search for: norm2

Hi, I am new to R. I have a matrix that I have assigned to the object ?colon?. > colon<-read.table("c:\\alon.txt",header=T,row.names=1) attach(colon) names(colon) The dimenstions are 2000 62. Each of the 62 columns (titled norm1, norm2, norm3, etc) has 2000 different numbers (?continuous? values) within it. I have also assigned a name for each of the 2000 rows of the dataframe with a prefix (i.e. g1 ?. g2000) using the code (not sure if I did this right): > colon<-paste("g",c(1:nrow(colon)),sep="") I...

...or(i in 1:length(myydata)){ lines(c(myxdata[i],myxdata[i],max(newdata)),c(min(newy),myydata[i],myydata[i]),lty=3) } y1=seq(16.95,17.25,0.003) y2=seq(16.35,16.65,0.003) x1=predict_amount(fit,y1,TRUE) x2=predict_amount(fit,y2,TRUE) norm1=normpdf(y1,17.1,0.045) norm1=min(newy)+norm1/max(norm1)*.5 norm2=normpdf(y2,16.5,0.045) norm2=min(newy)+norm2/max(norm2)*.5 lines(x1,norm1,col="#0000FF") lines(x2,norm2,col="#FF0000") normy1=normpdf(y1,17.1,0.045) normy1=normy1/max(normy1)*-.3e-10+max(newdata) normy2=normpdf(y2,16.5,0.045) normy2=normy2/max(normy2)*-.3e-10+max(newdata) lines(...

...sep="\t", na.strings="NA", dec=".", strip.white=TRUE) dataf <- read.table("FILENAME", header=FALSE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE) norm1 <- glm(dataf[1,1] ~ dataf[1,2], family= normal(log), data=dat) norm2 <- glm(dataf[1,1] ~ dataf[1,2], family= normal(identity), data=dat) and so on. But glm does not work on the data unless I write ABC and EFG there... I want to automate the script for multiple files. The other problem is to write the plot(GLM) to a file without displaying it at stdout. Thanks...

...The second reason might be that your dataset is named df, which is defined as a function an may produce problems. Try renaming the dataset. yhat=predict(reg1,newdata=x2) x1=seq(1:100) r.norm1=rnorm(100,0,20) x1=r.norm1+x1 x2=x1*2 r.norm2=rnorm(200,0,20) x2=r.norm2+x2 reg1=glm(y1~x1,binomial) yhat=predict(reg1) ##prediction works fine yhat=predict(reg1,newdata=x2) ##gives error message because of "newdata" >Error in eval(predvars, data, env) : numeric 'envir' arg not of l...

...what is returned by poly(). > poly(1:3, degree=2) 1 2 [1,] -7.071068e-01 0.4082483 [2,] -9.073264e-17 -0.8164966 [3,] 7.071068e-01 0.4082483 attr(,"degree") [1] 1 2 attr(,"coefs") attr(,"coefs")$alpha [1] 2 2 attr(,"coefs")$norm2 [1] 1.0000000 3.0000000 2.0000000 0.6666667 attr(,"class") [1] "poly" "matrix" > I've read the code for poly(), but $alpha and $norm2 are undocumented, and I still still can't see the inverse transformation -Michael -- Michael Friendly Email: fr...

...summarize(norm, llist(gtt,fdiab), function(norm) { n <- sum(!is.na(norm)) s <- sum(norm, na.rm=T) binconf(s, n) }, type='matrix') ) ie gtt fdiab norm.norm norm.norm2 norm.norm3 18 PL No 3.70370370 0.18997516 18.28346593 19 PL Yes 3.57142857 0.18319034 17.71219774 13 TT1 No 9.09090909 3.59221932 21.15923917 14 TT1 Yes 1.81818182 0.09326054 9.60577606 ... 10 HIGH No 26.53061224 16.21128213 40.26228897 11 HIGH Yes 10.00000000...

