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

[Rosa Oliveira]

Dear Thierry,


even using the code:

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)
f.age                        = cut(age, breaks = 8)
id.age                      = rep(f.age, each =5)
visit                         = rnorm(nsample, mean = 2, sd = .03)
f.visit                       = cut(visit, breaks = 3)
id.visit                     = rep(f.visit, each =5)

1st lmer(yy~1+id.age+time+(time|id.pat))

2nd lmer(yy~1+time+(time|id.pat)+(1|id.visit))

the problem remains:

variable lengths differ     - for the first fixed effect in the model id.age in the first or time in the second



Best,
RO




Atenciosamente,
Rosa Oliveira

-- 
____________________________________________________________________________


Rosa Celeste dos Santos Oliveira, 

E-mail: rosita21 at gmail.com
Tlm: +351 939355143 
Linkedin: https://pt.linkedin.com/in/rosacsoliveira
____________________________________________________________________________
"Many admire, few know"
Hippocrates

> On 11 Aug 2015, at 09:00, Thierry Onkelinx <thierry.onkelinx at inbo.be> wrote:
> 
> 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 at gmail.com <mailto:rosita21 at 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 at gmail.com <mailto:rosita21 at gmail.com>
> Tlm: +351 939355143 <tel:%2B351%20939355143>
> Linkedin: https://pt.linkedin.com/in/rosacsoliveira <https://pt.linkedin.com/in/rosacsoliveira>
> ____________________________________________________________________________
> "Many admire, few know"
> Hippocrates
> 
> ______________________________________________
> R-help at r-project.org <mailto:R-help at r-project.org> mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help <https://stat.ethz.ch/mailman/listinfo/r-help>
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html <http://www.r-project.org/posting-guide.html>
> and provide commented, minimal, self-contained, reproducible code.
>