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considering that I have a very small sample and that my residuals are non-normally distributed, I've decided to perform a lmer() with bootstrapping. This is my very first time doing this, this package lmeresampler seems to be the easiest one to do this, but I didn't find much on that, sonI have a few questions.

  • The data is:
CONT_Y = scores 
YEAR = A/B (2020 or 2021)
MY_GROUP = test1 (G1) or test2 (G2)

I have 21 participants, each participant took 2 tests (MY_GROUP) each year (YEAR) and I wanna see if there's an interaction between YEAR*MY_GROUP

  • The model is:
### my model lmer (which doens't have normally-distibuted residuals)

mod1 <- lmer(CONT_Y ~ YEAR * MY_GROUP + (1|PARTICIPANTS), data = data)

### check model: 

performance::check_model(mod1) 
ggResidpanel::resid_panel(mod1, smoother = TRUE, qqbands = TRUE, type = "pearson")
  • which gives me:

my model

  • My attempt to bootstrapp:
### Trying to bootstrap (following the package's vinagrete): 

library(lme4)
library(lmerTest)
library(lmeresampler) 

########################### bootstrap via lmersmapler: #######################################

mod1 <- lmer(CONT_Y ~ YEAR * MY_GROUP  + (1|PARTICIPANTS), data = data)
lmer_par_boot <- bootstrap(mod1, .f = fixef, type = "parametric", B = 100)

### output:

# summary(mod1) # here to compare with the original fit 

names(lmer_par_boot)
summary(lmer_par_boot)

# get confidence intervals:

confint(lmer_par_boot)
print(lmer_par_boot,  ci = TRUE)

### plot ## what does this plot even mean?...
plot(lmer_par_boot)

Questions:

  • 1 The package offers me different boot options such as "parametric", "residual", "case", "wild", or "reb" , I got a bit overwhelmed, so I stuck with the package's default "parametric". I've read a bit on the topic, but I still didn't get the difference between all types (as here or here (thanks, D). Is it ok to stick with "parametric" in my case?

  • 2 I don't get any p-values, is there a way to get them or should I divide the estimations by the standard errors in order to see if the t-statistic > 1.96, and then conclude that's significant?

  • 3 There are different options of confidence intervals, should I look at the "norm", "basic" or "perc" ?

  • 4 How many times should I bootstrapp the sample? The package's default is B= 100 , is that enough? I've seen a lot of interesting posts here concerning this, but I'm still confused

  • 5 I didn't see much on this package, but It seems to be a very simple way to bootstrap (specially if we consider that I'm a beginner), I've seen more on lme4::bootMer() and robustlmm::rlmer() (cf. notes below, please). Are they equivalent?

Notes:

  • I've also used lme4 's bootMer(), but it seemed to be harder, with more steps:

### I got this function from the bootMer's help:

if (interactive()) {
  fm01ML <- lmer(Score ~ Year * Test + (1|ID), data = data, REML = F)
  ## see ?"profile-methods"
  mySumm <- function(.) { s <- sigma(.)
  c(beta =getME(., "beta"), sigma = s, sig01 = unname(s * getME(., "theta"))) }
  (t0 <- mySumm(fm01ML)) # just three parameters
  ## alternatively:
  mySumm2 <- function(.) {
    c(beta=fixef(.),sigma=sigma(.), sig01=sqrt(unlist(VarCorr(.))))
  }
}

### now, finally, one can run the bootstrapped model:

mod1bot <- bootMer(mod1, mySumm, nsim = 100, seed = NULL, use.u = FALSE, re.form=NA,
               type = "parametric")

summary(mod1bot)

