Displaying 5 results from an estimated 5 matches for "inp2".
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2011 Dec 05
1
about error while using anova function
fit1<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc)
fit2<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.5,data=wbc)
fit3<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc)
fit4<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=...
2011 Dec 05
1
about interpretation of anova results...
quantreg package is used.
*fit1 results are*
Call:
rq(formula = op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 +
inp8 + inp9, tau = 0.15, data = wbc)
Coefficients:
(Intercept) inp1 inp2 inp3 inp4
inp5
-0.191528450 0.005276347 0.021414032 0.016034803 0.007510343
0.005276347
inp6 inp7 inp8...
2009 Mar 21
1
How to avoid switching on input type?
...hat it works for all input
types?
Is a switch depending on the input type really necessary? I am hoping the
answer is "no".
Thanks in advance.
- Ken
#
-----------------------------------------------------------------------------------
require( zoo );
inp <- c( 5, 9, 4, 2, 1 ); inp2 <- c( 6, 6, 0, 4, 2 );
inp.zoo <- zoo( cbind( inp, inp2 ), as.Date("2003-02-01") +
(0:(length(inp)-1)));
lag.zerofill.list <- function( m, shift=1, ...) {
c( rep(0,shift), m[-((length(m)-shift+1):length(m))] );
}
lag.zerofill.zoo <- function( m, shift=1, ...) {
k <-...
2013 Apr 17
1
Merging big data.frame
...t allocate vector of size
360.1 Mb. To overcome this, I am exploring option of using data.table
package. But its not helping in term of memory as merge in data.table is
fast but not memory efficient. Similar error is coming.
My inputs are
inp1
V1 V2
1 a i1
2 a i2
3 a i3
4 a i4
5 b i5
6 c i6
inp2
V1 V2
1 a x
2 b x
3 a y
4 c z
I want merge(x=inp1, y=inp2, by.x="V1", by.y="V1")
so the output
V1 V2.x V2.y
1 a i1 x
2 a i1 y
3 a i2 x
4 a i2 y
5 a i3 x
6 a i3 y
7 a i4 x
8 a i4 y
9 b i5 x
10 c i6...
2011 Dec 01
1
hi all.regarding quantile regression results..
i know this is not about R.
After applying quantile regression with t=0.5,0.6 on the data set WBC(
Wisconsin Breast Cancer)with 678 observations and 9 independent
variables(inp1,inp2,...inp9) and 1 dependent variable(op) i have got the
following results for beta values.
when t=0.5(median regression) beta values b1=0.002641,b2=0.045746,b3=0.
005282,b4=0.004397,b5=0.002641,b6=0.065807,b7=0.005282
,b8=0.031394,b9=0.004993 and intercept is -0.181388
and when t=0.6 beta values...