Cfrgtkky

up vote 0 down vote favorite I have a dataframe mydf containing 6 columns of time series data. I want to calculate the correlation of all this data, which can easily be done through cor(mydf) . However, I want to multiply each of the correlations by the square root of the relevant long-run variances (I adopt an arbitrary autocorrelation lag of 5) of each pairwise column. To demonstrate, val = cor(mydf[,1], mydf[,2]) cov_temp1 = acf(mydf[,1], type = "covariance", lag.max = 5, plot = FALSE, na.action = na.pass)$acf cov_temp2 = acf(mydf[,2], type = "covariance", lag.max = 5, plot = FALSE, na.action = na.pass)$acf s.e. = sqrt((cov_temp1[1]+2*sum(cov_temp1[-1]))/nrow(mydf) * (cov_temp2[1]+2*sum(cov_temp2[-1]))/nrow(mydf)) Then, the pairwise statistic for column 1 and 2 is val*s.e. . Assuming I have 6 colum

up vote 3 down vote favorite how to align dotfill and square symbol in a longtable with wrapped lines? I wrote this mwe: documentclass{report} usepackage{longtable} usepackage{booktabs} usepackage{multicol} usepackage{wasysym} usepackage{array} begin{document} newcommand{lsquare}{LargeSquare} newcommand{lcheck}{LargeCheckedBox} begin{longtable}{@{hspace{1cm}}p{12cm}<{dotfill}>{centeringletnewline\arraybackslashhspace{0pt}}m{1cm}} caption{Criteria for risk classification of petrol filling stations according vicinity} label{tab:criteria}\ toprule multicolumn{1}{c}{} & textbf{Yes/No} \ midrule endfirsthead caption{Criteria for risk classification of petrol filling stations according vicinity (continuation)}\ multicolumn{1}{c}{} & textbf{Yes/No} \midrule endh