search for: 6.25

##I want to remove the rows where the row sums are zero and this is as far as I have gotten ffg <- (structure(list(CD = c(0, 0, 0, 0, 3.125, 0, 0, 0, 0, 1.6, 3.125, 0, 0, 6.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.125, 0, 0, 0, 0, 0, 0, 0, 0,

useR's, I am trying to find a quick way to change some values in a list that are subject to a condition to be NA. Consider the 3x1 matrix: delta <- matrix(c(2.5,2.5,1), nrow = 1) And consider the list named v that has 3 elements > v v[[1]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 4.25 3.25 2.25 1.25 0.25 0.75 1.75 2.75 3.75 4.25

Dear R helpers I have following dataset rate_number = matrix(c(5, 15, 60, 15, 5, 0, 20, 60, 20,0, 10, 20, 40, 20, 10), nrow = 5, ncol = 3) range_mat = matrix(c(6.25, 6.75, 7.25, 8.75, 9.25, 9.75, 8.5, 9, 9.5, 10.5, 11, 11.5, 4.25, 4.75, 5.25, 5.75, 6.25, 6.75), nrow = 6, ncol = 3) > rate_number        [,1]   [,2]   [,3] [1,]    5     0      10 [2,]   15   20      20 [3,]   60   60      40

Is there any way besides looping to remove complete rows from a matrix or data frame where there is at least one NA in any of the columns? For example > a [,1] [,2] [1,] 0 2.6875 [2,] 8.366667 6.625 [3,] 15.6 4.375 [4,] 23.4 6.25 [5,] 29 5.09375 [6,] 18 NA [7,] 0 4.15625 [8,] 9.366667 6.25 [9,] 14.73333 5.875 [10,]

For the first error message: > win.metafile(file = "//.../plot1.wmf", + width = 8.5, height = 6.25) > lset( list( background = list(col = "white"))) Error in get(x, envir, mode, inherits) : variable "win.metafile://.../plot1.wmf" was not found > traceback() 4: get(device) 3: trellis.device(device = .Device, new = FALSE) 2: trellis.par.get(item) 1:

Hi All, I have the following text file (mytextfile.txt) 738307 527178 714456 557955 #N/A 17.42 6.22 4.73 #N/A 17.3 6.23 4.75 #N/A 17.29 6.17 4.7 #N/A 17.07 6.12 4.6 #N/A 17.27 6.19 4.7 #N/A 17.72 6.4 4.78 #N/A 17.12 6.19 4.75 #N/A 17.07 6.15 4.65 #N/A 17.03 6.07 4.64 #N/A 17.38 6.13 4.7 #N/A 17.38 6.13 4.7 #N/A 17.38 6.13 4.7 #N/A 17.38 6.13 4.7 #N/A

Hello, I have the following question, which I haven't been able to resolve after days of trying. I used to save my plots as jpegs using the savePlot command. However, that seems to result in lost resolution. So now I'm trying to use the jpeg( ) function, but am having trouble because it seems to be incompatible with the windows (width=, height=) command. It's important for me to

Hello, I would like to change the default size of the routinely opened graphics window - (width = 7, height = 7)- to -(width = 6.25, height = 6.25)- in rw1051 but I can't find out how? Sincerely Fredrik Lundgren -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Dear all, I am attempting to subset a data frame based on a range of latitude values. I want to extract the values of 'interception' where latitude ranges between 50 and 60. I am doing this using the following code, yet it doesn't return the results I expected: > test <- subset(int1901, Latitude>=50 & Latitude <60, select=c(Latitude, Interception)) > head(test)

Dear R-list members, I need an help with the Multidimensional Scaling analysis (MDS). So far I used the cmdscale() command in R, but I did not get the perceptual map I would love to see, and I would like to know if it is possible to get it using R, and if yes how. I also had a look to the functions isoMDS() and sammoc() but with no luck. I summarize the experiment I performed, and I would ask you

Hello, I would be very grateful if someone could give me a hand with my split plot design problems. So here is my design : I am studying the crossed-effects of water (wet/dry) and mowing (mowed/not-mowed = nm) on plant height (PH) within 2 types of plant communities (Xerobromion and Mesobromion) : - Within each type of communities, I have localised 4 blocks - In each block, I have defined

Hi all, I am trying to figure out the formula used by R's Spearman rho (using cor(method="spearman")) because I can't seem to get the same value as by calculating "by hand". Perhaps I'm using "cor" wrong, but I don't know where. Basically, I am running these commands: > y=read.table(file="tmp",header=TRUE,sep="\t") >

my reproducible example test<-structure(list(site = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L), .Label = c("A", "B", "C", "D", "E"), class = "factor"),

This surprised me. Is it the way it is supposed to be? > x<-c(1,2,3,4,5) > y<-c(1.2,1.3,1.4,1.5,NA) > x-y [1] -0.2 0.7 1.6 2.5 NA > (x-y)^2 [1] 0.04 0.49 2.56 6.25 NaN >>>so NA^2 = NaN? Why not still NA? > sum((x-y)^2) [1] NaN >>>yes that is reasonable So if you ever have a data set with missing observations (NAs), you can't do any nlm() least

Dear R-list, Could anybody tell me (or give me a tip) of how to implement the Duncan distribution in R? I've been trying to make a new and more flexible function for multiple comparison of means: Tukey, SNK and Duncan, from 'aov' objects, like TukeyHSD function. For while, it is running nice (Tukey and SNK), for simple design (completely randomized, randomized block and Latin

Hi I have a set of data like this: *Time of Day* *Pct of Daily Volume* 9:45 7.50% 10 6.25% 10:15 4.45% 10:30 4.80% 10:45 4.45% 11:00 4.20% 11:15 2.50% 11:30 2.30% 11:45 2.25% 12:00 2.45% 12:15 2.60% 12:30 2.00% 12:45 2.05% 13:00 2.40% 13:15 1.90% 13:30 3.10% 13:45 2.90% 14:00 2.80% 14:15 2.50% 14:30 3.40% 14:45 4.40% 15:00 5.40% 15:15 4.00% 15:30 4.70% 15:45 6.20% 16:00 8.50% I want to fit

Hello, this forum was very helpful yesterday with a simple question I had on working with tables. What function will I need to use to do the following. Move matrix a: A B C D x 1 2 3 4 y 5 6 7 8 to the mean of B&C and C&D: BC CD x 2.5 3.5 y 6.5 7.5 and then to the mean of B&CB, C&BC, C&CD, and D&CD: AAB BAB

Hi, Given some example data: dat <- seq(4, 7, by = 0.05) x <- sample(dat, 30) y <- sample(dat, 30) error <- x - y I have broken the rage of x into 10 groups and I can calculate the bias (mean(error)) for each of these 10 groups: groups <- cut(x, breaks = 10) max.bias <- aggregate(error, list(group = groups), mean) max.bias group x 1 (4,4.3] -0.7750000 2

Hi, I'm a new R user, coming from SPSS, and without a particularly strong stats background. I've got a data set that I'd like to do a mixed-design ANOVA with. No missing values. Here's the summary: summary(learnDat.ae) Type Subject idio struct TrainErrs cond 0:20 11 : 3 idio :28 ae :58 Min. : 0.00 idioae :28 2:19 12 : 3

Dear mailinglist members, I was wondering if there was a way to re-evaluate the instances of a tree (in the forest) again after I have manually changed a splitpoint (or split variable) of a decision node. Here's an illustration: library("randomForest") forest.rf <- randomForest(formula = Species ~ ., data = iris, do.trace = TRUE, ntree = 3, mtry = 2, norm.votes = FALSE) # I am