I am trying to rewrite code from Matlab in R and I am failing at the Hampel-Filter which seems to be distinct.
absmeasuredAccelerations<-(9.817899,9.923724,9.915009,9.414430,9.912013,9.822199,9.662423,9.809928,9.812976,9.883809)
The vector I am applying the Hampel-Filter in Matlab and R is identical as seen below.
Matlab Source:
Filtered = hampel(absmeasuredAccelerations,30,0.05);
In R:
Filtered<- hampel(absmeasuredAccelerations, k=15,t0=0.05)$y
Comparing the output between Matlab and R, there is a significant distinction as seen below:
Assuming the implementation of both filters is distinct, however, somehow it must be possible to reproduce results? Anyone has some ideas or what am I doing wrong?
Related
I'm generating random values from a Bernoulli distribution in both SPSS and R.
In R:
set.seed(9191972)
Y1C <- rbinom(nrow(data3), 1, 0.70)
In SPSS:
SET SEED=9191972.
IF (MISSING(Y1) = 0) Campione=RV.BERNOULLI(0.70).
EXECUTE.
I don't get why the generated distributions differ. I've also tried to set the parameter "kind" in the function set.seed() in R but still got different values from those of SPSS.
Also, I run R in SO Windows while SPSS in Mac. I'm wondering whether it could be due to the different SO used.
R and SPSS don't use the same random number generator. Even if they used very similar generators, it is unlikely that any specific implementation would be the same.
You need to think of another way to solve your problem.
I am currently translating some MATLAB scripts to R for Multivariate Data Analysis. Currently I am trying to generate the same data as the Coeffs.Linear and Coeffs.Const part of the fitdiscr function in MATLAB.
The code being used is:
fitcdiscr(data, groups, 'DiscrimType', 'linear');
The data consists of 3 groups.
Unfortunately the R function seems to do the LDA only for two LDs and MATLAB seems to always compare all groups in all constellations. Does anybody have an idea how I could obtain that data?
I suspect you mean information on the implementation of various MATLAB function, which would be doc <functionname> (doc fitcdiscr would yield this documentation page on fitcdscr) to get the documentation, and edit <functionname> to get the implementation, if it is not obscured by The MathWorks. If those two do not give you enough information, I'm afraid you're out of luck, since not all TMW codes are available non-obscured.
fitcdiscr is non-obscured, although very brief; it's just a wrapper for some other functions. Keep doing edit <functionname> and doc <functionname> and see how deep the rabbit hole takes you.
NB: there's no built-in function called fitdiscr, but the syntax you describe is that of fitcdiscr (note the c), so I used that as examples. If the actual function being called is named fitdiscr, it's custom-made and you'll have to spit through its file by edit fitdiscr and hope for the best.
I have this script which does a simple PCA analysis on number of variables and at the end attaches two coordinates and two other columns(presence, NZ_Field) to the output file. I have done this many times before but now its giving me this error:
I understand that it means there are negative eigenvalues. I looked at similar posts which suggest to use na.omit but it didn't work.
I have uploaded the "biodata.Rdata" file here:
covariance matrix is not non-negative definite
https://www.dropbox.com/s/1ex2z72lilxe16l/biodata.rdata?dl=0
I am pretty sure it is not because of missing values in data because I have used the same data with different "presence" and "NZ_Field" column.
Any help is highly appreciated.
load("biodata.rdata")
#save data separately
coords=biodata[,1:2]
biovars=biodata[,3:21]
presence=biodata[,22]
NZ_Field=biodata[,23]
#Do PCA
bpc=princomp(biovars ,cor=TRUE)
#re-attach data with auxiliary data..coordinates, presence and NZ location data
PCresults=cbind(coords, bpc$scores[,1:3], presence, NZ_Field)
write.table(PCresults,file= "hlb_pca_all.txt", sep= ",",row.names=FALSE)
This does appear to be an issue with missing data so there are a few ways to deal with it. One way is to manually do listwise deletion on the data before running the PCA which in your case would be:
biovars<-biovars[complete.cases(biovars),]
The other option is to use another package, specifically psych seems to work well here and you can use principal(biovars), and while the output is bit different it does work using pairwise deletion, so basically it comes down to whether or not you want to use pairwise or listwise deletion. Thanks!
