Hi Petr,
oh, that's really way more easier than the way I did it. Thanks for the hint!
The problem with "no prediction" is that these cases are already coded
within the "Prediction" variable.
0 codes "no prediction required" while 1 and 2 codes for different
predictions. Therefore, there are no NAs within this variable.
By applying the procedure suggested by you, I would receive 0-coding
for both trials with wrong predictions and trials without any
predictions.
But probably I can change coding within the Prediction-variable prior
to applying your procedure.
Thanks again!
Best,
Jonas
Zitat von PIKAL Petr <[email protected]>:
Hi
-----Original Message-----
From: [email protected] [mailto:r-help-bounces@r-
project.org] On Behalf Of Jonas Walter
Sent: Monday, February 25, 2013 2:38 PM
To: [email protected]
Subject: [R] creating variable that codes for the match/mismatch
between two other variables
Dear all,
I have got two vectors coding for a stimulus presented in the current
trial (mydat$Stimulus) and a prediction in the same trial
(mydat$Prediciton), respectively.
By applying an if-conditional I want to create a new vector that
indicates if there is a match between both vectors in the same trial.
That is, if the prediction equals the stimulus.
When I pick out some trials randomly, I get some trials with no match
(mydat$Stimulus[1] != mydat$Prediction[1]) as well as some trials with
a match (mydat$Stimulus[1] == mydat$Prediction[1]).
However, if I apply the following code, each trial is coded as a match.
Why, what do I wrong?
In some blocks, there was no prediction recorded. Therefore, I want
those trials to be labeled differently [that is, match = 7].
Coding-legend:
1 = match
0 = no match
7 = no prediction recorded
The code:
# create varialbe that codes match/mismatch of prediction vs. stimulus
mydat$match <- 0
for (i in seq_along(1:nrow(mydat))) {
# if there is a match, mydat$match[i] = 1 if
(mydat$Stimulus[i] == mydat$Prediction[i]) {
mydat$match = 1
# the next to conditions refer to blocks without prediction recording.
Therefore, the corresponding trials are coded with mydat$match[i] = 7.
} else if (mydat$BlockOrder[i] == 1 & mydat$Block_nr[i] == 1) {
mydat$match = 7
} else if (mydat$BlockOrder[i] == 2 & mydat$Block_nr[i] == 4) {
mydat$match == 7
}
}
Well, why so complicated?
(mydat$Stimulus == mydat$Prediction)*1
gives you vector of 1 when there is match and 0 when there is no match.
I do not understand your no prediction though. How is no prediction
coded? If NA, the resulting vector will have NA in corresponding
item too.
Regards
Petr
# The corresponding dataframe structure:
str(mydat)
'data.frame': 9302 obs. of 18 variables:
$ BlockOrder : int 1 1 1 1 1 1 1 1 1 1 ...
$ Block_nr : num 1 1 1 1 1 1 1 1 1 1 ...
$ Trial_nr : int 1 2 3 4 5 6 7 8 9 10 ...
$ PreSeq.Length : int 1 2 2 1 1 2 0 2 2 2 ...
$ PreSeq : int 21 12 21 20 20 12 0 21 22 11 ...
$ Sequence : int 121111 121212 121111 121111 112212 121221
121111 121111 122112 121111 ...
$ Category : int 2 1 3 2 1 1 3 3 1 3 ...
$ FixCross.Latency : int 1429 1043 1093 1297 1155 1449 1140 1396 1341
1427 ...
$ Stimulus : int 2 1 2 2 1 1 1 1 2 1 ...
$ RT : int 333 275 378 428 442 388 340 394 414 542 ...
$ RT.Button_pressed: int 2 1 2 2 1 1 1 1 2 1 ...
$ RT.Accuracy : int 1 1 1 1 1 1 1 1 1 1 ...
$ Prediction : int 0 0 0 0 0 0 0 0 0 0 ...
$ Confidence : int 0 0 0 0 0 0 0 0 0 0 ...
$ ITI : int 1053 1182 1467 1431 1103 1170 1232 1393 1356
1495 ...
$ Subject : num 4 4 4 4 4 4 4 4 4 4 ...
$ ITruns : num 0 0 0 1 0 1 2 3 0 0 ...
$ match : num 1 1 1 1 1 1 1 1 1 1 ...
# mydat$match, the new variable, only contains ones.
min(mydat$match)
[1] 1
> max(mydat$match)
[1] 1
# example: row 1699: no match Stimulus - Prediction
mydat$Stimulus[1699] == mydat$Prediction[1699] # [1] FALSE
# but:
mydat$match[1699]
# [1] 1
How can I get the right coding? Where is the mistake?
Thanks!
Best,
Jonas Walter
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