standard-error

I am using the plm library to run fixed effect regressions and the sandwich,lmtest libraries to compute robust standard errors. I have no problem running the regressions, but in some instances when I go to compute the standard errors I get the following error: library(plm) library(sandwich) library(lmtest) fe_reg <- plm(y ~ x + I(x^2)+factor(date), data=reg_data, index=c("id","date"), model="within") coeftest(fe_reg, vcov.=vcovHC(fe_reg, type="HC1")) RRuntimeError: Error in solve.default(crossprod(demX)) system is computationally singular: reciprocal condition number = 1.84726e-25 I do not

I'm unable to cluster standard errors using R and guidance based on this post . The cl function returns the error: Error in tapply(x, cluster1, sum) : arguments must have same length After reading up on tapply I'm still not sure why my cluster argument is the wrong length, and what is causing this error. Here is a link to the data set that I'm using. https://www.dropbox.com/s/y2od7um9pp4vn0s/Ec%201820%20-%20DD%20Data%20with%20Controls.csv Here is the R code: # read in data charter<-read.csv(file.choose()) View(charter) colnames(charter) # standardize NAEP scores charter$naep.standardized <-

I would like to plot a model with ggplot2. I have estimated a robust variance-covariance matrix which I would like to use when estimating the confidence interval. Can I tell ggplot2 to use my VCOV, or, alternatively, can I somehow force predict.lm to use my VCOV matrix? A dummy example: source("http://people.su.se/~ma/clmclx.R") df <- data.frame(x1 = rnorm(100), x2 = rnorm(100), y = rnorm(100), group = as.factor(sample(1:10, 100, replace=T))) lm1 <- lm(y ~ x1 + x2, data = df) coeftest(lm1) ## outputs coef.test, but can be modified to output VCOV clx(lm1, 1, df$group) It would be relatively

I have two categorical factors ('Habitat' and 'Locality'), and one continuous variable (T). 'Habitat' has two level and 'Locality' has eight levels. I want to change the default whiskers to represent the SE, and the median into the mean for each boxplot. Is there a way to do this and taking both of the categorical factors into account when plotting? Many thanks in advance. This is what I have done with the default setting of boxplot ggplot, showing the first and third quartiles with median intervals. ggplot(data,aes(x=Locality,y=T)) + geom_boxplot(aes(fill=interaction(Habitat,Locality), group

I'm posting this question because the very similar question here has not been answered until now. I have been asked to plot the mean +/- SEM of my whole cohort of patients over the xyplot() that depicts the values of all patients. The data used represents intraoperative cardiovascular findings from patients undergoing surgery. This is my data.frame called df dput(df) structure(list(Name = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("DE", "JS", "KG"

I am trying to plot a simple barplot with standard errors and its driving me crazy. I did look up some examples and got as far as this: rt5 <- data.frame(rtgrp=c(37.2,38.0,38.3,38.5,38.9), mort=c(35,11,16,8,4), se=c(0.08,0.01,0.005,0.01,0.02)) rt5 xvals=with(rt5, barplot(mort,names.arg=rtgrp, xlab="PTEMP_R group mean",ylab="%",ylim=c(0,max(mort+10+se)))) I am trying to get through the last line of script but have been on it for quite a while: with(rt5, arrows(xvals,mort,xvals,mort+se,length=45,angle=90,code=3)) I would really love to get over this one! Thanks, Baz length is the size of the

I have a panel data set in R (time and cross section) and would like to compute standard errors that are clustered by two dimensions, because my residuals are correlated both ways. Googling around I found http://thetarzan.wordpress.com/2011/06/11/clustered-standard-errors-in-r/ which provides a function to do this. It seems a bit ad-hoc so I wanted to know if there is a package that has been tested and does this? I know sandwich does HAC standard errors, but it doesn't do double clustering (i.e. along two dimensions). Frank Harrell's package rms (which used to be named Design ) has a function