Cox regression stratified by another column than the predictor

Cox regression stratified by another column than the predictor - r

I would like to perform a cox regression analysis with value_max_min as predictor, but according 2 groups peakand drop in the column peak_drop_status(to get 2 survival curves).
cox <- coxph(Surv(mace_months_date_vs_date_sample, mace) ~ value_max_min, data = df)
and to get the plot
fit <- survfit(Surv(mace_months_date_vs_date_sample, mace) ~ value_max_min,
data = df)
I thought about using the group_by or the slice functions?
Library used:
library(dplyr)
library(survival)
library(survminer)
Here is the console output:
structure(list(ID = c(136L, 136L, 200L, 200L, 146L, 146L, 153L,
153L, 137L, 137L, 214L, 214L, 85L, 85L, 96L, 96L, 172L, 172L,
182L, 182L, 87L, 87L, 93L, 93L, 210L, 210L, 69L, 69L, 132L, 132L,
68L, 68L, 74L, 74L, 9L, 9L, 159L, 159L, 7L, 7L, 154L, 154L, 94L,
94L, 113L, 113L, 124L, 124L, 131L, 131L, 143L, 143L, 151L, 151L,
213L, 213L, 225L, 225L, 160L, 160L, 226L, 226L, 133L, 133L, 105L,
105L, 119L, 119L, 156L, 156L, 208L, 208L, 117L, 117L, 227L, 227L,
97L, 97L, 221L, 221L, 187L, 187L, 155L, 155L, 189L, 189L, 219L,
219L, 178L, 178L, 181L, 181L, 184L, 184L, 165L, 165L, 233L, 233L,
180L, 180L, 192L, 192L, 79L, 79L, 183L, 183L, 139L, 139L, 199L,
199L, 81L, 81L, 162L, 162L, 104L, 104L, 237L, 237L, 128L, 128L,
73L, 73L, 229L, 229L, 138L, 138L, 218L, 218L, 95L, 95L, 110L,
110L, 190L, 190L, 72L, 72L, 127L, 127L, 164L, 164L, 111L, 111L,
194L, 194L, 216L, 216L, 188L, 188L, 71L, 71L, 67L, 67L, 88L,
88L, 123L, 123L, 173L, 173L, 223L, 223L, 152L, 152L, 238L, 238L,
63L, 63L, 10L, 10L, 75L, 75L, 109L, 109L, 197L, 197L, 193L, 193L,
89L, 89L, 106L, 106L, 205L, 205L, 125L, 125L, 121L, 121L, 100L,
100L, 234L, 234L, 6L, 6L, 211L, 211L, 228L, 228L, 175L, 175L,
84L, 84L, 191L, 191L, 243L, 243L, 115L, 115L, 220L, 220L, 242L,
242L, 2L, 2L, 222L, 222L, 203L, 203L, 201L, 201L, 224L, 224L,
4L, 4L, 102L, 102L, 76L, 76L, 239L, 239L, 231L, 231L, 195L, 195L,
134L, 134L, 171L, 171L, 83L, 83L, 217L, 217L, 5L, 5L, 141L, 141L,
3L, 3L, 112L, 112L, 235L, 235L, 185L, 185L, 103L, 103L, 120L,
120L, 207L, 207L, 166L, 166L, 174L, 174L, 116L, 116L, 8L, 8L,
140L, 140L, 14L, 14L, 27L, 27L, 30L, 30L, 23L, 23L, 54L, 54L,
13L, 13L, 16L, 16L, 39L, 39L, 44L, 44L, 42L, 42L, 51L, 51L, 245L,
245L, 59L, 59L, 28L, 28L, 45L, 45L, 34L, 34L, 49L, 49L, 43L,
43L, 24L, 24L, 19L, 19L, 12L, 12L, 41L, 41L, 47L, 47L, 32L, 32L,
50L, 50L, 31L, 31L, 18L, 18L, 15L, 15L, 60L, 60L, 52L, 52L, 21L,
21L, 29L, 29L, 38L, 38L, 55L, 55L, 33L, 33L, 56L, 56L, 244L,
244L, 36L, 36L, 20L, 20L, 17L, 17L, 57L, 57L, 35L, 35L, 22L,
22L, 26L, 26L, 25L, 25L, 37L, 37L, 58L, 58L, 61L, 61L), age = c(49L,
49L, 77L, 77L, 75L, 75L, 75L, 75L, 63L, 63L, 60L, 60L, 72L, 72L,
51L, 51L, 50L, 50L, 35L, 35L, 48L, 48L, 44L, 44L, 79L, 79L, 67L,
67L, 57L, 57L, 58L, 58L, 46L, 46L, 57L, 57L, 59L, 59L, 71L, 71L,
65L, 65L, 56L, 56L, 28L, 28L, 65L, 65L, 41L, 41L, 76L, 76L, 63L,
63L, 66L, 66L, 69L, 69L, 37L, 37L, 52L, 52L, 47L, 47L, 63L, 63L,
41L, 41L, 79L, 79L, 42L, 42L, 69L, 69L, 76L, 76L, 68L, 68L, 66L,
66L, 59L, 59L, 64L, 64L, 72L, 72L, 65L, 65L, 75L, 75L, 56L, 56L,
80L, 80L, 68L, 68L, 58L, 58L, 61L, 61L, 59L, 59L, 68L, 68L, 60L,
60L, 39L, 39L, 63L, 63L, 52L, 52L, 82L, 82L, 63L, 63L, 49L, 49L,
59L, 59L, 61L, 61L, 64L, 64L, 63L, 63L, 66L, 66L, 68L, 68L, 54L,
54L, 73L, 73L, 54L, 54L, 46L, 46L, 75L, 75L, 72L, 72L, 64L, 64L,
69L, 69L, 68L, 68L, 59L, 59L, 52L, 52L, 65L, 65L, 51L, 51L, 48L,
48L, 63L, 63L, 52L, 52L, 56L, 56L, 67L, 67L, 68L, 68L, 47L, 47L,
75L, 75L, 76L, 76L, 63L, 63L, 73L, 73L, 48L, 48L, 68L, 68L, 48L,
48L, NA, NA, 74L, 74L, 37L, 37L, 60L, 60L, 54L, 54L, 55L, 55L,
61L, 61L, 79L, 79L, 67L, 67L, 57L, 57L, 51L, 51L, 67L, 67L, 68L,
68L, 51L, 51L, 53L, 53L, 51L, 51L, 62L, 62L, 60L, 60L, 71L, 71L,
58L, 58L, 52L, 52L, 72L, 72L, 73L, 73L, 76L, 76L, 81L, 81L, 48L,
48L, 65L, 65L, 57L, 57L, 51L, 51L, 56L, 56L, 66L, 66L, 46L, 46L,
78L, 78L, 76L, 76L, 81L, 81L, 71L, 71L, 46L, 46L, 51L, 51L, 73L,
73L, 66L, 66L, 59L, 59L, 77L, 77L, 74L, 74L, 28L, 28L, 73L, 73L,
54L, 54L, 59L, 59L, 53L, 53L, 57L, 57L, 54L, 54L, 52L, 52L, 38L,
38L, 73L, 73L, 62L, 62L, 61L, 61L, 76L, 76L, 51L, 51L, 51L, 51L,
54L, 54L, 59L, 59L, 47L, 47L, 66L, 66L, 57L, 57L, 57L, 57L, 62L,
62L, 66L, 66L, 54L, 54L, 47L, 47L, 56L, 56L, 65L, 65L, 72L, 72L,
49L, 49L, 46L, 46L, 73L, 73L, 55L, 55L, 47L, 47L, 59L, 59L, 43L,
43L, 62L, 62L, 64L, 64L, 56L, 56L, 44L, 44L, 58L, 58L, 47L, 47L,
46L, 46L, 66L, 66L, 54L, 54L, 81L, 81L, 36L, 36L, 48L, 48L),
sex = c(1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L), mace = c(0L, 0L, 0L, 0L, 1L, 1L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), mace_months_date_vs_date_sample = c(61L,
61L, 62L, 62L, 21L, 21L, 1L, 1L, 47L, 47L, 3L, 3L, 61L, 61L,
31L, 31L, 2L, 2L, 44L, 44L, 46L, 46L, 43L, 43L, 61L, 61L,
3L, 3L, 4L, 4L, 62L, 62L, 60L, 60L, 49L, 49L, 48L, 48L, 43L,
43L, 46L, 46L, 45L, 45L, 62L, 62L, 4L, 4L, 61L, 61L, 47L,
47L, 62L, 62L, 5L, 5L, 48L, 48L, 59L, 59L, 32L, 32L, 59L,
59L, 4L, 4L, 63L, 63L, 8L, 8L, 4L, 4L, 49L, 49L, 1L, 1L,
7L, 7L, 45L, 45L, 45L, 45L, 1L, 1L, 1L, 1L, 62L, 62L, 62L,
62L, 45L, 45L, 1L, 1L, 61L, 61L, 46L, 46L, 55L, 55L, 44L,
44L, 45L, 45L, 1L, 1L, 58L, 58L, 27L, 27L, 47L, 47L, 48L,
48L, 25L, 25L, 55L, 55L, 50L, 50L, 44L, 44L, 63L, 63L, 50L,
50L, 10L, 10L, 23L, 23L, 47L, 47L, 19L, 19L, 48L, 48L, 62L,
62L, 62L, 62L, 1L, 1L, 15L, 15L, 47L, 47L, 61L, 61L, 45L,
45L, 46L, 46L, 47L, 47L, 49L, 49L, 1L, 1L, 46L, 46L, 48L,
48L, 45L, 45L, 15L, 15L, 55L, 55L, 1L, 1L, 62L, 62L, 55L,
55L, 46L, 46L, 2L, 2L, 46L, 46L, 46L, 46L, 63L, 63L, 43L,
43L, 16L, 16L, 55L, 55L, 1L, 1L, 55L, 55L, 8L, 8L, 46L, 46L,
46L, 46L, 19L, 19L, 48L, 48L, 50L, 50L, 48L, 48L, 41L, 41L,
50L, 50L, 4L, 4L, 62L, 62L, 62L, 62L, 17L, 17L, 25L, 25L,
48L, 48L, 48L, 48L, 3L, 3L, 1L, 1L, 53L, 53L, 46L, 46L, 46L,
46L, 51L, 51L, 59L, 59L, 55L, 55L, 59L, 59L, 55L, 55L, 1L,
1L, 46L, 46L, 43L, 43L, 1L, 1L, 5L, 5L, 46L, 46L, 10L, 10L,
11L, 11L, 16L, 16L, 55L, 55L, 3L, 3L, 6L, 6L, 71L, 71L, 68L,
68L, 72L, 72L, 71L, 71L, 69L, 69L, 73L, 73L, 74L, 74L, 30L,
30L, 69L, 69L, 1L, 1L, 11L, 11L, 79L, 79L, 71L, 71L, 73L,
73L, 13L, 13L, 28L, 28L, 74L, 74L, 77L, 77L, 78L, 78L, 71L,
71L, 73L, 73L, 69L, 69L, 73L, 73L, 70L, 70L, 72L, 72L, 69L,
69L, 43L, 43L, 76L, 76L, 74L, 74L, 75L, 75L, 77L, 77L, 78L,
78L, 70L, 70L, 69L, 69L, 70L, 70L, 60L, 60L, 5L, 5L, 5L,
5L, 77L, 77L, 74L, 74L, 77L, 77L, 77L, 77L, 77L, 77L, 73L,