### yey, same coeficients (maybe I'm doing the right thing)
### b0 = 17.61, b1 = 1.14, b2 = 0.91, b3 (int) = - 0.60

### confidence intervals:
confint(mod1bot)
### plot: ## I also didn't understand this plot
plot(mod1bot,index=3)
data <- structure(list(PARTICIPANTS = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
                                        3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 
                                        7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 
                                        10L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 
                                        14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 17L, 
                                        17L, 17L, 17L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 20L, 20L, 
                                        20L, 20L, 21L, 21L, 21L, 21L), CONT_Y = c(19.44, 20.07, 19.21, 
                                                                                  16.35, 11.37, 12.82, 19.42, 18.94, 19.59, 20.01, 19.7, 17.92, 
                                                                                  18.78, 19.21, 19.27, 18.46, 19.52, 20.02, 16.19, 19.97, 13.83, 
                                                                                  15.93, 14.79, 21.55, 18.8, 19.42, 19.27, 19.37, 17.14, 14.45, 
                                                                                  17.63, 20.01, 20.28, 17.93, 19.36, 20.15, 16.06, 17.04, 19.16, 
                                                                                  20.1, 16.44, 18.39, 18.01, 19.05, 18.04, 19.69, 19.61, 16.88, 
                                                                                  19.02, 20.42, 18.27, 18.43, 18.08, 17.1, 19.98, 19.43, 19.71, 
                                                                                  19.93, 20.11, 18.41, 20.31, 20.1, 20.38, 20.29, 13.6, 18.92, 
                                                                                  19.05, 19.13, 17.75, 19.15, 20.19, 18.3, 19.43, 19.8, 19.83, 
                                                                                  19.53, 16.14, 21.14, 17.37, 18.73, 16.51, 17.51, 17.06, 19.42
                                        ), CATEGORIES = structure(c(1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
                                                                    1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
                                                                    1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
                                                                    1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
                                                                    1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
                                                                    1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L), .Label = c("A", 
                                                                                                                            "B"), class = "factor"), MY_GROUP = structure(c(1L, 2L, 1L, 2L, 
                                                                                                                                                                            1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
                                                                                                                                                                            1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
                                                                                                                                                                            1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
                                                                                                                                                                            1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
                                                                                                                                                                            1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L
                                                                                                                            ), .Label = c("G1", "G2"), class = "factor")), row.names = c(NA, 
                                                                                                                                                                                         -84L), class = c("tbl_df", "tbl", "data.frame"))
 


### rename collumn:

data <- data %>%  rename(., YEAR = CATEGORIES)
  • I recognize that maybe this in not THE best solution to the model's problems, but I'm a beginner, I can't go much further right now (such as fitting a non-linear model, for example), but I believe that bootstrapping would be a first solution, right? Thanks in advance.

  • EDIT

While discussing the boot's type options in the comments, I've came across an interesting finding. So, before fitting the model, I've ran a couple of t-tests/Wilcoxon on this data, and the results only match if I choose the option "case" (which seems to fit the plot as well?):

  • t-tests on GROUP:

1 Paired Y Score 2020 ~ Group => significant, G2 > G1
2 Paired Y Score 2021 ~ Group => ns, (G2 sightly bigger, but ns)

  • t-tests on Year:

5 Paired Y G1 Score ~ YEAR => significant, 21 > 20
6 Paired Y G2 Score ~ YEAR => ns, 21 (sightly bigger, but ns)

As its in the discussion below, case seems to be the only option by which there's a Group effect in relation to the reference level/intercept (G1, 20) (in consonanse with test 1), isn't this interesting?

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1 Answer 1

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You can certainly use bootstrapping with the (new to me) lmeresampler package but, as you point out, there are only 21 students/participants. You should expect lots of uncertainty, esp. about the interaction.

It's often helpful to start by visualizing the data.

First let's look at the main effects. The plot below tells me — without any models — that:

  • There seems to be an YEAR effect: most segments have positive slope. This is of course not surprising: we expect students know more with every year they spend at school.
  • The might be a GROUP effect. Hard to tell from the plot actually.
  • The variance is higher during the first than the second year. This is not the question you are asking but it's an interesting observation. There can be different definitions of "quality of education/teaching" and one might be related to "consistency of student attainment".

And here is a plot of the interaction between YEAR and GROUP. An interaction means that the blue and red line cross. As you can see, there is lots of variability and the lines are (close to) parallel for quite a few of the students.

This plot should prepare you for the outcome that no matter how you do the bootstrapping and really, no matter what model you choose to fit, the interaction term is (likely) not going to be significant. This is not surprising: Interaction require bigger samples to estimate precisely. See You need 16 times the sample size to estimate an interaction than to estimate a main effect.

I specify B = 5,000 boostrap resamples. This dataset is small and my laptop can handle it (in about 1 minute). This is probably too many replicates. With large B we can only decrease one source of error: the sampling error due to the randomness in the bootstrapping. We can't reduce the error due to having data on only 21 students.

B <- 5000

tic()
mod1_boot <- bootstrap(mod1, .f = fixef, type = "residual", B = B)
toc()
#> 51.31 sec elapsed

I choose the "normal" confidence interval because the "basic" and "percentile" confidence intervals have worse statistical properties. See Is it true that the percentile bootstrap should never be used?

confint(mod1_boot, type = "norm")
#> # A tibble: 4 × 6
#>   term         estimate    lower  upper type  level
#>   <chr>           <dbl>    <dbl>  <dbl> <chr> <dbl>
#> 1 (Intercept)    17.6   16.9     18.3   norm   0.95
#> 2 YEAR2           1.14   0.181    2.11  norm   0.95
#> 3 GROUPB          0.915 -0.00817  1.86  norm   0.95
#> 4 YEAR2:GROUPB   -0.602 -1.94     0.718 norm   0.95

We get a confidence interval with the bootstrap procedure, not a p-value, but obviously, the confidence interval either contains 0 or it doesn't.

It might be instructive to try out different types of bootstrapping methods (I tried the "parametric" and "residual" bootstraps.) but not particularly interesting: the confidence interval for the interaction term is wide and as expected we can't draw much of a conclusion about the strength of the interaction based on this small dataset.


