search for: formule

Dear All: I need to have some data frame objects. First aa object: pH Formulation time Subject [1]1.2 F 0 1 [2]7.4 S 1 2 [3] MF 2 3 [4] 3 4 [5] n i Then, I need to produce 2*3(pH*formulation) different tables. This table includes column of (pH, Formulation, time S1 S2 S3 ?K..Si) and S1=

Hello list reader, I am trying to form some constraints for an optimization I am working on. I think I have understand the use of the constraints in matrix form. I use them like: constr_mat<- -diag(2) constr_vec<- rep(-0.05,2) constr_mat<- rbind(constr_mat, diag(2)) constr_vec<- c(constr_vec, rep(-1, 2)) To get parameters in the interval [-1, 0.05]. (And this works so far) Now I

Dear all: I am a new R-user and I have 2 questions about it. 1) I have a dataframe. Based on ?formulation? and ?subject?, a dataframe is split into 4 dataframes. The example is as follows. Moreover, I want to calculate ?test? value for these 4 dataframes. My question is that the ?test? values not correct and I do not know where the problem is. 2) There are 12 ?test? (y) values from

HI, sorry but i don't understand how to make a function with as.function() formula<-"2+3*x" formu<-as.symbol(formula) > formu 2+3*x formul<-as.function(alist(x=,formu)) curve(formul,1,5,col="blue") Error in xy.coords(x, y, xlabel, ylabel, log) : x and y lengths differ > typeof(formul) [1] "closure" and not plot the curve function, Why?

this is a clearer (I hope) version of an earlier post - My problem is formulating the random = argument to give estimates of all 9 random components for this kind of setup where there are (I think) 9 variance/covariance components ... Study.1 Study.2 ... Study.5 Treatment T1: subject: 1 2 3 4 5 6 ... 13 14 15 Treatment T2: subject: 16 17

I'm trying to fit a nested mixed model using lmer and have some questions about the output and my model formulations. I have replicate measures on Lines which are strictly nested within Populations. (a) So if I want to fit a model where Line is a random effect and Populations are fixed and the random Line effect is constant across Populations, I have: measure_ijk = mu + P_i + L_ij +

Dear all, I am using the pls package of R to perform partial least square on a set of multivariate data. Instead of fitting a linear model, I want to fit my data with a quadratic function with interaction terms. But I am not sure how. I will use an example to illustrate my problem: Following the example in the PLS manual: ## Read data data(gasoline) gasTrain <- gasoline[1:50,] ## Perform

...les gender and age response<-c(2.1,3.5,6.1,2.0,1.5) dataset<-data.frame(ID,gender,age,G1,G2,G3,response) GG<-c("G1","G2","G3") # here I construct a function that makes a basic fit, then updates with each variable from GG vector. trial_function(mydata,formule,expl,distr="binomial") { n_length(expl) fit.low_glm(formule, family = distr, data = mydata, na.action = na.exclude) for (j in 1:n) { fit_update(fit.low,~.+ mydata[,expl[j]]) print(mydata[,expl[j]]) } } result<- trial(mydata=dataset,formule=re...

Hi all - R2.0.1 on OSX;MASS library;nlme library I am trying to emulate the solution to a problem set that has normally been run in Genstat, using R. The problem that I am having at the moment is with the following glmm question (using glmmPQL from the MASS library): "We have two different forest habitats (first rotation thicket, and high forest) which we want to survey for the

Hi, I would like to see if a matching variable is an effect-modifier in a conditional logistic regression. Naturally, the matching variable can't enter directly in the model but as an interaction with terms that are in. However, I have problems in formulating the correct model the term that's already in the model is a factor. I am using treatment contrasts and the problem is that if I

Hello, I'm trying to analyse data from an incomplete design with four factor : - fr : number of the batch - op : ID of operator - meth : method used - mat : nature of the material used and one variable - mv : mesure - trmv : transformed mesure str(matvol) `data.frame': 120 obs. of 6 variables: $ fr : Factor w/ 30 levels "1","2","3","4",..:

Hi Daniel, I want to clarify that our analysis is not based on CFL-reachability. We apply CFL-reachability to matching context information where the exist from a function to a call-site must match the entry from the corresponding call-site. The problem is a simple balanced parentheses problem in CFL-reachability, and it can be computed efficiently. The paper you mentioned is a very nice paper

On Fri, Oct 18, 2013 at 7:27 AM, lian li <lianli at gmail.com> wrote: > Hi Daniel, > > I want to clarify that our analysis is not based on CFL-reachability. > We apply CFL-reachability to matching context information where the > exist from a function to a call-site must match > the entry from the corresponding call-site. Yes, sorry, I pulled the wrong quote, it was late.

Dear list, we are a statistical/epidemiological departement that - after a few years of rapid growth - finally is getting around to formulate a general data storage and retention policy - mainly to ensure that we can reproduce results from published papers/theses easier in the future, but also with the hope that we get more synergy between related projects. We have formulated what we

...neralized logistic model proposed by richards. with data such as these: x <- c(88,113,128,143,157,172,184,198,210,226,240,249,263,284,302,340) y <- c(0.04,0.16,1.09,2.65,2.46,2.43,1.88,2.42,1.51,1.70,1.92,1.35,0.89,0.34,0.13,0.10) I use the nls function to fit my data to the model. nls(formule, data=cbind.data.frame(x,y), start=list(Asym_inf =min(y),Asym_inf =max(y)-min(y), n1=1,n2=1,tmid1=120,tmid2=250,scal1=11,scal2=30)) and it always finished by one of those answers (even if I change the initial values) : - "Error in nls(formule, data = cbind.data.frame(x, y), start = list(A...

Dear ?, I'm sure that there are many ways to do what you want; here's one: > cbind(concept_df, category= + ifelse(apply( + sapply(chemical_df$chemical, + function(x) grepl(x, concept_df$concept)), + 1, any), + "chemical", "")) concept category 1 butan

In the almost current development version (2009-05-22 r48594) and also in https://svn.r-project.org/R/trunk/src/library/base/man/factor.Rd ?factor contains (compare the formulations marked by ^^^^^^) \section{Warning}{ The interpretation of a factor depends on both the codes and the \code{"levels"} attribute. Be careful only to compare factors with the same set of levels (in

On 06/10/2010 08:16 PM, Chris Pearce wrote: > I have no plans to change the 'index' packet format further. I have proposed an alternative formulation of the index packet at http://github.com/bemasc/OggIndex/blob/master/Proposed-modified-spec.txt That repository also contains a working implementation of the alternative formulation. I have reviewed the details with Chris extensively.

Below. -- Bert Bert Gunter On Thu, Jul 13, 2017 at 3:07 AM, Luigi Biagini <luigi.biagini at gmail.com> wrote: > I have two ideas about it. > > 1- > i) Entering variables in quadratic form is done with the command I > (variable ^ 2) - > plsr (octane ~ NIR + I (nir ^ 2), ncomp = 10, data = gasTrain, validation = > "LOO" > You could also use a new variable

I have two ideas about it. 1- i) Entering variables in quadratic form is done with the command I (variable ^ 2) - plsr (octane ~ NIR + I (nir ^ 2), ncomp = 10, data = gasTrain, validation = "LOO" You could also use a new variable NIR_sq <- (NIR) ^ 2 ii) To insert a square variable, use syntax I (x ^ 2) - it is very important to insert I before the parentheses. iii) If you want to