73L, 74L, 74L, 76L, 76L, 76L, 76L), trop = c(262L, 262L,
NA, NA, 1454L, 1454L, 663L, 663L, 2107L, 2107L, 86115L, 86115L,
24L, 24L, 3004L, 3004L, 9352L, 9352L, NA, NA, 1247L, 1247L,
NA, NA, 2888L, 2888L, NA, NA, 8421L, 8421L, NA, NA, NA, NA,
251L, 251L, 1211L, 1211L, NA, NA, 54592L, 54592L, 1241L,
1241L, 8669L, 8669L, 5204L, 5204L, 751L, 751L, 2840L, 2840L,
250L, 250L, NA, NA, NA, NA, 1411L, 1411L, 3789L, 3789L, 1675L,
1675L, 1557L, 1557L, 440L, 440L, NA, NA, 6979L, 6979L, 6155L,
6155L, 5110L, 5110L, 87355L, 87355L, 90L, 90L, 2234L, 2234L,
10000L, 10000L, NA, NA, 843L, 843L, 950L, 950L, 372L, 372L,
NA, NA, NA, NA, NA, NA, 6212L, 6212L, 871L, 871L, 776L, 776L,
24160L, 24160L, NA, NA, 9951L, 9951L, 3598L, 3598L, 2040L,
2040L, NA, NA, 6581L, 6581L, 349L, 349L, 11L, 11L, 6694L,
6694L, 63L, 63L, 15543L, 15543L, NA, NA, 33017L, 33017L,
2483L, 2483L, NA, NA, 961L, 961L, 1470L, 1470L, 2380L, 2380L,
11135L, 11135L, 1730L, 1730L, NA, NA, 11450L, 11450L, 769L,
769L, 16720L, 16720L, 57L, 57L, NA, NA, 4281L, 4281L, NA,
NA, 1258L, 1258L, NA, NA, 4299L, 4299L, 13374L, 13374L, NA,
NA, 2844L, 2844L, 1753L, 1753L, NA, NA, 5256L, 5256L, 3624L,
3624L, NA, NA, 21876L, 21876L, 8903L, 8903L, 844L, 844L,
5654L, 5654L, 3569L, 3569L, 45649L, 45649L, NA, NA, NA, NA,
4927L, 4927L, NA, NA, 2177L, 2177L, 5247L, 5247L, 50000L,
50000L, 438L, 438L, 1480L, 1480L, 50L, 50L, NA, NA, NA, NA,
27L, 27L, 2956L, 2956L, NA, NA, 3000L, 3000L, NA, NA, 6630L,
6630L, 1911L, 1911L, NA, NA, 2797L, 2797L, 6672L, 6672L,
1627L, 1627L, 123L, 123L, 7671L, 7671L, NA, NA, NA, NA, 2340L,
2340L, 10014L, 10014L, 7860L, 7860L, 67927L, 67927L, NA,
NA, NA, NA, 2413L, 2413L, 1035L, 1035L, 40273L, 40273L, 7120L,
7120L, 6440L, 6440L, 3340L, 3340L, 8450L, 8450L, 1500L, 1500L,
1970L, 1970L, 180L, 180L, 990L, 990L, 2600L, 2600L, 1800L,
1800L, 5280L, 5280L, 83L, 83L, 160L, 160L, 40L, 40L, 3710L,
3710L, 400L, 400L, NA, NA, 2100L, 2100L, 2390L, 2390L, 9320L,
9320L, 6020L, 6020L, 320L, 320L, 1420L, 1420L, 1710L, 1710L,
15300L, 15300L, 6490L, 6490L, 6390L, 6390L, 6300L, 6300L,
470L, 470L, 1740L, 1740L, 3600L, 3600L, NA, NA, 3930L, 3930L,
NA, NA, 2260L, 2260L, 1360L, 1360L, 846L, 846L, 15940L, 15940L,
NA, NA, 840L, 840L, 5010L, 5010L, NA, NA, 5330L, 5330L, 500L,
500L, 1080L, 1080L, NA, NA, NA, NA, 4470L, 4470L), egfr = c(90L,
90L, 48L, 48L, 65L, 65L, 35L, 35L, 84L, 84L, 90L, 90L, 64L,
64L, 61L, 61L, 86L, 86L, 56L, 56L, 90L, 90L, 62L, 62L, 75L,
75L, 56L, 56L, 86L, 86L, 90L, 90L, 89L, 89L, 84L, 84L, 86L,
86L, 65L, 65L, 86L, 86L, 61L, 61L, 90L, 90L, 73L, 73L, 61L,
61L, 77L, 77L, 60L, 60L, 58L, 58L, 80L, 80L, 58L, 58L, 90L,
90L, 64L, 64L, 68L, 68L, 90L, 90L, 61L, 61L, 80L, 80L, 90L,
90L, 36L, 36L, 90L, 90L, 90L, 90L, 59L, 59L, 90L, 90L, 77L,
77L, 64L, 64L, 52L, 52L, 90L, 90L, 33L, 33L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 69L, 69L, 90L, 90L, 46L,
46L, 90L, 90L, 75L, 75L, 54L, 54L, 90L, 90L, 54L, 54L, 90L,
90L, 82L, 82L, 49L, 49L, 35L, 35L, 90L, 90L, 66L, 66L, 90L,
90L, 86L, 86L, 90L, 90L, 45L, 45L, 72L, 72L, 68L, 68L, 51L,
51L, 90L, 90L, 90L, 90L, 90L, 90L, 58L, 58L, 84L, 84L, 42L,
42L, 90L, 90L, 86L, 86L, 90L, 90L, 90L, 90L, 90L, 90L, 87L,
87L, 67L, 67L, 51L, 51L, 81L, 81L, 74L, 74L, 63L, 63L, 90L,
90L, 56L, 56L, 87L, 87L, 84L, 84L, 78L, 78L, 63L, 63L, 63L,
63L, 63L, 63L, 67L, 67L, 64L, 64L, 68L, 68L, 78L, 78L, 68L,
68L, 90L, 90L, 69L, 69L, 90L, 90L, 90L, 90L, 75L, 75L, 85L,
85L, 85L, 85L, 52L, 52L, 69L, 69L, 90L, 90L, 76L, 76L, 90L,
90L, 54L, 54L, 86L, 86L, 90L, 90L, 61L, 61L, 72L, 72L, 76L,
76L, 69L, 69L, 85L, 85L, 86L, 86L, 42L, 42L, 72L, 72L, 71L,
71L, 58L, 58L, 68L, 68L, 86L, 86L, 75L, 75L, 84L, 84L, 63L,
63L, 63L, 63L, 78L, 78L, 90L, 90L, 48L, 48L, 55L, 55L, 81L,
81L, 87L, 87L, 99L, 99L, 77L, 77L, 56L, 56L, 69L, 69L, 66L,
66L, 67L, 67L, 85L, 85L, 90L, 90L, 65L, 65L, 68L, 68L, 76L,
76L, 84L, 84L, 90L, 90L, 59L, 59L, 88L, 88L, 79L, 79L, 85L,
85L, 90L, 90L, 90L, 90L, 77L, 77L, 90L, 90L, 49L, 49L, 62L,
62L, 71L, 71L, 87L, 87L, 51L, 51L, 90L, 90L, 90L, 90L, 79L,
79L, 90L, 90L, 90L, 90L, 52L, 52L, 75L, 75L, 71L, 71L, 68L,
68L, 83L, 83L, 88L, 88L, 51L, 51L, 87L, 87L, 99L, 99L, 78L,
78L, 90L, 90L), dm = c(0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L), smoke = c(1L, 1L,
0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L,
1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L), peak_drop_status = c("max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
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"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val"), value_max_min = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 308.408676147461, -283.636077880859,
NA, NA, 208.791275024414, -5.3211898803711, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, -14.9628820419311, -218.279922485352, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -15.319938659668,
-279.422790527344, 248.09851074219, -30.822647094727, 116.716430664065,
-8.8325366973877, NA, NA, 10.0856704711914, -29.1057052612305,
179.8525390625, -10.1883692741394, 130.585632324218, -39.6044845581057,
32.883270263672, -5.3593330383301, -17.5934886932374, -821.989379882808,
375.086456298828, -5.7297992706299, NA, NA, NA, NA, NA, NA,
419.108337402341, -1.28273773193359, 55.87646484375, -17.770830154419,
NA, NA, 44.05969238281, -6.7603330612182, 36.9793767929077,
-58.77816772461, 47.6982421875, -48.2563076019287, 180.041305541992,
-3.5863590240479, 19.503479003907, -66.49755859375, NA, NA,
33.036499023438, -3.0688781738281, 83.0613746643061, -562.289733886719,
-5.5973930358887, -162.939453124998, 18.5003929138184, -95.700927734375,
164.985534667969, 9.7361946105957, 27.7907447814941, -69.5900268554681,
159.863708496094, -22.477741241455, -21.2021789550781, -372.002563476562,
153.190795898438, 5.9852733612061, 15.9482421875, -32.590072631836,
243.90209960937, -24.0595645904541, 131.392028808594, -5.5808029174805,
192.978088378906, -56.510217666626, 104.543823242188, -173.209777832031,
NA, NA, 174.83570098877, -69.742797851558, 12.743041992187,
-502.216308593755, 28.5669360160827, -388.549621582031, -15.8679084777832,
-308.033813476562, -12.657926082611, -133.534645795822, -0.800338745117202,
-170.645233154297, -8.9742355346679, -44.6486473083496, 3.6423645019532,
-0.407896041870121, 163.853607177735, -13.0253372192383,
327.035522460937, -3.5568542480469, 336.011077880859, -9.2046012878418
)), row.names = c(NA, -364L), class = c("tbl_df", "tbl",
"data.frame"))
Thank you,

To do a stratified Cox model, you would specify strata(var) in your model formula as follows:
library("survival")
library("survminer")
library("dplyr")
cox <- coxph(
Surv(mace_months_date_vs_date_sample, mace) ~ value_max_min + strata(peak_drop_status),
data = df
)
Your survfit isn't going to be very useful by the way, as you're supplying a numeric variable it's fitting a KM curve to every unique value of value_max_min. So you get a whole bunch of non-informative Kaplan-Meier curves:
fit <- survfit(
Surv(mace_months_date_vs_date_sample, mace) ~ value_max_min,
data = df
)
plot(fit)
It might be better for visualisation purposes to split your data into groups based on quantiles of the predictor:
med <- median(df$value_max_min, na.rm=TRUE)
fit <- survfit(
Surv(mace_months_date_vs_date_sample, mace) ~ (value_max_min > med),
data = df
)
plot(fit)

Related

Why I having problem on assigning data for NMDS in R?

I want learn Non-matric multidimensional scale, I have these data downloaded from https://cougrstats.wordpress.com/2019/12/11/non-metric-multidimensional-scaling-nmds-in-r/
data are
library(vegan)
dput(orders)
structure(list(Amphipoda = c(0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 39L, 0L, 0L, 0L, 0L, 0L,
8L, 10L, 52L, 11L, 51L, 14L, 96L, 7L, 93L, 0L, 29L, 4L, 0L, 0L,
0L, 0L, 0L, 0L, 4L, 0L, 0L, 0L, 36L, 10L, 5L, 15L, 14L, 3L, 11L,
6L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 2L, 4L, 4L, 3L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
5L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 54L, 51L, 47L, 212L,
26L, 51L, 0L, 0L, 4L, 0L, 0L, 0L, 4L, 6L, 14L, 34L, 8L, 284L,
1L, 2L, 6L, 92L, 134L, 98L, 38L, 8L, 116L, 0L, 0L, 8L, 264L,
104L, 114L, 138L, 152L, 42L, 46L, 10L, 67L, 25L, 0L, 0L, 1L,
12L, 0L, 26L, 0L, 67L, 456L, 7L, 2L, 46L, 155L, 82L, 124L, 596L,
0L, 36L, 1L, 1L, 588L, 0L, 0L, 16L, 0L, 0L, 470L, 0L, 6L, 262L,
2L, 476L, 0L, 6L, 14L, 0L, 342L, 0L, 6L, 4L, 24L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 6L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 74L, 170L, 37L, 228L, 144L, 21L, 189L, 117L, 45L, 132L, 108L,
35L, 0L, 0L, 0L, 0L, 0L, 0L), Coleoptera = c(42L, 5L, 7L, 14L,
2L, 43L, 7L, 2L, 15L, 5L, 6L, 23L, 25L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 142L, 96L, 202L, 306L,
917L, 748L, 139L, 148L, 115L, 216L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 12L, 36L, 48L, 30L, 10L, 12L, 11L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L,
0L, 0L, 381L, 397L, 229L, 51L, 266L, 102L, 201L, 189L, 80L, 292L,
271L, 583L, 641L, 318L, 729L, 520L, 582L, 262L, 59L, 209L, 134L,
139L, 108L, 79L, 99L, 96L, 13L, 60L, 22L, 15L, 27L, 1L, 11L,
2L, 3L, 3L, 6L, 1L, 1L, 0L, 20L, 48L, 129L, 82L, 16L, 70L, 114L,
77L, 190L, 27L, 163L, 125L, 244L, 43L, 70L, 88L, 202L, 52L, 39L,
70L, 10L, 5L, 26L, 108L, 37L, 38L, 81L, 5L, 52L, 14L, 33L, 0L,
26L, 12L, 41L, 3L, 5L, 15L, 1L, 10L, 18L, 31L, 40L, 22L, 23L,
21L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 4L, 17L, 7L, 59L, 21L, 0L, 84L,
41L, 95L, 83L, 52L, 100L, 90L, 34L, 31L, 19L, 27L, 51L, 62L,
7L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 4L, 6L, 0L, 0L, 0L, 2L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 6L, 0L, 6L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L,
0L, 2L, 2L, 0L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 29L, 68L, 119L, 156L,
114L, 73L, 81L, 115L, 5L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 4L, 131L, 135L, 102L, 219L, 165L, 207L, 149L, 105L, 147L,
195L, 332L, 316L, 22L, 55L, 17L, 12L, 50L, 49L), Diptera = c(210L,
54L, 336L, 80L, 210L, 647L, 171L, 948L, 1495L, 751L, 877L, 912L,
1130L, 170L, 105L, 72L, 26L, 190L, 138L, 91L, 46L, 96L, 39L,
49L, 66L, 87L, 82L, 69L, 29L, 548L, 1240L, 810L, 999L, 521L,
784L, 504L, 800L, 1190L, 360L, 539L, 331L, 742L, 1041L, 742L,
154L, 787L, 479L, 411L, 1181L, 1350L, 1423L, 747L, 1827L, 1758L,
2L, 172L, 1L, 278L, 145L, 250L, 121L, 294L, 121L, 219L, 254L,
278L, 305L, 269L, 212L, 248L, 229L, 229L, 225L, 311L, 236L, 209L,
257L, 226L, 655L, 440L, 416L, 39L, 398L, 323L, 461L, 670L, 934L,
401L, 686L, 619L, 1043L, 1578L, 767L, 432L, 1754L, 1228L, 2164L,
585L, 1336L, 933L, 928L, 454L, 833L, 928L, 745L, 604L, 69L, 1052L,
1228L, 15L, 1835L, 1459L, 1408L, 170L, 1367L, 146L, 14L, 164L,
101L, 780L, 779L, 259L, 537L, 576L, 480L, 1076L, 577L, 119L,
58L, 853L, 529L, 724L, 1329L, 381L, 194L, 428L, 1240L, 1349L,
29L, 42L, 249L, 881L, 1122L, 456L, 837L, 162L, 751L, 281L, 421L,
36L, 803L, 553L, 562L, 1769L, 151L, 1019L, 34L, 158L, 736L, 472L,
254L, 666L, 853L, 1175L, 795L, 1627L, 1229L, 960L, 1659L, 1719L,
713L, 0L, 5L, 216L, 199L, 335L, 64L, 466L, 98L, 1385L, 1162L,
1545L, 1457L, 1215L, 614L, 1247L, 1697L, 620L, 895L, 1297L, 902L,
12L, 264L, 76L, 4L, 2L, 36L, 44L, 2L, 326L, 6L, 66L, 9L, 70L,
13L, 2L, 8L, 0L, 0L, 11L, 42L, 2L, 2L, 4L, 2L, 70L, 4L, 120L,
138L, 126L, 14L, 1L, 93L, 10L, 40L, 3L, 15L, 186L, 54L, 304L,
12L, 34L, 34L, 8L, 296L, 80L, 50L, 36L, 0L, 0L, 10L, 40L, 4L,
0L, 0L, 98L, 68L, 2L, 0L, 7L, 8L, 6L, 186L, 148L, 0L, 6L, 14L,
106L, 0L, 0L, 2L, 2L, 62L, 4L, 4L, 318L, 742L, 1099L, 298L, 553L,
867L, 716L, 556L, 91L, 154L, 89L, 16L, 114L, 21L, 49L, 130L,
46L, 94L, 58L, 349L, 967L, 828L, 857L, 765L, 847L, 459L, 725L,
731L, 409L, 432L, 805L, 565L, 967L, 953L, 1398L, 999L, 1081L,
1104L), Ephemeroptera = c(27L, 9L, 2L, 1L, 0L, 38L, 11L, 4L,
234L, 3L, 1L, 218L, 44L, 0L, 0L, 0L, 0L, 1L, 8L, 1L, 2L, 3L,
23L, 5L, 7L, 6L, 8L, 3L, 3L, 173L, 718L, 1264L, 825L, 464L, 478L,
456L, 816L, 481L, 811L, 652L, 146L, 686L, 563L, 372L, 190L, 419L,
158L, 63L, 244L, 141L, 267L, 236L, 100L, 99L, 0L, 0L, 0L, 10L,
3L, 1L, 0L, 3L, 0L, 14L, 9L, 0L, 5L, 5L, 1L, 29L, 21L, 0L, 45L,
29L, 1L, 14L, 9L, 1L, 134L, 300L, 15L, 46L, 170L, 272L, 100L,
325L, 146L, 436L, 544L, 27L, 9L, 40L, 41L, 103L, 63L, 84L, 103L,
629L, 133L, 584L, 74L, 25L, 191L, 489L, 212L, 304L, 118L, 78L,
76L, 0L, 20L, 238L, 373L, 4L, 69L, 3L, 0L, 0L, 121L, 266L, 273L,
104L, 209L, 356L, 203L, 461L, 53L, 60L, 5L, 130L, 25L, 135L,
163L, 56L, 81L, 884L, 358L, 432L, 32L, 98L, 1L, 26L, 18L, 10L,
11L, 1L, 68L, 3L, 9L, 0L, 32L, 5L, 41L, 106L, 85L, 240L, 27L,
15L, 113L, 613L, 786L, 572L, 394L, 306L, 84L, 0L, 76L, 11L, 11L,
261L, 192L, 40L, 35L, 30L, 266L, 34L, 7L, 293L, 41L, 167L, 253L,
103L, 93L, 233L, 362L, 408L, 173L, 440L, 145L, 162L, 11L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 8L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 467L, 430L, 177L, 291L, 392L,
231L, 82L, 361L, 29L, 0L, 31L, 0L, 16L, 0L, 3L, 17L, 8L, 15L,
27L, 45L, 111L, 82L, 133L, 163L, 96L, 85L, 76L, 72L, 121L, 127L,
69L, 109L, 443L, 221L, 114L, 421L, 183L, 156L), Hemiptera = c(27L,
2L, 1L, 1L, 0L, 3L, 1L, 0L, 10L, 6L, 0L, 8L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 3L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 1L, 2L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 4L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
2L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 5L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 3L, 0L, 0L, 2L, 10L, 0L, 0L, 0L, 2L, 2L, 50L, 8L,
47L, 0L, 320L, 98L, 5L, 0L, 287L, 314L, 16L, 14L, 236L, 14L,
2L, 627L, 279L, 6L, 254L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 16L, 4L, 0L, 8L, 12L, 36L, 6L, 14L, 104L,
0L, 5L, 94L, 10L, 0L, 82L, 10L, 94L, 48L, 2L, 0L, 2L, 44L, 8L,
6L, 0L, 16L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 2L, 0L, 2L, 0L, 1L, 20L, 1L, 4L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Trichoptera = c(0L,
0L, 11L, 0L, 4L, 1L, 0L, 25L, 3L, 2L, 3L, 0L, 9L, 0L, 0L, 2L,
2L, 12L, 12L, 7L, 8L, 4L, 8L, 1L, 11L, 9L, 12L, 15L, 10L, 307L,
332L, 224L, 92L, 210L, 213L, 239L, 195L, 75L, 372L, 5L, 6L, 12L,
14L, 12L, 2L, 17L, 35L, 30L, 33L, 17L, 13L, 33L, 10L, 8L, 0L,
0L, 0L, 26L, 4L, 3L, 4L, 7L, 1L, 22L, 7L, 6L, 11L, 4L, 10L, 35L,
11L, 4L, 61L, 21L, 6L, 19L, 17L, 16L, 417L, 250L, 225L, 34L,
375L, 396L, 84L, 188L, 55L, 55L, 98L, 1145L, 713L, 342L, 2387L,
1404L, 908L, 685L, 44L, 692L, 691L, 101L, 35L, 14L, 296L, 145L,
44L, 274L, 62L, 31L, 49L, 1L, 135L, 24L, 219L, 2L, 60L, 6L, 0L,
0L, 120L, 31L, 126L, 68L, 62L, 182L, 153L, 27L, 61L, 31L, 51L,
153L, 185L, 190L, 174L, 372L, 170L, 81L, 180L, 218L, 3L, 22L,
5L, 161L, 23L, 10L, 54L, 1L, 22L, 11L, 17L, 0L, 19L, 12L, 74L,
13L, 29L, 64L, 1L, 1L, 1L, 193L, 561L, 97L, 112L, 241L, 19L,
9L, 14L, 16L, 5L, 5L, 5L, 71L, 22L, 75L, 239L, 44L, 16L, 346L,
31L, 169L, 353L, 120L, 117L, 187L, 361L, 210L, 28L, 181L, 53L,
19L, 3L, 0L, 0L, 0L, 0L, 3L, 0L, 10L, 26L, 4L, 0L, 18L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 20L, 0L, 0L, 0L, 22L, 11L, 8L, 10L, 4L,
0L, 0L, 5L, 2L, 5L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 8L,
4L, 13L, 0L, 0L, 2L, 0L, 4L, 0L, 1L, 0L, 0L, 0L, 4L, 0L, 0L,
0L, 0L, 0L, 24L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 0L, 2L, 0L, 0L,
107L, 230L, 52L, 14L, 46L, 44L, 29L, 85L, 7L, 0L, 11L, 0L, 2L,
0L, 0L, 5L, 3L, 4L, 0L, 7L, 90L, 97L, 166L, 243L, 160L, 62L,
122L, 72L, 297L, 139L, 102L, 145L, 40L, 19L, 16L, 12L, 3L, 7L
), Trombidiformes = c(6L, 1L, 59L, 1L, 4L, 16L, 3L, 1L, 3L, 2L,
2L, 49L, 12L, 0L, 0L, 0L, 1L, 2L, 3L, 8L, 1L, 8L, 10L, 11L, 0L,
15L, 1L, 5L, 8L, 31L, 31L, 59L, 48L, 111L, 155L, 153L, 116L,
102L, 210L, 4L, 3L, 2L, 2L, 4L, 0L, 6L, 5L, 52L, 215L, 76L, 107L,
103L, 116L, 100L, 0L, 0L, 0L, 0L, 0L, 2L, 3L, 0L, 0L, 1L, 0L,
2L, 1L, 0L, 2L, 1L, 0L, 4L, 1L, 5L, 10L, 3L, 0L, 1L, 5L, 19L,
7L, 5L, 13L, 7L, 8L, 2L, 2L, 6L, 0L, 1L, 0L, 0L, 0L, 3L, 1L,
2L, 0L, 0L, 0L, 50L, 21L, 22L, 41L, 26L, 4L, 70L, 2L, 8L, 16L,
0L, 48L, 35L, 6L, 3L, 16L, 6L, 2L, 0L, 7L, 8L, 43L, 17L, 9L,
26L, 32L, 24L, 52L, 16L, 39L, 34L, 26L, 29L, 6L, 51L, 53L, 75L,
198L, 93L, 49L, 29L, 37L, 59L, 92L, 45L, 66L, 4L, 38L, 33L, 36L,
2L, 116L, 31L, 70L, 9L, 32L, 8L, 2L, 8L, 8L, 80L, 92L, 51L, 187L,
75L, 130L, 143L, 128L, 83L, 80L, 67L, 76L, 0L, 2L, 1L, 47L, 14L,
0L, 105L, 14L, 52L, 50L, 54L, 20L, 54L, 48L, 34L, 6L, 47L, 23L,
10L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 16L, 2L, 8L, 0L, 13L, 8L, 0L, 0L, 29L, 12L, 2L, 2L, 3L, 1L,
0L, 44L, 23L, 1L, 12L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 2L, 0L,
0L, 0L, 0L, 0L, 0L, 8L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L,
36L, 0L, 2L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L,
23L, 93L, 26L, 257L, 61L, 36L, 179L, 56L, 20L, 0L, 61L, 0L, 66L,
0L, 3L, 0L, 3L, 0L, 0L, 27L, 66L, 76L, 113L, 44L, 30L, 15L, 16L,
18L, 23L, 39L, 95L, 41L, 37L, 28L, 45L, 22L, 21L, 9L), Tubificida = c(20L,
0L, 13L, 1L, 34L, 77L, 11L, 379L, 147L, 184L, 267L, 197L, 313L,
2L, 1L, 10L, 1L, 2L, 9L, 15L, 25L, 9L, 4L, 7L, 21L, 20L, 4L,
30L, 3L, 17L, 11L, 15L, 0L, 2L, 8L, 139L, 133L, 292L, 158L, 94L,
13L, 42L, 73L, 53L, 81L, 79L, 277L, 15L, 2L, 14L, 42L, 54L, 41L,
59L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 15L, 112L, 0L, 7L, 18L,
1L, 15L, 18L, 4L, 5L, 67L, 0L, 9L, 41L, 4L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 0L, 0L, 2L, 34L, 5L, 33L, 5L, 22L, 25L, 48L,
0L, 3L, 16L, 9L, 0L, 152L, 10L, 1L, 13L, 4L, 0L, 25L, 1L, 65L,
3L, 10L, 18L, 11L, 33L, 13L, 38L, 0L, 29L, 36L, 21L, 10L, 11L,
16L, 16L, 73L, 2L, 0L, 538L, 773L, 88L, 347L, 58L, 54L, 0L, 2L,
14L, 0L, 0L, 5L, 23L, 12L, 60L, 10L, 13L, 21L, 14L, 8L, 2L, 29L,
4L, 5L, 23L, 11L, 21L, 41L, 196L, 128L, 0L, 0L, 0L, 0L, 0L, 9L,
5L, 3L, 67L, 19L, 3L, 7L, 0L, 0L, 3L, 3L, 4L, 0L, 14L, 3L, 77L,
188L, 73L, 78L, 163L, 13L, 73L, 13L, 20L, 61L, 33L, 2L, 0L, 0L,
0L, 0L, 0L, 12L, 410L, 124L, 80L, 0L, 42L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 116L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 14L,
8L, 2L, 0L, 0L, 0L, 0L, 0L, 6L, 2L, 3L, 96L, 0L, 10L, 148L, 12L,
17L, 2L, 0L, 0L, 18L, 0L, 0L, 0L, 2L, 2L, 0L, 2L, 3L, 2L, 0L,
34L, 16L, 0L, 24L, 0L, 82L, 0L, 0L, 0L, 0L, 18L, 0L, 0L, 6L,
18L, 39L, 41L, 16L, 27L, 31L, 27L, 44L, 0L, 136L, 5L, 32L, 0L,
256L, 164L, 305L, 224L, 244L, 160L, 63L, 63L, 68L, 37L, 209L,
52L, 47L, 51L, 81L, 12L, 45L, 49L, 1L, 28L, 0L, 0L, 22L, 1L),
aquaticSiteType = c("stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "lake", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "lake", "lake", "lake", "lake", "lake", "lake",
"lake", "lake", "lake", "lake", "lake", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream", "stream",
"stream", "stream", "stream", "stream", "stream")), class = "data.frame", row.names = c(NA,
-303L))
I run the NMDS code using the code below
set.seed(1)
metaMDS(comm = orders[,1:8], # Define the community data
distance = "bray", # Specify a bray-curtis distance
try = 100) # Number of iterations
It worked properly, when i assign it to another object, there is no solution
set.seed(1)
nmds = metaMDS(comm = orders[,1:8], # Define the community data
distance = "bray", # Specify a bray-curtis distance
try = 100) # Number of iterations
Best solution was not repeated -- monoMDS stopping criteria:
2: no. of iterations >= maxit
16: stress ratio > sratmax
2: scale factor of the gradient < sfgrmin
why is this happenning? i also tried with several seeds and without seeds also, but the problem is the same.
and then when i tried the score value to data frame
data_scores = as.data.frame(scores(nmds))
Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, :
arguments imply differing number of rows: 303, 8
Why I am getting this error?

