[R] LMER - generate data and define model (2 fixed effects and 1 random effect or 1 fixed effect and 2 random effects)

[Thierry Onkelinx]

Dear Rosa,

1) use cut() to convert a continuous variable into a factor. See ?cut for
the details.
2) The syntax for factors is the same as for continuous variables. Just add
the name of the factor variable to the formula
fAge <- cut(age)
yy~1+fAge+time+(time|id.pat)
3) Add + (1|fAge) to the formula. Note that adding fAge to both the fixed
and the random effect doesn't make sense.
yy~1+time+(time|id.pat) + (1|fAge)

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2015-08-11 1:44 GMT+02:00 Rosa Oliveira <rosita21 op gmail.com>:

>
>
>
> ###############################################################################
>
> # Clear memory and set the working directory and the seed
>
>
> ###############################################################################
>
> rm(list = ls())
>
> setwd("/Dropbox/LMER/R ")
>
> set.seed(7010)
>
>
> ###############################################################################
>
> # Load up needed packages and do a require
>
>
> ###############################################################################
>
> # install.packages("gdata")
>
>
>
> library(nlme)
>
> library(lme4)
>
>
>
> nsample                                       = 1000                    #
> Number of subjects
>
> n.longitudinal.observations  = 5                                  # number
> of observations per subject
>
>
>
>
> ###############################################################################
>
> # Set the other parameters
>
>
> ###############################################################################
>
> id.pat                       = rep(seq(1:nsample), each
> =n.longitudinal.observations)
>
> time                         = rep(seq(1:n.longitudinal.observations)-1,
> nsample)
>
> age                          = rnorm(nsample, mean = 36, sd = .8)
> id.age                      = rep(seq(1: n.longitudinal.observations),
> each =age)
>
>
>
>
>
> ###############################################################################
> MODEL WITHOUT AGE
>
> boldBeta_individual_blup = coef(lmer(yy~1+time+(time|id.pat)   ))$id.pat
>  #mixed model
>
>
>
>
>
> ###############################################################################
> MODEL WITH AGE
>
> boldBeta_individual_blup = coef(lmer(yy~1+age+time+(time|id.pat)
>  ))$id.pat   #mixed model
>
>
>
> Dear all,
>
>
>
>
>
> I’m trying to use LMER in my simulation problem, and I’m having problems
> ate the very begging L I’m new in LMER. Can you please help me?
>
>
>
>
>
> 1st problem:
>
> how do I generate age so I can use it as a fixed factor?
>
> 2nd problem:
>
> how do I insert age as a fixed factor?
>
> 3rd problem:
>
>  what if I wanted to insert a 2nd random effect based on age?
>
>
>
> Best,
>
> RO
>
>
>
> Atenciosamente,
> Rosa Oliveira
>
> --
>
> ____________________________________________________________________________
>
>
> Rosa Celeste dos Santos Oliveira,
>
> E-mail: rosita21 op gmail.com
> Tlm: +351 939355143
> Linkedin: https://pt.linkedin.com/in/rosacsoliveira
>
> ____________________________________________________________________________
> "Many admire, few know"
> Hippocrates
>
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