search for: 0.185

I have a list of entrants (1-14 in this example) in a competitive event and corresponding win probabilities for each entrant. [(1, 0.049), (2, 0.129), (3, 0.043), (4, 0.013), (5, 0.015), (6, 0.040), (7, 0.066), (8, 0.038), (9, 0.204), (10, 0.022), (11, 0.234), (12, 0.044), (13, 0.068), (14, 0.035)] So, of course Sum(ps) = 1. In order to make some subsequent computations more

Hello! I have the following problem: I have a function to construct three surfaceplots with a marker for an optimum, each of the plots has as title paste("Estimated ",pred.var.lab," for ",var.lab[1]," vs. ",var.lab[2],sep="") with different var.lab[1,2] each time. My problem is now that I need to allow for plotmath expressions in the variables pred.var.lab

Hi, I have the following type of data: 86 subjects in three independent groups (high power vs low power vs control). Each subject solves 8 reasoning problems of two kinds: conflict problems and noconflict problems. I measure accuracy in solving the reasoning problems. To summarize: binary response, 1 within subject var (TYPE), 1 between subject var (POWER). I wanted to fit the following model:

Hello R'ers, I have a large set of data which has many y samples for each unit x. The data might look like: Seconds Response_time ---------- ---------------- 0 0.150 0 0.202 0 0.065 1 0.110 1 0.280 2 0.230 2 0.156 3 0.070 3 0.185 3 0.255 3 0.311 3 0.120 4 .... and so on When I do a basic plot with type=l or the default of points it obviously plots every

Hello, I’m trying to follow the syntax of a script from a journal website. In order to create a regression formula used later in the script, the regression matrix must have column names “X1”, “X2”, etc. I have tried to assign these column names to my matrix ScoutRSM.mat using a for loop, but I don’t know how to interpret the error message. Suggestions? Thanks, Paul

How do I change the tick text on the x-axis of a plot and rotate them 90 degree? > labs [1] "[-0.185,-0.0997]" "(-0.0997,-0.0549]" "(-0.0549,-0.0293]" [4] "(-0.0293,-0.00948]" "(-0.00948,0.00534]" "(0.00534,0.0178]" [7] "(0.0178,0.035]" "(0.035,0.0566]" "(0.0566,0.0932]" [10] "(0.0932,0.183]"

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Hi everybody, I noticed a strange behavior when using loops versus apply() on a data frame. The example below "explicitly" computes a distance matrix given a dataset. When the dataset is a matrix, everything works fine. But when the dataset is a data.frame, the dist.for function written using nested loops will take a lot longer than the dist.apply ######## USING FOR ####### dist.for

I have the manual for S+ 6 and I'm trying to use R for the Principle Component Analysis example and I'm getting a few interesting answers... The log is as follows: R : Copyright 2001, The R Development Core Team Version 1.3.0 (2001-06-22) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type `license()' or

Hi there, I am doing multiple comparisons for data that is not normally distributed. For this purpose I tried both functions kruskal{agricolae} and kruskalmc{pgirmess}. It confuses me that these functions do not yield the same results although they are doing the same thing, don't they? Can anyone tell my why this happens and which function I can trust? kruskalmc() tells me that there are no

Thank you very much for your reply. Your code work well with this example. I modified a little to fit my real data, I got an error massage. Error in split.default(x = seq_len(nrow(x)), f = f, drop = drop, ...) : Group length is 0 but data length > 0 On Thu, Jan 31, 2013 at 12:21 PM, arun kirshna [via R] < ml-node+s789695n4657196h87@n4.nabble.com> wrote: > Hi, > Try this: >

My data frame looks like: SightingID PA1 PA2 PlotID InOverlap Area1 2001 1 -99 392 Y 0.22 2002 1 -99 388 Y 0.253 2008 1 NA 104 N 0.344 2010 1 NA 71 N 0.185 2012 1 NA 61 N 0.166 2013 1 NA 61 N 0.227 2014 1 NA 62

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

Hi I have a set of data with both quantitative and categorical predictors. After scaling of response variable, i looked for multicollinearity (VIF values) among the predictors and removed the predictors who were hinding some of the other significant predictors. I'm curious to know whether the predictors (who are not significant) while doing simple 'lm' will be involved in

Dear R gurus I am running a GLMM that looks at whether chimpanzees spend time in shade more than sun (response variable 'y': used cbind() on counts in the sun and shade) based on the time of day (Time) and the availability of shade (Tertile). I've included some random factors too which are the chimpanzee in question (Individual) and where they are in a given area (Zone). There are

Using R 1.8.1, and the negative binomial glm implemented in MASS, the default when using anova and a chi-square test is to divide the deviance by the estimated dispersion. Using my UNIX version of S-plus (v 3.4), and the same MASS functions, the deviances are *not* divided by the estimated dispersion. Firstly, I'm wondering if anyone can enlighten about the correct procedure (I thought

For mean() and sd() you need to convert the data frame to a matrix (I'm guessing here since you did not show us the structure of your data). The min() and max() functions should work on the data frame just fine. If you have other columns in the data frame, extract the monthly columns first. > set.seed(42) > x <- data.frame(matrix(rnorm(100), 20, 5)) > str(x) 'data.frame':

Thanks Bill. Do you and others think that a link to this guide (or another)should be included in the Posting Guide and/or R FAQ? -- Bert On Tue, May 17, 2011 at 4:07 PM, <Bill.Venables at csiro.au> wrote: > Amen to all of that, Bert. ?Nicely put. ?The google style guide (not perfect, but a thoughtful contribution on these kinds of issues, has avoiding attach() as its very first line.

Hi I have a set of data with both quantitative and categorical predictors. After scaling of response variable, i looked for multicollinearity (VIF values) among the predictors and removed the predictors who were hinding some of the other significant predictors. I'm curious to know whether the predictors (who are not significant) while doing simple 'lm' will be involved in

Hello, I did a cross-validation using cvlm from DAAG package but wasn't sure how to assess the result. Does this result means my model is a good model? I understand that the overall ms is the mean of sum of squares. But is 0.0987 a good number? The response (i.e. gailRel5yr) has min,1st Quantile, median, mean and 3rd Quantile, and max as follows: (0.462, 0.628, 0.806, 0.896, 1.000, 2.400) ?