metaMDS worked OK and gave you results. No problem.
Your problem was that you assumed that scores gives you a simple matrix-like object that can be converted to a data.frame. It does not, but it gives you a list of sample scores and species scores:
> str(scores(nmds))
List of 2
$ sites : num [1:303, 1:2] -0.051 0.426 0.129 0.385 0.127 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:303] "1" "2" "3" "4" ...
.. ..$ : chr [1:2] "NMDS1" "NMDS2"
$ species: num [1:8, 1:2] -1.178 0.351 0.269 0.339 -1.177 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:8] "Amphipoda" "Coleoptera" "Diptera" "Ephemeroptera" ...
.. ..$ : chr [1:2] "NMDS1" "NMDS2"
You have two alternatives:
Request only one kind of scores. For instance this gives you only sample scores.
> str(scores(nmds, display="sites"))
num [1:303, 1:2] -0.051 0.426 0.129 0.385 0.127 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:303] "1" "2" "3" "4" ...
..$ : chr [1:2] "NMDS1" "NMDS2"
Request "tidy" scores that pack species and site scores in one data frame and add a variable that identifies the type of scores:
> str(scores(nmds, tidy=TRUE))
'data.frame': 311 obs. of 4 variables:
$ NMDS1: num -0.051 0.426 0.129 0.385 0.127 ...
$ NMDS2: num -0.2518 -0.1687 -0.0795 0.069 0.2453 ...
$ score: chr "sites" "sites" "sites" "sites" ...
$ label: chr "1" "2" "3" "4" ...

There are two issues here. The first is the notice that "best solution was not repeated" and the second is the error trying to coerce the scores to a data.frame.
As noted in the answer by Jari Oksanen, the latter error is a consequence of trying to force an object to be a data.frame when it has dimensions that do not allow it to be coerced to a data.frame.
However, the much bigger issue is that you should not trust the scores in the first place because the model has not converged. From the metaMDS documentation:
Non-linear optimization is a hard task, and the best possible solution
(“global optimum”) may not be found from a random starting
configuration. Most software solve this by starting from the result of
metric scaling (cmdscale). This will probably give a good result, but
not necessarily the “global optimum”. Vegan does the same, but metaMDS
tries to verify or improve this first solution (“try 0”) using several
random starts and seeing if the result can be repeated or improved and
the improved solution repeated. If this does not succeed, you get a
message that the result could not be repeated. However, the result
will be at least as good as the usual standard strategy of starting
from metric scaling or it may be improved. You may not need to do
anything after such a message, but you can be satisfied with the
result. If you want to be sure that you probably have a “global
optimum” you may try the following instructions.
That different starting points (a) produce a solution but (b) the solution differs based on initial starts means the optimizer has found a local solution that can be arbitrarily far from the global solution. In other words, the results you obtain can be arbitrarily wrong and shouldn't be trusted. In other software, this would throw and not provide you the results because it wouldn't trust you not to use the known-to-be-unreliable results.
For some reason, the authors of this don't do that but you should not let their decision to make this a warning instead of an error be a reason to ignore the fundamental issues with the results.
You can follow the steps discussed in the documentation linked above to address this.

