lme4 + lspline + marginaleffects: problems with CI?
I ran this code
library(lme4)
library(lspline)
library(marginaleffects)
m_weight <- lmer(delta_weight ~ lspline(MINUTES, knots = c(5))*period + ( 1 | ID) , data = dat )
summary(m_weight)
weight1 <- comparisons(
m_weight,
variables = "period",
include_random = FALSE,
newdata = datagrid(
MINUTES = seq(from = 0, to = 30, by = 5)
))
weight1
which gives this output:
Questions:
conf.low and high, as well predicted_hi and lo, don't give results compatible with "comparsion" or "predicted". Is there any issue here?
Also, which do you tend to report here? comparsion or predicted?
Lastly, is it possible to change the reference level to "pre" in "weight1"?
dat <- structure(list(delta_weight = c(0, -14.5, -0.5, -12.5, -7.5, -17.5,
-7.5, 0, -7.5, -17.5, -14.5, -12.5, -4.5, -12.5, 0, -6, -4, 2,
-5, -1, -3, 0, -15.5, -30.5, -33.5, -31.5, -30.5, -32.5, 0, -9,
-20, -15, -3, -8, -12, 0, -8, -11, -8, -6, -9, -5, 0, -8, -19,
-20, -19, -11, -7, 0, -24, -7, -22, -24, -19, -19, 0, -5.5, -9.5,
-9.5, -6.5, -4.5, -7.5, 0, -21.5, -26.5, -20.5, -26.5, -20.5,
-29.5, 0, -9, -5, -4, -6, -1, -3, 0, -13, -9, -10, -4, -5, -12,
0, -3.5, -17.5, -6.5, -13.5, -5.5, -18.5, 0, 0, 1, -1, -5, -3,
-12, 0, -5.5, 0.5, -6.5, -10.5, -1.5, -4.5, 0, -13.5, -10.5,
-13.5, -17.5, -9.5, -24.5, 0, -2, -5, -4, -9, -5, -9, 0, -16.5,
-20.5, -13.5, -10.5, -14.5, -16.5, 0, 5, 6, 2, -1, 8, 5, 0, 0.5,
-0.5, 0.5, 3.5, -3.5, 4.5, 0, -13, -22, -31, -33, -25, -24, 0,
-15, -32, -34, -26, -32, -39, 0, -14.5, -5.5, -15.5, -0.5, -6.5,
-10.5, 0, -17, -15, -20, -19, -9, -18, 0, 14.5, -2.5, -4.5, -4.5,
-0.5, -8.5, 0, -24.5, -10.5, -4.5, -22.5, -9.5, -9.5, 0, -0.5,
-3.5, -2.5, -0.5, 13.5, 1.5, 0, -4, -3, -3, -4, -9, -7, 0, -14.5,
-13.5, -15.5, -13.5, -13.5, -14.5, 0, -10, -4, -13, -8, -9, -7,
0, 8.5, -2.5, -13.5, -3.5, -4.5, 2.5, 0, -10, -15, -13, -12,
-14, -7, 0, -13.5, -8.5, -43.5, -14.5, -15.5, -12.5, 0, -19,
-9, -17, -20, -22, -12, 0, -4, -3, -3, -7, -4, -4, 0, -6, -15,
-6, -10, -14, -12, 0, -9, -15, -19, -13, -15, -14, 0, -24.5,
-22.5, -24.5, -19.5, -19.5, -24.5, 0, -4.5, -5.5, -5.5, -5.5,
-6.5, -3.5, 0, -8, -10, -9, -11, -3, -6, 0, -15, -21, -26, -15,
-15, -15, 0, 5, 1, -4, -4, 4, -1, 0, 1.5, 3.5, 5.5, 5.5, -8.5,
10.5, 0, -5, -6, -13, -14, -24, -10, 0, 5, -1, -6, -7, -3, -5,
0, -12, -22, -17, -15, -20, -23, 0, 0, -4, -6, -9, -9, -12, 0,
-19, -18, -22, -14, -21, -11), ID = c(10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,
15, 15, 15, 15, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
17, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 20, 20, 20, 20, 20,
20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21,
21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22,
22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 28,
28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 31, 31, 31, 31, 31, 31, 31,
31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 32, 32, 32,
32, 32, 32, 32, 32, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
34, 34, 34, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,
35, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 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, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9), MINUTES = c(0, 5, 10, 15, 20, 25, 30, 0, 5,
10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20,
25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0,
5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15,
20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30,
0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10,
15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25,
30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5,
10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20,
25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0,
5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15,
20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30,
0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10,
15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25,
30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5,
10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20,
25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0,
5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15,
20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30,
0, 5, 10, 15, 20, 25, 30, 0, 5, 10, 15, 20, 25, 30, 0, 5, 10,
15, 20, 25, 30), period = structure(c(2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = c("pre", "post"
), class = c("ordered", "factor"))), row.names = c(15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 57L,
58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L,
71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L,
103L, 104L, 105L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L,
121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L,
132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L,
143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L,
154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L,
165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 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, 208L,
209L, 210L, 211L, 212L, 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,
260L, 261L, 262L, 263L, 264L, 265L, 266L, 267L, 268L, 269L, 270L,
271L, 272L, 273L, 274L, 275L, 276L, 277L, 278L, 279L, 280L, 281L,
282L, 283L, 284L, 285L, 286L, 287L, 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, 348L, 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, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L,
388L, 389L, 390L, 391L, 392L, 400L, 401L, 402L, 403L, 404L, 405L,
406L, 407L, 408L, 409L, 410L, 411L, 412L, 413L), class = "data.frame")
Solution
The CRAN version of marginaleffects does not support lspline. Thankfully, recent improvements in the insight package (an upstream dependency) should now allow you should to do this.
