search for: grandmean

...8 Residual standard error: 0.4264094 > confint<-95 > a<-(1-(confint/100))/2 > #str(BGaov) # get structure of BGAOV object > #str(summary(BGaov)) # get structure of BGaov summary > #summary(aov.1.e)[[2]][[1]]$"Df" # Could also use this syntax > grandmean<-as.vector(BGaov$"(Intercept)"[[1]][1]) # Grand Mean (I think) > within<-summary(BGaov)$"Error: Within"[[1]]$"Mean Sq" # S2^2Mean Square Value for Within Machine = 0.1819 > dfMachine<-summary(BGaov)$"Error: Machine"[[1]]$"Df" #...

...be more appropriate, or am I (likely) misinterpreting this? Thanks! Tyler MyContrast <- function(Var, Group, G1, G2, G3=NULL) { ## Var == data vector, Group == factor ## G1, G2, G3 == character vectors of factor levels to contrast nG1 = sum(Group %in% G1) nG2 = sum(Group %in% G2) GrandMean = mean(Var[Group %in% c(G1, G2, G3)]) G1Mean = mean(Var[Group %in% G1]) G2Mean = mean(Var[Group %in% G2]) if(is.null(G3)) MScontr = nG1 * ((G1Mean - GrandMean)^2) + nG2 * ((G2Mean - GrandMean)^2) else { nG3 = sum(Group %in% G3) G3Mean = mean(Var[Group %in% G3])...

Hi, I have a matrix: mymat <- matrix(runif(10*4), ncol=4) I wish to subtract the column means, down the colums, subtract the row means from the rows and add back the grand mean - all the means should be the means of mymat rather than of the new matrix. How can I do this? Any help much appreciated. Thanks Tom _________________________________________________________________

...on(variable,groupvariable) { # find out how many values the group variable can take groupvariable2 <- as.factor(groupvariable[[1]]) levels <- levels(groupvariable2) numlevels <- length(levels) # calculate the overall grand mean: grandmean <- mean(variable) # get the mean and standard deviation for each group: numtotal <- 0 denomtotal <- 0 for (i in 1:numlevels) { leveli <- levels[i] levelidata <- variable[groupvariable==leveli,] levelile...

...11]*2))+rnorm(mean=0, sd=.05, 22) fit=lm(y~categ*cont) scatterplot(y~cont|categ) # shows interaction scatterplot(y~categ|cont) # shows effect of the categorical variable, irrespective of cont summary(fit); anova(fit) coeff=coef(summary(fit))[,1] # column of estimates to "coeff" grandmean=coeff[1]+ mean(cont)*coeff[3] #GM = intercept + mean(cont)*regression_slope = 45.00677 slope_A=coeff[3]+coeff[4] #slope of individual regression for A = 3.998 slope_B=coeff[3]+(-coeff[4]) #slope of individual regression for B = 2.002 tapply(y, categ, mean) #these are what I want to get back ou...