search for: fact1

Dear R-users, consider the two following outputs, ## 1 and ## 2 \begin{Scode}{Setup, echo = FALSE, print = FALSE, eval = TRUE} with(expand.grid(Fact1 = 1:3, Fact2 = 1:40), table(Fact1, Fact2)) ## 1 xtable(with(expand.grid(Fact1 = 1:3, Fact2 = 1:40), table(Fact1, Fact2))) ## 2 \end{Scode} The first line with(expand.grid(Fact1 = 1:3, Fact2 = 1:40), table(Fact1, Fact2)) has an output that easily fit the A4 page, while the s...

I am trying to construct a two-way table where, instead of printing the two-way frequencies in the table, I would like to print the values of a third variable that correspond to the frequencies. For example, the following is easily constructed in R > fact1 <- factor(sample(LETTERS[1:3],10,replace=TRUE)) > fact2 <- factor(sample(LETTERS[25:26],10,replace=TRUE)) > fact3 <- letters[1:10] > data.frame(fact1,fact2,fact3) fact1 fact2 fact3 1 C Z a 2 A Y b 3 A Y c 4 C Z d 5...

I am using R to make two-way ANOVA on a number of variables using g <- aov(var ~ fact1*fact2) where var is a matrix containing the variables. However the outcome seem to be dependent on the order of fact1 and fact2 (i.e. fact2*fact1) gives a slightly (factor of 1.5) different result. Any ideas why this is? Thanks for any help Anders

...message for aov1 was "Estimated effects <may> be unbalanced". The effects are not unbalanced. The design is 'orthogonal'. The problem is that there are not enough degrees of freedom to estimate all those error terms. If you change the model to: aov1 <- aov(RT~fact1*fact2*fact3+Error(sub/(fact1+fact2+fact3)),data=myData) or to aov2 <- aov(RT~fact1*fact2*fact3+Error(sub/ ((fact1+fact2+fact3)^2)),data=myData) all is well. This last model (aov2) seems to me to have an excessive number of error terms. The lme model lme(RT~fact1*fact2*fact3, random=...

Dear ExpeRts, Suppose I have a typical psychological experiment that is a within-subjects design with multiple crossed variables and a continuous response variable. Subjects are considered a random effect. So I could model > aov1 <- aov(resp~fact1*fact2+Error(subj/(fact1*fact2)) However, this only holds for orthogonal designs with equal numbers of observation and no missing values. These assumptions are easily violated so I seek refuge in fitting a mixed-effects model with the nlme library. > lme1 <- lme(resp~fact1*fact2, random=~1...

Dear r-help, With lme, is there a way to specify multiple fixed factors under one level of grouping? For example, for a single fixed factor, I use the following: fm1.lme <- lme(fixed=resp ~ fact1, random=~1|subj/fact1, data=mydata) I would like to have multiple factors under subj, like the following for a two-way design, but I get an error: fm2.lme <- lme(fixed=resp ~ fact1*fact2, random=~1|subj/(fact1+fact2), data=mydata) Error in getGroups.data.frame(dataMix, groups): Inv...

In my undertstanding of the problem, the model lme1 <- lme(resp~fact1*fact2, random=~1|subj) should be ok, providing that variances are homogenous both between & within subjects. The function will sort out which factors & interactions are to be compared within subjects, & which between subjects. The problem with df's arises (for lme() in nlme, bu...

Dear R-helpers, I'm trying to selectively swap labels between two factors, depending on an indicator variable i. Can you point me to a solution, and perhaps how I could have found it? labels(fact1) is a character vector of r row numbers levels(fact1) is a character vector of the n < r unique levels How do I then get the character vector of length r of the levels of fact1? Once I have that, is there anything else I need to know about swapping labels between factors in a df? __________...

Hello, I am a new user or R, and am so far very impressed with its capabilities. However, I have no programming experience, and am having some issues in trying to tell the software what I want done. There are basically two issues which I am currently grappling with. The first, I have a data matrix, with two factors and dozens of response variables. I am interested on conducting ANOVAs on

I am trying to put 3 dotcharts side-by-side with minimal space between each. Each chart is for a different variable, but the vertical axes are the same. I want to have vertical axis labels on the lefthand chart but no vertical axis labels on the other two. Plus, I would like very little space between charts 1 & 2 and between charts 2 & 3. I have one approach but am not too happy with

Hi All, Can someone help me?The way to do this may be very easy but i do not know. *Question1:----* factanal() function produces the results in this way:-- *RESULTS:--* *>fact1<- factanal(data_withNA,factors=1,rotation="none") >fact1$"loadings"* Loadings: Factor1 i1 0.784 i2 0.874 i3 0.786 i4 0.839 i5 0.778 i6 0.859 i7 0.850 i8 0.763 i9 0.810 i10 0.575 i11 0.745 i12 0.777 i13 0.674 i14 0.644 i15 0.796 * Factor...

Hello all, I have some trouble in reconstructing a valid expression within a function, here is my question. I am building a function : SUB<-function(DF,subset=TRUE) { #where DF is a data frame, with Var1, Var2, Fact1, Fact2, Fact3 #and subset would be an expression, eg. Fact3 == 1 #in a first time I want to build a subset from DF #I managed to, with an expression like eg. DF$Fact3, # but I would like to skip the DF$ for convenience # so I tried something like this : tabsub<-deparse(substitute(subset)) dDF...

Dear all, I'm having a few problems trying to reshape a data frame. I tried with reshape{stats} and melt{reshape} but I was missing something. Any help is very welcome. Please find details below: ################################# # data in its original shape: indiv <- rep(c("A","B"),c(10,10)) level.1 <- rpois(20, lambda=3) covar.1 <- rlnorm(20, 3, 1) level.2

Hello to all users and wizards. I am regulary using 'boxplot' function or its analogue - 'bwplot' from the 'lattice' library. But they are, as far as I understand, totally flawed in functionality: they miss ability to select what they would draw 'in the middle' - median, mean. What the box means - standard error, 90% or something else. What the whiskers mean -

...factorlist, FUN, condition) ## DF is the original dataframe with raw results from my experiments ## var is the var that I want to print ## eg. DF$var1 ## factorlist is a list of the two factor I want to mean against (but I'm noot sure List is the best form for this argument), ## eg. list(DF$fact1, DF$fact2) ## FUN would be the function I want to use during aggregation, ## eg. Mean( ) ## condition would be a condition for building a subset ## eg. DF$fact3==1 { sDF <- subset(DF, condition) A <- aggregate(sDF, factorlist, FUN) M <- matrix(data=NA, nrow = length(levels(factorlist...

...AAAAAAAAAAAAAA --Apple-Mail-28-953181987-- --Apple-Mail-27-953181986 Content-Disposition: attachment; filename=bug.txt Content-Transfer-Encoding: quoted-printable Content-Type: text/tab-separated-values; x-mac-creator=5843454C; x-unix-mode=0644; x-mac-type=54455854; name="bug.txt" fact1 fact2 covar time delta=0D57 1 71 100 = 0=0D24 27 73 100 0=0D54 42 74 46 1=0D24 = 27 77 100 0=0D60 42 78 100 0=0D33 1 = 78 100 0=0D60 42 79 100 0=0D54 42 79 = 100 0=0D24 1 79 100 0=0D48 27 80 78 = 1=0D54 1 80 100 0=0D54 42 81 34 1=0D24 = 27 81 48 1=0D24 1 81 100 0=0D48 40 = 81 100 0=0D58 1 82 13 1=0...