...orrelation for hierarchical clustering. Any idea if anything is violated in case pearson's correlation is used with Ward's linkage function? the dissimilarity of pearson's correlation can be defined as d = sqrt(1-pearsonsimilarity^2). can that be considered as norm1 distance? and thus norm2 if we square it? so that the wikipedia's statement "To apply a recursive algorithm under this objective function, the initial distance between individual objects must be (proportional to) squared Euclidean distance." is valid? Best, Pierre [[alternative HTML version deleted]]

...rodives irq affinity - Choose txq based on processor id References: - V4: https://lkml.org/lkml/2012/6/25/120 - V3: http://lwn.net/Articles/467283/ Test result: 1) 1 vm 2 vcpu 1q vs 2q, 1 - 1q, 2 - 2q, no pinning - Guest to External Host TCP STREAM sessions size throughput1 throughput2 norm1 norm2 1 64 650.55 655.61 100% 24.88 24.86 99% 2 64 1446.81 1309.44 90% 30.49 27.16 89% 4 64 1430.52 1305.59 91% 30.78 26.80 87% 8 64 1450.89 1270.82 87% 30.83 25.95 84% 1 256 1699.45 1779.58 104% 56.75 59.08 104% 2 256 4902.71 3446.59 70% 98.53 62.78 63% 4 256 4803.76 2980.76 62% 97.44 54.68 56% 8 256 51...

...rodives irq affinity - Choose txq based on processor id References: - V4: https://lkml.org/lkml/2012/6/25/120 - V3: http://lwn.net/Articles/467283/ Test result: 1) 1 vm 2 vcpu 1q vs 2q, 1 - 1q, 2 - 2q, no pinning - Guest to External Host TCP STREAM sessions size throughput1 throughput2 norm1 norm2 1 64 650.55 655.61 100% 24.88 24.86 99% 2 64 1446.81 1309.44 90% 30.49 27.16 89% 4 64 1430.52 1305.59 91% 30.78 26.80 87% 8 64 1450.89 1270.82 87% 30.83 25.95 84% 1 256 1699.45 1779.58 104% 56.75 59.08 104% 2 256 4902.71 3446.59 70% 98.53 62.78 63% 4 256 4803.76 2980.76 62% 97.44 54.68 56% 8 256 51...

...10 0.5224893 0.5896255 - thats the same as last time. Extrapolate? > predict(mmn[[3]],newdata=data.frame(x=c(9,10,11))) Error: variable 'poly(x, i)' was fitted with type "nmatrix.3" but type "nmatrix.5" was supplied In addition: Warning message: In Z/rep(sqrt(norm2[-1L]), each = length(x)) : longer object length is not a multiple of shorter object length it falls over. I can't see the difference between the objects, summary() looks the same. Is something wrapped up in an environment somewhere, or some lazy evaluation thing, or have I just done somethin...

Dear All, Need some help in polynomials transformation to get the coefficients. I have tried "poly.transform" as applied in S-plus but it does not work. Thanks in advanced for any helps. Regards. Abd. Rahman Kassim (PhD) Head Forest Ecology Branch Forest Management & Ecology Program Forestry and Conservation Division Forest Research Institute Malaysia Kepong 52109 Selangor,

Hi all - I'm stumped by the following mdl <- glm(resp ~ . , data = df, family=binomial, offset = ofst) WORKS yhat <- predict(mdl) WORKS yhat <- predict(mdl,newdata = df) FAILS Error in drop(X[, piv, drop = FALSE] %*% beta[piv]) : subscript out of bounds I've tried without offset, quoting binomial. The offset variable ofst IS in df. Previous postings indicate possible

Hi I Have a very large dataset that I would like to conduct ANOVA tests on. Im not a very strong programmer so any help would be appreciated. the format is Identifier             A1       A2        B1      B2       C1   C2      Norm1         Norm2 1234                  1        1            NA     NA      4       3        NA               NA 4567                  2        2              4      4         8       8       9                    9 and so on I have 10 runs for 3 different doses plus the normal state. Any help greatly appreciated...

