Thank you again! But somehow the which(function) didn't work...
The problem is, that the columns I want to drop in further calculations will
not have the same name, that's why I'm looking for a solution to make a
vector of the columns that should be omitted, like
omit<-c("Restriction 1","Restricti
Hello
I've got a matrix of the following form
BARNBCGEBCVNBEANRestriction 1 Restriction 2
Restriction 3 Restriction 4
Restriction 5
alpha.1 0.000172449 7.87E-05-0.0032710440.000921609
9.28E-192.00E-05
-0.000608211NA NA
alpha.2 0
Thank you for the fast answer!
Is there any way to use the first solution way, but work with the column
names instead of the number of the column. Because in further calculation I
need to drop more columns than just the restriction ones and the real
dataset has to many columns to work with the pos
Hi R Team
I've got the following problem
I'd like to run a time series regression of the following form
Regression1:
At = α + β1 * Bt + β2 * Bt-1 + β3 [(Bt-2 + Bt-3 + Bt-4)/3] + εt
The B's are the input values and the A's are the output values, the
subscript stands for the lag.
The real Beta o
Thank you, it worked!
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Thank you a lot A.K.!
One more question:
I'd like to compute the Spearman's rank correlation coefficients for V1
(from dat1) and V1 (from dat2) and so on... Do you know how to do that?
I managed to write the Pearson's correlation product moment coefficient with
your sapply-approach, but I have no
Thank you for your answer. But further calculations will be much more
difficult, like
(1-b)^2 * Var(V1) for all matching columns
where b is the slope from a regression V1 (from datset 1) on V1 (dataset 2)
and Var(V1) the variance from V1(from dataset2).
So what I'm looking for is somethi
Hi thereI've got two datasets of the following form (just an example, the
real dataset got a lot more columns)dataset1V1 V2 V32 6 84
3 41 9 8and
dataset 2V1 V2 V36 8 42 0 78 1 3First,
I'd like to calculate the
followin
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