search for: mean

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 montana.edu

...ork, 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) factor<-factor(factor) traits1<-c(rnorm(100, mean=1, sd=1), rnorm(100, mean=3, sd=3), rnorm(100, mean=6, sd=6)) traits2<-c(rnorm(100, mean=1, sd=1), rnorm(100, mean=3, sd=3), rnorm(100, mean=6, sd=6)) traits3<-c(rnorm(100, mean=1, sd=1), rnorm(100, mean=3, sd=3), rnorm(100, mean=6, sd=6)) traits4<-c(rnorm(100, mean=1, sd=1), rnorm(100, me...

...ork, 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) factor<-factor(factor) traits1<-c(rnorm(100, mean=1, sd=1), rnorm(100, mean=3, sd=3), rnorm(100, mean=6, sd=6)) traits2<-c(rnorm(100, mean=1, sd=1), rnorm(100, mean=3, sd=3), rnorm(100, mean=6, sd=6)) traits3<-c(rnorm(100, mean=1, sd=1), rnorm(100, mean=3, sd=3), rnorm(100, mean=6, sd=6)) traits4<-c(rnorm(100, mean=1, sd=1), rnorm(100, me...

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...

...ars 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 <- rmnorm(n=1500,mean=c(mean(sanad[,1]),mean(sanad[,2]),mean(sanad[,3])),varcov=covmat) The middle column of 'datamat' is simulated data for age. Obviously some of the simulated values are not going to be sensible. Therefore I'd like to set up a function that looks at each row of 'datamat' and if...

...prevRP + owning + adv_binc_ratio * (repmethIO + repmethSR + no_dis_no_def + prevLO + prevOO + prevRP + owning) + agem1 * (repmethIO + repmethSR + no_dis_no_def + prevLO + prevOO + prevRP + owning), data = a) This is part of my result: > summary(resultt1,adv_binc_ratio=mean(a$adv_binc_ratio),agem1=mean(a $agem1),repmethIO=c(0,mean(a$repmethIO),1),repmethSR=c(0,mean(a $repmethSR),1),no_dis_no_def=c(0,mean(a$no_dis_no_def),1),prevLO=c (0,mean(a$prevLO),1),prevOO=c(0,mean(a$prevOO),1),prevRP=c(0,mean(a $prevRP),1),regEA=c(0,mean(a$regEA),1),regEM=mean(a$regEM),regGL=...

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 random but the p values do not go negative. I have highlighted the differences in the script that I noticed. I am think some combination of these...

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: > by(data=mtcars, INDICES=list(mtcars$am), FUN=mean) : 0 [1] NA ------------------------------...

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

...utine 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??? <- apply(data,2,mean,na.rm=TRUE) +?? Std.dev <- apply(data,2,sd,? na.rm=TRUE) +?? Min <- apply(data,2,min,na.rm=TRUE) +?? Max <- apply(data,2,max,na.rm=TRUE) +?? Obs <- dim(data)[1] +?? out <-round(cbind(Mean,Std.dev,Min,Max,Obs),digits) +?? out + # invisible(list(Mean=Mean,Std...

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, '\n') return(x) } I am also giving the outputs below to illustrate my point. I understand the point that as 'a' is less than 2.0, the var...

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

...5, of a famous book "R by Example" written by "Jim Albert and Maria Rizzo" published in Springers (2012) in a Use R! Series. The output of the by() function printed in the book is being reproduced below for information to all: > by(data=brain[, -1], INDICES=brain$Gender, FUN=mean, na.rm=TRUE) brain$Gender: Female FSIQ VIQ PIQ Weight Height MRI_Count 111.900 109.450 110.450 137.200 65.765 862654.600 ------------------------------------------------------------ brain$Gender: Male FSIQ VIQ PIQ Weight Height MRI_Count 115.00000 115.25000 111.60000 166.44444 71.431...

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 Cost-Effectiveness Ratio....

...an expression level different between any two of the tissue types, how many of them have an expression level unique to only one tissue (i.e., gene20 is expressed at a very low level (0.1) in Liver, but expressed at the same level (~0.7) across all the other tissue types). So my hypotheses are: H0: Mean(B)=Mean(L)=Mean(K)=Mean(H) H1: Mean(B)?Mean(L)=Mean(K)=Mean(H) or Mean(L)?Mean(B)=Mean(K)=Mean(H) or Mean(K)?Mean(B)=Mean(L)=Mean(H) or Mean(H)?Mean(B)=Mean(K)=Mean(L) H2: Mean(B)?Mean(L)?Mean(K)?Mean(H) In my analysis, the gene expression level is the dependent value (Y), the tissue-typ...

...ly 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 = .05. You presume that the population means will have the following values: uA = 17.5, uB = 19, uC = 25, and uD = 20.5. You intend to run 80 subjects in all, with equal n's across all 4 groups. You plan on conducting a one-way ANOVA. Compute your power to reject the null hypothesis under these conditions. I did: within.var = 9 ^ 2 mea...

...55372 58012 55546 57309 M336T90 11063 10312 10674 10840 11208 M427T91 12064 11956 12692 12340 11924 M429T91 4594 3890 4096 4019 4204 M447T90 26553 27647 26889 26751 26929 Now I'm trying to transform each value column-wise to make columns to all have the same mean with: df * mean(mean(df)) / mean(df). I just can't get my head around this: mean(df) gives me the correct column means vector, and mean(mean(df)) gives me the correct total mean. The above operation works correctly for individual rows, i.e. if I do df[1,]*mean(mean(df))/mean(df) Can someone...

...ns is smaller than 2. Nothing to split, exiting."); } if ( length(data) < 2 ) { stop("The length of the data is really short. Is that ok?"); } if ( bins * n != length(data) ) { stop("Cannot construct bins of equal length."); } t(array(data, c(n,bins))); } mean.bins <- function(data) { # For the vincentizing procedures in vincentize() and binify(), # it made sense to check the data array/vector/matrix. Here, # we now just need to check that data is a matrix. if ( !is.matrix(data) ) { stop("The data is not in matrix form."); } means...

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

...e 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 = gl(3, 10, labels = letters[4:6])) df %>% group_by(f1, f2) %>% summarise(x1m = mean(X1), x2m = mean(X2), x3m = mean(X3), x4m = mean(X4)) df %>% group_by(f1) %>% summarise(x1m = mean(X1), x2m = mean(X2), x3m = mean(X3), x4m = mean(X4)) # My fail attempt s <- function() { dplyr::summarise(x1m =...