Create a condition for legend in ggsurvplot

At the moment, the legend is Quartile 4. HR 0.62 (95%CI 0.10-3.72), P=0.60.
I would like to create a condition when as follow: if P-value is >=0.95, I would like to write Quartile 4. P=0.99. So without the HR and the 95% CI.
HR and Ci make nonsense writing like this for now.
With this code:
#libraries
library(readxl)
library(tidyverse)
library(tidytidbits)
library(survivalAnalysis)
library(dplyr)
library(survival)
library(survminer)
library(ggplot2)
library(ggthemes)
library(ggpubr)
df$quantile <- df$delta_mon1_baseline_to_d3
#define quartile
df$Quartile <- findInterval(df$quantile, quantile(df$quantile, na.rm = TRUE)[-5])
#factor Quartile
df$Quartile <- factor(df$Quartile)
#cox regression
cox <- coxph(Surv(mace_months_date_vs_date_sample, mace) ~ Quartile, data = df)
# create the tags
coxP <- data.frame(summary(cox)$coefficients)[,5]
coxConf <- data.frame(summary(cox)$conf.int) %>%
rownames_to_column() %>%
mutate(p = coxP,
p2 = case_when( # determine direction
round(p, 3) > p ~ '=',
round(p, 3) < p ~ '=',
round(p, 3) == p ~ '='
),
p3 = ifelse(round(p, 2) == 1, T, F), # id if p value is 1 (too high!)
# gsub adds space, round, keep trailing zeros
tag = paste0(rowname %>% gsub("(\\D)(\\d)", "\\1 \\2", .),
". HR ", exp.coef. %>% sprintf(fmt = "%.2f", .),
" (95% CI ",
lower..95 %>% sprintf(fmt = "%.2f", .),
"-", upper..95 %>% sprintf(fmt = "%.2f", .),
"), P", p2, "",
ifelse(p3,
yes = "0.99", # if p rounded = 1
no = sprintf(fmt = "%.2f", p)))) %>%
select(tag)
# validate as expected
coxConf
I have got this legend in the graph
Here are my data:
ID age sex mace mace_months_date_vs_date_sample trop egfr dm smoke delta_mon1_baseline_to_d3
1 44 52 1 1 30 2600 56 1 0 -822.
2 32 66 1 0 73 1710 90 1 0 -562.
3 20 56 1 1 5 NA 75 0 1 -502.
4 17 44 1 0 77 840 71 0 0 -389.
5 52 49 1 0 74 1740 71 0 1 -372.
6 57 58 1 0 74 5010 68 0 1 -308.
7 79 68 1 0 45 776 90 0 0 -284.
8 14 74 1 1 6 7120 78 0 0 -279.
9 223 63 1 0 46 4281 90 0 0 -218.
10 56 43 1 0 70 1360 90 1 0 -173.
11 50 54 1 0 70 15300 90 0 1 -163.
12 31 47 0 0 72 6490 77 1 1 -95.7
13 35 47 1 0 77 NA 83 0 0 -71.0
14 36 64 1 1 5 15940 52 0 1 -69.7
15 15 65 1 1 43 6300 49 1 0 -69.6
16 12 57 1 0 71 6020 88 0 1 -66.5
17 43 59 0 0 74 2100 84 0 1 -58.8
18 22 46 1 0 77 5330 88 0 1 -29.3
19 54 59 1 0 71 1500 81 1 1 -25.7
20 26 66 1 0 77 500 51 0 0 -12.5
21 29 73 0 0 77 NA 51 0 0 -2.99
22 25 54 1 0 73 1080 87 0 0 2.81
23 39 54 1 0 74 990 77 0 0 32.9
24 47 62 1 0 69 1420 85 0 0 33.0
25 49 54 1 1 28 NA 76 1 0 44.1
26 24 47 1 0 77 2390 90 0 1 47.7
27 45 51 0 0 73 3710 65 0 1 55.9
28 30 73 0 0 68 3340 48 1 1 117.
29 16 57 1 0 73 180 99 0 1 131.
30 55 47 1 0 70 NA 90 0 1 131.
31 37 81 1 0 74 NA 99 1 1 147.
32 21 46 1 0 75 3600 87 0 1 153.
33 60 72 1 0 76 470 62 0 0 160.
34 18 56 1 0 69 6390 90 0 1 165.
35 13 53 1 0 69 1970 87 1 1 180.
36 19 66 1 0 78 9320 59 0 0 180.
37 33 59 1 0 69 2260 79 0 1 193.
38 139 39 0 0 58 NA 90 0 1 209.
39 38 55 1 0 78 3930 90 1 0 244.
40 27 28 1 0 71 6440 90 0 1 248.
41 58 36 1 0 76 NA 78 1 1 327.
42 61 48 1 0 76 4470 90 0 1 336.
43 42 38 1 0 69 1800 69 0 1 375.
44 28 76 1 0 71 40 90 1 1 419.
Here is the console output:
structure(list(ID = c(44L, 32L, 20L, 17L, 52L, 57L, 79L, 14L,
223L, 56L, 50L, 31L, 35L, 36L, 15L, 12L, 43L, 22L, 54L, 26L,
29L, 25L, 39L, 47L, 49L, 24L, 45L, 30L, 16L, 55L, 37L, 21L, 60L,
18L, 13L, 19L, 33L, 139L, 38L, 27L, 58L, 61L, 42L, 28L, 121L,
192L, 120L, 68L, 41L, 23L, 216L, 136L, 88L, 87L, 182L, 93L, 154L,
94L, 116L, 145L, 228L, 76L, 63L, 59L, 219L, 175L, 164L, 181L,
234L, 146L, 242L, 71L, 67L, 187L, 128L, 151L, 215L, 132L, 173L,
124L, 119L, 224L, 140L, 221L, 172L, 115L, 103L, 73L, 194L, 106L,
193L, 148L, 156L, 203L, 100L, 81L, 190L, 206L, 233L, 189L, 105L,
220L, 85L, 11L, 205L, 131L, 1L, 225L, 183L, 213L, 7L, 147L, 134L,
86L, 69L, 212L, 199L, 75L, 137L, 191L, 245L, 111L, 153L, 112L,
89L, 243L, 109L, 165L, 95L, 231L, 5L, 168L, 159L, 6L, 179L, 77L,
155L, 171L, 174L, 84L, 102L, 207L, 230L, 138L, 188L, 241L, 72L,
235L, 211L, 127L, 237L, 70L, 210L, 110L, 133L, 2L, 218L, 180L,
229L, 65L, 130L, 96L, 226L, 152L, 197L, 178L, 141L, 195L, 92L,
162L, 201L, 217L, 222L, 208L, 104L, 160L, 66L, 74L, 185L, 177L,
123L, 184L, 204L, 227L, 125L, 83L, 8L, 143L, 9L, 3L, 117L, 10L,
198L, 244L, 108L, 34L, 214L, 4L, 97L, 200L, 113L, 80L, 166L,
98L, 238L, 239L, 114L, 167L, 64L, 157L, 90L, 149L, 129L, 170L,
91L, 135L, 122L, 240L, 99L, 236L, 144L, 53L, 176L, 107L, 232L,
163L, 142L, 118L, 126L, 158L, 186L, 82L, 78L, 48L, 62L, 209L,
196L, 46L, 150L, 161L, 169L, 101L, 202L, 51L, 40L), age = c(52L,
66L, 56L, 44L, 49L, 58L, 68L, 74L, 63L, 43L, 54L, 47L, 47L, 64L,
65L, 57L, 59L, 46L, 59L, 66L, 73L, 54L, 54L, 62L, 54L, 47L, 51L,
73L, 57L, 47L, 81L, 46L, 72L, 56L, 53L, 66L, 59L, 39L, 55L, 28L,
36L, 48L, 38L, 76L, NA, 59L, 71L, 58L, 57L, 54L, 69L, 49L, 65L,
48L, 35L, 44L, 65L, 56L, 66L, 41L, 55L, 52L, 67L, 61L, 65L, 61L,
75L, 56L, 37L, 75L, 68L, 59L, 52L, 59L, 59L, 63L, 62L, 57L, 48L,
65L, 41L, 60L, 77L, 66L, 50L, 51L, 81L, 61L, 64L, 48L, 63L, 78L,
79L, 51L, 74L, 52L, 73L, 82L, 58L, 72L, 63L, 67L, 72L, 51L, 68L,
41L, 66L, 69L, 60L, 66L, 71L, 45L, 81L, 52L, 67L, 58L, 63L, 47L,
63L, 67L, 62L, 72L, 75L, 46L, 73L, 57L, 75L, 68L, 68L, 73L, 51L,
59L, 59L, 60L, 54L, 62L, 64L, 48L, 73L, 79L, 58L, 46L, 75L, 63L,
68L, 60L, 54L, 78L, 54L, 46L, 49L, 67L, 79L, 54L, 47L, 51L, 66L,
61L, 64L, 79L, 73L, 51L, 52L, 52L, 76L, 75L, 56L, 76L, 54L, 82L,
62L, 57L, 53L, 42L, 63L, 37L, 66L, 46L, 76L, 39L, 51L, 80L, 69L,
76L, 48L, 65L, 59L, 76L, 57L, 66L, 69L, 68L, 72L, 62L, 56L, 51L,
60L, 71L, 68L, 77L, 28L, 62L, 51L, 61L, 56L, 72L, 79L, 62L, 68L,
68L, 49L, 75L, 64L, 48L, 51L, 68L, 68L, 70L, 73L, 54L, 47L, 79L,
40L, 52L, 58L, 69L, 61L, 44L, 57L, 55L, 43L, 61L, 44L, 77L, 35L,
74L, 72L, 60L, 44L, 53L, 61L, NA, 80L, 73L, 72L), sex = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L,
0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L,
1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L,
0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 1L), mace = c(1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L,
0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L,
0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L), mace_months_date_vs_date_sample = c(30L,
73L, 5L, 77L, 74L, 74L, 45L, 6L, 46L, 70L, 70L, 72L, 77L, 5L,
43L, 71L, 74L, 77L, 71L, 77L, 77L, 73L, 74L, 69L, 28L, 77L, 73L,
68L, 73L, 70L, 74L, 75L, 76L, 69L, 69L, 78L, 69L, 58L, 78L, 71L,
76L, 76L, 69L, 71L, 43L, 44L, 5L, 62L, 73L, 72L, 47L, 61L, 47L,
46L, 44L, 43L, 46L, 45L, 16L, 62L, 8L, 48L, 15L, 79L, 62L, 46L,
62L, 45L, 55L, 21L, 41L, 45L, 46L, 45L, 50L, 62L, 43L, 4L, 1L,
4L, 63L, 17L, 3L, 45L, 2L, 50L, 1L, 44L, 15L, 46L, 46L, 7L, 8L,
62L, 16L, 47L, 19L, 61L, 46L, 1L, 4L, 48L, 61L, 43L, 46L, 61L,
44L, 48L, 1L, 5L, 43L, 43L, 46L, 58L, 3L, 55L, 27L, 1L, 47L,
19L, 11L, 1L, 1L, 1L, 2L, 48L, 62L, 61L, 23L, 1L, 55L, 19L, 48L,
1L, 43L, 47L, 1L, 46L, 11L, 46L, 48L, 46L, 32L, 50L, 61L, 62L,
48L, 46L, 55L, 62L, 55L, 43L, 61L, 47L, 59L, 50L, 10L, 55L, 63L,
55L, 48L, 31L, 32L, 48L, 55L, 62L, 59L, 53L, 48L, 48L, 62L, 59L,
4L, 4L, 25L, 59L, 48L, 60L, 43L, 58L, 49L, 1L, 8L, 1L, 63L, 51L,
55L, 47L, 49L, 55L, 49L, 55L, 13L, 60L, 2L, 13L, 3L, 25L, 7L,
62L, 62L, 60L, 10L, 61L, 45L, 3L, 11L, 51L, 47L, 1L, 46L, 63L,
60L, 43L, 62L, 58L, 61L, 1L, 2L, 46L, 1L, 13L, 54L, 48L, 54L,
45L, 45L, 31L, 48L, 42L, 49L, 43L, 61L, 1L, 31L, 1L, 63L, 11L,
47L, 39L, 7L, 42L, 1L, 1L, 1L), trop = c(2600L, 1710L, NA, 840L,
1740L, 5010L, 776L, 7120L, 4281L, 1360L, 15300L, 6490L, NA, 15940L,
6300L, 6020L, 2100L, 5330L, 1500L, 500L, NA, 1080L, 990L, 1420L,
NA, 2390L, 3710L, 3340L, 180L, NA, NA, 3600L, 470L, 6390L, 1970L,
9320L, 2260L, NA, 3930L, 6440L, NA, 4470L, 1800L, 40L, 21876L,
871L, 7860L, NA, 320L, 8450L, 1730L, 262L, 16720L, 1247L, NA,
NA, 54592L, 1241L, 2413L, NA, 45649L, NA, NA, 160L, 843L, NA,
1470L, 372L, 844L, 1454L, 50000L, 11450L, 769L, 2234L, 349L,
250L, 3654L, 8421L, NA, 5204L, 440L, NA, 40273L, 90L, 9352L,
2177L, 10014L, 11L, 11135L, 5256L, 1753L, NA, NA, 50L, 8903L,
3598L, 2483L, NA, NA, NA, 1557L, 5247L, 24L, 2993L, 3624L, 751L,
NA, NA, 24160L, NA, NA, 5687L, 1911L, NA, NA, 1855L, 9951L, 13374L,
2107L, 4927L, 83L, 2380L, 663L, NA, NA, NA, NA, NA, NA, NA, 1627L,
NA, 1211L, 5654L, NA, NA, 10000L, NA, NA, NA, 2956L, 67927L,
NA, 63L, NA, 4790L, NA, NA, 3569L, 961L, 6581L, 253L, 2888L,
33017L, 1675L, 438L, 15543L, 6212L, 6694L, NA, 1945L, 3004L,
3789L, NA, 2844L, 950L, 123L, 6630L, 3220L, 2040L, NA, 6672L,
1480L, 6979L, NA, 1411L, 5711L, NA, 2340L, NA, 57L, NA, 33L,
5110L, NA, 2797L, 1035L, 2840L, 251L, 7671L, 6155L, 4299L, NA,
846L, 2339L, 400L, 86115L, 27L, 87355L, NA, 8669L, NA, NA, NA,
1258L, 3000L, NA, 137L, 3866L, NA, 1312L, NA, NA, NA, NA, NA,
2103L, 1586L, 601L, 1472L, 1692L, NA, 2102L, 6452L, NA, NA, 1244L,
2051L, 1007L, NA, NA, NA, 1726L, 3400L, 2143L, NA, 236L, 3930L,
31026L, NA, NA, NA, NA, 5280L, 1230L), egfr = c(56L, 90L, 75L,
71L, 71L, 68L, 90L, 78L, 90L, 90L, 90L, 77L, 83L, 52L, 49L, 88L,
84L, 88L, 81L, 51L, 51L, 87L, 77L, 85L, 76L, 90L, 65L, 48L, 99L,
90L, 99L, 87L, 62L, 90L, 87L, 59L, 79L, 90L, 90L, 90L, 78L, 90L,
69L, 90L, 87L, 90L, 58L, 90L, 79L, 55L, 51L, 90L, 58L, 90L, 56L,
62L, 86L, 61L, 84L, 63L, 63L, 90L, 90L, 85L, 64L, 67L, 45L, 90L,
78L, 65L, 69L, 90L, 90L, 59L, 54L, 60L, 68L, 86L, 42L, 73L, 90L,
85L, 63L, 90L, 86L, 68L, 71L, 90L, 68L, 63L, 81L, 76L, 61L, 75L,
84L, 90L, 90L, 48L, 90L, 77L, 68L, 90L, 64L, 90L, 90L, 61L, 84L,
80L, 69L, 58L, 65L, 86L, 86L, 46L, 56L, 90L, 46L, 87L, 84L, 68L,
67L, 72L, 35L, 86L, 74L, 78L, 67L, 90L, 90L, 90L, 76L, 90L, 86L,
63L, 63L, 53L, 90L, 90L, 75L, 64L, 69L, 68L, 52L, 49L, 90L, 65L,
86L, 42L, 63L, 90L, 90L, 73L, 75L, 66L, 64L, 90L, 35L, 90L, 82L,
90L, 84L, 61L, 90L, 86L, 51L, 52L, 69L, 54L, 90L, 75L, 85L, 72L,
90L, 80L, 54L, 58L, 90L, 89L, 72L, 90L, 84L, 33L, 74L, 36L, 56L,
61L, 63L, 77L, 84L, 85L, 90L, 90L, 51L, 90L, 90L, 68L, 90L, 52L,
90L, 48L, 90L, 82L, 86L, 67L, 90L, 76L, 14L, 63L, 59L, 82L, 90L,
39L, 77L, 90L, 78L, 74L, 54L, 36L, 58L, 69L, NA, 53L, 90L, 90L,
90L, 88L, 90L, 90L, 90L, 90L, 90L, 63L, 87L, 48L, 90L, 55L, 70L,
65L, 90L, NA, 90L, 90L, 72L, 66L, 55L), dm = c(1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L,
0L, 0L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, NA, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L), smoke = c(0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L,
0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L,
0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L,
0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
NA, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L), delta_mon1_baseline_to_d3 = c(-821.989379882808,
-562.289733886719, -502.216308593755, -388.549621582031, -372.002563476562,
-308.033813476562, -283.636077880859, -279.422790527344, -218.279922485352,
-173.209777832031, -162.939453124998, -95.700927734375, -70.961883544922,
-69.742797851558, -69.5900268554681, -66.49755859375, -58.77816772461,
-29.29504394531, -25.714111328125, -12.548919677734, -2.99462890625,
2.80639648437602, 32.883270263672, 33.036499023438, 44.05969238281,
47.6982421875, 55.87646484375, 116.716430664065, 130.585632324218,
131.392028808594, 146.8232421875, 153.190795898438, 159.863708496094,
164.985534667969, 179.8525390625, 180.041305541992, 192.978088378906,
208.791275024414, 243.90209960937, 248.09851074219, 327.035522460937,
336.011077880859, 375.086456298828, 419.108337402341, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA), stratum = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA), .Label = c("1", "2", "3", "4"), class = "factor"), surv = structure(c(30,
73, 5, 77, 74, 74, 45, 6, 46, 70, 70, 72, 77, 5, 43, 71, 74,
77, 71, 77, 77, 73, 74, 69, 28, 77, 73, 68, 73, 70, 74, 75, 76,
69, 69, 78, 69, 58, 78, 71, 76, 76, 69, 71, 43, 44, 5, 62, 73,
72, 47, 61, 47, 46, 44, 43, 46, 45, 16, 62, 8, 48, 15, 79, 62,
46, 62, 45, 55, 21, 41, 45, 46, 45, 50, 62, 43, 4, 1, 4, 63,
17, 3, 45, 2, 50, 1, 44, 15, 46, 46, 7, 8, 62, 16, 47, 19, 61,
46, 1, 4, 48, 61, 43, 46, 61, 44, 48, 1, 5, 43, 43, 46, 58, 3,
55, 27, 1, 47, 19, 11, 1, 1, 1, 2, 48, 62, 61, 23, 1, 55, 19,
48, 1, 43, 47, 1, 46, 11, 46, 48, 46, 32, 50, 61, 62, 48, 46,
55, 62, 55, 43, 61, 47, 59, 50, 10, 55, 63, 55, 48, 31, 32, 48,
55, 62, 59, 53, 48, 48, 62, 59, 4, 4, 25, 59, 48, 60, 43, 58,
49, 1, 8, 1, 63, 51, 55, 47, 49, 55, 49, 55, 13, 60, 2, 13, 3,
25, 7, 62, 62, 60, 10, 61, 45, 3, 11, 51, 47, 1, 46, 63, 60,
43, 62, 58, 61, 1, 2, 46, 1, 13, 54, 48, 54, 45, 45, 31, 48,
42, 49, 43, 61, 1, 31, 1, 63, 11, 47, 39, 7, 42, 1, 1, 1, 1,
0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1,
0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0,
0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0,
0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0,
0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1), .Dim = c(245L, 2L), .Dimnames = list(
NULL, c("time", "status")), type = "right", class = "Surv")), row.names = c(NA,
-245L), class = c("tbl_df", "tbl", "data.frame"))
Thank you very much for your help,

