search for: subj

Hi , I am new to R. I am trying to run a simple R script as shown below: aov.R ------ data1<-c(49,47,46,47,48,47,41,46,43,47,46,45,48,46,47,45,49,44,44,45,42,45,45,40 ,49,46,47,45,49,45,41,43,44,46,45,40,45,43,44,45,48,46,40,45,40,45,47,40) matrix(data1, ncol= 4, dimnames = list(paste("subj", 1:12), c("Shape1.Color1", "Shape2.Color1", "Shape1.Color2", "Shape2.Color2"))) Hays.df<-data.frame(rt = data1, subj=factor(rep(paste("subj", 1:12, sep=""), 4)), shape=factor(rep(rep(c("shape1","shape2"), c(12...

I am extremely puzzled by this behavior in R. I have a data frame called Trials in which I have results from an experiment. I am trying to do a subjects analysis, but getting weird results. Each row has 1 trial in it, which includes a column for the subject number I get the list of subject numbers like so: > Subj=unique(sort(Trials$Subj)) Then I loop over them. But I get strange results. As a test, I tried the following: > i=1 > Sub...

...;-c(2,1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2,1,1,2) d<-c(2,2,1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2,1,1) e<-c(1739,1633,1481,1837,1780,2073,1374,1629,1555,1385,1756,1522,1566,1643, ???? 1939,1615,1475,1759,1388,1483,1127,1682,1542,1247,1235,1605,1598,1718 ) KK<-data.frame(subj=as.factor(a), drug=as.factor(b), per=as.factor(c),? seqe=as.factor(d), Cmax=e) M<- lm(Cmax ~ seqe+ subj:seqe + per + drug , data=KK) anova(M) drop1(M, test="F") The result of Type I SS: Analysis of Variance Table Response: Cmax ????????? Df Sum Sq Mean Sq F value Pr(>F) seqe??????...

...i, I have a data frame and want to merge adjacent rows if some condition is met. There's an obvious solution using a loop but it is prohibitively slow because my data frame is large. Is there an efficient canonical solution for that? > head(d) rt dur tid mood roi x 55 5523 200 4 subj 9 5 56 5523 52 4 subj 7 31 57 5523 209 4 subj 4 9 58 5523 188 4 subj 4 7 70 4016 264 5 indic 9 51 71 4016 195 5 indic 4 14 The desired result would have consecutive rows with the same roi value merged. dur values should be added and x values averaged, other values don&...

...variables specifying different lines were numeric, whereas in the current data frame one of those variables (challenge) is a factor with 2 levels. Any suggestions for getting this to plot as intended would be much appreciated. Thank you! ************ This is meant to plot a separate line for each subject for each challenge************* for (subj in unique(lab.samples$subid)) { #par(new=T) plot.new() par(mfrow=c(2,1)) par(mfg=c(1,1)) plot(data=lab.samples, subset=(subid==subj), cortisol ~ Sample, type='n', main=paste('Cortisol and Amylase for subject ', as.character(subj))...

...ls, Hmisc documentation, contributed manuals, help archives, and Internet. I am running R 1.7.1 under Windows 2000 (I will upgrade when my imminent OS upgrade happens). My data was successfully entered and displayed as data.df whose first row is column labels, whose subsequent rows are separate subject results, whose 2nd to 7th columns are numeric control results (for observations at 0, 30, 60, 90, 120, and 180 min), and whose 8th to 13th columns are numeric treatment results. I am trying to enter the control and test area under the curve values from the Hmisc trap.rule function into &quot...

...h discrete levels, e.g. 1,2,4,6), and then as factor, with the latter then accounting for the deviation from linearity. If x contains the values 1,2,4,6, the model formula then would be y ~ x + as.factor(x) A complete example is here: library(nlme) d <- data.frame(x=rep(c(1,2,4,6),each=2),subj=factor(rep(1:16,each=2))) d$y <- rnorm(32) + rnorm(length(levels(d$subj)))[d$subj] anova(lme(y~x,random=~1|subj,data=d)) ## works anova(lme(y~as.factor(x),random=~1|subj,data=d)) ## works anova(lme(y~x+as.factor(x),random=~1|subj,data=d)) ## fails: # Error in MEEM(object, con...

Hi All. I'm trying to run a simple model from Baayan, Davidson, & Bates and getting a confusing error message. Any ideas what I'm doing wrong here? # Here's the data..... Subj <- factor(rep(1:3,each=6)) Item <- factor(rep(1:3,6)) SOA <- factor(rep(0:1,3,each=3)) RT <- c(466,520,502,475,494,490,516,566,577,491,544,526,484,529,539,470,511,528) priming <- data.frame(cbind(Subj,Item,SOA,RT)) str(priming) 'data.frame': 18 obs. of 4 va...

...(Intercept) (Intercept) 8.519916 attr(,"sc") scale 3.215072 > VarCorr(fm)[[1]][1] [1] 8.519916 > VarCorr(fm)[[2]][1] Error in VarCorr(fm)[[2]] : subscript out of bounds ########################################################## set.seed(500) n.timepoints <- 4 n.subj.per.tx <- 20 sd.d <- 5; sd.p <- 2; sd.res <- 1.3 drug <- factor(rep(c("D", "P"), each = n.timepoints, times = n.subj.per.tx)) drug.baseline <- rep( c(0,5), each=n.timepoints, times=n.subj.per.tx ) #Patient <- rep(1:(n.subj.per.tx*2), each = n.timepoints) Pat...

