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Hearing development in barn owls

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Tried a couple of methods for smoothing the V(t) data to use for analysis. Data is noisy, but setting spar on smooth.spline seems to do the job - need to apply smooth.spline on the raw data and calculate the first derviative through diffV/difft. However, I am uncertain as to how to justify the actual value of spar. Seems that something around 0.25 does a pretty good job - but would like to have some way or rationalising what that value should be. One way might be to look at the noise on the spont trace, and extract from there some value of noise that I can then use to determine spar? Mmmm... MF Kubke 19:42, 27 October 2013 (EDT)

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Fabiana

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<body> <h1>Trying to get case name from user</h1>

List the available folders and prompt user to choose case:

<pre><code>## [1] "2013-10-27-MFK.Rmd" "2013-10-27.html" "2013-10-27.R"

1. [4] "2013-10-27.txt" "2013-10-28-MFK.html" "2013-10-28-MFK.md"
2. [7] "2013-10-28-MFK.Rmd" "Copy189L0A.ABR" "Copy224l0a.txt"
3. [10] "Copy233L0B.ABR.log" "Copy233L0B.ABR.txt" "figure"
4. [13] "knitr-testr.html" "knitr-testr.md" "knitr-testr.Rmd"
5. [16] "kntR2.html" "kntR2.md" "kntR2.Rmd"
6. [19] "my first knitr.html" "my first knitr.md" "my first knitr.Rmd"
7. [22] "mydata_new" "mydata_new.txt" "readrawabr.R"
8. [25] "Sandbox1.R" "Sandbox1.txt" "texput.log"

</code></pre>

<pre><code class="r">newdir <- readline("enter case number: ") </code></pre>

<pre><code>## enter case number: </code></pre>

<pre><code class="r">print(newdir) </code></pre>

I can use this then to change the wd() to where the new cases are.

<pre><code class="r">basedir <- getwd() casedir <- paste(basedir, newdir, sep = "/") setwd(casedir) dir() #test </code></pre>

<pre><code>## [1] "2013-10-27-MFK.Rmd" "2013-10-27.html" "2013-10-27.R"

1. [4] "2013-10-27.txt" "2013-10-28-MFK.html" "2013-10-28-MFK.md"
2. [7] "2013-10-28-MFK.Rmd" "Copy189L0A.ABR" "Copy224l0a.txt"
3. [10] "Copy233L0B.ABR.log" "Copy233L0B.ABR.txt" "figure"
4. [13] "knitr-testr.html" "knitr-testr.md" "knitr-testr.Rmd"
5. [16] "kntR2.html" "kntR2.md" "kntR2.Rmd"
6. [19] "my first knitr.html" "my first knitr.md" "my first knitr.Rmd"
7. [22] "mydata_new" "mydata_new.txt" "readrawabr.R"
8. [25] "Sandbox1.R" "Sandbox1.txt" "texput.log"

</code></pre>

I should then read the files in and go back to base dir so that I can call the functions. Another way of going about this

<pre><code class="r">dir.create(file.path(basedir, casedir), showWarnings = FALSE) setwd(file.path(basedir, casedir)) </code></pre>

<pre><code>## Error: cannot change working directory </code></pre>

<h2>Try to smooth the ABR trace and calculate local max and min</h2>

<pre><code class="r">mydatanew <- read.table("mydata_new") head(mydatanew) </code></pre>

<pre><code>## msec left right spont bin

1. 1 0.00 1000 1000 1000 1000
2. 2 0.02 1000 1000 1000 1000
3. 3 0.04 1000 1000 1000 1000
4. 4 0.06 1000 1000 1000 1000
5. 5 0.08 1000 1000 1000 1000
6. 6 0.10 1000 1000 1000 1000

</code></pre>

Realised that after adding the bincomp column to the datafile, I forgot to overwrite the data file - so mydata_new still had the original variables but not the new one - fixing that.