Plot visreg over an boxplot (GLM with binominal predictor)

I fitted some GLMs with a binominal predictor and would like to plot them with visreg. I usually plot the raw data with par(new=T) as well for better clarity. I don't really like the normal outcome here (x-axis 0-1 in 0.2 steps, a lot of data points just at 0 and 1) and was thinking about plotting the visreg over boxplot since they look much better with binominal data. However, I can't get the two plots to align since there are always two different "starts" and "ends" in the plot. How can I make it so that the visreg line starts at the "No" and ends at the "Yes" of the boxplot?
fit <- glm (Cov.herb ~ Fire, family=gaussian, data=data)
boxplot(data$Cov.herb ~ data$Fire, ylim=c(0,100), axes=F, ylab="Herb cover [%]", xlab="Fire")
axis(1, xaxp=c(1,2,1), xaxt="n")
mtext(text=c("No","Yes"),side=1,line=0.5,at=c(1,2))
axis(2, las=1)
box()
par(new=T)
visreg(fit, scale = "response", type="conditional",line=list(col="red", lwd=1), ylim=c(0,100), xlim=c(0,1), rug=F, axes=F, ann=F)
example plot
Cheers,
Alex
data:
structure(list(Cov.herb = c(40L, 80L, 30L, 2L, 40L, 8L, 5L, 5L,
20L, 45L, 55L, 55L, 35L, 40L, 65L, 70L, 2L, 15L, 1L, 1L, 1L,
25L, 10L, 1L, 10L, 5L, 5L, 15L, 10L, 5L, 15L, 5L, 5L, 35L, 1L,
1L, 35L, 1L, 10L, 5L, 5L, 10L, 5L, 10L, 10L, 20L, 10L, 0L, 3L,
1L, 2L, 4L, 1L, 10L, 30L, 10L, 1L, 2L, 0L, 15L, 25L, 50L, 15L,
35L, 30L, 5L, 5L, 1L, 1L, 1L, 10L, 0L, 0L, 5L, 2L, 1L, 10L, 0L,
2L, 1L, 1L, 5L, 1L, 15L, 1L, 1L, 1L, 0L, 5L, 25L, 3L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 3L, 1L, 1L, 0L, 5L, 1L, 1L, 1L, 1L, 7L, 1L,
1L, 1L, 1L, 5L, 0L, 2L, 3L, 5L, 3L, 1L, 1L, 2L, 0L, 2L, 0L, 10L,
1L, 20L, 3L, 5L, 20L, 3L, 20L, 5L, 10L, 15L, 30L, 0L, 20L, 45L,
1L, 1L, 2L, 1L, 3L, 0L, 5L, 0L, 35L, 1L, 5L, 25L, 0L, 0L, 40L,
3L, 15L, 10L, 3L, 50L, 30L, 10L, 1L, 0L, 5L, 10L, 10L, 2L, 2L,
5L, 1L, 2L, 1L, 1L, 0L, 0L, 1L, 2L, 5L, 15L, 0L, 1L, 1L, 1L,
1L, 0L, 1L, 5L, 1L, 5L, 35L, 1L, 0L, 1L, 0L, 5L, 1L, 1L, 3L,
15L, 1L, 3L, 1L, 0L, 0L, 0L, 15L, 0L, 1L, 1L, 3L, 35L, 80L, 10L,
2L, 10L, 3L, 3L, 2L, 10L, 50L, 20L, 40L, 2L, 40L, 45L, 25L, 5L,
25L, 50L, 35L, 15L, 45L, 10L, 5L, 15L, 2L, 30L, 2L, 3L, 15L,
5L, 45L, 35L, 20L, 70L, 20L, 10L, 30L, 25L, 8L, 4L, 45L, 60L,
35L, 5L, 40L, 30L, 0L, 30L, 3L, 4L, 25L, 15L, 10L, 15L, 25L,
20L, 7L, 25L, 25L, 40L, 35L, 30L, 40L, 25L, 50L, 30L, 25L, 60L,
15L, 25L, 25L, 50L, 30L, 20L, 2L, 3L, 20L, 25L, 35L, 30L, 10L,
15L, 65L, 10L, 20L, 20L, 2L, 7L, 20L, 25L, 30L, 30L, 9L, 20L,
40L, 7L, 20L, 15L, 15L, 30L, 20L, 35L, 8L, 40L, 20L, 3L, 55L,
35L, 10L, 10L, 65L, 20L, 35L, 60L, 45L, 20L, 10L, 35L, 15L, 20L,
15L, 40L, 10L, 10L, 60L, 60L, 40L, 10L, 10L, 25L, 8L, 20L, 40L,
15L, 25L, 5L, 20L, 20L, 20L, 25L, 30L, 35L, 20L, 110L, 50L, 20L,
20L, 10L, 45L, 25L, 20L, 55L, 10L, 5L, 15L, 15L, 1L, 10L, 15L,
15L, 10L, 30L, 20L, 40L, 55L, 55L, 20L, 30L, 10L, 50L, 40L, 5L,
15L, 10L, 30L, 15L, 20L, 5L, 45L, 50L, 25L, 45L, 30L, 7L, 25L,
30L, 5L, 7L, 50L, 60L, 50L, 10L, 30L, 50L, 15L, 15L, 30L, 15L,
25L, 40L, 10L, 2L, 60L, 20L, 65L, 5L, 15L, 3L, 15L, 40L, 50L,
45L, 30L, 5L, 45L, 15L, 25L, 65L, 15L, 50L, 55L, 30L, 10L, 35L,
15L, 20L, 20L, 10L, 20L, 15L, 45L, 40L, 10L, 7L, 25L, 20L, 60L,
4L, 7L, 40L, 60L, 50L, 50L, 10L, 50L, 5L, 10L, 50L, 20L, 40L,
20L, 25L, 25L, 35L, 10L, 2L, 15L, 60L, 25L, 30L, 20L, 25L, 10L,
10L, 20L, 40L, 40L, 45L, 10L, 35L, 60L, 50L, 10L, 40L, 50L, 25L,
20L, 25L, 25L, 45L, 20L, 30L, 65L, 30L, 35L, 40L, 25L, 15L, 10L,
50L, 25L, 45L, 40L, 20L, 5L, 65L, 5L, 10L, 15L, 7L, 20L, 45L,
15L, 5L, 20L, 20L, 20L, 50L, 15L, 20L, 30L, 25L, 45L, 45L, 35L,
40L, 45L, 4L, 10L, 20L, 20L, 30L, 15L, 30L, 50L, 35L, 45L, 25L,
25L, 10L, 5L, 30L, 30L, 10L, 70L, 25L, 25L, 7L, 20L, 5L, 20L,
8L, 15L, 10L, 20L, 10L, 7L, 15L, 15L, 40L, 50L, 15L, 20L, 8L,
45L, 40L, 15L, 25L, 40L, 20L, 35L, 40L, 70L, 20L, 20L, 40L, 5L,
20L, 7L, 40L, 10L, 5L, 45L, 20L, 10L, 20L, 20L, 45L, 15L, 7L,
30L, 30L, 35L, 10L, 20L, 5L, 15L, 35L, 40L, 40L, 10L, 5L, 15L,
70L, 20L, 85L, 15L, 7L, 55L, 55L, 5L, 20L, 25L, 5L, 30L, 20L,
8L, 30L, 40L, 25L, 10L, 5L, 30L, 10L, 5L, 10L, 35L, 2L, 10L,
10L, 10L, 90L, 45L, 60L, 7L, 1L, 15L), Fire = c(0L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L,
0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("Cov.herb",
"Fire"), class = "data.frame", row.names = c(2L, 3L, 4L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 29L, 30L, 31L, 32L, 33L,
34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 74L, 75L, 76L,
77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 87L, 88L, 89L, 90L,
91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L,
103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L,
114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L,
125L, 126L, 153L, 154L, 155L, 161L, 162L, 163L, 164L, 165L, 166L,
167L, 169L, 170L, 171L, 173L, 174L, 175L, 176L, 177L, 178L, 179L,
180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L,
191L, 192L, 193L, 194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L,
202L, 203L, 204L, 205L, 206L, 207L, 209L, 211L, 213L, 214L, 215L,
216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L,
227L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L,
238L, 239L, 240L, 241L, 242L, 243L, 244L, 245L, 246L, 247L, 248L,
249L, 250L, 251L, 252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L,
260L, 261L, 262L, 263L, 269L, 270L, 274L, 275L, 276L, 277L, 279L,
280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L, 288L, 289L, 290L,
291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L, 299L, 300L, 301L,
302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 310L, 311L, 312L,
313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L, 323L,
324L, 325L, 326L, 327L, 328L, 329L, 330L, 331L, 332L, 333L, 334L,
335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L, 343L, 344L, 345L,
346L, 347L, 349L, 350L, 351L, 352L, 353L, 354L, 355L, 356L, 357L,
358L, 359L, 360L, 361L, 362L, 363L, 364L, 365L, 366L, 367L, 368L,
369L, 370L, 371L, 372L, 373L, 374L, 375L, 376L, 377L, 378L, 380L,
381L, 382L, 383L, 384L, 385L, 386L, 387L, 388L, 389L, 390L, 391L,
392L, 393L, 394L, 395L, 396L, 397L, 398L, 399L, 400L, 401L, 402L,
403L, 404L, 405L, 406L, 407L, 408L, 409L, 410L, 411L, 412L, 413L,
414L, 415L, 416L, 417L, 418L, 419L, 420L, 421L, 422L, 423L, 424L,
425L, 426L, 427L, 428L, 429L, 430L, 431L, 432L, 433L, 434L, 435L,
436L, 437L, 438L, 439L, 440L, 441L, 443L, 444L, 445L, 446L, 447L,
448L, 449L, 450L, 451L, 453L, 454L, 455L, 457L, 458L, 459L, 460L,
461L, 463L, 464L, 465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L,
473L, 474L, 475L, 476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L,
484L, 485L, 486L, 487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L,
495L, 496L, 497L, 498L, 499L, 500L, 501L, 502L, 503L, 504L, 505L,
506L, 507L, 508L, 509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L,
517L, 518L, 519L, 520L, 521L, 522L, 523L, 524L, 525L, 526L, 527L,
528L, 529L, 530L, 531L, 532L, 533L, 534L, 535L, 536L, 537L, 538L,
539L, 540L, 541L, 542L, 543L, 544L, 545L, 546L, 547L, 548L, 549L,
551L, 552L, 553L, 554L, 555L, 556L, 557L, 558L, 559L, 560L, 561L,
