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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

Hi, I am curious about how to interpret the table produced by anova(ols(...)), from the Design package. I have a multiple linear regression model, with some interaction, defined by: ols(formula = log(ksat * 60 * 60) ~ log(sar) * pol(activity, 3) + log(conc) * pol(sand, 3), data = sm.clean, x = TRUE, y = TRUE) n Model L.R. d.f. R2 Sigma 1834 1203

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

Dear r-helpers, Following up on http://finzi.psych.upenn.edu/R/Rhelp02a/archive/ 81159.html where Douglas Bates gives a helpful application of lmer() to data(sleepstudy, package = 'lme4'), I need a bit more help in order to plot the correct confidence intervals of a designed experiment such as: > data(ratdrink, package = 'faraway') I follow the steps Douglas took in

Hello folks, I am on learning phase of R. I have developed Regression Model over six predictor variables. while development, i found my all data are not very linear. So, may because of this the prediction of my model is not exact. Here is the summary of model : Call: lm(formula = y ~ x_1 + x_2 + x_3 + x_4 + x_5 + x_6) Residuals: Min 1Q Median 3Q Max -125.302

Hello, I have fit a simple spline model to the following data. Data x y 0 1.298 2 0.605 3 0.507 4 0.399 5 0.281 6 0.203 7 0.150 8 0.101 Model Sp.1=lm(y~bs(x,df=4)) Now I wish to inverse predict the x for y=.75, say. Optimize works fine for a polynomial but I can figure out how to get the spline model into the function argument. Can anyone help me out. Thanks!! Jeff Jeff Morris Sanofi

Dear R community members, I think I am asking a very simple question, but I really looked up in the faqs and manuals and found nothing helpful. I am trying to plot a data frame with the following structure (this is just a small extract): glo conc odor line series X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 1 0 AIR LN1 UP -0.488

Hi all, I am using "lm" to fit some anova factor models with interactions. The default setting for my unordered factors is "treatment". I understand the resultant "lm" coefficients for one factors, but when it comes to the interaction term, I got confused. > options()$contrasts unordered ordered "contr.treatment"

Hi! The kernlab function kpca() mentions that new observations can be transformed by using predict. Theres also an example in the documentation, but as you can see i am getting an error there (As i do with my own data). I'm not sure whats wrong at the moment. I haven't any predict functions written by myself in the workspace either. I've tested it with using the matrix version and the

Hi, I have three questions concerning GLMMs. First, I ' m looking for a measure for the significance of the random variable in a glmm. I'm fitting a glmm (lmer) to telemetry-locations of 12 wildcat-individuals against random locations (binomial response). The individual is the random variable. Now I want to know, if the individual ("TIER") has a significant effect on the model

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 List, My goal is to force R not to print in scientific notation in the sixth column (rel_diff - for the p-value) of my data frame (not a matrix). I have used the format.pval () and printCoefmat () functions on the data frame. The R script is appended below. This issue is that use of the format.pval () and printCoefmat () functions on the data frame gives me the desired results, but coerces

Hi, I downloaded both gee and geepack, and I am trying to understand the differences between the two libraries. I used the same data and estimated the same model, with a correlation structure autoregressive of order 1. Surprisingly for me, I found very different results. Coefficients are slightly different in value but sometimes opposite in sign. Moreover, the estimate of rho (correlation

Ah.? Here you go. # grep A5077100E776C /var/log/maillog Jul 19 13:15:55 mailcampaign1 postfix/pickup[19675]: A5077100E776C: uid=0 from=<root> Jul 19 13:15:55 mailcampaign1 postfix/cleanup[19797]: A5077100E776C: warning: header Subject: Cron <root at mailcampaign1> run-parts /etc/cron.hourly from local; from=<root at csusb.edu> Jul 19 13:15:55 mailcampaign1

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

Dear R readers: I have written a short lme.R function, which adds normalized coefficients and White heteroskedasticity-adjusted statistics to the standard output. Otherwise, it behaves like lm. This is of course trivial for experts, but for me and other amateur users perhaps helpful. y= rnorm(15); x= rnorm(15); z= rnorm(15); m= lme( y ~ x + z); print(summary(m)); produces something

Hello, My tests are messing with one another because it seems that they aren''t cleaning the database after execution. I have the tables headlines and articles. And the articles table has a foreign key to the headlines table. My schema structure is created by this script ---- drop table if exists changes; drop table if exists articles; drop table if exists headlines; create table

Hi, I'm interested in building a Cox PH model for survival modeling, using 2 covariates (x1 and x2). x1 represents a 'baseline' covariate, whereas x2 represents a 'new' covariate, and my goal is to figure out where x2 adds significant predictive information over x1. Ideally, I could get a p-value for doing this. Originally, I thought of doing some kind of likelihood ratio

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,] 

Seems like the message got filtered on BUGTRAQ, I'd still think at least some of the people on this list may have an interest in parts of the data below. Cheers. ---------- Forwarded message ---------- Date: Sat, 29 Jul 2000 06:11:54 +0300 (EET DST) From: Ville <viha@cryptlink.net> To: bugtraq@securityfocus.com Subject: Linux traceroute (oddness & localhost memory-alloc) Hi.