search for: meaning

I am trying to write a function that will solve a simple system of nonlinear equations for the parameters that describe the beta distribution (a,b) given the mean and variance. mean = a/(a+b) variance = (a*b)/(((a+b)^2) * (a+b+1)) Any help as to where to start would be welcome. -- Scott Story Graduate Student MSU Ecology Department 319 Lewis Hall Bozeman, Mt 59717 406.994.2670 sstory at

Dear R-help listers, I want to add 95% confidential interval as vertical lines to x axe in density plot. I have found the library(hdrcde) can do this work, but I do not know how to handle functions of this library when I used ggplot2 to draw the graph. Thank you in advance. The data and codes followed: # dummy data factor<-rep(c("Alice","Jone","Mike"),each=100)

Dear R-help listers, Under the helps from the ggplot2 list, I have set up method of drawing a graph with multiple density plots arranged one by one in one page. Now, I want to add 95% confidential interval as vertical lines to x axe in density plot. I have found the library(hdrcde) can do this work, but I do not know how to handle functions of this library when I used ggplot2 to draw the graph.

Hi there: I have a question about generating mean value of a data.frame. Take iris data for example, if I have a data.frame looking like the following: --------------------- Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2

Dear all, I know there have been various questions posted over the years about loops but I'm afraid that I'm still stuck. I am using Windows XP and R 2.9.2. I am generating some data using the multivariate normal distribution (within the 'mnormt' package). [The numerical values of sanad and covmat are not important.] > datamat <-

Dear All: I run a logistic regression (using lrm in the Design package), and after that, I use the command "summary" to get the marginal effects of each variable. But one strange thing happens on my binary dependent variable: The marginal effect of it jumping from 0 to 1 is 1.77. I believe the marginal effect of binary variable x1 has interpretation should be P(Y=1|x1=1,

I am new to R and have two scripts written slightly different but should to relatively the same thing but my lack of experience with the program I can not figure out the what I need to do to correct it. The first script gives me a consistent mean fold change values with every run but can generate negative p values for some. For the second version of the script, the fold changes seem to be very

Dear Sirs, I am Professor at Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh, India. While taking classes, I found the *by() *function producing following error when I use FUN=mean or median and some other functions, however, FUN=summary works. Given below is the output of the example I used on a built-in dataset "mtcars", along with error message reproduced herewith: >

I have 3 columns of numbers, and i want to find the mean of each separately, and then the mean of all of the means. ive tried: av3daysB<- mean(mean(avr1), mean(avr2), mean(avr3)) av3daysB but it only gives me the mean of the first column (avr1) Any suggestions? Thanks, Emilija

How can I have both printout and saved results at the same time. The subroutine first return "out" and the printout gets printed, but not saved. I then run the "invisible" line. Results got saved and accessible but no printout. How can I have both printout and also have the results saved? Thank you! > dstat4 <- function(data,digits=3){ +?? Mean??? <-

Dear All, I am trying to generate some Pareto random variates using the inverse method. This is really straightfoward and my R function looks as below : pareto <- function(c, a, cnt=1000) { u <- runif(cnt) x <- (c / ((u ^ (1 / a)))) mean.theo <- ((c * a) / (a - 1)) mean.gen <- mean(x) cat('Pareto mean : theoritical', mean.theo, 'generated', mean.gen,

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

Dear All, Thanks for your help. However, I would like to draw your attention to the following: Actually, I was replicating the Example 2.3, using the dataset "brainsize.txt" given in Section 2.3.3 ("Summarize by group") at page 55, of a famous book "R by Example" written by "Jim Albert and Maria Rizzo" published in Springers (2012) in a Use R! Series. The

Hi all: I get the following error message that I am not able to resolve. Error in if (const(t, min(1e-08, mean(t)/1e+06))) { : missing value where TRUE/FALSE needed It appears right before the last data.frame statement. Below is the program that simulates data from one way random effects model and then computes normality and bootstrap confidence interval for

Hi Dear all, I have some gene expression data samples from different tissue types ----------------------------------------------- - 120 samples from blood (B) - 20 samples from Liver (L) - 15 samples from Kidney (K) - 6 samples from heart (H) ----------------------------------------------- All the samples are from different individuals, so there are in total 161 individuals from which the DNA was

I sent a message a couple days ago about doing calculations for power of the ANOVA. Several people got back to me very quickly which I really appreciated. I'm working now on a similar problem, but instead of a balanced ANOVA, I have an unbalanced one. The first part of the question was: You assume that the within-population standard deviations all equal 9. You set the Type 1 error rate at á

Dear group: sorry for my beginners question, but I'm rather new to R and was searching high and low without success: I have a data frame (df) with variables in the rows and observations in the columns like (the actual data frame has 15 columns and 1789 rows): early1 early2 early3 early4 early5 M386T1000 57056 55372 58012 55546 57309 M336T90 11063 10312 10674 10840 11208

Hi! Apologies for sending the mail without any code. Apparently somewhere along the way the .R attachments got filtered out. I have included the code below as clean as possible. My original mail is below the code. Thank you again for your time. regards, Paul vincentize <- function(data, bins) { if ( length(data) < 2 ) { stop("The data is really short. Is that ok?"); }

I would like to generate pseudo-random numbers between two numbers using R, up to a given distribution, for instance, rnorm. That is something like rnorm(HowMany,Min,Max,mean,sd) over rnorm(HowMany,mean,sd). I am wondering if dnorm(runif(HowMany, Min, Max), mean, sd) is good. Any idea? Thanks. -james

Dear group, Is there a way I could avoid the sort of duplication illustrated below? i.e., I have the same dplyr::summarise function on different group_by arguments. So I'd like to create a single summarise function that could be applied to both. My attempt below fails. df <- data.frame(matrix(rnorm(40), 10, 4), f1 = gl(3, 10, labels = letters[1:3]), f2 =