similar to: unbalanced number of rows

HiI have a data frame with rows specifying companies (codes are assigned to companies) and columns specify months (monthly data). The data is based on male (M) and female (F) information for each month. Following is an example of how data looks like: 01 02 03 04001 na M M M001 M M M F002 M F F na003 M na na M003 F M M F003 F F M M na= no male/female. Now, I want to firstly add rows with similar

Hi, I've got a microarray dataset (Illumina) coming from a blood assay with a case-control factor of interest. I also have several other covariates (gender, weight, etc...). I know that the experimental design is highly unbalanced with respect to Gender: female male control 12 7 case 7 17 Therefore, if there is a Gender effect, then it really

With VMWare (server), you can pre-allocate your image in several vmdk files. But I have not bee able to mount that in guestfs. On file (e.g. guest.vmdk) is a text file pointing to all the other files (e.g. guest-f001.vmdk, guest-f002.vmdk,...). I tried the following scenarios without success: 1- just adding the base vmdk: add_drive guest.vmdk 2- adding all the files: add_drive guest-f001.vmdk

Trying to import a bunch of data files named like f001, f002, ....f999. Some of the files may be missing and the missing files vary from time to time. Used for loop and read.table. When it reaches the missing file (say f100), it shows: Error in file(file, "r") : cannot open the connection In addition: Warning message: In file(file, "r") : cannot open file 'f100': No

Dear all, I noticed the following behaviour of plot.lm: > fm1 <- lm(time~dist, data=hills, weights=c(0,0,rep(1,33))) > par(mfrow=c(2,2)) > plot(fm1) Warning messages: 1: longer object length is not a multiple of shorter object length in: res/(sd * (1 - hat)) 2: longer object length is not a multiple of shorter object length in: (res/(sd * (1 - hat)))^2 * hat which seems to be

Dear list members, I want to use a GLM with an unbalanced factor and continuous variables. My factor F has 12 unbalanced levels:

Dear all, I need an help because I am really not able to find over internet a good example in R to analyze an unbalanced table with Anova with repeated measures. For unbalanced table I mean that the questions are not answered all by the same number of subjects. For a balanced case I would use the command aov1 = aov(response ~ stimulus*condition + Error(subject/(stimulus*condition)), data=scrd)

Hello R-users, I am trying to obtain Type III SS for an ANOVA with subsampling. My design is slightly unbalanced with either 3 or 4 subsamples per replicate. The basic aov model would be: fit <- aov(y~x+Error(subsample)) But this gives Type I SS and not Type III. But, using the drop() option: drop1(fit, test="F") I get an error message: "Error in

Hi there, I am looking to fit a two-way random effects model to an *unblalanced* layout, y_ijk = mu + a_i + b_j + eps_ijk, i=1,...,R, j=1,...,C, k=1,...,K_ij. I am interested first of all in estimates for the variance components, sigsq_a, sigsq_b and sigsq_error. In the balanced case, there are simple (MM, MLE) estimates for these; In the unbalanced setup,

Greetings from Rio de Janeiro, Brazil. I am looking for advice / references on binary logistic regression with weighted least squares (using lrm & weights), on the following context: 1) unbalanced sample (n0=10000, n1=700); 2) sampling weights used to rebalance the sample (w0=1, w1=14.29); e 3) after modelling, adjust the intercept in order to reflect the expected % of 1?s in the population

Hello, I was wondering if there was an easy way to calculate the rate of change in a variable for an unbalanced panel data set. Below is a detailed description in R of what I am asking. Thank you. Geoff #Suppose I have the following unbalanced panel data; Person <- c(rep('Frank',5), rep('Tony',4), rep('Edward',4)); Year <-

Hi all, Say I have the following data: a<-data.frame(col1=c(rep("a",5),rep("b",7)),col2=runif(12)) a_aov<-aov(a$col2~a$col1) summary(aov) Note that there are 5 observations for a and 7 for b, thus is unbalanced. What would be the correct way of doing anova for this set? Thanks, Sachin [[alternative HTML version deleted]]

It seems that the standardized deviance residulas, that one gets on plots of a glm.object x with plot(x) are calculated as r <- residuals(x) s <- sqrt(deviance(x)/df.residual(x)) w <- weights(x) hii <- lm.influence(x)$hat r.w <- if (is.null(w)) r else (sqrt(w) * r) rs <- r.w/(s * sqrt(1 - hii)) This implies that, for example, for binomial B(ni,pi) data the devaince residials

I have an unbalanced design I would like to run a power analysis on. What I have been able to find has pointed me to using the pwr.f2.test function as described below. My problem is that I don't know how to appropriately define the numerator and demoninator df. If someone can help here is some more info about my design. It is an unbalanced 2^3 x 3 design where the factor with 3 levels is a

Dear R users What would be the best way to approach estimating a panel regression with twoways random effects, on unbalanced data? Unfortunately, the "plm" package has no implementation of twoways random effects for unbalanced data. Currently I'm considering two approaches: - extend "plm" to cover this type of panel regression. (For the authors, cc'ed:) Would

Hello all, I am currently working with rpart to classify vegetation types by spectral characteristics, and am comming up with poor classifications based on the fact that I have some vegetation types that have only 15 observations, while others have over 100. I have attempted to supply prior weights to the dataset, though this does not improve the classification greatly. Could anyone supply some

Hi! I am using R 1.8.1 and the svm of the e1071 package for classification. The problem is that I have unbalanced classes e.g. the first one is much bigger than the second one and therfore the svm is biased to the first class. If I manually adjust the class size the bias disappears. The question is then how to include this unequal class distribution to the svm (e.g. via wheights or costs)?

Hi everyone! I need to perform a Wilcoxon rank sum test, but I have some ties and the groups have different size also. When I deal with ties I use the wilcox.exact function, how can I solve the different size problem using this function? thanks net -- View this message in context: http://r.789695.n4.nabble.com/Wilcoxon-rank-sum-in-unbalanced-design-tp3447400p3447400.html Sent from the R help

Dear listserv members, In Croissant (2008) “Panel Data Econometrics in R: The plm Package” the authors seem to indicate that the nlme package for R cannot correctly handle unbalanced panel data: “Moreover, economic panel datasets often happen to be unbalanced (i.e., they have a different number of observations between groups), which case needs some adaptation to the methods and is not

Hello, I'm wondering what's the best way to analyse an unbalanced design with a low number of replicates. I'm not a statistician, and I'm looking for some direction for this problem. I've a 2 factor design: Factor batch with 3 levels, and factor dose within each batch with 5 levels. Dose level 1 in batch one is replicated 4 times, level 3 is replicated only 2 times. all