...ing, for the one that interested in this please see: http://www.mail-archive.com/kvm at vger.kernel.org/msg64111.html Detail result: Test results: smp = 2 pin vhosts and vcpus in the same node - 1 sq 2 mq(q=2) - TCP_MAERTS (Guest to external Host): sessions size throughput1 throughput2 norm1 norm2: 1 64 424.91 401.49 94% 13.20 12.35 93% 2 64 1211.06 878.31 72% 24.35 15.80 64% 4 64 1292.46 1081.78 83% 26.46 20.14 76% 8 64 1355.57 826.06 60% 27.88 15.32 54% 1 256 1489.37 1406.51 94% 46.93 43.72 93% 2 256 4936.19 2688.46 54% 100.24 46.39 46% 4 256 5251.10 2900.08 55% 107.98 50.47 46% 8 256 5270...

...ing, for the one that interested in this please see: http://www.mail-archive.com/kvm at vger.kernel.org/msg64111.html Detail result: Test results: smp = 2 pin vhosts and vcpus in the same node - 1 sq 2 mq(q=2) - TCP_MAERTS (Guest to external Host): sessions size throughput1 throughput2 norm1 norm2: 1 64 424.91 401.49 94% 13.20 12.35 93% 2 64 1211.06 878.31 72% 24.35 15.80 64% 4 64 1292.46 1081.78 83% 26.46 20.14 76% 8 64 1355.57 826.06 60% 27.88 15.32 54% 1 256 1489.37 1406.51 94% 46.93 43.72 93% 2 256 4936.19 2688.46 54% 100.24 46.39 46% 4 256 5251.10 2900.08 55% 107.98 50.47 46% 8 256 5270...

...86846110 -0.08282554 -0.008363512 -0.21685556 [20,] 0.1753745 -0.0033219134 0.09871464 0.017312033 -0.23746062 attr(,"degree") [1] 1 2 1 2 2 attr(,"coefs") attr(,"coefs")[[1]] attr(,"coefs")[[1]]$alpha [1] 0.5477073 0.4154115 attr(,"coefs")[[1]]$norm2 [1] 1.00000000 20.00000000 1.55009761 0.08065872 Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, Jul 13, 2017 at 11:1...

...first post requesting help to this mailing list. I am new to R. My apologies for any breach in posting etiquette. I am new to this language and just learning my way around. I am attempting to run some sample code and and am confused by the error message: Loading required package: rrcov Error in fitNorm2(fdat[, "FSC-H"], fdat[, "SSC-H"], scalefac = ScaleFactor) : Required package rrcov could not be found. In addition: Warning message: there is no package called 'rrcov' in: library(package, lib.loc = lib.loc, character.only = TRUE, logical = TRUE, > that I get...

...> [20,] 0.1753745 -0.0033219134 0.09871464 0.017312033 -0.23746062 > attr(,"degree") > [1] 1 2 1 2 2 > attr(,"coefs") > attr(,"coefs")[[1]] > attr(,"coefs")[[1]]$alpha > [1] 0.5477073 0.4154115 > > attr(,"coefs")[[1]]$norm2 > [1] 1.00000000 20.00000000 1.55009761 0.08065872 > > Cheers, > Bert > > > Bert Gunter > > "The trouble with having an open mind is that people keep coming along > and sticking things into it." > -- Opus (aka Berkeley Breathed in his "Bloom Co...

Bert, The 'degree' argument follows the "..." argument in the function declaration: poly(x, ..., degree = 1, coefs = NULL, raw = FALSE, simple = FALSE) Generally, any arguments after the "..." must be explicitly named, but as per the Details section of ?poly: "Although formally degree should be named (as it follows ...), an unnamed second argument of length 1

Dear all, Can any one tell me how can i perform Orthogonal Polynomial Regression parameter estimation in R? -------------------------------------------- Here is an "Orthogonal Polynomial" Regression problem collected from Draper, Smith(1981), page 269. Note that only value of alpha0 (intercept term) and signs of each estimate match with the result obtained from coef(orth.fit). What