similar to: selected observations based several variables

Hello, See my problem below. a<-data.frame(c("s","c","c","n","n","n"),c(rep(1,3),rep(2,3)),c(rep(2,3),rep(1,3)),c("01/01/1999","10/02/2000","13/02/2000","11/02/2000","15/02/2000","23/02/2000"))

Hello. I am probably wrong... I am wondering if it could have a mistake in the code of the ansari.test function. For me, it seems that the function do not recover the p value at the correct side of the normal law N(0, 1) when it use the normal approximation (presence of ties) in a one tailed test. Exemple : quanti<-c(197, 205, 228, 234, 237, 195, 233, 226, 244, 227, 259, 185, 198, 253, 207,

Let's say I have a within-subject experiment with 2 observables, obs1 and ob2 and 2 independent factors, fac1 and fac2. I can do summary( aov( obs1~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) summary( aov( obs2~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) to test the 2 observables separately. > summary( fit<-manova( cbind(obs1,obs2)~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) gives

Hi everyones, my function like; e <- rnorm(n=50, mean=0, sd=sqrt(0.5625)) x0 <- c(rep(1,50)) x1 <- rnorm(n=50,mean=2,sd=1) x2 <- rnorm(n=50,mean=2,sd=1) x3 <- rnorm(n=50,mean=2,sd=1) x4 <- rnorm(n=50,mean=2,sd=1) y <- 1+ 2*x1+4*x2+3*x3+2*x4+e x2[1] = 10 #influential observarion y[1] = 10 #influential observarion data.x <- matrix(c(x0,x1,x2,x3,x4),ncol=5) data.y

Hola.. yo lo haría de la siguiente manera ... En excel:genero una columna con la serie de fechas continuas ... con la función buscarv, agrego las variables que tienen dato a esta serie.. en base a la fecha muy largo y mecánico para mi gusto.... En R ..de igual manera genero el vector de fechas require(chron)# crear el vector continuo de fechasfch01 <- data.frame(''fch'' =

Hi, I' am interested in differences between sample's result when samples consist of full elements and consist of only distinct elements. When sample consist of full elements it take about 120 sec., but when consist of only distinct elements it take about 4.5 or 5 times more sec. I expected that opposite of this result, because unique(sample) has less elements than full sample. Code as

Dear Help, I'm trying to calculate a weighted correlation matrix from a data frame with 6 columns (variables) and 297 observations extracted from the regression. The last column is a weight column which I want to apply. $ model :'data.frame': 297 obs. of 6 variables: ..$ VAR1 : num [1:297] 5.21 9.82 8.08 0.33 8.7 6.82 3.94 4 0 5 ... ..$ VAR2 : num [1:297]

I apologize in advance for the simplicity of this question. I use R 2-3 times a year, and I seem to forget more in the intervening months than I learn during my days of panicked reading.... I HAVE tried looking at the help resources; I'm just not very good at understanding them. I have a dataframe with 18 observations of 5 different things, and a second dataframe with and estimate for those

Hi R-experts I have many text files to read and combined them into one into R that are output from other programs. My textfile have unbalanced number of rows for example: ;this is example ; r help Var1 Var2 Var3 Var4 Var5 0 0.05 0.01 12 1 0.04 0.06 18 A 2 0.05 0.08 14 3 0.01 0.06

Dear R-users, I’d like to make a nested anova on my data, and since I’m discovering both R and statistic, I’d like to be sure that I’m not doing something stupid. Here is my data: I’ve measured some variable responses (Y, for example leaf size) for different plants grown on three differing conditions (A, B and C). These plants come from two different species (sp1 and sp2) and within each species,

Hi, newbie question. I have a data-frame with 3 named columns: Name, Obs1, Obs2. The Name column members are made of alphanumeric characters: T1, T2, T3 etc. I would like to acess only that subset of the data-frame with Name == T44. X <- dataframe[dataframe$Name=='T44'] does not work. Any ideas on how to do this? I'm sure I'm missing a simple concept here. Thanks, Anjan --

Dear everyone, I have looked all over the internet but I cannot find a way to solve my problem. In my data I want to sum a couple of variables. Some of these variables have NA values, and when I add them together, the result is NA dat <- data.frame( id = gl(5,1), var1 = rnorm(5, 10), var2 = rnorm(5, 7), var3 = rnorm(5, 6), var4 = rnorm(5, 3), var5 = rnorm(5, 8) ) dat[3,3] <- NA dat[4,5]

Dear All Sorry for this simple question, I could not solve it by spending days. My data looks like this: # data set.seed(1234) clvar <- c( rep(1, 10), rep(2, 10), rep(3, 10), rep(4, 10)) # I have 100 level for this factor var; yvar <- rnorm(40, 10,6); var1 <- rnorm(40, 10,4); var2 <- rnorm(40, 10,4); var3 <- rnorm(40, 5, 2); var4 <- rnorm(40, 10, 3); var5 <- rnorm(40, 15,

Dear all, I am trying (!!!) to generate pdfs that have 8 plots on one page: df = data.frame( day = c(1,2,3,4), var1 = c(1,2,3,4), var2 = c(100,200,300,4000), var3 = c(10,20,300,40000), var4 = c(100000,20000,30000,4000), var5 = c(10,20,30,40), var6 = c(0.001,0.002,0.003,0.004), var7 = c(123,223,123,412), var8 = c(213,123,234,435), all = as.factor(c(1,1,1,1)))

Hi, I have a problem converting a XML file, via the XML package, to a data.frame. The XML file looks like this: <Transaction> <ID value="0044"/> <Var1 value="XYZ159"/> <Var2 value="_"/> <Var3 value="AMR1.0-INT-1005"/> <Var4 value="2010-05-25 10:44:16:673"/> <Var5 value="1"/>

Hi R users, I have a question about plotting. The following two datasets are an example. What I have in mind is like the attached figure, but just have two rows, top row is for DF1, bottom row is for DF2. The top and bottom rows, have x-axis as var1, var2, var3, etc, while the y-axis represents A. For each row, only the leftmost panel has y-axis ticks and label. For the two rows, only the bottom

Dear all, I have a dataset that looks like id var1 var2 var4 var7 var8 1 0.0 0.1 0.3 0.9 0.0 2 0.4 0.6 0.0 0.0 0.2 3 0.0 0.0 0.0 0.8 0.7 Some columns are missed, for example, here the fourth (var3), sixth(var5) and seventh (var6) columns. I want to first determine which columns are missed in a huge dataset and then add the missed

Dear list, after fitting an lrm with the Design package (stored as "mymodel") I try running a summary, but I get the following error: dim(mydata) [1] 235 9 names(mydata) [1] "id" "VAR1" "VAR2" "VAR3" "VAR4" "VAR5" "VAR6" "VAR7" "VAR8" summary(mymodel) Error in `contrasts<-`(`*tmp*`,

Dear R Users! I have a dataframe with duplicate cases. Var1 duplicated by var2. var1 var2 var3 var4 var5 var6 1 4 500 1 2 a 1 3 200 2 5 b 1 8 125 1 9 b

Sorry! I try it again Dear R Users! I have a dataframe with duplicatecases. Var1 duplicated by var2. var1 var2 var3 var4 var5 1 4 500 1 2 1 3 200 2 5 1 8 125 1 9 2 2 120 2 52 2 6 22 1 20 2 9 400 1 22 3 1 100 2 8 3 2 200 5 40 4