I'm implementing a custom bootstrap resampling procedure in R. This procedure resamples clusters of data points obtained by different subjects in an experiment. Since the bootstrap samples need to have the same size as the original dataset, `target.set.size`, I select speakers compute their data point contributions to make sure I have a set of the right size. set.seed(1) target.sample.size = 1742 count.lookup = rbin...

Dear R-help, I'm facing a problem with defining a repeated measures anova with weighted data. Here's the code to reproduce the problem: # generate some data seed=11 rtrep <- data.frame(rt=rnorm(100),ti=rep(1:5,20),subj=gl (20,5,100),we=runif(100)) # model with within factor for subjects/repeated measurements, no problem aov(rt~ti + Error(subj/ti),data=rtrep) #model with weights and subj as between factor, ie ignoring repeated measures, #again, no problem aov(rt~ti+subj,data=rtrep,weights=we) #combination...

...: [1] grid stats graphics grDevices utils datasets methods base other attached packages: [1] effects_1.0-12 lattice_0.17-8 loaded via a namespace (and not attached): [1] Matrix_0.999375-11 lme4_0.999375-24 nlme_3.1-89 tools_2.7. set.seed(500) n.timepoints <- 4 n.subj.per.tx <- 20 sd.d <- 5; sd.p <- 2; sd.res <- 1.3 drug <- factor(rep(c("D", "P"), each = n.timepoints, times = n.subj.per.tx)) drug.baseline <- rep( c(0,5), each=n.timepoints, times=n.subj.per.tx ) Patient <- rep(1:(n.subj.per.tx*2), each = n.timepoints) Pati...

Hi all, I have a strange problem and rigth now I can't figure out a solution. Trying to calculate an ANOVA with one between subject factor (group) and one within (hemisphere). My dependent variable is source localization (data). My N = 25. My data.frame looks like this: > ML.dist.stack subj group hemisphere data 1 1 tin left 0.7460840 2 2 tin left 1.0300600 3 3 tin left 0.802...

...eriment involving noise from Maxwell, S. E. & Delaney, H. D. (1990) Designing Experiments and Analyzing Data: A model comparison perspective. Pacific Grove, CA: Brooks/Cole., which was referenced in the Baron and Li link above. The experimental concept I'm trying to envision is 2 groups of subjects (Treat and Control), 3 subjects in each. In one session, all 6 patients from both groups are tested for reaction time by making 3 replicate tests on some hypothetical RT test (I call this the "pre" testing phase below or one could call this baseline ). In a second session (the &quot...

...has much more programming experience, but a comparable level of knowledge of R, has been stumped. What follows are 3 questions pertaining to an lme() model, one on the nested hierarcy, 1 on a strategy for a piecewise approach to the variance given I have ~24 hours of data (sampled at 32Hz, 1hr per subject), and one on the corStruct or how to get rid of serial dependencies before lme(). I'm analyzing skin temperature continuously recorded at 32Hz in Baseline (10 min), Testing (~5 min), and Recovery (20 min) epochs of a face recognition experiment. Stimuli are the same in Baseline and Recovery...

...eley Breathed in his "Bloom County" comic strip ) On Sat, Aug 19, 2017 at 7:39 AM, Aleksander G??wka <aglowka at stanford.edu> wrote: > I'm implementing a custom bootstrap resampling procedure in R. This > procedure resamples clusters of data points obtained by different subjects > in an experiment. Since the bootstrap samples need to have the same size as > the original dataset, `target.set.size`, I select speakers compute their > data point contributions to make sure I have a set of the right size. > > set.seed(1) > target.sample.size = 1742...

#Dear All, #I'm having a bit of a trouble here, please help me... #I have this data set.seed(4) mydata <- data.frame(var = rnorm(100), temp = rnorm(100), subj = as.factor(rep(c(1:10),5)), trt = rep(c("A","B"), 50)) #and this model that fits them lm <- lm(var ~ temp * subj, data = mydata) #i want to plot the results with lattice anf fit the regression line, predicted with my model, trough them #to do so, I...

...re drawn? The challenge is that each panel has different y-ranges (in my real example the y-ranges and y-intervals are even more different). For example, I wish I could use the yScale list as the h parameter in abline, but it does not work with a list. Thanks for any help. Rene library(lattice) Subj <- rep(1:4,each=3) Time <- rep(1:3,4) + 0.1 Conc <- (1:12) + 0.1 df <- data.frame(Subj,Time,Conc) xScale <- 1:3 yScale <- list(1:3,4:6,7:9,10:12) xyplot(Conc ~ Time | Subj, data = df, layout = c(2,2), type="b", scales=list( x=list(at...

Hi, all. I have a simple design I'm comparing to output from SPSS. the design is 1 repeated measure (session) and 1 between measure (cond). my dependent measure is rl. here is the data I'm using (in a data.frame): mig <- data.frame(subj=factor(rep(subj,3)), cond=factor(rep(cond,3)), session=factor(c(rep(1,nsubj),rep(2,nsubj),rep(3,nsubj))), rl) > mig subj cond session rl 1 401.1 NW 1 6.4081 2 402.1 NW 1 5.8861 3 500.1 NWC 1 5.3492 4 502...

Dear All, Comparing linear mixed effect models in SAS and R, I found the following discrepancy: SAS R random statement random subj(program); random = ~ 1 | Subj -2*loglik 1420.8 1439.363 random effects variance(Intercept) 9.6033 9.604662 variance(residual) 1.1969 1.187553 the first 3 fixed effects intercept...