my data is nested on a group level. There are five different treatments. Within every treatment, four participants are grouped. It is about donation behavior of those participants (dependent variable = Donation, metric, in €) under competition ( Explanatory variable = Treatment, ordinal). The data structure is like this:
Treatment Session player.cumulative_donation:
CG uk4rlbdo 2.5
CG uk4rlbdo 1.4
CG uk4rlbdo 0
CG uk4rlbdo 1
CG dg0bqvit 0
CG dg0bqvit 0
CG dg0bqvit 0.5
CG dg0bqvit 0
TG1 g6n3z46r 1
TG1 g6n3z46r 0
TG1 g6n3z46r 0
TG1 g6n3z46r 0.2
After computing the ANOVA based on Rcompanion, I want to perform a Posthoc test using the multcomp function.
however, if I run
library(multcomp)
posthoc = glht(model,
linfct = mcp(Treatment="Tukey"))
I get this error message which I don't understand
Error in model.frame.lme(object) : object does not contain any data
Error in factor_contrasts(model) :
no ‘model.matrix’ method for ‘model’ found!
There is data stored in model:
> model
Linear mixed-effects model fit by REML
Data: NULL
Log-restricted-likelihood: -166.8703
Fixed: Donation ~ Treatment
(Intercept) TreatmentTG1 TreatmentTG2 TreatmentTG3 TreatmentTG4
0.7492227 1.3343727 0.2981268 1.4943010 0.5274175
Random effects:
Formula: ~1 | Session
(Intercept) Residual
StdDev: 0.1759392 1.651152
Number of Observations: 88
Number of Groups: 27
The variables are:
$ player.cumulative_donation: num 2.5 1.4 0 1 0 0 0.5 0 1 0 ...
$ player.treatmentgroup : chr "CG" "CG" "CG" "CG" ...
$ Session code : chr "uk4rlbdo" "uk4rlbdo" "uk4rlbdo" "uk4rlbdo" ...
EDIT: The R command to create model:
library(nlme)
model = lme(Donation ~ Treatment, random=~1|Session,
method="REML")
anova.lme(model,
type="sequential",
adjustSigma = FALSE)
Output of dput(head(SPSS_Data.df,10)):
structure(list(Participant_id = c(1, 2, 3, 4, 5, 6, 7, 8, 9,
10), participant.id_in_session = c(1, 2, 3, 4, 1, 2, 3, 1, 2,
3), participant.code = c("hcj5o43a", "ugiv2jlq", "53vepzb7",
"j2k7noqy", "njm1sr5d", "c2phh8p1", "5xaot5ii", "lvfkfw72", "05pjmgwp",
"o0yt5qbt"), `Session code` = c("uk4rlbdo", "uk4rlbdo", "uk4rlbdo",
"uk4rlbdo", "dg0bqvit", "dg0bqvit", "dg0bqvit", "8stn6uxo", "8stn6uxo",
"8stn6uxo"), player.cumulative_donation = c(2.5, 1.4, 0, 1, 0,
0, 0.5, 0, 1, 0), player.treatmentgroup = c("CG", "CG", "CG",
"CG", "CG", "CG", "CG", "CG", "CG", "CG"), TG_coded = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0), CG_Dummy = c(1, 1, 1, 1, 1, 1, 1, 1,
1, 1), TG1_Dummy = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), TG2_Dummy = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), TG3_Dummy = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0), TG4_Dummy = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Gruppe = c(1,
1, 1, 1, 2, 2, 2, 3, 3, 3), `Perceived Competition` = c(2, 1,
1, 1, 3, 1, 2, 2, 1, 1), `Influenced behavior` = c(2, 2, 1, 1,
3, 1, 4, 1, 1, 1), `Donate more` = c(3, 3, 1, 1, 1, 3, 2, 1,
1, 1), `Donate less` = c(3, 3, 1, 1, 3, 3, 3, 1, 1, 1)), row.names = c(NA,
10L), class = "data.frame")
The regression works if the data is a variable in the environment, but for downstream analysis, they required it to be stored as a data.frame inside the lme object:
For example, this works perfectly
library(nlme)
library(multcomp)
SPSS_Data.df = data.frame(
"player.treatmentgroup"=sample(c("TG1","TG2","TG3"),100,replace=TRUE),
"player.cumulative_donation"=rnorm(100),
"Session code" = sample(c("uk4rlbdo","dg0bqvit"),100,replace=TRUE),
check.names=FALSE)
df = setNames(SPSS_Data.df[,c("player.cumulative_donation",
"player.treatmentgroup","Session code")],
c("Donation","Treatment","Session")
)
model = lme(Donation ~ Treatment, random=~1|Session,data=df)
glht(model,linfct=mcp(Treatment="Tukey"))
Whereas when you put the variable into the environment, i get the same error:
Donation = df$Donation
Treatment = df$Treatment
Session =df$Session
model = lme(Donation ~ Treatment, random=~1|Session)
glht(model,linfct=mcp(Treatment="Tukey"))
Error in model.frame.lme(object) : object does not contain any data
Error in factor_contrasts(model) :
no ‘model.matrix’ method for ‘model’ found!