Let me clear my last part
qt2 <- qtd[-1:-2]
# second function
myfun2 <- function(x) {
vydv <- sum(ydv)*0.25
sumD <- sum(ydv * x)
Rt <- vydv / sumD
return(Rt)
}
# ignoring grouping with ped, the following is output
qt2 <- apply(qtd, 2, myfun2)
qtd1 <- qt2[-1:-2]
# The following result seems to pool at X1, X2, and X3
require(plyr)
dfsumd1 <- ddply(qtd,.(qtd$ped),myfun2)
qtd$ped V1
1 1 0.2296159
2 2 0.1045569
3 3 -5.8861942
But I need a twoway table
Ped X1 X2 X3
1
2
3
Still unsuccessful, sorry !
On Sat, Sep 3, 2011 at 7:57 AM, Maya Joshi <maya.d.jo...@gmail.com> wrote:
> Dear R experts:
>
> Thank you Dennis and David ...
>
> As David indicated sorry of language and I have tried to explain what I
> intend to do... I would this with Dennis's solution code:
>
>
> ped <- rep(1:3, c(4, 3, 3))
>> y <- rnorm(10, 8, 2)
>> # This replaces all of your sample() statements, and is equivalent:
>> smat <- matrix(sample(1:3, 120, replace = TRUE), ncol = 12)
>> colnames(smat) <- c('M1a', 'M1b', 'M1aP1', 'M1bP2',
>> 'M2a', 'M2b', 'M2aP1', 'M2bP2',
>> 'M3a', 'M3b', 'M3aP1', 'M3bP2')
>> mydf <- as.data.frame(cbind(ped, y, smat))
>>
>
>
>>
>> mmat <- matrix
> (c("M1a","M2a","M3a","M1b","M2b","M3b","M1aP1","M2aP1","M3aP1",
> "M1bP2","M2bP2","M3bP2"), ncol = 4)
> [,1] [,2] [,3] [,4]
>
> [1,] "M1a" "M1b" "M1aP1" "M1bP2"
> [2,] "M2a" "M2b" "M2aP1" "M2bP2"
> [3,] "M3a" "M3b" "M3aP1" "M3bP2"
>
> I want to compare [,1] and [,3] names of mydf (mydf[x[1]] ==
> mydf[x[3]]) . for all three rows in the nmat. nmat is guiding me which
> variable I want to pick while working on mydf. In my real dataset I have
> 1000 such set of variables.
>
> # first function
> myfun <- function(x) {
> x<- as.vector(x)
> ot1 <- ifelse(mydf[x[1]] == mydf[x[3]], 1, -1)
> ot2 <- ifelse(mydf[x[2]] == mydf[x[4]], 1, -1)
> qt <- ot1 + ot2
> return(qt)
> }
> qt <- apply(mmat, 1, myfun)
>
> Solution of this will create a matrix with number of set of variables by
> number of rows in the mydf
> [,1] [,2] [,3]
> [1,] 0 -2 0
> [2,] -2 0 -2
> [3,] 0 -2 0
> [4,] 0 0 2
> [5,] 0 -2 -2
> [6,] -2 0 -2
> [7,] -2 -2 0
> [8,] -2 0 0
> [9,] -2 0 2
> [10,] 0 0 0
>
> ydv <- c((y - mean(y))^2) # calculates mean of y and deviations from it
> for each y values
> [1] 9.5012525 0.2578341 1.6676271 6.3102202 12.8701830 9.5509480
> [7] 0.8661107 3.1828185 0.9215140 1.0909813
>
> qtd <- data.frame(ped, ydv, qt) # new data.frame with above function's
> output with ped variable
> ped ydv X1 X2 X3
> 1 1 9.5012525 0 -2 0
> 2 1 0.2578341 -2 0 -2
> 3 1 1.6676271 0 -2 0
> 4 1 6.3102202 0 0 2
> 5 2 12.8701830 0 -2 -2
> 6 2 9.5509480 -2 0 -2
> 7 2 0.8661107 -2 -2 0
> 8 3 3.1828185 -2 0 0
> 9 3 0.9215140 -2 0 2
> 10 3 1.0909813 0 0 0
>
> Now I want to calculate Rt for each X1, X2, X3 (in real data world I will
> have 1000 of them). The expected result of the following function should
> look like 3 x 3 matrix. This is just example, I do have Ped around 200 and
> X1 is around 1000.
> # Rt values
> Ped X1 X2 X3
> 1
> 2
> 3
>
> # second function
> myfun2 <- function(dataframe) {
> vydv <- sum(ydv)*0.25
> sumD <- sum(ydv * qt)
> Rt <- vydv / sumD
> return(Rt)
> }
>
> # using plyr
> require(plyr)
> dfsumd1 <- ddply(mydf,.(mydf$ped),myfun2)
>
> dfsumd1
> mydf$ped V1
> 1 1 -0.1047935
> 2 2 -0.1047935
> 3 3 -0.1047935
>
> This is not what I want. I want ped wise Rt values for each of X variables
> in above qtd matrix.
> # Rt values
> Ped X1 X2 X3
> 1
> 2
> 3
>
> Then in I can sum Ped$X1, Ped$X2, Ped$X3. The idea is to calculated separate
> Rt values for each variable group by Ped variables separately. Then add the
> values.
>
> Thank you so much for your time. Hope I had made it clear now.
>
> Maya
>
>>
>>
>
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