similar to: Simple question about formulae in R!?

Hi, I often find myself using bits of code like this inside ActiveRecord, perhaps it''s useful for others, or others can improve on it: ########### # fix user input before validating it before_validation :sanitize_input # santize input before actual validation is called # this uses the little methods defined below def sanitize_input trim %w(adres postcode woonplaats email naam

Hi, This is a problem about writing R documentation (R-exts 2.6). The command "\deqn" defined in "Rd.sty" is: \newcommand{\deqn}[2]{\[#1\]} which will put mathematical formulae in the "displaymath" environment; that means line breaks are not allowed (or will not be shown) in formulae, but sometimes we do need multiple lines of formulae. One solution is to write

In working through material on p.272 of MASS (4th ed.), I came across the following model formula: pet1.lm <- lm(Y ~ No/EP - 1, Petrol) I was at a loss to understand the use of "/" until I looked in "An Introduction [!] to R," where I found the explanation. My request is that more complete material on model formulae be lifted from "Introduction to R" (or

Can someone direct me to a resource or resources that list the formulae used by R functions (i.e. predict.lm ) to calculate the statistic reported. I am not a programmer and studying the r code is extremely slow going. I have searched r-project.org and all the function help files without success. For example I have attempted to replicate by hand the se.fit calculation from a lm object

Hi, all Does anybody know if it is possible to include TeX-style formulae in R plots? The aim is to export such plots to LaTeX documents using the postscript() device. Many thanks in advance, Alberto Munoz --------------------------------------------------------------------- Alberto Munoz Phone: +34-91- 624 95 79 Dpto. de Estadistica y Econometria Fax: +34-91- 624

I'd like to check within a whether an argument is a formulae of the type ~X or similar. What's the right way to do so? I'm using: is.language(obj) but not sure this is right Thanks in advance P.J. Paulo Justiniano Ribeiro Jr Dept Maths & Stats - Fylde College Lancaster University Lancaster LA1 4YF - U.K. e-mail: paulojus at est.ufpr.br http://www.maths.lancs.ac.uk/~ribeiro

Hi, Given that R reports Type I sums of squares, isn't it a bit anachronistic that it re-orders terms in formulae? > d <- expand.grid(y=rnorm(8), + A=factor(c(1,2)), + B=factor(c(1,2)), + C=factor(c(1,2))) > summary(aov(y ~ A+B+A:B+C,data=d)) Df Sum Sq Mean Sq F value Pr(>F) A 1 8.294e-34 8.294e-34 1.027e-33 1

Hi, Is there a way of annotating a ggplot plot with mathematical formulae? I can do geom_text(aes(label="some text", ... but I can't do geom_text(aes(label=expression(x^{n-1}), ... It gives the error Error: geom_text requires the following missing aesthetics: label Is there a convenient equivalent? Cheers, Paul

Hello All, I have searched but haven't been able to find an answer to this question. I'm writing a function that needs to be able evaluate the right-hand side of a general non-linear formula for different parameter values, similar to what nls() does. I looked through the nls() code but it isn't clear what is going on. Could someone point me to a reference or a way I might do this?

I am attempting to extract the derivative/ gradient from this expression df1p <- deriv(f1, "P") > df1p expression({ .value <- s - c - a * P .grad <- array(0, c(length(.value), 1L), list(NULL, c("P"))) .grad[, "P"] <- -a attr(.value, "gradient") <- .grad .value }) So in this case I want to extract the "-a".

Dear R-Community, I am relatively new with R, so sorry for things which for you might be obvious... I am trying to automatically update lmer formulae. the variables of the model are: depM= my dependent measure Sb2= a random factor OS = a predictor VR= another predictor So, I am building the first model with random intercept only: model = lmer(depM ~ (1 |Sb2)) then I update the formula

> cmpq %rbx, %r14 > jne .LBB0_1 > > LLVM can perform compare-jump fusion, it already does in certain cases, but > not in the case above. We can remove the cmp above if we were to perform > the following transformation: Do you mean branch-fusion (https://en.wikichip.org/wiki/macro-operation_fusion)? Is there any more limitation why these two or not fused? > -----Original

Dear R Mailing Subscribers, I just have a question of how R handles interacting variables in model creation using glm for instance. if I write : >> model=glm(solution~descriptor1+descriptor2+descriptor3,family=binomial(link="logit")) I should end up with coefficients for a logistic model that I can introduce easily in the mathematical form of such a model (weighted sum of

Hello again, It appears that get_all_vars() incorrectly handles model formulae that use a right-hand side (rhs) matrix. For example, consider these two substantively identical models: # model using named variables mpg <- mtcars$mpg wt <- mtcars$wt hp <- mtcars$hp m1 <- lm(mpg ~ wt + hp) # model using matrix y <- mtcars$mpg x <- cbind(mtcars$wt, mtcars$hp) m2 <- lm(y ~ x)

What is a good way to enter a very long model formula. For example: y ~ Input.2 + Input.3 + ... + Input.1000 (assuming the corresponding dataframe has many other columns). Is there a way to convert a character string to a formula? Are there command line expansions in R besides the simple '.'? Thanks. [[alternative HTML version deleted]]

Hi, I'm trying to create a linear model for a dataset that has a breakpoint e.g. # dummy dataset x <- 1:20 y <- c(1:10,seq(10.5,15,0.5)) plot(x,y) I've modelled this using the following formula: temp <- lm(y ~ x*(x<=10)+x*(x>10)) I want to be able to omit the intercept (i.e. force the line through zero) from the first of these segments (x<=10) so that I'm only

Several bugs (no solutions, yet). These might be well known. 1) If one does, e.g., mymod <- lm(y ~ x); formula(mymod) then one does not get back the formula (one gets, Error: invalid formula) 2) if x is of mode numeric, then the model formula mymod <- lm(y ~ x + x^2) is not processed as S would do it. The model is fit ignoring the x^2 term, however mymod$call includes the x^2 term.

Several bugs (no solutions, yet). These might be well known. 1) If one does, e.g., mymod <- lm(y ~ x); formula(mymod) then one does not get back the formula (one gets, Error: invalid formula) 2) if x is of mode numeric, then the model formula mymod <- lm(y ~ x + x^2) is not processed as S would do it. The model is fit ignoring the x^2 term, however mymod$call includes the x^2 term.

Hi everyone, I want to do test on moderating effect. I have three factors, A, B, and C. A has influence on B, and C moderating the influence. The relationship looks like this: A -----> B ^ | C A, B, and C are all scale variables. I think I can test the moderating effect by adding a interactive variable between A and C. But I'm not sure how to do. Is there a default way to

Hi, I think the time (150 messages and counting) has come to ask someone of the CentOS team to start moderating this list. I don't mind people discussing whatever topic they like, but not at the expense of the mailinglist and its members. I don't think the topic really doesn't matter in these discussions, and if done in private they would stand for long anyway. I would propose