similar to: lags for unbalanced panel data

Hello, I have an unbalanced panel data set that looks like: ID,YEAR,HEIGHT Tom,2007,65 Tom,2008,66 Mary,2007,45 Mary,2008,50 Harry,2007,62 Harry,2008,62 James,2007,68 Jack,2007,70 Jordan,2008,72 That is, James, Jack, and Jordan are missing a YEAR. Is there any command that will "fill in" the missing YEAR such that the end result will be balanced and look like: ID,YEAR,HEIGHT

Dear all I am trying to fit Variable Coefficients Models on Unbalanced Panel Data. I managed to fit such models on balanced panel data (the example from the "plm" vignette), but I failed to do so on my real, unbalanced panel data. I can reproduce the error on a modified example from the vignette: > require(plm) > data("Hedonic") > Hed <- pvcm(mv ~ crim + zn + indus

Appreciate any suggestions regarding how to fit an unbalanced panel data to a Tobit model using R functions. I am trying to analyze how real estate capital expenditures (CapEx) are affected by market conditions using a panel Tobit model. The CapEx is either positive or 0, so it is censored. The data are unbalanced panel, including the CapEx of about 5000 properties over about 40 quarters, with the

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

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,

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

I have a three-way unbalanced ANOVA that I need to calculate (fixed effects plus interactions, no random effects). But word has it that aov() is good only for balanced designs. I have seen a number of different recommendations for working with unbalanced designs, but they seem to differ widely (car, nlme, lme4, etc.). So I would like to know what is the best or most usual way to go about working

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)

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

Hi, I have an unbalanced dataset on which I would like to perform a one-way anova test using R (aov). According to Wannacott and Wannacott (1990) p. 333, one-way anova with unbalanced data is possible with a few modifications in the anova-calculations. The modified anova calculations should take into account different sample sizes and a modified definition of the average. I was wondering if the

Hello Everyone, *Question: *How do you calculate the sum of squares for a two-way _unbalanced_ ANOVA? *What I have done:* I have found many useful tutorials online for running a balanced two-way ANOVA but I haven't had much luck for running a unbalanced two-way ANOVA. From what I have read, the trouble with running an unbalanced two-way ANOVA, is that things get tricky when calculating

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

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

I am a student who is doing empirical work for his thesis and trying to switch to R. I am familiar with Stata, and at the moment I am trying to replicate some of my previous work. I have a large unbalanced panel data set, observations for different countries between 1970 and 2007. My dependent variable is an overdispersed count. So far I have used fixed-effects negative binomial regression,

Hi all, I'm new to R and have the following problem: I have a 2 factor design (a has 2 levels, b has 3 levels). I have an object kidney.aov which is an aov(y ~ a*b), and when I ask for model.tables(kidney.avo, se=T) I get the following message along with the table of effects: Design is unbalanced - use se.contrast() for se's but the design is NOT unbalanced... each fator level

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

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

Hi all I'm trying to complete a textbook example originally designed for SPSS in R, and I therefore need to find out how to compute an unbalanced ANOVA in R. I did a search on the mailinglist archives an found a post by Prof. Ripley saying one should use the lme function for (among other things) unbalanced ANOVAs, but I have not been able to use this object. My code gives me an error.. Why

Hi folks, I have a dataset from a trial measuring the subjects' pupils. There are many measurements, all of which must be analysed in a similar fashion; so if I get the analysis right for one of them, I've got them all. For simplicity, let us call any measurement we may be interested as "response". The study design is an unbalanced latin square, with 5 periods, 5 treatments and

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]]