similar to: family question

Let''s say I have three hypothetical MySQL tables: ? people, with columns id, gender, and source_id belongs_to :boys and :girls ? boys, with columns id and name has_many :people ? girls, with columns id and name has_many :people The gender column in people specifies which of the two source tables the source_id refers to. For example, if we have values: 1, boy, 1 in people,

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Hi There, There are 300 boy students and 100 girl students in a class. One interesting question is whether boy is smarter than girl or not. first given the exam with a difficulty level 1, the number of the student who got A is below 31 for boy, 10 for girl. Then we increase the difficulty level of the exam to level 2, the number of the student who got A is below 32 for boy, 10 for girl. We

I have some data that I read in via read.csv: sales2007 <- read.csv("Total2007.dat", header=TRUE) The data looks like: > sales2007[1:605,] Year DayOfYear Sku Quantity CatId Category SubCategory 1 2007 1 100091 1 10862 HOLIDAY Christmas 2 2007 1 100138 1 11160 PET COSTUMES Famous (Licensed) 3 2007

How does one implement a likelihood-ratio test, to test whether the variances of the random effects differ between two groups of subjects? Suppose your data consist of repeated measures on subjects belonging to two groups, say boys and girls, and you are fitting a linear mixed-effects model for the response as a function of time. The within-subject errors (residuals) have the same variance in

Hi R, I am using select.list names=c("Ravi", "Raj","Shubha","Nivriti") select.list(names) provides a drop down to choose one of the 4 names. However I would like to know if it is possible to create a classification something like this select.list(names) should give Boys Ravi Raj Girls Shubha Nivriti I should be able to choose

hi, as far I know, iproute QoS works in interface, not in all interfaces. I want give one inner interface priority over other inner, like PRIO one IP over other. HOW? -- Andrius K. Kasparavičius _______________________________________________ LARTC mailing list / LARTC@mailman.ds9a.nl http://mailman.ds9a.nl/mailman/listinfo/lartc HOWTO: http://lartc.org/

Hi! I have used the following command: densityplot(~PV1CIV, groups=SGENDER, data=ISGFINC2, lwd=2, col=1, lty=c(1,2), pch=c("+","o"), key=list(text=list(lab=levels(ISGFINC2$SGENDER), col=1), space="bottom", columns=2, border=T, lines=T, lwd=2, lty=c(1,2), col=1), ref=T, plot.points=F) to produce a lattice density plot presenting the distribution of

R Users: I am trying to estimate a model of fertility behaviour using birth history data with maximum likelihood. My code works but is extremely slow (because of several for loops and my programming inefficiencies); when I use the genetic algorithm to optimize the likelihood function, it takes several days to complete (on a machine with Intel Core 2 processor [2.66GHz] and 2.99 GB RAM). Computing

Hi, lme() users, Can some one tell me how to do this. I model Orthodont with the same G for random variables, but different R{i}'s for boys and girls, so that I can get sigma1_square_hat for boys and sigma2_square_hat for girls. The model is Y{i}=X{i}beta + Z{i}b + e{i} b ~ iid N(0,G) and e{i} ~ iid N(0,R{i}) i=1,2 orth.lme <- lme(distance ~ Sex * age, data=Orthodont, random=~age|Subject,

Greeting R Community, I'm trying to learn Logistic Regression on my own and am using An Introduction to Logistic Regression Analysis and Reporting (Peng, C., Lee, K., & Ingersoll, G. ,2002). This article uses a Score Test Stat as a measure of overall fit for a logistic regression model. The author calculates this statistic using SAS. I am looking for an [R] function that can compute

Hello boys and girls In the ever-loving spirit of OSS, here''s another feature for s.a.u. In particular it''s called Effect.Accordion, and is a scriptaculified version of openrico''s Accordion. Work in progress, I''ll submit further modifications as the come in, but this is 90% of what needs to be done. I anticipate some bugs when I start using it more. Tested

I'm working on a quick tutorial for my students, and was planning on using Mann-Whitney U as one of the tests. I have the following (fake) data grade <- c("MVG", "VG", "VG", "G", "MVG", "G", "VG", "G", "VG") sex <- c( "male", "male", "female", "male",

Estimados Usuarios-R: Estoy convirtiendo un programa en Matlab a R. El original lo saqu de: Lai, M., & Poltera, Y. (2009). Lecture with computer exercises: Modelling and simulating social systems with matlab. Tech. rep., Swiss Federal Institute of Technology (December 2009). 27. Ahora estoy convirtiendo la siguiente funcin: function sizes = gridsizes(N,varargin) % gridsizes(N) calculates

Could some kindly tell me if I am supposed to be getting the same test statistic value with var.equal=TRUE and var.equal=FALSE in t.test ? set.seed(1066) x1 <- rnorm(50) x2 <- rnorm(50) t.test(x1, x2, var.equal=FALSE)$statistic # 0.5989774 t.test(x1, x2, var.equal=TRUE)$statistic # 0.5989774 ??? Here are my own calculations that shows that perhaps the result when var.equal=TRUE is

Hi R lovers I would like to know how to step to the next line in the R console editor without breaking the continuity of my code more clearly : if for example I write a function, so far i have to write the all code inside on the same line wich may become obscure as the function is more and more complex. I would like to do like in the example of the manuels: >twosam <- function(y1, y2) {

I'm solving 4 complex equations simultaneously. Code is below. The code returns only zero's for the solution though there should also be a non-zero result. I'm pretty confident that the equations are correct because they are straight from a published paper and I checked them pretty thoroughly. The parameter values I used are from the published paper as well. Any suggestions for how

I still need to do some repetitive statistical analysis on some outcomes from a dataset. Take the following as an example; id sex hiv age famsize bmi resprate 1 M Pos 23 2 16 15 2 F Neg 24 5 18 14 3 F Pos 56 14 23 24 4 F Pos 67 3 33 31 5 M Neg 34 2 21 23 I want to know if there are statistically detectable differences in all of the continuous variables in

The problem seems that ":" always coerces to integer, but should not.. 9.9:12 ## R: [1] 9 10 11 ## S-plus 3.4: [1] 9.9 10.9 11.9 ## and many more examples.... -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Hi, I am trying to aggregate the sum of my test data.frame as follow: testDF <- data.frame(v1 = c("a", "a", "a", "a", "a", "b", "b", "b", "b", "b", "c", "c", "c", "c", "c", "d", "d", "d", "d",