similar to: sd() for numeric row entries

I have a vector with lots of different numbers. I need to make a graph showing the Uniform Distribution of the figures. I have created a graph showing all the different values, but now want individual Gaussian Kernel round each point. This is what I have but each time it comes up with an error as I have just based it on the Normal Distribution, but I'm not sure what I need to change to make it

Dear useRs (called Frank Harrell, most likely), after having preached for years to my medical colleagues to be cautious with stepwise selection procedures, they chanted back asking for an alternative when using mixed models. There is a half dozen laXXX packages around for all types of linear models, but as far I see there is none for mixed models such as lme. Even boot.stepAIC (which I

Hi Everyone, I just don't know how to extract the information I want from the summary of a linear regression model fitting. For example, I fit the following simple linear regression model: results = lm(y_var ~ x_var) summary(results) gives me: Call: lm(formula = y_var ~ x_var) Residuals: Min 1Q Median 3Q Max -5.9859 -1.5849 0.4574 2.0163 4.6015 Coefficients:

hello, here are some problems with ks.test. Do you think these values are consistent? (2 e^{-1} means 0.2 ) data file name& sample size& D & pvalue A&10.000&0.4202&<2.2 e^{-16}\\ B&10.000&04294&<2.2 e^{-16}\\ C &10.000&0.4484&<2.2 e^{-16}\\ D&10.000&0.4569&<2.2 e^{-16}\\ E&50.000&0.4015&<2.2 e^{-16}\\

I am having problems with comparing individual means in a within-subjects ANOVA. From my understanding, TukeyHSD is not appropriate in this context. So I am trying to compute contrasts, as follows: seven subjects participated in each of 6 conditions (intervals). > subject = factor(rep(c(1:7), each = 6)) > interval = factor(rep(c(1:6), 7)) and here is the dependent variable: > dv

Dear all, this is probably more a statistics question than an R question but probably there is somebody who can help me nevertheless. I'm running a regression with four predictors (a, b, c, d) and all their interaction effects using lm. Based on theory I assume that a influences y positively. In my output (see below) I see, however, a negative regression coefficient for a. But several of the

Hello. I'm a new user of R, and I have a question regarding the use of aov and lm-functions. I'm doing a fractional factorial experiment at our production site, and I need to familiarize myself with the analysis before I conduct the experiment. I've been working my way through the examples provided at http://www.itl.nist.gov/div898/handbook/pri/section4/pri472.htm

can anybody help with the following error message: Error in 1:object$rank : NA/NaN argument I get it with comparisons of single means in an ANOVA. Example data below. Thanks, Steffen seven subjects participated in each of 6 conditions (intervals). > subject = factor(rep(c(1:7), each = 6)) > interval = factor(rep(c(1:6), 7)) and here is the dependent variable: > dv = c(3.3868,

R : Copyright 2002, The R Development Core Team Version 1.4.1 (2002-01-30) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type `license()' or `licence()' for distribution details. R is a collaborative project with many contributors. Type `contributors()' for more information. Type `demo()' for some demos,

Dear list, I used glm to analyze the effect of treatment, measurement-methods etc. on lesion size in a time course experiments. Could anyone points me out how to extract detailed comparisons of interest? For example: Measurement.T2.M6 vs. Measurement.T2.Baseline, Or even Measurement.T2.M6.Treatment vs. Measurement.T2.M6.placebo (I did not include Treatment as a factor in the query below yet).

I'm attempting to sort a 3 dimensional array that looks like this > x , , 1 [,1] [,2] [1,] 9 9 [2,] 7 9 , , 2 [,1] [,2] [1,] 6 5 [2,] 4 6 , , 3 [,1] [,2] [1,] 2 1 [2,] 3 2 Such that it ends up like this .... > y , , 1 [,1] [,2] [1,] 2 1 [2,] 3 2 , , 2 [,1] [,2] [1,] 6 5 [2,] 4 6 , , 3 [,1] [,2]

Hello everyone, I am using *mlogit* to analyse my choice experiment data. I have *3 alternatives* for each individual and for each individual I have *9 questions*. I have a response from *516 individuals*. So it is a panel of 9*516 observations. I have arranged the data in long format (it contains 100 columns indicating different variables and identifiers). In mlogit I tried the following

Hi R Users, I created interaction plots in ggplot2 and was trying to add output of two way ANOVA models, especially only interaction ( example treatment*control F(XX, XX) = xxx, p = xxx) into figures, but i was not able to add. Would you mind to help on how I can add information into each figure? I have attached the example data and the code that I used for this. dat<-structure(list(Sites