similar to: integer codes of factors

Hello! I am running analysis on the data from 4 experiments, with approximately 4600 rows (cases). My working model is: fitA1 = lme(RT~F1+F2+L,random=~1|Experiment/Subject,data=data) Model works very fine, but if I try to check whether the effect of L depends on Experiments/Subjects with: fitA2 = lme(RT~F1+F2+L,random=~1+L|Experiment/Subject,data=data) [with the idea to make: anova(fitA1,fitA2)]

I'm interested in plotting a y with an x factor as the combination of 2 factors and colour with respect to a third, which the code below does with interaction.plot(). However, this is because I redefine the x to be 1 factor. Is there a way of getting it to plot without redefining it, and ideally to not join up the lines BETWEEN levels a and b, but just join those between after and before for

Hello, I am using R to analyze a large multilevel data set, using lmer() to model my data, and using anova() to compare the fit of various models. When I run two models, the output of each model is generated correctly as far as I can tell (e.g. summary(f1) and summary(f2) for the multilevel model output look perfectly reasonable), and in this case (see below) predictor.1 explains vastly more

Dear all, I need an advice in the following problem. I have to maximize two functions of the form f1(x)=f(y1,x,alpha1,beta1) and f2(x)=(y2,x,alpha2,beta2), the maximization is with respect to alpha1, alpha2, beta1, beta2. I can maximize each function separately using nlm. The problem is that I have to add the constraint of the form g(alpha1)=g(alpha2). The total number of parameters is

Hello, I'd like to produce a ggplot where the order of factors within facets is based on the average of another variable. Here's a reproducible example. My problem is that the factors are ordered similarly in both facets. I would like to have, within each facet of `f1', boxplots for 'x' within each factor `f2', where the boxplots are ordered based on the average of x

Hi I know this is a basic question and I know I have done it before but I can't find the answer any more. I have a data set, say: F1, F2, Value F1 and F2 are Factors. I would like to plot plot(TheCombinationOf(F1, F2), Value) I remember there was a function for TheCombinationOf() but I don't remember the name. I could create a new factor based on F1 and F2, but I would prefer the

Hi all, I hope it's not too trivial for the list - I'm trying to concatenate two factor arrays, and obtain the following: > f1<-factor(c("a","a","b")) > f1 [1] a a b Levels: a b > f2<-factor(c("b","b","a")) > f2 [1] b b a Levels: a b > c(f1,f2) [1] 1 1 2 2 2 1 Instead of getting: [1] a a b b b a Levels: a

Hello, I use the function 'aggregate' a lot. One small annoyance is that it is necessary to name the factors in the 'by' list to get the names in the resulting data.frame (else, they appear as Group.1, Group.2...etc). For example, I am forced to write: aggregate(y,list(f1=f1,f2=f2),mean) instead of aggregate(y,list(f1,f2),mean) (for two factors with short names, it is not such

Greetings, I am attempting to run a function, which produces a vector and requires two input variables, across two nested factor levels. I can do this using by(X, list(factor1, factor2), function), however I haven't found a simple way to extract the list output into an organized vector form. I can do this using nested loops but it isn't exactly an optimal approach. Thank you

The following example from the help page does not work: > f1_ gl(2,3); f1 > f2_ gl(3,2); f2 > f1:f2 # a factor, the ``cross'' f1 x f2 yields Warning in f1:f2 : Numerical expression has 6 elements: only the first used [1] 1 whereas R-0.61.2 yields > f1:f2 # a factor, the ``cross'' f1 x f2 [1] 1:1 1:1 1:2 2:2 2:3 2:3 Note that the new behavior is consistent with

hello, I am trying to use predict.lm to make point forecasts based on a model with continuous and categorical independent variables I have no problems fitting the model using lm, but when I try to use predict to make point predictions. it reverts back to the original dataframe and gives me the point predictions for the fitted data rather than for the new data, I imagine that I am missing

Hi everyone, I'm fairly new to R, and I don't have a background in statistics, so please bear with me. ;-) I'm dealing with 2^k factorial designs, and I was just wondering if there's any way to analyze more than two factors of a gage R&R study in R. For example, Minitab has an "expanded gage R&R" function that lets you include up to eight additional factors

#I have a dataset with two factor. I want to combine those factors into a single factor and count the number of data values for each new factor. The following gives a comparable dataframe: a <- rep(c("a", "b"), c(6,6)) b <- rep(c("c", "d"), c(6,6)) df <- data.frame(f1=a, f2=b, d=rnorm(12)) df # I use the 'interaction' function to combine

Hi, I have a data frame with two factors, and I would like to create a new one by "combining" them. I have already a solution, but it is very "heavy", and I'm sure there is a basic function which can do the same. I tried to find it in the R-help, but without result. See the example : # just to create a dataframe f1 <- factor(rep(c("A","B"),3)) f2

Dear R helpers: Sorry to bother for a basic question about model.matrix. Basically, I want to apply the dummy coding rule in a dataframe with complete factor levels to another dataframe with incomplete factor levels. I used model.matrix, but could not get what I want. The following is an example. #Suppose I have two dataframe A and B

I'm using specify.model for the sem package. I can't figure out how to represent the residual errors for the observed variables for a CFA model. (Once I get this working I need to add some further constraints.) Here is what I've tried: model.sa <- specify.model() F1 -> X1,l11, NA F1 -> X2,l21, NA F1 -> X3,l31, NA F1 -> X4,l41, NA F1 -> X5, NA, 0.20

Hi, Is there any way to suppress plotting of panels that don't actually contain any information? I have tried using 'drop.unused.levels=TRUE', but there doesn't seem to be any effect. Here is an example: library(lattice) # some fake data: d <- data.frame(x=runif(20), x.class=rep(letters[1:5], each=4), f1=rep(letters[1:2], each=10), f2=rep(letters[10:19], each=2) ) # plot

Dear All, I want to compare formulas resulting from using stepAIC() on glm() objects. I have 2 different formulas, say F1 and F2, which consist of about 10 factors (Y~A+B+C+...) but one has an interaction term (A:B) while the other doesn't. But when I perform F1==F2 it comes back as TRUE. Is there a proper way to compare formulas? ------------------------------------------- Jeffrey M

This is just my curiousity working. Suppose I write: f1 <- function(x) x + 1 f2 <- function(x) 2 * f1(x) f2(10) # 22 f1 <- function(x) x - 1 f2(10) # 18 This is quite obvious. But is there any way to define f2 in such a way that we "freeze" the definition of f1? f1 <- function(x) x + 1 f2 <- function(x) # put something here 2 * f1(x) # probably put something else here

Hi. I have defined a function, f1, that calls another function, f2. Inside f1 an intermediate variable called nm1 is created; it is a matrix. f2 takes a matrix argument, and I defined f2 (schematically) as follows: f2<-function(nmArg1=nm1,...){nC<-ncol(nmArg1); ... } so that it expects nm1 as the default value of its argument. f1 is defined (schematically) as: