I am new to mixed-effects modeling, so this is probably an embarrassingly
easy problem to solve.  I have a repeated measures design where, in each
block of trials, subjects (N=10) performed a reaching response to 10
different target stimuli that were presented 10 times each (once per level
of 'FactorA'), for a total of 100 trials per block.  There were 5
experimental blocks.  The dependent measure is the 'angle' of the reaching
trajectory, and this was sampled/calculated at 5 different times per
response.

I would like to plot angles ~ Sample for each level of FactorA (with CIs),
but have not been able to figure out how to do this.  The amer package
provides a nice example of how to do this, using the following call to amer
with the 'dog' data:
d1<- amer(y ~ -1 + group + tp(time, k=6, by = group) + (1|dog), data = dog)
plotF(d1, legend = 'topleft', lwd=3)

But when I run a similarly structured analysis with my data, it fails.  For
example:
fm1<- amer(angles ~ -1 + FactorA + tp(Sample, k=4, by = FactorA) + (1|Subj)
+ (1|TargItem), data = VelInts)
Error in `*tmp*`[[ind]] : subscript out of bounds

I suspect that this is because of the repeated-measures design, but don't
know how to get around that.

I have run the following analysis successfully with lmer:

lmer1 <- lmer(angles ~ FactorA + Sample + (1|TargItem) + (0+key|Subj), data
= VelInts)

which produces the following:

Fixed effects:
              Estimate Std. Error t value
(Intercept)  1.6177246  0.0124393  130.05
FactorA2    -0.0300888  0.0110805   -2.72
FactorA3     0.0746474  0.0111035    6.72
FactorA4     0.0506684  0.0110861    4.57
FactorA5     0.1578699  0.0111209   14.20
FactorA6     0.0272700  0.0110862    2.46
FactorA7     0.0613134  0.0111036    5.52
FactorA8    -0.0470794  0.0111091   -4.24
FactorA9    -0.0248243  0.0111382   -2.23
FactorA10   -0.1258571  0.0110807  -11.36
Sample      -0.0038139  0.0000701  -54.41


But I can't figure out how to go from this to visualizing 'angles ~ Sample'
for each level of FactorA.

Any help would be much appreciated.

The structure of my data is as follows:

> str(VelInts)
'data.frame':   24205 obs. of  31 variables:
 $ .id         : chr  "101" "101" "101" "101" ...
 $ Subj        : Factor w/ 10 levels "S1","S2","S3",..: 1 1 1 1 1 1 1 1 1 1
...
 $ block       : num  2 2 2 2 2 2 2 2 2 2 ...
 $ trial_n     : num  1 1 1 1 1 2 2 2 2 2 ...
 $ FactorA     : Factor w/ 10 levels "1","2","3","4",..: 6 6 6 6 6 1 1 1 1 1
...
 $ fixedpattern: num  1 1 1 1 1 0 0 0 0 0 ...
 $ pattern     : num  2 2 2 2 2 0 0 0 0 0 ...
 $ TargPos     : num  2 2 2 2 2 1 1 1 1 1 ...
 $ LiftOff     : num  241 241 241 241 241 290 290 290 290 290 ...
 $ Acc         : num  1 1 1 1 1 1 1 1 1 1 ...
 $ FixDur      : num  529 529 529 529 529 662 662 662 662 662 ...
 $ Missing     : num  0 0 0 0 0 0 0 0 0 0 ...
 $ TargItem    : num  6 6 6 6 6 1 1 1 1 1 ...
 $ RT          : num  712 712 712 712 712 959 959 959 959 959 ...
 $ x           : num  0.477 25.26 68.063 121.514 213.708 ...
 $ y           : num  7.09 30.97 69.54 149.23 270.24 ...
 $ z           : num  10.8 73.9 138.2 191.8 240.5 ...
 $ t           : num  270 336 402 469 535 ...
 $ presT       : num  270 336 402 468 534 ...
 $ key         : num  101 101 101 101 101 102 102 102 102 102 ...
 $ pathoffset  : num  0.00583 -0.00899 -0.03706 -0.043 -0.06842 ...
 $ xvel        : num  92.8 571.6 746.9 869.1 1803.9 ...
 $ yvel        : num  150 431 817 1476 2092 ...
 $ tangvel     : num  177 716 1107 1713 2762 ...
 $ xaccel      : num  5310 2967 3155 5330 1387 ...
 $ yaccel      : num  7812 1132 10343 6860 2647 ...
 $ angles      : num  1.503 0.887 0.796 0.887 0.902 ...
 $ Sample      : int  0 25 50 75 100 0 25 50 75 100 ...
 $ G           : num  6 6 6 6 6 1 1 1 1 1 ...
 $ S           : num  51 51 51 51 51 1 1 1 1 1 ...
 $ SK          : Factor w/ 4841 levels "100103","100113",..: 2228 2228 2228
2228 2228 96 96 96 96 96 ...


And if it is helpful, the full data frame can be accessed here:
 http://www.maccs.mq.edu.au/~mfinkbei/VelInts.RData


Thank you kindly for suggestions,

Matthew

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