562L, 563L, 564L, 565L, 566L, 567L, 568L, 569L, 570L, 571L, 572L,
573L, 574L, 575L, 576L, 577L, 578L, 579L, 580L, 581L, 582L, 583L,
584L, 585L, 587L, 588L, 589L, 590L, 591L, 592L, 593L, 594L, 595L,
596L, 597L, 598L, 599L, 600L, 601L, 602L, 603L, 604L, 605L, 606L,
607L, 608L, 609L, 610L, 611L, 612L, 613L, 614L, 615L, 616L, 617L,
618L, 619L, 620L, 621L, 622L, 623L, 624L, 625L, 626L, 628L, 629L,
631L, 632L, 633L, 634L, 635L, 636L, 637L, 638L, 639L, 640L, 641L,
642L, 643L, 644L, 645L, 646L, 648L, 649L, 650L, 651L, 652L, 653L,
654L, 655L, 656L, 657L, 658L, 659L, 660L, 661L, 662L, 663L, 664L,
665L, 666L, 667L, 668L, 669L, 670L, 671L, 672L, 673L, 674L, 675L,
676L, 677L, 678L, 679L, 680L, 682L, 683L, 684L, 685L, 686L, 687L,
689L, 690L, 691L, 692L, 693L, 694L, 697L, 698L, 699L, 700L, 701L,
702L, 704L, 705L, 706L, 707L))

So, my point was that doing it this way would give you more flexibility with your plotting. For example,
# Fit model
fit <- glm (Cov.herb ~ Fire, family=gaussian, data=data)
# Get model data for plotting
vis.out <- visreg(fit, scale = "response", plot = FALSE)
# Load library
library(ggplot2)
# Create plot
p <- ggplot(data = data)
p <- p + geom_boxplot(aes(x = as.factor(Fire), y = Cov.herb, fill = as.factor(Fire)), alpha = 0.3, outlier.alpha = 1)
p <- p + xlab("Fire") + ylab("Herb cover [%]")
p <- p + geom_ribbon(data = vis.out$fit, aes(x = Fire + 1, ymin = visregLwr, ymax = visregUpr), fill = "lightgrey")
p <- p + geom_line(data = vis.out$fit, aes(x = Fire + 1, y = visregFit), colour = "salmon", size = 1.25)
p <- p + scale_x_discrete(labels = c("No", "Yes"))
p <- p + theme(legend.position = "none")
print(p)
gives,
Is that the sort of thing you're looking for? (You could also add all the data points using geom_point to plot on top of the boxes. I think that usually looks pretty cool.)

Merge two legends (size and color) into one [duplicate]

This question already has answers here:
How to combine scales for colour and size into one legend?
(2 answers)
Closed 7 years ago.
What is the code to make the two legends into one: A circles legend with color?
I think, a single legend with circles colored according to "size" and "# total number of crimes" is the best way to show the legend.
Desired output:
1) There should be one legend: the circles, instead of black should be colored: 0 circle = "yellow" to 800 circle = "red".
My code:
library(maps)
library(ggmap)
Get map from Google Maps
lima <- get_map(location = "lima", zoom = 11, maptype = c("terrain"))
Plot
ggmap(lima) + geom_point(data = limanov2, aes(x = LONGITUD , y = LATITUD, color = TOTALES,
size = TOTALES)) +
scale_size_continuous(name = "Cantidad\ndelitos",range = c(2,12)) +
scale_color_gradient(name = "Cantidad\ndelitos", low = "yellow", high = "red") +
theme(legend.text= element_text(size=14)) +
ggtitle("TOTAL DELITOS - LIMA NOV 2012") +
theme(plot.title = element_text(size = 12, vjust=2, family="Verdana", face="italic"),
legend.position = 'left')
My data:
structure(list(DISTRITO = c("SAN JUAN DE LURIGANCHO", "CALLAO",
"LOS OLIVOS", "ATE", "LIMA", "SAN MARTIN DE PORRES", "SANTIAGO DE SURCO",
"CHORILLOS", "COMAS", "INDEPENDENCIA", "EL AGUSTINO", "LA VICTORIA",
"SAN JUAN DE MIRAFLORES", "VILLA EL SALVADOR", "SAN MIGUEL",
"CARABAYLLO", "MIRAFLORES", "SAN BORJA", "VENTANILLA", "SURQUILLO",
"BREÑA", "ANCON", "PTE. PIEDRA", "RIMAC", "BARRANCO", "LA MOLINA",
"SAN LUIS", "SANTA ANITA", "LURIGANCHO", "P. LIBRE", "MAGDALENA DEL MAR",
"LA PERLA", "CHACLACAYO", "PUENTE PIEDRA", "SAN ISIDRO", "JESUS MARIA",
"BELLAVISTA", "LINCE", "CARMEN DE LA LEGUA REYNOSO", "CIENEGUILLA",
"SANTA ROSA", "LURIN", "PUNTA NEGRA", "PUCUSANA", "LA PUNTA",
"PUNTA HERMOSA", "PACHACAMAC", "SAN BARTOLO", "SANTA MARIA"),
TOTALES = c(861L, 696L, 696L, 642L, 516L, 479L, 442L, 378L,
371L, 368L, 361L, 333L, 325L, 291L, 282L, 251L, 239L, 196L,
193L, 188L, 185L, 174L, 165L, 161L, 138L, 134L, 128L, 119L,
115L, 105L, 67L, 65L, 63L, 58L, 58L, 56L, 45L, 38L, 23L,
23L, 11L, 8L, 6L, 5L, 3L, 3L, 2L, 0L, 0L), HOMICIDIOS = c(1L,
7L, 0L, 1L, 2L, 0L, 0L, 1L, 7L, 4L, 4L, 4L, 0L, 0L, 0L, 2L,
0L, 0L, 7L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 2L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), LESIONES = c(100L, 72L, 61L, 43L, 44L, 8L, 10L,
15L, 44L, 40L, 50L, 15L, 52L, 28L, 7L, 33L, 15L, 3L, 21L,
7L, 36L, 33L, 15L, 19L, 14L, 1L, 8L, 6L, 16L, 4L, 4L, 9L,
1L, 12L, 2L, 9L, 5L, 2L, 5L, 7L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), VIO..DE.LA.LIBERTAD.PERSONAL = c(0L, 7L, 6L,
5L, 6L, 1L, 1L, 0L, 3L, 1L, 2L, 0L, 2L, 0L, 1L, 0L, 1L, 0L,
1L, 1L, 0L, 3L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L,
0L, 1L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), VIO..DE.LA.LIBERTAD.SEXUAL = c(56L, 14L, 12L, 15L, 7L,
10L, 2L, 9L, 11L, 13L, 8L, 9L, 7L, 14L, 4L, 15L, 4L, 2L,
17L, 7L, 3L, 4L, 6L, 12L, 2L, 1L, 5L, 3L, 11L, 4L, 1L, 2L,
0L, 6L, 2L, 0L, 3L, 0L, 2L, 2L, 0L, 4L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), HURTO.SIMPLE.Y.AGRAVADO = c(217L, 203L, 296L, 230L,
260L, 167L, 226L, 217L, 130L, 117L, 154L, 133L, 121L, 46L,
163L, 72L, 161L, 119L, 69L, 120L, 64L, 19L, 64L, 21L, 57L,
44L, 39L, 2L, 48L, 60L, 30L, 19L, 48L, 20L, 41L, 25L, 19L,
27L, 7L, 11L, 9L, 0L, 6L, 0L, 2L, 3L, 1L, 0L, 0L), ROBO.SIMPLE.Y.AGRAVADO = c(460L,
289L, 308L, 344L, 186L, 277L, 198L, 130L, 165L, 184L, 137L,
149L, 134L, 188L, 104L, 126L, 58L, 72L, 64L, 51L, 77L, 115L,
79L, 76L, 64L, 88L, 73L, 108L, 40L, 36L, 30L, 32L, 14L, 17L,
12L, 22L, 12L, 8L, 6L, 3L, 1L, 3L, 0L, 2L, 1L, 0L, 1L, 0L,
0L), MICRO.COM.DE.DROGAS = c(26L, 100L, 13L, 3L, 10L, 15L,
5L, 5L, 11L, 8L, 3L, 23L, 9L, 15L, 3L, 3L, 0L, 0L, 8L, 2L,
5L, 0L, 0L, 28L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 2L, 0L, 2L,
0L, 0L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L
), TENENCIA.ILEGAL.DE.ARMAS = c(1L, 4L, 0L, 1L, 1L, 1L, 0L,
1L, 0L, 1L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 6L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), LONGITUD = c(-77,
-77.12, -77.08, -76.89, -77.04, -77.09, -76.99, -77.01, -77.05,
-77.05, -77, -77.02, -76.97, -76.94, -77.09, -76.99, -77.03,
-77, -77.13, -77.01, -77.05, -77.11, -77.08, -76.7, -77.02,
-76.92, -77, -76.96, -76.86, -77.06, -77.07, -77.12, -76.76,
-77.08, -77.03, -77.05, -77.11, -77.04, -77.09, -76.78, -77.16,
-76.81, -76.73, -76.77, -77.16, -76.76, -76.83, -76.73, -76.77
), LATITUD = c(-11.99, -12.04, -11.95, -12.04, -12.06, -12,
-12.16, -12.2, -11.93, -11.99, -12.04, -12.08, -12.16, -12.23,
-12.08, -11.79, -12.12, -12.1, -11.89, -12.11, -12.06, -11.69,
-11.88, -11.94, -12.15, -12.09, -12.08, -12.04, -11.98, -12.08,
-12.09, -12.07, -11.99, -11.88, -12.1, -12.08, -12.06, -12.09,
-12.04, -12.07, -11.81, -12.24, -12.32, -12.47, -12.07, -12.28,
-12.18, -12.38, -12.42)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -49L), .Names = c("DISTRITO", "TOTALES",
"HOMICIDIOS", "LESIONES", "VIO..DE.LA.LIBERTAD.PERSONAL", "VIO..DE.LA.LIBERTAD.SEXUAL",
"HURTO.SIMPLE.Y.AGRAVADO", "ROBO.SIMPLE.Y.AGRAVADO", "MICRO.COM.DE.DROGAS",
"TENENCIA.ILEGAL.DE.ARMAS", "LONGITUD", "LATITUD"))