I would like to run some Matlab scripts. Nevertheless we don't have the Matlab licence so it is necessary a conversion from Matlab to R language. Unfortunately I'm totally new in Matlab but not in R. Is it possible to read Matlab scripts using R or is there an easy way to translate Matlab scripts in R?
Rewriting from one language to another can be a painstaking process, especially because your have to take great care that the outcomes of both sets of codes are the same. I see roughly four approaches:
Digest the goal of the scripts, put aside the matlab code, and rewrite in R
Try and mimic the matlab code in R
Run the matlab code in octave, and interface with R
Run the code in Octave entirely
These are roughly in order of amount of work. If you just want to get the Matlab code working, definitely use Octave, which should run the code with minimal changes. If you want to convert the code to R, and continue developing in R, I would go for the first option. In that way you can leverage the real strenghts of R, as R is quite different (link with info, comparison R and matlab). But it does take the largest amount of time. Even if you reimplement in R, I would recommend getting the code running in Octave to be able to see if your results in R fit with the Matlab code.
Completely new to R here. I ran R in SPSS to solve some complex polynomials from SPSS datasets. I managed to get the result from R back into SPSS, but it was a very inelegant process:
begin program R.
z <- polyroot(unlist(spssdata.GetDataFromSPSS(variables=c("qE","qD","qC","qB","qA"),cases=1),use.names=FALSE))
otherVals <- spssdata.GetDataFromSPSS(variables=c("b0","b1","Lc","tInv","sR","c0","c1","N2","xBar","DVxSq"),cases=1)
b0<-unlist(otherVals["b0"],use.names=FALSE)
b1<-unlist(otherVals["b1"],use.names=FALSE)
Lc<-unlist(otherVals["Lc"],use.names=FALSE)
tInv<-unlist(otherVals["tInv"],use.names=FALSE)
sR<-unlist(otherVals["sR"],use.names=FALSE)
c0<-unlist(otherVals["c0"],use.names=FALSE)
c1<-unlist(otherVals["c1"],use.names=FALSE)
N2<-unlist(otherVals["N2"],use.names=FALSE)
xBar<-unlist(otherVals["xBar"],use.names=FALSE)
DVxSq<-unlist(otherVals["DVxSq"],use.names=FALSE)
z2 <- Re(z[abs(c(abs(b0+b1*Re(z)-tInv*sR*sqrt(1/(c0+c1*Re(z))^2+1/N2+(Re(z)-xBar)^2/DVxSq))-Lc))==min(abs(c(abs(b0+b1*Re(z)-tInv*sR*sqrt(1/(c0+c1*Re(z))^2+1/N2+(Re(z)-xBar)^2/DVxSq))-Lc)))])
varSpec1 <- c("Xd","Xd",0,"F8","scale")
dict <- spssdictionary.CreateSPSSDictionary(varSpec1)
spssdictionary.SetDictionaryToSPSS("results", dict)
new = data.frame(z2)
spssdata.SetDataToSPSS("results", new)
spssdictionary.EndDataStep( )
end program.
Honestly, it was mostly pieced together from somewhat-related examples and seems more complicated than it should be. I had to take the new dataset created by R and run MATCH FILES with my original dataset. All I want to do is a) pull numbers from SPSS into R, b) manipulate them-in this case, finding a polyroot that fit certain criteria- , and c) put the results right back into the SPSS dataset without messing up any of the previous data.
Am I missing something that would make this more simple? Keep in mind that I have zero R experience outside of this attempt, but I have decent experience in programming SPSS and matlab.
Thanks in advance for any help you give!
R in SPSS can create new SPSS datasets, but it can't modify an existing one. There are a lot of situations where the data from R would be dimensionally inconsistent with the active SPSS dataset. So you need to create a dictionary and data frame using the apis above and then do whatever is appropriate on the SPSS side if you need to match back. You might want to submit an enhancement request for SPSS at suggest#us.ibm.com