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Hi, I've got a problem building a contrast matrix for the Dunnet contrast in package multcopm. The following works fine: > summary(simtest(adiff ~ trial)) Simultaneous tests: Dunnett contrasts Data: adiff by trial Contrast matrix: trial1 trial2 trial3 trial4 trial5 trial2-trial1 -1 1 0 0 0 trial3-trial1 -1 0 1 0 0

Dear r-helpers, xx <- c(0.000, 0.210, 0.714, 0.514, 1.000, 0.190, 0.590, 0.152) yy <- c(0.000, 0.265, 0.256, 0.521, 0.538, 0.761, 0.821, 1.000) aa <- c(19, 19, 19, 21, 19, 21, 21, 21) x0 <- xx[c(1, 1, 2, 2, 2, 3, 3, 4, 4, 4, 5, 6, 6, 7)] y0 <- yy[c(1, 1, 2, 2, 2, 3, 3, 4, 4, 4, 5, 6, 6, 7)] x1 <- xx[c(2, 3, 3, 4, 6, 4, 5, 5, 6, 7, 7, 7, 8, 8)] y1 <- yy[c(2, 3, 3, 4, 6, 4, 5,

Good Morning. Below I like statement like j<-grep(".r\\b",colnames(mydata),value=TRUE); j with the \\b option which I read long time ago which Ive found useful. Are there more or these options, other than ? grep? Thanks. dstat is just my own descriptive routine. > x ?[1] "age"????????? "sleep"??????? "primary"????? "middle" ?[5]

Dear Sirs: I've been working with several variables in a dataframe that serve as part of a calculation that I need to perform in a different way depending on its value. Let me explain: The main dataframe is called llmcc llmcc : 'data.frame': 283 obs. of 11 variables: $ Area : num 308.8 105.6 51.4 51.4 52.9 ... $ mFondo : num 30.1 10 10.2 10.2 40.4 ... $ mFachada :

Hi R User, Would you give me your hand to standardize some columns in a matrix? I have included the example table. > dput(test) structure(list(X = c(1, 2, 3, 4, 5, 6), Y = c(4, 5, 6, 7, 8, 9), temp = c(0.818, 0.113, 0.256, 0.587, 0.955, 0.207), oxy = c(0.797, 0.487, 0.727, 0.128, 0.514, 0.031)), .Names = c("X", "Y", "temp", "oxy"), row.names =

My question is about the "Signif. codes" and the p-value, specifically, the output when I run summary(nameofregression.lm) So you get this little key: Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 And on a regression I ran, next to the intercept data, I get '***' Coefficients: > > Estimate Std. Error t value Pr(>|t|) > >

I've figured out how to use optim (barely) to estimate 2 parameter = weibull distributions. I can't get over how easy this is. What I need to = do is use a weight in the observations..... For example,=20 the tree diameters and weights are are=20 4.70 , 100 6.00, 98 7.10, 75.0 8.10, 86.3 8.60, 80.456 8.90, 20.5 9.50, 16.6 11.40, 12.657 11.80, 12.47 14.50,

Dear all I use aggregate with variables of type numeric and dates. For type numeric functions, such as sum() are very fast, but similar simple functions, such as min() are much slower for the variables of type 'dates'. The difference gets bigger the larger the 'id' var is - but see this sample code: dts <- dates(c("02/27/92", "02/27/92",

?s 02:10 de 02/08/2024, Steven Yen escreveu: > Good Morning. Below I like statement like > > j<-grep(".r\\b",colnames(mydata),value=TRUE); j > > with the \\b option which I read long time ago which Ive found useful. > > Are there more or these options, other than ? grep? Thanks. > > dstat is just my own descriptive routine. > > > x > ?[1]

Hello, this is probably more a statistical question than an R-specific problem, but I'll risk it. I've fitted a Cox Proportional hazard model with one factor Treatment (seven levels) as a predictor variable. The general Null hypothesis (all groups show the same survival behaviour) is clearly rejected. Now, is there any (statistically sensible) way of doing pairwise comparisons and/or

Dear HelpeRs, I have some data: "ice" <- structure(c(0.386, 0.374, 0.393, 0.425, 0.406, 0.344, 0.327, 0.288, 0.269, 0.256, 0.286, 0.298, 0.329, 0.318, 0.381, 0.381, 0.47, 0.443, 0.386, 0.342, 0.319, 0.307, 0.284, 0.326, 0.309, 0.359, 0.376, 0.416, 0.437, 0.548, 41, 56, 63, 68, 69, 65, 61, 47, 32, 24, 28, 26, 32, 40, 55, 63, 72, 72, 67, 60, 44, 40, 32, 27, 28, 33,

This is probably simple, but I just can't see it... I want to calculate the R^2s for a series of linear models where each term is dropped in turn. I can get the RSS from drop1(), and the r.squared from summary() for a given model, but don't know how to use the result of drop1() to get the r.squared for each model with one term dropped. Working example: library(vcd) # for

Windows 2000 (v.5.00.2195), R 1.5.1 I have an lme object, fm0, which I examine with anova() and intervals(). The anova output indicates one of the interaction terms is significant, but the intervals output shows that the single parameter for that term includes 0.0 in its 95% CI. I believe that the anova is a conditional (sequential) test; is intervals marginal or approximate? Which should I

Hello all, I am working with a 1000x1000 matrix, and I would like to return a 1000x1000 matrix that tells me which value in the matrix is greater than a theshold value (1 or 0 indicator). i have tried mat2<-as.matrix(as.numeric(mat1>0.25)) but that returns a 1:100000 matrix. I have also tried for loops, but they are grossly inefficient. THanks for all your help in advance. Lanre

Hello, I've been looking through ?phantom and ?expression and this forum for examples of how I might be able to manipulate some of the names that appear on the y-axis of the barplot below. For example, the "gw" in "ECgw" would appear as a subscript...or "qr" would be the theta symbol followed by subscript "r". My attempts haven't even come close

Hi All, I have run many regression analyses (14000 +) and want to collect the coefficients and pvalues into an excel file. I can get the statements below to work up to step 4. I can printout the regressionresults (sample output below). So my hope is to run something like step 5 and 6 and put the pvalues (and then coefficients) into an excel file. Can anyone suggest what I am doing wrong or a

Hi, I have a Community (COM) composed of 6 species: A, B, C, D, E & F. The density of my Community is thus (Eq.1): dCOM = dA + dB + dC + dE + dF I would like to calculate and plot a linear regression between the density of each of my species and the density of the whole community (illustrating how the density of each species varies with variations of the whole community). For example, I would

Hi folks, We recently generated some data that seemed interesting enough to share here. This is a comparison between compilers that ignores the performance of the generated code and focuses only on compiler correctness. volatile checksum errors errors avr-gcc-3.4 1.879% 0.378% avr-gcc-4.1 0.037% 0.256% avr-gcc-4.2

Hi, I am using R to do a principal components analysis for a class which is generally using SPSS - so some of my question relates to SPSS output (and this might not be the right place). I have scoured the mailing list and the web but can't get a feel for this. It is annoying because they will be marking to the SPSS output. Basically I'm getting different values for the component

suppose I have two sets of vectors: x1,x2,...,xN and y1,y2,...,yN. I want N correlations: cor(x1,y1), cor(x2,y2), ..., cor(xN,yN). my sets of vectors are arranged as data frames x & y (vector=column): x <- data.frame(a=rnorm(10),b=rnorm(10),c=rnorm(10)) y <- data.frame(d=rnorm(10),e=rnorm(10),f=rnorm(10)) cor(x,y) returns a _matrix_ of all pairwise correlations: cor(x,y)