search for: 0.474

Hi all, I am using Hartigan & Hartigan's [1] "dip test" of unimodality via the diptest package in R. The function dip() returns the value of the test statistic but I am having problems calculating the p-value associated with that value. I'm hoping someone here is familiar with this process and can explain it. In the original article there is an example using n=63 and a

Dear All I am trying to do a repeated measures analysis using lmer and have a number of issues. I have non-orthogonal, unbalanced data. Count data was obtained over 10 months for three treatments, which were arranged into 6 blocks. Treatment is not nested in Block but crossed, as I originally designed an orthogonal, balanced experiment but subsequently lost a treatment from 2 blocks. My

Dear R users I have a big matrix like 6021 1188 790 290 1174 1015 1990 6613 6288 100714 6021 1 0.658 0.688 0.474 0.262 0.163 0.137 0.32 0.252 0.206 1188 0.658 1 0.917 0.245 0.331 0.122 0.148 0.194 0.168 0.171 790 0.688 0.917 1 0.243 0.31 0.122 0.15 0.19 0.171 0.174 290 0.474

Well pointed out, Jim! It is infortunate that the documentation for options(digits=...) does not mention that these are *significant digits* and not *decimal places* (which is what Joshua seems to want): "?digits?: controls the number of digits to print when printing numeric values." On the face of it, printing the value "0,517" of 'ccc' looks like printing 4

Hello, I am trying to filter a data set like below so that the peaks in the Phase value are more obvious and can be identified by a peak finding function following the useful advise of Carl Witthoft. I have written the following for(i in length(data$Phase)){ newphase=if(abs(data$Phase[i+1]-data$Phase[i])>6){ data$Phase[i+1] }else{data$Phase[i] } } I get the following error which I have not

>>>>> Ted Harding >>>>> on Thu, 31 May 2018 07:10:32 +0100 writes: > Well pointed out, Jim! > It is infortunate that the documentation for options(digits=...) > does not mention that these are *significant digits* > and not *decimal places* (which is what Joshua seems to want): Since R 3.4.0 the help on ?options *does* say

Hi Joshua, Because there are no values in column ddd less than 1. itemInfo[3,"ddd"]<-0.3645372 itemInfo aaa bbb ccc ddd eee skill 1.396 6.225 0.517 5.775 2.497 predict 1.326 5.230 0.462 5.116 -2.673 waiting 1.117 4.948 NA 0.365 NA complex 1.237 4.170 0.220 4.713 5.642 novelty 1.054 4.005 0.442 4.260 2.076 creative 1.031 3.561 0.362 3.689

R version 3.5.0 (2018-04-23) -- "Joy in Playing" Platform: x86_64-pc-linux-gnu (64-bit) options(digits=3) itemInfo <- structure(list("aaa" = c(1.39633732316667, 1.32598263816667, 1.11658324066667, 1.23651072616667, 1.05368679983333, 1.03100737383333, 0.9630728395, 0.7483865045, 0.620086646166667, 0.5411017985, 0.496397607833333, 0.459528044666667, 0.427877047833333,

Dear List: the CODA and BOA packages for the analysis of MCMC output yield different results on two dignostic test of convergence: 1) Geweke's convergence diagnostic; 2) Heidelberger and Welch's convergence diagnostic. Does that imply that the CODA and BOA packages implement different ``flavors'' of the same test? I paste below an example. Geweke's test

Hi all, I have a dataframe of behavioral observations from 360 fish, each with 241 observation points(rows), which looks like this: > head(d) fish treatment tank trial video tid pid ang.chg abs.ac t len vel d2p x y 1 1 3 1 1 1 1 1 NA NA 0.0 0.000 NA NA 5.169 9.617 2

I just had a manuscript returned with the biggest problem being the analysis. Instead of using principal components in a regression I've been asked to analyze a few variables separately. So that's what I'm doing. I pulled a feather from young birds and we quantified certain aspects of the color of those feathers. Since I often have more than one sample from a nest, I thought I

Hi, In my analysis of impacts of insecticide-treated bednets on malaria, I look at the relationship between malaria incidence and mosquito behaviors. The condensed data set is copied here. Ordinary regression (lm) shows that Incidence was negatively related to Mortality. This makes sense because the latter reflected the strength of killing mosquitoes by insecticide-treated nets. Since the

Dear all, When analyzing data from a climate change experiment using linear mixed-effects models, I recently came across a situation where: - the summary(model) showed a significant difference between the levels of a two-level factor, - while the anova(model) showed no significance for that factor (see below). My question now is: Is the anova.lme() approach correct for that model? And why does

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

Is there a way to set up a regression in R that forces two coefficients to be equal but opposite in sign? I'm trying to setup a model where a subject appears in a pair of environments where a measurement X is made. There are a total of 5 environments, one of which is a baseline. But each observation is for a subject in only two of them, and not all subjects will appear in each

Dear all, I'm calling Winbugs (1.4.3) through R2WinBugs (2.1-18 coda_0.14-7) to fit a switching random walk model, but come up with an instant trap with the log only displaying 'check('. I will paste the trap with session info below; I'd be very grateful for any ideas. Couple of leads: 1. I presume the problem relates to the r package itself or the way I call bugs(), because I

Dear All, I'm not sure of the interpretation of interactions with contrasts. Can anyone help? I do an ANCOVA, dryweight is covariate, block and treatment are factors, c4 the response variable. model<-aov(log(c4+1)~dryweight+treatment+block+treatment:block) summary(model); Df Sum Sq Mean Sq F value Pr(>F) dryweight 1 3.947 3.947 6.6268 0.01076 *

Dear R community, I want to estimate coefficients in a (non-linear) system of equations using 'mle2' from the "bbmle" package. Right now the whole data is read in as just one long time series, when it's actually 9 cross sections with 30 observations each. I would like to be able to test and correct for autocorrelation but haven't found a way to do this in this package.

My name is Giovanna and I am a PhD student in Norway. I am a beginner with statistics and R, hence my ignorance. Apologies from now..... I have been collecting data on time performances of 5 subjects using a 1:3 scale tower yarder. The task was consisting in yarding 5 small logs placed on permanently marked course. Four subjects had different previous experiences (None, Some) and the fifth was a

Hi all, I can't come over a problem with formula. Suppose I have a coxmod model with the following formula: > somemod$formula Surv(lebzeit, tot == 1) ~ sex + (alter >= 65) + diff3k + zelltyp_k_c + q_nuc_3k + kar_k80_g80 + stadium and I want to drop the stadium explanatory variable from the model with > update(somemod, ". ~ . - stadium") I get the following messages: