similar to: merging unbalanced rows

Hello everyone, I want to get a 1km by lkm grid raster image using my csv data. If I call latitude=a, longitude=b and preciptation=c. a<-(1,2,3,4,5) b<-(6,7,8,9,10) c<-(10,20, 30,40, 50) Then I found an example in r help which goes like pts = read.table("file.csv",......) library(sp) library(rgdal) proj4string(pts)=CRS("+init=epsg:4326") # set it to lat-long pts =

Please have a look at http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example and/or http://adv-r.had.co.nz/Reproducibility.html It looks like you have posted in HTML and the post is close to unreadable. The data is welcome but we really should have in in dput() form. Do ?dput() for more information. John Kane Kingston ON Canada > -----Original Message-----

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

Hello list, I have a data frame M like: BAC chr pos s1 s2 RP11-80G24 1 77465510 -1 0 RP11-198H14 1 78696291 -1 0 RP11-267M21 1 79681704 -1 0 RP11-89A19 1 80950808 -1 0 RP11-6B16 1 82255496 -1 0 RP11-210E16 1 228801510 0 -1 RP11-155C15 1 230957584 0 -1 RP11-210F8 1

Dear all, I have a matrix, called newdata1, > dim(newdata1) [1] 34176 83 It looks like: EntrezID Name S1 S2 S3 S4 S5..... 1 4076 CAPRIN1 0.1 0.2 0.3... 2 139170 WDR40B 0.4 0.5 0.6... 3 5505 PPP1R2P1 0.3 0.3 0.7... 4 4076 CAPRIN1 0.7 0.3 0.2... 5 139170 WDR40B null 0.8

Hi, I am relatively new to R and I am trying to figure out how to select rows of my data based on a condition. For example in the dataset below multiple observers recorded data at the same point on the same date and the data was recorded on separate rows, but I only need one of the rows of data. Is it possible to use grep to search for rows that have matching PtName and Date and then select the

Hi all, I have a matrix which looks like the following, x=matrix(c(1,2,3,4,3,5,4,6,4,9),5,2,byrow=T) Now, I want to replace the 2nd columns of those 4 rows which have duplicate entries in the 1st column. For example, x[2,1]=x[3,1]=3 and I want to replace x[3,2] by x[2,2]. Similarly, I want to replace x[5,2] with x[4,2], which means that for duplicate entries in the 1st column, I want to replace

more /etc/xen/scalix2 kernel="/boot/vmlinuz-2.6.18-8.1.6.el5xen" memory = 512 name = "hating-xen" vif = [''ip = "10.0.1.150", bridge=xenbr0''] disk = [ ''/dev/XEN/scalixroot,sda1,w'', ''/dev/XEN/scalixvar,sda3,w'', ''/dev/XEN/scalixswap,sda2,w'' ] It

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,

I have a multidimensional data which looks like the following: "S1-a" "S2-b" "S3-c" "S4-d" "S5-a" "S6-b" "S7-c" "S8-d" "T1-A" "T1-B" "T1-C" "T1-D" "T2-A" "T2-B" "T2-C" "T2-D" I read it from csv file and would like to have 16

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]]

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)?