<pre><code class="r">mydata <- read.table("Copy233L0B.ABR.txt", header = T) head(mydata) #passed test </code></pre>

<pre><code>## msec left right spont bin

1. 1 0.00 1000 1000 1000 1000
2. 2 0.02 1000 1000 1000 1000
3. 3 0.04 1000 1000 1000 1000
4. 4 0.06 1000 1000 1000 1000
5. 5 0.08 1000 1000 1000 1000
6. 6 0.10 1000 1000 1000 1000

</code></pre>

<pre><code class="r">write.table(mydata, "mydata_new") mydata_new <- read.table("mydata_new", header = T) head(mydata_new) #passed test </code></pre>

<pre><code>## msec left right spont bin

1. 1 0.00 1000 1000 1000 1000
2. 2 0.02 1000 1000 1000 1000
3. 3 0.04 1000 1000 1000 1000
4. 4 0.06 1000 1000 1000 1000
5. 5 0.08 1000 1000 1000 1000
6. 6 0.10 1000 1000 1000 1000

</code></pre>

<pre><code class="r">mydata_new$bincomp[1:1000] <- (mydata_new$bin[1:1000] - (mydata_new$left[1:1000] +

   mydata_new$right[1:1000]))

head(mydata_new) </code></pre>

<pre><code>## msec left right spont bin bincomp

1. 1 0.00 1000 1000 1000 1000 -1000
2. 2 0.02 1000 1000 1000 1000 -1000
3. 3 0.04 1000 1000 1000 1000 -1000
4. 4 0.06 1000 1000 1000 1000 -1000
5. 5 0.08 1000 1000 1000 1000 -1000
6. 6 0.10 1000 1000 1000 1000 -1000

</code></pre>

<pre><code class="r">write.table(mydata_new, "mydata_new.txt") #passed test </code></pre>

Now I have a working file that is separate from the original one which I can put in the analysis folder (not sure I really want that in the end, but ok for now)

Options:

<h3>diff function</h3>

Tried the diff function - but (unsurprisingly) I get heaps of noise

<pre><code class="r">mydata_new$diffleft[2:1000] <- diff(mydata_new$left)/diff(mydata_new$msec) par(mfrow = c(1, 2)) plot(mydata_new[c(1, 2)], type = "l", main = "original left") plot(mydata_new[c(1, 7)], ylim = c(-3000, 3000), type = "l", main = "firs diff of left") </code></pre>

(Need to bound to [2:1000] because the diff function returns N-1 values. )

<h3>smooth.spline</h3>

Trying smooth.spline. Trying to smooth on the diff doesnt work probably because of infinite values.

The smooth.spline gives me a rather noisy plot

<pre><code class="r">test <- smooth.spline(mydata_new$msec, mydata_new$left) par(mfrow = c(1, 3)) plot(mydata_new$msec, mydata_new$left, type = "l", main = "original left") plot(test$x, test$y, type = "l", main = "splined left") mydata_new$diffleft2[2:1000] <- diff(test$y)/diff(test$x) plot(mydata_new$msec, mydata_new$diffleft2, ylim = c(-3000, 3000), type = "l",

   main = &quot;first diff of splined left&quot;)

</code></pre>

but can smoot by setting spar to some value other than NULL

<pre><code class="r">test <- smooth.spline(mydata_new$msec, mydata_new$left, spar = 0.5) par(mfrow = c(1, 3)) plot(mydata_new$msec, mydata_new$left, type = "l", main = "original left") plot(test$x, test$y, type = "l") mydata_new$diffleft2[2:1000] <- diff(test$y)/diff(test$x) plot(mydata_new$msec, mydata_new$diffleft2, ylim = c(-3000, 3000), type = "l",

   main = &quot;first diff of splined left&quot;)

</code></pre>

Comparisons of different spar values:

<pre><code class="r"> par(mfrow = c(1, 3)) test <- smooth.spline(mydata_new$msec, mydata_new$left) plot(mydata_new$msec, mydata_new$left, type = "l", main = "original left") plot(test$x, test$y, type = "l", main = "spar = NULL on left") mydata_new$diffleft2[2:1000] <- diff(test$y)/diff(test$x) plot(mydata_new$msec, mydata_new$diffleft2, ylim = c(-3000, 3000), type = "l",

   main = &quot;first diff of splined left&quot;)