Update: The biggest difference is between type="case" and the other types of bootstrap. With "case", the procedure samples students but not the four observations by the same student. This actually might be the most appropriate procedure given the structure of your data.

tic()
mod1_boot <- bootstrap(mod1, .f = fixef, type = "case", B = B,
                       resample = c(TRUE, FALSE))
toc()
#> 86.396 sec elapsed

confint(mod1_boot, type = "norm")
#> # A tibble: 4 × 6
#>   term         estimate  lower  upper type  level
#>   <chr>           <dbl>  <dbl>  <dbl> <chr> <dbl>
#> 1 (Intercept)    17.6   16.7   18.5   norm   0.95
#> 2 YEAR2           1.14   0.184  2.11  norm   0.95
#> 3 GROUPB          0.915  0.133  1.69  norm   0.95
#> 4 YEAR2:GROUPB   -0.602 -1.78   0.598 norm   0.95

R code to reproduce the figures and the analysis.

library("tictoc")
library("broom.mixed")
library("lme4")
library("lmeresampler")
library("tidyverse")

data <- data %>%
  rename(
    SCORE = CONT_Y,
    YEAR = CATEGORIES,
    GROUP = MY_GROUP,
    PARTICIPANT = PARTICIPANTS
  ) %>%
  mutate(
    GROUP = recode(GROUP,
                   "G1" = "GroupA",
                   "G2" = "GroupB"
    ),
    YEAR = recode(YEAR,
                  "A" = "Year1",
                  "B" = "Year2"
    )
  )

data %>%
  ggplot(
    aes(
      YEAR, SCORE,
      group = PARTICIPANT,
      color = GROUP
    )
  ) +
  geom_line() +
  facet_grid(
    ~GROUP
  ) +
  theme(
    axis.title.x = element_blank(),
    legend.position = "none"
  )

data %>%
  ggplot(
    aes(
      YEAR, SCORE,
      group = GROUP,
      color = GROUP
    )
  ) +
  geom_line() +
  facet_wrap(
    ~PARTICIPANT,
    ncol = 7
  ) +
  theme(
    axis.title.x = element_blank(),
    legend.position = "none"
  )

mod1 <- lmer(
  SCORE ~ YEAR * GROUP + (1 | PARTICIPANT),
  data = data,
  REML = FALSE
)

B <- 5000

tic()
mod1_boot <- bootstrap(mod1, .f = fixef, type = "residual", B = B)
toc()

confint(mod1_boot, type = "norm")

tic()
mod1_boot <- bootstrap(mod1,
                       .f = fixef, type = "case", B = B,
                       resample = c(TRUE, FALSE)
)
toc()

confint(mod1_boot, type = "norm")
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  • 1
    $\begingroup$ You can't fit (YEAR * MY_GROUP |ID) because you have only 4 observations per ID. That's what the message "number of observations (=84) <= number of random effects (=84) " is telling you. You can try adding one or two random slopes. You may get a "boundary (singular) fit" error. None of this will change the fact that you can't estimate the interaction with any precision. So if the interaction is your goal you are wasting your time a bit. $\endgroup$
    – dipetkov
    Oct 3, 2022 at 1:17
  • 1
    $\begingroup$ Ah, I think I see what you mean: you look at the plot and see lots of variability. But you have to keep in mind: a) most of this variability is actually residual variability. (There is a lot of randomness in observation of people. Think about it this way: Do you expect to have seen close to the same scores if the tests were taken 1 day later?) b) your intuition about the random slopes is a bit wrong: what matters is how many observations you have per student. Actually, there are effectively two observations per student to estimate YEAR and GROUP random effect. That's hard. $\endgroup$
    – dipetkov
    Oct 3, 2022 at 1:58
  • 1
    $\begingroup$ More about b): The random effects are "student-specific differences" from the fixed model. How many observations do you have (per student) to estimate any student-specific differences? To estimate the random intercept (1|ID) there are 4 data points per student because all 4 scores by the same student share the same student-specific intercept. What about student-specific YEAR2 or student-specific GROUPB? $\endgroup$
    – dipetkov
    Oct 3, 2022 at 2:15
  • 1
    $\begingroup$ oh, I guess that now I got it what you've said about only participant having only 2 observations to estimate the random slopes (not 4). The student-specific difference would only be, then, GroupB 2021 - GroupB 2020 and GroupA 2021 - GroupA 2020 to estimate these random slopes, right? ps: thanks for the code! Now I've reproduced the other plot as well :) $\endgroup$ Oct 3, 2022 at 2:20
  • 1
    $\begingroup$ It seems to me that type="case" is more appropriate given the actual constraints of the experiment. The other bootstrap types add randomness within students too. Makes no difference for the interaction. It makes difference for the group main effect. If you are interested in the group main effect, then there is something to look into. $\endgroup$
    – dipetkov
    Oct 3, 2022 at 2:33

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