 1L, 3L,
1L, 2L, 3L, 3L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 2L, 3L,
3L, 1L, 3L, 1L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 3L,
3L, 1L, 2L, 3L, 2L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 2L, 1L, 3L, 2L,
3L, 3L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 1L,
3L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 2L, 2L, 3L, 2L, 3L,
3L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 2L, 2L, 1L, 3L, 3L, 2L, 3L,
3L, 3L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 2L,
1L, 1L, 3L, 1L, 3L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 3L, 3L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 3L,
3L, 1L, 2L, 3L, 1L, 1L, 3L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 2L, 1L,
2L, 3L, 3L, 2L, 1L, 2L, 3L, 1L, 2L, 2L, 1L, 3L, 3L, 1L, 1L, 1L,
3L, 2L, 3L, 1L, 2L, 2L, 3L, 2L, 1L, 3L, 1L, 2L, 2L, 3L, 3L, 2L,
1L, 3L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 3L, 2L, 2L, 1L, 2L, 2L, 3L, 1L, 1L, 2L, 3L, 2L, 3L, 3L,
3L, 3L, 1L, 3L, 2L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 3L, 2L, 1L, 1L,
2L, 1L, 2L, 3L, 3L, 3L, 1L, 2L, 2L, 3L, 1L, 3L, 1L, 3L, 3L, 1L,
1L, 3L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 3L, 2L, 3L, 2L, 3L,
1L, 2L, 2L, 1L, 2L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 3L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 3L, 2L, 1L, 3L, 3L,
3L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 2L, 1L, 3L, 1L, 1L, 2L, 2L, 3L,
1L, 3L, 3L, 3L, 1L, 3L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 3L, 3L,
3L, 3L, 3L, 2L, 2L, 3L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 3L, 1L, 1L,
1L, 3L, 3L, 1L, 1L, 3L, 1L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 3L, 3L,
3L, 1L, 3L, 1L, 3L, 1L, 3L, 2L, 3L, 2L, 2L, 1L, 2L, 1L, 3L, 2L,
2L, 3L, 1L, 2L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 3L, 3L, 2L, 1L, 2L, 3L, 2L, 3L,
2L, 3L, 1L, 3L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 2L, 3L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 3L, 1L, 2L, 2L, 2L, 1L, 1L, 3L, 3L, 1L,
1L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 2L, 1L, 3L, 2L, 2L,
1L, 1L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, 2L, 3L, 1L,
2L, 2L, 3L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 3L, 1L, 3L,
1L, 1L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 3L, 2L, 2L,
1L, 2L, 3L, 2L, 1L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L,
3L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 1L, 1L, 2L,
2L, 3L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 3L, 2L, 3L, 1L, 2L, 3L,
2L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 2L, 1L, 2L, 3L, 1L, 3L, 2L, 3L,
1L, 2L, 3L, 2L, 1L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 2L,
1L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 3L, 1L, 1L, 2L, 1L, 3L, 1L,
2L, 2L, 3L, 2L, 2L, 3L, 3L, 1L, 3L, 1L, 3L, 2L, 1L, 3L, 1L, 2L,
1L, 3L, 1L, 3L, 1L, 1L, 2L, 1L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 1L, 2L, 3L, 2L, 3L, 1L,
3L, 3L, 3L, 1L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 2L, 3L,
2L, 2L, 1L, 3L, 2L), .Label = c("No norm", "Norm", "Provincial"
), class = "factor"), discount_i = structure(c(1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L), .Label = c("0", "1"), class = "factor"),
rank_i = structure(c(1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
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"582.1", "583.1", "584.1", "585.1", "586.1", "587.1", "588.1",
"589.1", "590.1", "591.1", "592.1", "593.1", "594.1", "595.1",
"596.1", "597.1", "598.1", "599.1", "600.1", "601.1", "602.1",
"603.1", "604.1", "605.1", "606.1", "607.1", "608.1", "609.1",
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"624.1", "625.1", "626.1", "627.1", "628.1", "629.1", "630.1",
"631.1", "632.1", "633.1", "634.1", "635.1", "636.1", "637.1",
"638.1", "639.1", "640.1", "641.1", "642.1", "643.1", "644.1",
"645.1", "646.1", "647.1", "648.1", "649.1", "650.1", "651.1",
"652.1", "653.1", "654.1", "655.1", "656.1", "657.1", "658.1",
"659.1", "660.1", "661.1", "662.1", "663.1", "664.1", "665.1",
"666.1", "667.1", "668.1", "669.1", "670.1", "671.1", "672.1",
"673.1", "674.1", "675.1", "676.1", "677.1", "678.1", "679.1",
"680.1", "681.1", "682.1", "683.1", "684.1", "685.1", "686.1",
"687.1", "688.1", "689.1", "690.1", "691.1", "692.1", "693.1",
"694.1", "695.1", "696.1", "697.1", "698.1", "699.1", "700.1"
), class = "data.frame")

Caveat: Have not added the ylab="" to the second overlaid plot call and you probably want to use a non-default ylab for the first one, too. Since the details of this analysis are still opaque to me, I am not standing behind its validity. (Just turning the crank on the machinery.) And there would need to be some further work on the legend. Furthermore, teh ylims were different so would probably want to set them to min and max for c(newpred0, newpred1).
newpred0 <- predict(m1, newdata = predframe[predframe$discount_i=="0", ] ,
REform = NA)
interaction.plot(droplevels(predframe[predframe$discount_i=="0", ])$rank_i,
droplevels(predframe[predframe$discount_i=="0", ])$msg,
newpred0)
newpred1 <- predict(m1, newdata = predframe[predframe$discount_i=="1", ] ,
REform = NA)
par(new=TRUE); # This is the way to overlay base graphics on top of each other
interaction.plot(droplevels(predframe[predframe$discount_i=="1", ])$rank_i,
droplevels(predframe[predframe$discount_i=="1", ])$msg, newpred1,
col="red")

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