similar to: Subset of matrix

OK, Let's try this again! Here is the reproducible script; it is long because I had to copy the panel dataset here. My question is related to systemfit; I don't know how to get the result for the entire panel. #Reproducible script Empdata<- read.csv("/Users/ngwinuiazenui/Documents/UPLOADemp.csv") View(Empdata) install.packages("systemfit")

Sadly you failed to set your email program to send plain text and the data is corrupted at my end. I also think you need to reduce the size of the data set... the intent here is to increase your understanding, not debug your particular analysis. I will say that I am having a very challenging time understanding what you are trying to accomplish though. What are the equations that you think need

... and the mailing list is picky about attachments... whatever you attached did not conform to the stringent requirements mentioned in the Posting Guide. Pasting the code right into the email is usually safest, though you DO have to post using plain text (as the Posting Guide indicates) or your code may get mangled by the automatic html format removal. On May 15, 2018 7:04:31 AM PDT, Bert Gunter

Hi all, I'm seeing this failure on my PPC G4 box running TOT with llvm-gcc 4.2. Is anyone else seeing this? I'm sure it's related to the byval stuff that's recently gone into LLVM. I'm attaching the output of this command: $ llvm-gcc -emit-llvm -O3 -S -o - -emit-llvm /Users/wendling/llvm/ llvm.src/test/CFrontend/2008-01-25-ByValReadNone.c As you can see in it, there

Unless there is good reason not to, always cc the list -- there are lots of smarter folks than I on it who can help. I may or may not have time to look at this. Hopefully someone else will. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Hello, I did a cross-validation using cvlm from DAAG package but wasn't sure how to assess the result. Does this result means my model is a good model? I understand that the overall ms is the mean of sum of squares. But is 0.0987 a good number? The response (i.e. gailRel5yr) has min,1st Quantile, median, mean and 3rd Quantile, and max as follows: (0.462, 0.628, 0.806, 0.896, 1.000, 2.400) ?

Dear all,I am a new user of WinBUGS and need your help. After running the following code, I got parameters of beta0 through beta4 (stats, density), but I don't know how to get the prediction of the last value of h, the variable I set to NA and want to model it using the following code.Does anyone can given me a hint? Any advice would be greatly appreciated.Best

Hello all, I am working with a 1000x1000 matrix, and I would like to return a 1000x1000 matrix that tells me which value in the matrix is greater than a theshold value (1 or 0 indicator). i have tried mat2<-as.matrix(as.numeric(mat1>0.25)) but that returns a 1:100000 matrix. I have also tried for loops, but they are grossly inefficient. THanks for all your help in advance. Lanre

Hi there, I am doing multiple comparisons for data that is not normally distributed. For this purpose I tried both functions kruskal{agricolae} and kruskalmc{pgirmess}. It confuses me that these functions do not yield the same results although they are doing the same thing, don't they? Can anyone tell my why this happens and which function I can trust? kruskalmc() tells me that there are no

Dear all, I am attempting to explain patterns of arthropod family richness (count data) using a regression model. It seems to be able to do a pretty good job as an explanatory model (i.e. demonstrating relationships between dependent and independent variables), but it has systematic problems as a predictive model: It is biased high at low observed values of family richness and biased low at

I am trying to use t.test on the following data: date type INTERVAL nCASES MTF SDF MTO SDO nFST MF nOBS MO MB BIASCV BIASEV ME MAE RMSE CRCF 2001-06-15 avn GE1.00 4385 0.246 0.300 1.502 0.556 1367 1.373 4385 1.502 1.471 0.285 0.164 -1.256 1.266 1.399 0.056 2001-06-15 avn

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

Dear list, I am currently working with a rather large data set on body temperature regulation in wintering birds. My original model contains quite a few dependent variables, but I do not (of course) wish to keep them all in my final model. I've fitted the following model to the data: >

Hi, In the following results I interpret exp(coef) as the factor that multiplies the base hazard rate if the corresponding variable is TRUE. For example, when the bucket is ks008 and fidelity <= 3, then the rate, compared to the base rate h_0(t), is h(t) = 0.200 h_0(t). My question is then, to what case does the base hazard rate correspond to? I would expect the reference to be the first

Hi, the following strange error appears when I use qda: > qda1 <- qda(as.data.frame(mfilters[cvtrain,]),as.factor(traingroups)) Error: function is not a closure That's also strange: > qda1 <- qda(mfilters[cvtrain,],as.factor(traingroups)) Error in qda.default(mfilters[cvtrain, ], as.factor(traingroups)) : length of dimnames must match that of dims Some backgroud: >

Hi, In my analysis of impacts of insecticide-treated bednets on malaria, I look at the relationship between malaria incidence and mosquito behaviors. The condensed data set is copied here. Ordinary regression (lm) shows that Incidence was negatively related to Mortality. This makes sense because the latter reflected the strength of killing mosquitoes by insecticide-treated nets. Since the

I have a data frame, lets call it dat, with 3 columns ( mc, yr, ret) which represent market cap, year, and return. mc is a factor, mc, and ret are real numbers. I want to add a column to the data calculated as follows. For each year, I want to split the data by mc decile, then calculate the mean ret within that mc decile, and finally subtract that year's decile mean from the raw return. Then

Dear R users, I noticed a problem in the anova command when applied on a single coxph object if there are missing observations in the data: This example code was run on R-2.6.1: > library(survival) > data(colon) > colondeath = colon[colon$etype==2, ] > m = coxph(Surv(time, status) ~ rx + sex + age + perfor, data=colondeath) > m Call: coxph(formula = Surv(time, status) ~ rx +

Dear list, I would like to run an ANCOVA using car::Anova with repeated measures factors, but I can't figure out how to do it. My (between-subjects) covariate always interacts with my within-subject factors. As far as I understand ANCOVA, covariates usually do not interact with the effects of interest but are simply additive (or am I wrong here?). More specifically, I can add a covariate as