similar to: Merging big data.frame

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 inp9 0.058708544

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=0.15,data=wbc)

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.

*Problem-1* CASE-I---------(works fine) > var1<-"tom" > var1 [1"tom" > var1<-as.character(var1) > var1 [1] "tom" > var2<-c("tom","harry","kate") > logc<-(var1 %in% var2) > logc [1] TRUE > typeof(var1) [1] "character" > typeof(var2) [1] "character"

I have dataframe with 17factors variables (for example every factor have 3levels) I have maybe 5000 observation. And i need to do table where is in every raw 1 of possible combination of this factors and the numbur how many time is this combination in my dataset. I wrote one code, but this is very slow and dumb. it looks like this: i<-0 for(i1 in levels(hivdat$pohl)){

Hi all, Can anyone suggest me how to insert rows in between data frames and also keep the ordering of row numbers correct? Estimate Std. Error t value Pr(>|t|) recmeanC2 9.275880e-17 6.322780e-17 1.467057e+00 0.14349903 recmeanC3 1.283534e-17 2.080644e-17 6.168929e-01 0.53781390

https://bugzilla.netfilter.org/show_bug.cgi?id=1140 Bug ID: 1140 Summary: nft dump invalid (flow table) Product: nftables Version: unspecified Hardware: x86_64 OS: other Status: NEW Severity: major Priority: P5 Component: nft Assignee: pablo at netfilter.org

It seems that that it's hard to vectorize int64 in LLVM. For example, LLVM 3.4 generates very complicated code for the following IR. I am running on a Haswell processor. Is it because there is no alternative AVX/2 instructions for int64? The same thing also happens to zext <2 x i32> -> <2 x i64> and trunc <2 x i64> -> <2 x i32>. Any ideas to optimize these

How to NATURAL sort a vector or data frame* by row* , in ascending order ? V1 V2 V3 V4 i1 5.000000e-01 1.036197e-17 4.825338e+16 0.00000000 i10 4.001692e-18 1.365740e-17 2.930053e-01 0.76973827 i12 -1.052843e-17 1.324484e-17 -7.949081e-01 0.42735000 i13 2.571236e-17 1.357336e-17 1.894325e+00 0.05922715 i2

hello everyone. Look at the following R idiom: a <- array(1:30,c(3,5,2)) M <- (matrix(1:15,c(3,5)) %% 4) < 2 a[M,] <- 0 Now, I think that "a[M,]" has an unambiguous meaning (to a human). However, the last line doesn't work as desired, but I expected it to...and it recently took me an indecent amount of time to debug an analogous case. Just to be explicit, I would

I have huge list of edges with weights. a1 b1 w1 a2 b2 w2 a3 b3 w3 a1 b1 w4 a3 b1 w5 I have to convert it into 2 dim matrix b1 b2 b3 a1 max(w1,w4) 0 0 a2 0 w2 0 a3 w5 0 w3 if edges repeated take the maximum weights. How do this efficiently without using for loop? Any idea. thanks Avi [[alternative

I am having problems with comparing individual means in a within-subjects ANOVA. From my understanding, TukeyHSD is not appropriate in this context. So I am trying to compute contrasts, as follows: seven subjects participated in each of 6 conditions (intervals). > subject = factor(rep(c(1:7), each = 6)) > interval = factor(rep(c(1:6), 7)) and here is the dependent variable: > dv

Can any one help it will be very kind, loop statements I have this table and some more records, I want to reshape it V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 TP53 Dis1 Dis2 Dis3 Dis4 Dis5 Dis6 DCI New1 New2 New3 New4 FDI Hi2 H3 H4 GHD I1 I3 I4 I5 I6 I7 I8 I want my new table or matrix to be some thing like this V1 V2 V3 Tp53 Dis1 Dis2 Tp53 Dis1 Dis3 Tp53 Dis1 Dis4 Tp53 Dis1 Dis5 Tp53 Dis1 Dis6 Tp53 Dis2

> On 24.07.2015, at 08:06, zhi chen <zchenhn at gmail.com> wrote: > > It seems that that it's hard to vectorize int64 in LLVM. For example, LLVM 3.4 generates very complicated code for the following IR. I am running on a Haswell processor. Is it because there is no alternative AVX/2 instructions for int64? The same thing also happens to zext <2 x i32> -> <2 x

I'm looking for guidance on how to implement forward stepwise regression using lmStepAIC in Caret. The stepwise "direction" appears to default to "backward". When I try to use "scope" to provide a lower and upper model, Caret still seems to default to "backward". Any thoughts on how I can make this work? Here is what I tried: itemonly <-

Hello: When I use SPSS I execute the AGGREGATE DATA comand for the next data: 2112141123212213212213334 3143244113442312121213344 2114141123112214212113344 2112211122212413421213221 3114444123442414343413344 2312231223212222323223322 2143241123212313131213234 2113241113212313222213333 2113141123112214212113344 2114141123412111114413344 2113211122342314222313234 2114141123112414212113344

Hello, I've been working with "nnet" and now I'd like to use the weigths, from the fitted model, to iterpret some of variables impornatce. I used the following command: mts <- nnet(y=Y,x=X,size =4, rang = 0.1, decay = 5e-4, maxit = 5000,linout=TRUE) X is (m x n) Y is (m x 1) And then I get the coeficients by: Wts<-coef(mts) b->h1 i1->h1

* * Hi , Can anyone help me please with this problem?* * *CASE-I* all_raw_data_NAomitted is my data frame.It has columns with names i1 ,i2, i3,i4…, till i15.It has 291 rows actually ,couldn’t show here. The data frame looks like this:-- i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13 i14 i15 2 2 2 2 2 2 2 2 2 2 2 1 2 2 3 2 3 2 2 2 2 3 2 2 3 3

Hi All, ? I am trying to use the lordif package in R and can't figure out the reason for the above error when I try to run the calctheta() function. ################## # read data using foreign package - must be Stata 11 or earlier racedata<- read.dta("race.dta")??? # item responses resp.data<-racedata[,-c(1:2)] # grouping variable is in the second column

Hi, Suppose I've got a matrix, and the first few elements look like "x1 + x3 + x4 + x5 + x1:x3 + x1:x4" "x1 + x2 + x3 + x5 + x1:x2 + x1:x5" "x1 + x3 + x4 + x5 + x1:x3 + x1:x5" and so on (have got terms from x1 ~ x14). If I want to replace all the x1 with i7, all x2 with i14, all x3 with i13, for example. Is there an easy way? I tried to put what I want