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POPerforming KS.test on empirical weighted distribution functions in R
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copied!<p>I want to compare two datasets with differing amounts of data and differing start/end points. I wanted to use the KS.test as I am doing something similar in a C program I wrote (compares data via GSL histogram -> GSL cdf -> self-written KS test) but as this program is unpublished still I can't use it for this. I did however want to use the same principle in R but can't seem to grasp how to do this in R.</p> <p>I have two empircal weighted distribution functions (created with the spatstat library) and am trying to perform the KS.test on them but it gives me an error that I am not quite sure how to interpret.</p> <p>R-code that I performed:</p> <p>predicted & experimental contains an array of xy values (differing lengths, start and end X values).</p> <pre><code>cdf_e<-ewcdf(experimental[,1],weights=experimental[,2]) cdf_p<-ewcdf(predicted[,1],weights=predicted[,2]) ks.test(cdf_e,cdf_p) </code></pre> <p>The message i get is:</p> <pre><code>Error in x[!is.na(x)] : object of type 'closure' is not subsettable In addition: Warning message: In is.na(x) : is.na() applied to non-(list or vector) of type 'closure' </code></pre> <p>Can anyone tell me what this means specifically (as the ?ks.test doesn't mention anything that I can relate to this error) or if I should instead use a different function call or what not.</p> <p>PS: The first 10 lines of each data file is as follows:</p> <p>predicted</p> <pre><code>m/z I 292.1 1272 322.0 815 324.9 780 350.0 922 363.9 781 366.0 87049 366.9 12773 367.9 1620 383.6 1258 </code></pre> <p>Experimental</p> <pre><code>m/z I 366.139 1229.62 367.142 186.775 368.145 10.9435 528.192 19.8701 529.195 4.39351 579.435 11.5899 680.996 73.8311 681.861 7.2526 745.032 5.31373 </code></pre>

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