I've found a solution. Reading the documention for GGPLOT2 V. 0.9
It is the new function: guide_legend() that should be used inside guides().
This is a function that lets you have more control over legend labels.
This is the end code with the resulting output (See the last line):
ggmap(lima) + geom_point(data = limanov2, aes(x = LONGITUD , y = LATITUD, color = TOTALES,
size = TOTALES)) +
scale_size_continuous(name = "Cantidad\ndelitos",range = c(2,12)) +
scale_color_gradient(name = "Cantidad\ndelitos", low = "yellow", high = "red") +
theme(legend.text= element_text(size=14)) +
ggtitle("TOTAL DELITOS - LIMA NOV 2012") +
theme(plot.title = element_text(size = 12, vjust=2, family="Verdana", face="italic"),
legend.position = 'left') +
guides(colour = guide_legend())

R: ggmap: containing missing values (geom_point) when plottinng but no NAs values found in data.frame

I'm plotting some points over a map with ggmap package.
The problem is that i get the message: "Removed 12 rows containing missing values (geom_point)".
But i don't have any NAs. I've looked the data, and used:
sum(is.na(limanov2)) #Gives 0
to prove it.
This is my code:
library(maps)
library(ggmap)
lima <- get_map(location = "lima", zoom = 11)
ggmap(lima) + geom_point(data = limanov2, aes(x = LONGITUD , y = LATITUD, color = TOTALES,
size = TOTALES)) +
scale_color_gradient(low = "yellow", high = "red")
My data:
structure(list(DISTRITO = c("SAN JUAN DE LURIGANCHO", "CALLAO",
"LOS OLIVOS", "ATE VITARTE", "LIMA CERCADO", "SAN MARTÍN", "SANTIAGO DE SURCO",
"CHORILLOS", "COMAS", "INDEPENDENCIA", "EL AGUSTINO", "LA VICTORIA",
"SAN JUAN DE MIRAFLORES", "VILLA EL SALVADOR", "S. MIGUEL", "CARABAYLLO",
"MIRAFLORES", "PTE. PIEDRA", "SAN BORJA", "VENTANILLA", "SURQUILLO",
"BREÑA", "ANCÓN", "EL RIMAC", "BARRANCO", "LA MOLINA", "SAN LUIS",
"STA. ANITA", "LURIGANCHO", "P. LIBRE", "MAGDALENA", "LA PERLA",
"CHACLACAYO", "SAN ISIDRO", "J. MARÍA", "BELLAVISTA", "LINCE",
"C. DE LA LEGUA", "CIENEGUILLA", "STA.ROSA", "LURÍN", "PTA.NEGRA",
"PUCUSANA", "LA PUNTA", "PTA. HERMOSA", "PACHACAMAC", "SAN BARTOLO",
"SANTA MARÍA"), TOTALES = c(861L, 696L, 696L, 642L, 516L, 479L,
442L, 378L, 371L, 368L, 361L, 333L, 325L, 291L, 282L, 251L, 239L,
223L, 196L, 193L, 188L, 185L, 174L, 161L, 138L, 134L, 128L, 119L,
115L, 105L, 67L, 65L, 63L, 58L, 56L, 45L, 38L, 23L, 23L, 11L,
8L, 6L, 5L, 3L, 3L, 2L, 0L, 0L), HOMICIDIOS = c(1L, 7L, 0L, 1L,
2L, 0L, 0L, 1L, 7L, 4L, 4L, 4L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 7L,
0L, 0L, 0L, 4L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), LESIONES = c(100L,
72L, 61L, 43L, 44L, 8L, 10L, 15L, 44L, 40L, 50L, 15L, 52L, 28L,
7L, 33L, 15L, 27L, 3L, 21L, 7L, 36L, 33L, 19L, 14L, 1L, 8L, 6L,
16L, 4L, 4L, 9L, 1L, 2L, 9L, 5L, 2L, 5L, 7L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L), VIO..DE.LA.LIBERTAD.PERSONAL = c(0L, 7L,
6L, 5L, 6L, 1L, 1L, 0L, 3L, 1L, 2L, 0L, 2L, 0L, 1L, 0L, 1L, 1L,
0L, 1L, 1L, 0L, 3L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), VIO..DE.LA.LIBERTAD.SEXUAL = c(56L,
14L, 12L, 15L, 7L, 10L, 2L, 9L, 11L, 13L, 8L, 9L, 7L, 14L, 4L,
15L, 4L, 12L, 2L, 17L, 7L, 3L, 4L, 12L, 2L, 1L, 5L, 3L, 11L,
4L, 1L, 2L, 0L, 2L, 0L, 3L, 0L, 2L, 2L, 0L, 4L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), HURTO.SIMPLE.Y.AGRAVADO = c(217L, 203L, 296L, 230L,
260L, 167L, 226L, 217L, 130L, 117L, 154L, 133L, 121L, 46L, 163L,
72L, 161L, 84L, 119L, 69L, 120L, 64L, 19L, 21L, 57L, 44L, 39L,
2L, 48L, 60L, 30L, 19L, 48L, 41L, 25L, 19L, 27L, 7L, 11L, 9L,
0L, 6L, 0L, 2L, 3L, 1L, 0L, 0L), ROBO.SIMPLE.Y.AGRAVADO = c(460L,
289L, 308L, 344L, 186L, 277L, 198L, 130L, 165L, 184L, 137L, 149L,
134L, 188L, 104L, 126L, 58L, 96L, 72L, 64L, 51L, 77L, 115L, 76L,
64L, 88L, 73L, 108L, 40L, 36L, 30L, 32L, 14L, 12L, 22L, 12L,
8L, 6L, 3L, 1L, 3L, 0L, 2L, 1L, 0L, 1L, 0L, 0L), MICRO.COM.DE.DROGAS = c(26L,
100L, 13L, 3L, 10L, 15L, 5L, 5L, 11L, 8L, 3L, 23L, 9L, 15L, 3L,
3L, 0L, 2L, 0L, 8L, 2L, 5L, 0L, 28L, 0L, 0L, 1L, 0L, 0L, 0L,
2L, 2L, 0L, 0L, 0L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L,
0L, 0L), TENENCIA.ILEGAL.DE.ARMAS = c(1L, 4L, 0L, 1L, 1L, 1L,
0L, 1L, 0L, 1L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 6L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), LONGITUD = c(-77, -77.12,
-77.08, -76.89, -77.04, -77.09, -76.99, -77.01, -77.05, -77.05,
-77, -77.02, -76.97, -76.94, -77.09, -76.99, -77.03, -77.08,
-77, -77.13, -77.01, -77.05, -77.11, -76.7, -77.02, -76.92, -77,
-76.96, -76.86, -77.06, -77.07, -77.12, -76.76, -77.03, -77.05,
-77.11, -77.04, -77.09, -76.78, -77.16, -76.81, -76.73, -76.77,
-77.16, -76.76, -76.83, -76.73, -76.77), LATITUD = c(-11.99,
-12.04, -11.97, -12.04, -12.06, -12, -12.16, -12.2, -11.93, -11.99,
-12.04, -12.08, -12.16, -12.23, -12.08, -11.79, -12.12, -11.88,
-12.1, -11.89, -12.11, -12.06, -11.69, -11.94, -12.15, -12.09,
-12.08, -12.04, -11.98, -12.08, -12.09, -12.07, -11.99, -12.1,
-12.08, -12.06, -12.09, -12.04, -12.07, -11.81, -12.24, -12.32,
-12.47, -12.07, -12.28, -12.18, -12.38, -12.42)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -48L), .Names = c("DISTRITO",
"TOTALES", "HOMICIDIOS", "LESIONES", "VIO..DE.LA.LIBERTAD.PERSONAL",
"VIO..DE.LA.LIBERTAD.SEXUAL", "HURTO.SIMPLE.Y.AGRAVADO", "ROBO.SIMPLE.Y.AGRAVADO",
"MICRO.COM.DE.DROGAS", "TENENCIA.ILEGAL.DE.ARMAS", "LONGITUD",
"LATITUD"))

You have values outside of the base map zoom range... try changing your zoom parameter.
library(maps)
library(ggmap)
lima <- get_map(location = "lima", zoom = 10)
ggmap(lima) +
geom_point(data = limanov2,
aes(x = LONGITUD , y = LATITUD,
color = TOTALES, size = TOTALES)) +
scale_color_gradient(low = "yellow", high = "red")

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