similar to: Help using smooth.spline with zoo object

I've had a play with this and, due to my own short-comings, remain none the wiser. In particular, I'm not sure what value of 'spar' is consistent with the magic lambda=1/1600 for quarterly data. I initially interpreted spar as lambda and tried setting spar=1/1600. This results in almost no smoothing while spar=1600 causes an error. The smooth.spline function seems to want

Could someone, please, write me, how to compute the spar-value for the smooth.spline-routine to get the same HP-filtered time-series with a parameter lambda for a function (see mail "Hodrick-Prescott-Filter example" from ggrothendieck at yifan.net): hpf <- function(y,lambda{ eye <- diag(length(y)) d <- diff(eye,d=2) z <- solve(eye+lambda*crossprod(d),y)} ? Second, is

Dear list, if I do smooth.spline(tmpSec, tmpT, all.knots=T) with the attached data, I get this error-message: Error in smooth.spline(tmpSec, tmpT, all.knots = T) : smoothing parameter value too small If I do smooth.spline(tmpSec[-single arbitrary number], tmpT[-single arbitrary number], all.knots=T) it works! I just don't see it. It works for hundrets other datasets, but not for

Hello, I come from a non statistics background, but R is available to me, and I needed to test an implementation of smoothing spline that I have written in c++, so I would like to match the results with R (for my unit tests) I am following http://www.nabble.com/file/p25569553/SPLINES.PDF SPLINES.PDF where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) +

Dear All, How to do Hodrick-Prescott Filter in R? Thanks! [[alternative HTML version deleted]]

Has anyone written any code for the Hodrick-Prescott filter? I have a some uncompiled FORTRAN code from Ed Prescott but I'd like to save myself some programming time if possible. Thanks for your help. Nick Davis Crown Financial Policy Asset and Liability Management Branch The New Zealand Treasury Direct: +64-4-471-5924 Fax: +64-4-499-0143 Email:

Hi, I posted some days ago a question concerning the computation of lambda in the smooth.spline function (which I repreat at the bottom of the mail) given _df_ . Unfortunately the documentation is not clear to me. Maybee someone can help to answer in my view the basic question: If the penalized log likelihood is L = (y - f)' W (y - f) + lambda c' Sigma c how the _lambda_ in the above

Dear R listers, When using smooth.spline to interpolate data, results are generally good. However, some cases produce totally unreasonable results. The data are values of pressure, temperature, and salinity from a probe that is lowered into the ocean, and the objective is to interpolate temperature and salinity to specified pressures. While smooth.spline provides excellent values at the

Hi r-helpers. Please forgive my ignorance, but I would like to plot a smoothing spline (smooth.spline) from package "stats", and show the knots in the plot, and I can't seem to figure out where smooth.spline has located the knots (when I use nknots). Unfortunately, I don't know a lot about splines, but I know that they provide me an easy way to estimate the location of local

Dear list, I have an irregular time series saved and exported as a zoo object. What is the trick to force zoo to ignore the missing dates when reading it back in? Thanks. > str(g) ?zoo? series from 1948-11-02 to 2012-11-06 Data: num [1:14881, 1:8] 1 0 0 0 0 0 0 0 0 0 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:8] "session" "midterm"

I have a simple question that irritatingly I haven't been able to figure out on my own. It seems that some functions from the "Pspline" package are successfully installed while others are not. The code with which I'm working is more complicated, but the following highlights my problem. If I run the following code > tt <- seq (0,1,length=20) > xt <- tt^3 > fit

Dear R users I am currently investigating time series analysis using an irregular time series. Our study is looking at vegetation change in areas of alien vegetation growth after clearing events. The irregular time series is sourced from Landsat ETM+ data, over a six year period I have 38 scenes. For certain periods I have monthly data while for others, images are up to three months apart. So far

Hi all, Forecasting from an arima model is easy with predict. But I can't manage to backcast : invent data from the model before the begining of the sample. The theory is easy : take your parameters, reverse your data, forecast, and then reverse the forecast I've tried to adapt the predict function to do that (i'm not sure that the statistical procedure is fine (with the residuals),

Everyone, One of our developers was kind enough to insert some bug checking into the mod_auth_pam and mod_auth_sys_group so that we could see a little more of what was going on with our authentication failures. Here is what we just saw. Two of our users NA\connelmp and NA\guminssa both started getting messages that they were not part of the required group. Here is the log for

Hello, I come from a non statistics background, but R is available to me, and I needed to test an implementation of smoothing spline that I have written in c++, so I would like to match the results with R (for my unit tests). I am following Smoothing Splines, D.G. Pollock (available online) where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) + e_i

We got all rows by: library(XML) doc = htmlParse('http://www.statcan.gc.ca/daily-quotidien/090520/t090520b1-eng.htm') rows = xpathSApply(doc, '//table/tbody/tr') The last row is: row_last = rows[15] row_last [[1]] <tr><td id="t1stub17" class="stub1 RGBShade"><b>Unsmoothed composite leading indicator</b></td>&#13; <td

Dear all, I'm trying to use the Pspline add-on package to fit a quintic spline (norder =3), but I keep running into a Singularity error. > traj.spl <- smooth.Pspline(time, x, norder=3 ) Error in smooth.Pspline(time, x, norder = 3) : Singularity error in solving equations > Playing around with the other parameters produces an "unused arguments" error: > traj.spl

Hello I need to make site views changeable I found how to change layouts but it is not enough, becouse templates can use different ways to display and organize content. I found one way to do this, but i need to make subfolders in my ''view/layouts'', ''view/controller_name'' both and to create additional subfolder somewhere in ''public'' to put

R-help, I have the following file I want to import to R (some lines removed) Calibrated CTD data for station:00280001 Calibrated:23/8 2001, Salinity Unsmoothed, Fluorescence Uncalibrated Maximum observed depth: 36 m QUAL has one digit for each of pressure, temp., sal. and fluor. QUAL=1:Uncal., QUAL=2:OK, QUAL=6:Interp., QUAL=9:No data

What's the neat way to create a dummy from a list? The code below is not replicable, but hopefully self-explanatory... d$treatment<-rep(1,length(d)) notreat<-c("AR", "DE", "MS", "NY", "TN", "AK", "LA", "MD", "NC", "OK", "UT", "VA") #i would really like this to work: