search for: 0.408

the following the the lower.tri matrix in a file named luxry.car and i want to read it in R as a lower.tri matrix.how can i do? i have try to use help.search("read"),but no result what i want. 1.000 0.591 1.000 0.356 0.350 1.000

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I have the following data: > y [1] 0.000 0.004 0.008 0.016 0.024 0.032 0.044 0.064 0.072 0.088 0.108 0.140 [13] 0.156 0.180 0.208 0.236 0.264 0.296 0.320 0.360 0.408 0.444 0.472 0.524 [25] 0.576 > x [1] 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 [16] 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 I'd

Dear list, I cannot make the latex command to output a ftable objet the way I want it. Is it posible? I found a post in the archives saying that one should use the rgroup and n.rgroup arguments to supply the row names, but so far I have been unsuccessful. This is what I have: >

Hi all, Can anyone help me understand why an implicit print (i.e. just typing df at the console), is so much slower than an explicit print (i.e. print(df)) in the example below? I see the difference in both Rstudio and in a terminal. # Construct large df as quickly as possible dummy <- 1:18e6 df <- lapply(1:10, function(x) dummy) names(df) <- letters[1:10] class(df) <-

Dear all, I am perplexed when trying to get the same results using pairwise.t.test and t.test. I'm using examples in the ISwR library, >attach(red.cell.folate) I can get the same result for pairwise.t.test and t.test when I set the variances to be non-equal, but not when they are assumed to be equal. Can anyone explain the differences, or what I'm doing wrong? Here's an example

This is probably simple, but I just can't see it... I want to calculate the R^2s for a series of linear models where each term is dropped in turn. I can get the RSS from drop1(), and the r.squared from summary() for a given model, but don't know how to use the result of drop1() to get the r.squared for each model with one term dropped. Working example: library(vcd) # for

I've just found the lavaan package, and I really appreciate it, as it seems to succeed with models that were failing in sem::sem. I need some clarification, however, in the output, and I was hoping the list could help me. I'll go with the standard example from the help documentation, as my problem is much larger but no more complicated than that. My question is, why is there one latent

Hi Sir, Thanks for making package available to us. I am facing few problems if you can give some hints: Problem-1: The model summary and residual deviance matched (in the mail below) but I didn't understand why AIC is still different. > AIC(m1) [1] 532965 > AIC(m1big_longer) [1] 101442.9 Problem-2: chunksize argument is there in bigglm but not in biglm, consequently,

Good Morning. Below I like statement like j<-grep(".r\\b",colnames(mydata),value=TRUE); j with the \\b option which I read long time ago which Ive found useful. Are there more or these options, other than ? grep? Thanks. dstat is just my own descriptive routine. > x ?[1] "age"????????? "sleep"??????? "primary"????? "middle" ?[5]

While browsing some code I discovered a call to lm that used a formula y ~ X - 1, where X was a matrix. Looking through the documentation of formula, lm, model.matrix and maybe some others I couldn't find this useage (R 2.10.1). Is it anything I can count on in future versions? Is there documentation I've overlooked? For the curious: model.frame on the above equation returns a

Dear R users I have a big matrix like 6021 1188 790 290 1174 1015 1990 6613 6288 100714 6021 1 0.658 0.688 0.474 0.262 0.163 0.137 0.32 0.252 0.206 1188 0.658 1 0.917 0.245 0.331 0.122 0.148 0.194 0.168 0.171 790 0.688 0.917 1 0.243 0.31 0.122 0.15 0.19 0.171 0.174 290 0.474

Dear Sir or Madam? I am a doctor of urology,and I am engaged in developing a nomogram of bladder cancer. May I ask for your help on below issue? I set up a dataset which include 317 cases. I got the Binary Logistic Regression model by SPSS.And then I try to reconstruct the model ?lrm(RECU~Complication+T.Num+T.Grade+Year+TS)? by R-Project,and try to internal validate the model through

Dear all, I am using the bigglm package to fit a few GLM's to a large dataset (3 million rows, 6 columns). While trying to fit a Poisson GLM I noticed that the coefficient estimates were very different from what I obtained when estimating the model on a smaller dataset using glm(), I wrote a very basic toy example to compare the results of bigglm() against a glm() call. Consider the

?s 02:10 de 02/08/2024, Steven Yen escreveu: > Good Morning. Below I like statement like > > j<-grep(".r\\b",colnames(mydata),value=TRUE); j > > with the \\b option which I read long time ago which Ive found useful. > > Are there more or these options, other than ? grep? Thanks. > > dstat is just my own descriptive routine. > > > x > ?[1]

Dear All- I have a dataset that is comprised of the following: doy yr mon day hr hgt1 hgt2 hgt3 co21 co22 co23 sig1 sig2 sig3 dif flag 244.02083 2005 09 01 00 2.6 9.5 17.8 375.665 373.737 373.227 3.698 1.107 0.963 -0.509 PRE 244.0625 2005 09 01 01 2.6 9.5 17.8 393.66 384.773 379.466 15.336 11.033 5.76 -5.307 PRE 244.10417 2005 09 01 02 2.6 9.5 17.8 411.162 397.866 387.755 6.835 5.61 6.728

Hi: I am using a subset of the below dataset to predict PRED_SUIT for the whole dataset but I am having trouble with 'newdata'. The model was created with 153 records and want to predict for 208 records. wolf2 <- structure(list(gridcell = c(367L, 444L, 533L, 587L, 598L, 609L, 620L, 629L, 641L, 651L, 662L, 674L, 684L, 695L, 738L, 748L, 804L, 805L, 872L, 919L, 929L, 938L, 950L, 958L,

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Hi, I have a file that looks like this : one,0 two,0.591,0 three,0.356,0.350,0 four,-0.098,0.072,0.380,0 five,0.573,0.408,0.382,0.062,0 six,0.156,0.232,0.517,0.424,0.303,0 seven,0.400,0.414,0.611,0.320,0.401,0.479,0 eight,0.282,0.375,0.512,0.346,0.308,0.463,0.605,0 nine,0.519,0.484,0.467,0.167,0.455,0.311,0.574,0.557,0 I want to create a data matrix out of it, so I tried this :

Suharto, If you're interested in performance with subscripting, you might want to look at pqR (pqR-project.org). It has some substantial performance improvements for subscripting over R Core versions. This is especially true for the current development version of pqR (probably leading to a new release in about a month). You can look at a somewhat-stable snapshot of recent pqR development

Hello, I am not having success in a simple task. Using the effects package, I would like to add reference lines at probability values of 0.1 – 0.6 on a plot of the effects. The plot command works, but following up with an abline command produces the message “plot .new has not been called yet”, and of course the reference lines were not added. Looking through past R help lists, there was a