First, install the development version of both packages (will not be necessary next month, after CRAN release):
library(remotes)
install_github("easystats/insight")
install_github("vincentarelbundock/marginaleffects")
RESTART R COMPLETELY to make sure the change takes effect.
Then, you can use the variables argument: the order in which you enter values determines the reference level.
library(lme4)
#> Loading required package: Matrix
library(lspline)
library(marginaleffects)
m_weight <- lmer(
delta_weight ~ lspline(MINUTES, knots = c(5)) * period + (1 | ID),
data = dat)
weight1 <- comparisons(
m_weight,
variables = list("period" = c("post", "pre")),
include_random = FALSE,
newdata = datagrid(MINUTES = seq(from = 0, to = 30, by = 5)))
weight1
#> rowid type term contrast estimate std.error statistic
#> 1 1 response period pre - post 1.550531e-15 1.9043454 8.142070e-16
#> 2 2 response period pre - post -5.480159e+00 1.3782640 -3.976131e+00
#> 3 3 response period pre - post -5.632540e+00 1.0347416 -5.443426e+00
#> 4 4 response period pre - post -5.784921e+00 0.8100799 -7.141173e+00
#> 5 5 response period pre - post -5.937302e+00 0.8100799 -7.329279e+00
#> 6 6 response period pre - post -6.089683e+00 1.0347416 -5.885221e+00
#> 7 7 response period pre - post -6.242063e+00 1.3782640 -4.528932e+00
#> p.value conf.low conf.high predicted predicted_hi predicted_lo
#> 1 1.000000e+00 -3.732448 3.732448 -1.904410e-14 -1.749357e-14 -1.904410e-14
#> 2 7.004543e-05 -8.181507 -2.778811 -7.045635e+00 -1.252579e+01 -7.045635e+00
#> 3 5.226536e-08 -7.660596 -3.604483 -7.263492e+00 -1.289603e+01 -7.263492e+00
#> 4 9.253795e-13 -7.372648 -4.197193 -7.481349e+00 -1.326627e+01 -7.481349e+00
#> 5 2.313945e-13 -7.525029 -4.349574 -7.699206e+00 -1.363651e+01 -7.699206e+00
#> 6 3.975236e-09 -8.117739 -4.061626 -7.917063e+00 -1.400675e+01 -7.917063e+00
#> 7 5.928265e-06 -8.943411 -3.540716 -8.134921e+00 -1.437698e+01 -8.134921e+00
#> delta_weight period ID MINUTES
#> 1 -9.017857 post 10 0
#> 2 -9.017857 post 10 5
#> 3 -9.017857 post 10 10
#> 4 -9.017857 post 10 15
#> 5 -9.017857 post 10 20
#> 6 -9.017857 post 10 25
#> 7 -9.017857 post 10 30
The confidence intervals seem fine:
all(weight1$estimate > weight1$conf.low)
#> [1] TRUE
all(weight1$estimate < weight1$conf.high)
#> [1] TRUE
Whether you want to report predicted or comparison depends on your scientific/analytic interest; it is not a question for StackOverflow. Please read the vignettes on the website to learn the difference about those two quantities: https://vincentarelbundock.github.io/marginaleffects/
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