search for: samp3

...dean distance. I took a subset of this dataset (3 samples x 3 loci) to test how euclidean distance is calculated: 3x3 subset used Locus1 Locus2 Locus3 Samp1 GG <NA> GG Samp2 AG CA GA Samp3 AG CA GG The euclidean distance function is defined as: sqrt(sum((x_i - y_i)^2)) My assumption was that the difference between x_i and y_i would be the number of allelic differences at each base pair site between samples. For example, the euclidean distance b...

...7 random plots for 100 times. (There are 50 plots in total) So I created a list of 100 vectors, each vector has 7 elements. samp <- lapply(1:100, function(i) sample(LETTERS)) samp2 <- lapply(samp2, "[", 1:7) How can I select the 26 plots from 'data' using 'samp'? samp3 <- sample(LETTERS, 7) samp4 <- subset(data, plot %in% samp3) # this works samp5 <- subset(data, plot %in% samp2[[1]]) # this works as well, but I used a for loop to get it to select 7 plots 100 times. for (i in nrow(samp2)) { samp6 <- subset(data, plot %in% samp2[[i]]) } # this d...

> 3x3 subset used > Locus1 Locus2 Locus3 > Samp1 GG <NA> GG > Samp2 AG CA GA > Samp3 AG CA GG > > The euclidean distance function is defined as: sqrt(sum((x_i - y_i)^2)) My > assumption was that the difference between x_i and y_i would be the number > of allelic differences at each base pair site between samples. Base R does not...

...2, -4.60517, 2.121427, 1.973118, -4.60517, 2.251568, -4.60517, 2.270724, 0.70338, 0.963816, -4.60517, 0.023703, -4.60517, 2.043382, 1.070586, 2.768289, 1.085169, 0.959334, -0.02428, -4.60517, 1.371895, 1.533227) "zzzanova" <- structure(list(Intensity = c(t(Samp1), t(Samp2), t(Samp3), t(Samp4)), Group = structure(c(1,1,1,1,1,1,1,1,1, 2,2,2,2,2,2,2,2, 3,3,3,3,3,3,3,3,3, 4,4,4,4,4,4,4,4,4,4, 5,5,5,5,5,5,5,5,5, 6,6,6,6,6,6,6,6,6), .Label = c("Group1", "Group2", "Group3", "Group4", "Group5&q...

Dear All, I have a dissimilarity matrix which I happily convert to a distance object by running: X <- as.dist(Y) and I can happily now run either hclust(X) or agnes(X). So that the various bits of output are labelled correctly I would dearly like to be able to give names to the columns and rows of X, as would happen if I ran: mydata<-read.table("clipboard",header=T)

...hod (aov) assumes equal variances. How can I adjust this to do an ANOVA with unequal variances #####SCRIPT STARTS #Creates a structured list suitable for ANOVA analysis # Intensity Group (1,2,3,4) Sample(1:62) "zzzanova" <- structure(list(Intensity = c(t(Samp1), t(Samp2), t(Samp3), t(Samp4)), Group = structure(c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1, 2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2, 3,3,3,3,3,3,3,3,3,3,3,3,3,3, 4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4), .Label = c("Group1", "Group2", "Group3", "Group4"), class = "fac...