My name is Giovanna and I am a PhD student in Norway.
I am a beginner with statistics and R, hence my ignorance. Apologies from
now.....
I have been collecting data on time performances of 5 subjects using a 1:3
scale tower yarder. The task was consisting in yarding 5 small logs placed on
permanently marked course. Four subjects had different previous experiences
(None, Some) and the fifth was a trainer (Control).
Each cycle time per each log was registered, the sum of the 5 logs' cycle time
was giving the replication time. We had 6 replication per subject .
I would like to predict the time necessary to perform the task.
I have been modelling the time to perform the task (prod.time)versus the
replication number (Trial-in the dataset), the previous experience (factor) and
their interaction. As random effect I have been using the subjects.
> ma<-lme(prod.time~Trial+Previous.experience+Trial*Previous.experience, data=
> Data27_04, random=~1|Student, method="ML")
> summary(ma)
Linear mixed-effects model fit by maximum likelihood
Data: Data27_04
AIC BIC logLik
1517.445 1541.259 -750.7226
Random effects:
Formula: ~1 | Student
(Intercept) Residual
StdDev: 7.337648 42.42332
Fixed effects: prod.time ~ Trial + Previous.experience + Trial *
Previous.experience
Value Std.Error DF t-value p-value
(Intercept) 102.44173 9.561987 137 10.713435 0.0000
Trial -6.48494 2.252271 137 -2.879291 0.0046
Previous.experience1 -37.36173 14.786033 2 -2.526826 0.1274
Previous.experience2 47.22627 12.451072 2 3.792948 0.0630
Trial:Previous.experience1 6.55351 3.496401 137 1.874360 0.0630
Trial:Previous.experience2 -7.55163 2.940879 137 -2.567813 0.0113
Correlation:
(Intr) Trial Prvs.1 Prvs.2 Tr:P.1
Trial -0.841
Previous.experience1 0.253 -0.208
Previous.experience2 -0.234 0.199 -0.540
Trial:Previous.experience1 -0.207 0.264 -0.835 0.447
Trial:Previous.experience2 0.199 -0.226 0.447 -0.836 -0.550
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.3519731 -0.6903211 -0.1031114 0.6503216 4.6699702
Number of Observations: 145
Number of Groups: 5
>
Do you think this is good enough to demonstrate a learning effect.
Learning curves are exponential. I have been trying to log transform the
response variable but then p-values are saying that previous experience has no
significance.
> mb<-lme(log.prodtime~Trial+Previous.experience+Trial*Previous.experience,
> data= Data27_04, random=~1|Student, method="ML")
> summary(mb)
Linear mixed-effects model fit by maximum likelihood
Data: Data27_04
AIC BIC logLik
225.1042 248.9181 -104.5521
Random effects:
Formula: ~1 | Student
(Intercept) Residual
StdDev: 0.04484554 0.495812
Fixed effects: log.prodtime ~ Trial + Previous.experience + Trial *
Previous.experience
Value Std.Error DF t-value p-value
(Intercept) 4.448206 0.10593072 137 41.99165 0.0000
Trial -0.060150 0.02629765 137 -2.28726 0.0237
Previous.experience1 -0.333664 0.16351518 2 -2.04057 0.1781
Previous.experience2 0.368358 0.13776525 2 2.67381 0.1160
Trial:Previous.experience1 0.051714 0.04084708 137 1.26604 0.2076
Trial:Previous.experience2 -0.043036 0.03435150 137 -1.25282 0.2124
Correlation:
(Intr) Trial Prvs.1 Prvs.2 Tr:P.1
Trial -0.886
Previous.experience1 0.248 -0.221
Previous.experience2 -0.237 0.209 -0.535
Trial:Previous.experience1 -0.220 0.266 -0.881 0.473
Trial:Previous.experience2 0.208 -0.225 0.474 -0.883 -0.551
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.7119095 -0.8005032 0.1127388 0.8621127 2.1988560
Number of Observations: 145
Number of Groups: 5
>
The model is surely better (AIC, BIC) also the residuals are looking better but
then should I reduce the model leaving only the Trial number?
How would you present the results in a clear way? I am still struggling to
figure it out. The concept of mixed models is clear in my head but it is hard
to present it.
How should I then plot the learning curve?
I have been plotting the data I have adding a smooth line. Is this good enough?
Looking forward for your response
Best regards
Giovanna
Giovanna Ottaviani Aalmo
Stipendiat/Ph..D. Student
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