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I have the following model, how do I add a random intercept(no random effects included yet):

  modelid3 <- nlme(y ~ TPMRM0(M, v0, v1, v2, v3, v4, v5, v6, v7, v8, b, b, b, b, b, b, b, b),
             fixed  =list(v0 + v1 + v2 + v3 + v4 + v5 + v6 + v7 + v8 ~ 1,
                          b ~ act + 0),
             random =  -----, 
             groups = ~ id,
             data   = dat,
             weights     = varIdent(form = ~ 1 | visit),
             correlation = corSymm(value = covmmrmvec, form = ~ as.numeric(visit) | id),
             start  = init, 
             method = "ML",
             control=nlmeControl(maxIter=1000, msMaxIter = 1000, returnObject = TRUE),
             verbose = TRUE)

given that:

TPMRM0 <- function(t, v0, v1, v2, v3, v4, v5, v6, v7, v8, b1, b2, b3, b4, b5, b6, b7, b8) {
months <- seq(0,24,3)
b <- cbind(0, b1, b2, b3, b4, b5, b6, b7, b8) 
  t_out <- (1 - b[cbind(1:length(t), match(t, months))]) * t
  
  spline(x = months,
         y = c(v0[1], v1[1], v2[1], v3[1], v4[1], v5[1], v6[1], v7[1], v8[1]),
         method = 'natural',
         xout = t_out)$y
}

Tried random = 1 ~id. Still getting the same error. Here is some of the data if that helps:

id visit  M        y trt act mod_trt act_vis
1  1     1  0 109.3006 pbo   0     pbo   pbo.0
2  1     2  3 111.0854 pbo   0     pbo   pbo.3
3  1     3  6 102.1718 pbo   0     pbo   pbo.6
4  1     4  9 106.1363 pbo   0     pbo   pbo.9
.
.
.
8997 1000     6 15 92.61685 act   1     act  act.15
8998 1000     7 18 86.69760 act   1     act  act.18
8999 1000     8 21 87.87717 act   1     act  act.21
9000 1000     9 24 94.08110 act   1     act  act.24

Would this give the wanted random intercept, without interfering with the rest of the model?

  nlme(y ~ TPMRM0(M, v0, v1, v2, v3, v4, v5, v6, v7, v8, b, b, b, b, b, b, b, b) + b0,
             fixed  =list(v0 + v1 + v2 + v3 + v4 + v5 + v6 + v7 + v8 ~ 1,
                          b ~ act + 0),
             random =  list(b0 ~ 1), 
             groups = ~ id,
             data   = dat,
             weights     = varIdent(form = ~ 1 | visit),
             correlation = corSymm(value = covmmrmvec, form = ~ as.numeric(visit) | id),
             start  = init, 
             method = "ML",
             control=nlmeControl(maxIter=1000, msMaxIter = 1000, returnObject = TRUE),
             verbose = TRUE)
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1 Answer 1

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That's a very interesting model ! I'm curious about what kind of data you are dealing with, and your research questions?

Anyway in nlme you can usually just specify random interecepts for your grouping variable id:

random = ~ 1 | id
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  • 1
    $\begingroup$ Currently working on my masters thesis, looking into some of the work described in onlinelibrary.wiley.com/doi/full/10.1002/sim.9581 . That was also my initial guess, however I get the error: Error in nlme.formula(y ~ TPMRM0(M, v0, v1, v2, v3, v4, v5, v6, v7, v8, : 'random' must be a formula or list of formulae $\endgroup$
    – Sandra Sørensen
    Commented Feb 19 at 13:54
  • 1
    $\begingroup$ Very interesting. I will take a look at that paper when I get chance, In the meantime, try this instead: random = list(~1 | id), Without access to your data it is a bit hard to help, but I hope we can get there in the end ! On another note, you might want to consider using the mmrm package which is specificallyt designed for Mixed Models with Repeated Measures $\endgroup$
    – Robert Long
    Commented Feb 19 at 21:28
  • $\begingroup$ Answered below to give you a snippet of the data $\endgroup$
    – Sandra Sørensen
    Commented Feb 21 at 8:21
  • $\begingroup$ "Usually random effects and correlated errors are mutually exclusive." See my answer at stackoverflow.com/questions/36643713 on lme. The compound-symmetry correlation structure already specifies certain individual-specific patterns. $\endgroup$
    – DrJerryTAO
    Commented Feb 21 at 11:20

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