</code></pre>

<pre><code class="r"> par(mfrow = c(1, 3)) test <- smooth.spline(mydata_new$msec, mydata_new$left, spar = 0.25) plot(mydata_new$msec, mydata_new$left, type = "l", main = "original left") plot(test$x, test$y, type = "l", main = "spar = 0.25 on left") mydata_new$diffleft2[2:1000] <- diff(test$y)/diff(test$x) plot(mydata_new$msec, mydata_new$diffleft2, ylim = c(-3000, 3000), type = "l",

   main = &quot;first diff of splined left&quot;)

</code></pre>

<pre><code class="r"> par(mfrow = c(1, 3)) test <- smooth.spline(mydata_new$msec, mydata_new$left, spar = 0.5) plot(mydata_new$msec, mydata_new$left, type = "l", main = "original left") plot(test$x, test$y, type = "l", main = "spar = 0.5 on left") mydata_new$diffleft2[2:1000] <- diff(test$y)/diff(test$x) plot(mydata_new$msec, mydata_new$diffleft2, ylim = c(-3000, 3000), type = "l",

   main = &quot;first diff of splined left&quot;)

</code></pre>

<pre><code class="r"> par(mfrow = c(1, 3)) test <- smooth.spline(mydata_new$msec, mydata_new$left, spar = 0.75) plot(mydata_new$msec, mydata_new$left, type = "l", main = "original left") plot(test$x, test$y, type = "l", main = "spar = 0.75 on left") mydata_new$diffleft2[2:1000] <- diff(test$y)/diff(test$x) plot(mydata_new$msec, mydata_new$diffleft2, ylim = c(-3000, 3000), type = "l",

   main = &quot;first diff of splined left&quot;)

</code></pre>

<pre><code class="r"> par(mfrow = c(1, 3)) test <- smooth.spline(mydata_new$msec, mydata_new$left, spar = 1) plot(mydata_new$msec, mydata_new$left, type = "l", main = "original left") plot(test$x, test$y, type = "l", main = "spar = 1 on left") mydata_new$diffleft2[2:1000] <- diff(test$y)/diff(test$x) plot(mydata_new$msec, mydata_new$diffleft2, ylim = c(-3000, 3000), type = "l",

   main = &quot;first diff of splined left&quot;)

</code></pre>

Package ppc from <a href="http://www-stat.stanford.edu/%7Etibs/PPC/Rdist/index.html">http://www-stat.stanford.edu/~tibs/PPC/Rdist/index.html</a> => seem to be unable to install package - get an error that it is not an OS X binary package

<a href="http://finzi.psych.upenn.edu/R/library/ppc/html/ppc.peaks.html">http://finzi.psych.upenn.edu/R/library/ppc/html/ppc.peaks.html</a>

Trying package zoo

</body>

</html>

Andy

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Oris

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 =General Entries=
-* Tried a couple of methods for smoothing the V(t) data to use for analysis. Data is noisy, but setting spar on smooth.spline seems to do the job - need to apply smooth.spline on the raw data and calculate the first derviative through diffV/difft. However, I am uncertain as to how to justify the actual value of spar. Seems that something around 0.25 does a pretty good job - but would like to have some way or rationalising what that value should be. One way might be to look at the noise on the spont trace, and extract from there some value of noise that I can then use to determine spar? Mmmm... [[User:M Fabiana Kubke|MF Kubke]]
+* Tried a couple of methods for smoothing the V(t) data to use for analysis. Data is noisy, but setting spar on smooth.spline seems to do the job - need to apply smooth.spline on the raw data and calculate the first derviative through diffV/difft. However, I am uncertain as to how to justify the actual value of spar. Seems that something around 0.25 does a pretty good job - but would like to have some way or rationalising what that value should be. One way might be to look at the noise on the spont trace, and extract from there some value of noise that I can then use to determine spar? Mmmm... [[User:M Fabiana Kubke|MF Kubke]] 19:42, 27 October 2013 (EDT)
 =Personal Entries=

Kubke Lab:Research/ABR/Notebook/2013/10/28: Difference between revisions

Revision as of 16:42, 27 October 2013

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