Error in lm prediction
On Mon, Jul 24, 2017 at 1:10 PM, Ed Herranz <ed.herranz at gmail.com> wrote:
Hi Amol, My guess is that you can't use lm() directly on xts objects. See this: https://stackoverflow.com/questions/21692560/linear-regression-with-xts-object
Bad guess. :) library(xts) data(sample_matrix) xtsObject <- as.xts(sample_matrix) xtsObject$t <- seq_len(nrow(xtsObject))-1 lm(Open ~ t, data=xtsObject)
Regards, -Ed On Sun, Jul 16, 2017 at 4:31 PM, amol gupta <amolgupta87 at gmail.com> wrote:
Hi I am most likely committing an error in trying to predict using linear regression lm model. please help me figure out what am I doing wrong. I am trying to regress a index and its constituents. Here is the code #split ts inttwo parts a<-300; x1<-x[1:a,]; y1<-y[1:a,]; x2<-x[(a+1):nrow(x),]; y2<-y[(a+1):nrow(y),]; #regression m1<-lm( y1~x1) r1<-residuals(m1) coef(m1) ##out of sample y_hat<-predict.lm(m1,x2); r2<-y_hat-y2; x,y are xts. X contains multiple time series. The y_ hat turns out to be of 300 samples only, whereas x2 contains 1400 samples. Please help me figure out how to predict using model that I have found using regression.
It's very difficult to help if you do not provide a reproducible example. Most people do not have, and will not spend, the time it takes to imagine and create data required to reproduce the issue you describe. Please see: https://stackoverflow.com/q/5963269/271616
--
Regards
Amol
+91-9897860992
+91-8889676918
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On Mon, Jul 24, 2017 at 1:10 PM, Ed Herranz <ed.herranz at gmail.com> wrote:
Hi Amol, My guess is that you can't use lm() directly on xts objects. See this: https://stackoverflow.com/questions/21692560/linear-regression-with-xts-object Regards, -Ed On Sun, Jul 16, 2017 at 4:31 PM, amol gupta <amolgupta87 at gmail.com> wrote:
Hi
I am most likely committing an error in trying to predict using linear
regression lm model. please help me figure out what am I doing wrong. I am
trying to regress a index and its constituents. Here is the code
#split ts inttwo parts
a<-300;
x1<-x[1:a,];
y1<-y[1:a,];
x2<-x[(a+1):nrow(x),];
y2<-y[(a+1):nrow(y),];
#regression
m1<-lm( y1~x1)
r1<-residuals(m1)
coef(m1)
##out of sample
y_hat<-predict.lm(m1,x2);
r2<-y_hat-y2;
x,y are xts. X contains multiple time series. The y_ hat turns out to be of
300 samples only, whereas x2 contains 1400 samples.
Please help me figure out how to predict using model that I have found
using regression.
--
Regards
Amol
+91-9897860992
+91-8889676918
[[alternative HTML version deleted]]
_______________________________________________ R-SIG-Finance at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
[[alternative HTML version deleted]]
_______________________________________________ R-SIG-Finance at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
Joshua Ulrich | about.me/joshuaulrich FOSS Trading | www.fosstrading.com R/Finance 2017 | www.rinfinance.com