Convert LaTeX output from outreg to HTML markup
outreg2HTML.RdThis function is deprecated. Instead, please use outreg(type = "html")
Details
This will write the html on the screen, but if a filename argument is supplied, it will write a file. One can then open or insert the file into Libre Office or other popular "word processor" programs.
Author
Paul E. Johnson pauljohn@ku.edu
Examples
dat <- genCorrelatedData2(means = c(50,50,50,50,50,50),
sds = c(10,10,10,10,10,10), rho = 0.2, beta = rnorm(7), stde = 50)
#> [1] "The equation that was calculated was"
#> y = 0.1700512 + 0.698106162851769*x1 + -1.31746411123807*x2 + 0.336734754149924*x3 + -0.566377227766106*x4 + 0.210669422412191*x5 + 0.909036647675649*x6
#> + 0*x1*x1 + 0*x2*x1 + 0*x3*x1 + 0*x4*x1 + 0*x5*x1 + 0*x6*x1
#> + 0*x1*x2 + 0*x2*x2 + 0*x3*x2 + 0*x4*x2 + 0*x5*x2 + 0*x6*x2
#> + 0*x1*x3 + 0*x2*x3 + 0*x3*x3 + 0*x4*x3 + 0*x5*x3 + 0*x6*x3
#> + 0*x1*x4 + 0*x2*x4 + 0*x3*x4 + 0*x4*x4 + 0*x5*x4 + 0*x6*x4
#> + 0*x1*x5 + 0*x2*x5 + 0*x3*x5 + 0*x4*x5 + 0*x5*x5 + 0*x6*x5
#> + 0*x1*x6 + 0*x2*x6 + 0*x3*x6 + 0*x4*x6 + 0*x5*x6 + 0*x6*x6
#> + N(0,50) random error
m1 <- lm(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x1*x2, data = dat)
summary(m1)
#>
#> Call:
#> lm(formula = y ~ x1 + x2 + x3 + x4 + x5 + x6 + x1 * x2, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -114.373 -34.081 -5.618 37.459 136.442
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -150.84458 163.96711 -0.920 0.3600
#> x1 3.56052 3.15428 1.129 0.2619
#> x2 2.16538 3.46545 0.625 0.5336
#> x3 0.49006 0.50651 0.968 0.3358
#> x4 -0.72974 0.45666 -1.598 0.1135
#> x5 0.16985 0.53253 0.319 0.7505
#> x6 1.02774 0.60891 1.688 0.0948 .
#> x1:x2 -0.06328 0.06543 -0.967 0.3360
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 50.57 on 92 degrees of freedom
#> Multiple R-squared: 0.1381, Adjusted R-squared: 0.07247
#> F-statistic: 2.105 on 7 and 92 DF, p-value: 0.05062
#>
m1out <- outreg(list("Great Regression" = m1), alpha = c(0.05, 0.01, 0.001),
request = c("fstatistic" = "F"), runFuns = c(AIC = "AIC"),
float = TRUE)
#> \begin{table}
#> \caption{A Regression}\label{regrlabl}
#> \begin{tabular}{@{}l*{2}{l}@{}}
#> \hline
#> &\multicolumn{1}{l}{Great Regression }\tabularnewline
#> &\multicolumn{1}{l}{Estimate}\tabularnewline
#> &\multicolumn{1}{l}{(S.E.)}\tabularnewline
#> \hline
#> \hline
#> (Intercept) & -150.845 \tabularnewline
#> &(163.967)\tabularnewline
#> x1 & 3.561 \tabularnewline
#> &( 3.154)\tabularnewline
#> x2 & 2.165 \tabularnewline
#> &( 3.465)\tabularnewline
#> x3 & 0.490 \tabularnewline
#> &( 0.507)\tabularnewline
#> x4 & -0.730 \tabularnewline
#> &( 0.457)\tabularnewline
#> x5 & 0.170 \tabularnewline
#> &( 0.533)\tabularnewline
#> x6 & 1.028 \tabularnewline
#> &( 0.609)\tabularnewline
#> x1:x2 & -0.063 \tabularnewline
#> &( 0.065)\tabularnewline
#> \hline
#> N&\multicolumn{1}{l}{100} \tabularnewline
#> RMSE&50.572\tabularnewline
#> $R^2$&0.138\tabularnewline
#> adj $R^2$&0.072\tabularnewline
#> F($df_{num}$,$df_{denom}$)&\multicolumn{1}{c}{2.11(7,92)}\tabularnewline
#> AIC&\multicolumn{1}{c}{1078.13}\tabularnewline
#> \hline
#> \hline
#>
#> \multicolumn{2}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline
#> \end{tabular}
#> \end{table}
#>
##html markup will appear on screen
outreg2HTML(m1out)
#> <tr><td>\begin{table
#> \caption{A Regression\label{regrlabl
#> <table>
#> <tr><td> </td><td colspan = '1'> Great Regression \tabularnewline
#> <tr><td></td><td colspan = '1'> Estimate\tabularnewline
#> <tr><td></td><td colspan = '1'> (S.E.)\tabularnewline
#> <tr><td> (Intercept) </td><td> -150.845 \tabularnewline
#> <tr><td></td><td>(163.967)\tabularnewline
#> <tr><td> x1 </td><td> 3.561 \tabularnewline
#> <tr><td></td><td>( 3.154)\tabularnewline
#> <tr><td> x2 </td><td> 2.165 \tabularnewline
#> <tr><td></td><td>( 3.465)\tabularnewline
#> <tr><td> x3 </td><td> 0.490 \tabularnewline
#> <tr><td></td><td>( 0.507)\tabularnewline
#> <tr><td> x4 </td><td> -0.730 \tabularnewline
#> <tr><td></td><td>( 0.457)\tabularnewline
#> <tr><td> x5 </td><td> 0.170 \tabularnewline
#> <tr><td></td><td>( 0.533)\tabularnewline
#> <tr><td> x6 </td><td> 1.028 \tabularnewline
#> <tr><td></td><td>( 0.609)\tabularnewline
#> <tr><td> x1:x2 </td><td> -0.063 \tabularnewline
#> <tr><td></td><td>( 0.065)\tabularnewline
#> <tr><td>N</td><td colspan = '1'> 100 \tabularnewline
#> <tr><td>RMSE</td><td>50.572\tabularnewline
#> <tr><td>R<sup>2</sup></td><td>0.138\tabularnewline
#> <tr><td>adj R<sup>2</sup></td><td>0.072\tabularnewline
#> <tr><td>F( df_{num , df_{denom )</td><td colspan = '1'> 2.11(7,92)\tabularnewline
#> <tr><td>AIC</td><td colspan = '1'> 1078.13\tabularnewline
#> <tr><td></tr></td>
#> <tr><td colspan = '3'>* p≤ 0.05 *\!\!* p≤ 0.01 *\!\!*\!\!* p≤ 0.001 \tabularnewline
#> </table>
#> <tr><td>\end{table
#>
## outreg2HTML(m1out, filename = "funky.html")
## I'm not running that for you because you
## need to be in the intended working directory
m2 <- lm(y ~ x1 + x2, data = dat)
m2out <- outreg(list("Great Regression" = m1, "Small Regression" = m2),
alpha = c(0.05, 0.01, 0.01),
request = c("fstatistic" = "F"), runFuns = c(BIC = "BIC"))
#> \begin{tabular}{@{}l*{3}{l}@{}}
#> \hline
#> &\multicolumn{1}{l}{Great Regression } &\multicolumn{1}{l}{Small Regression }\tabularnewline
#> &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline
#> &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline
#> \hline
#> \hline
#> (Intercept) & -150.845 & 30.068 \tabularnewline
#> &(163.967)&(36.741)\tabularnewline
#> x1 & 3.561 & 0.773 \tabularnewline
#> &( 3.154)&( 0.502)\tabularnewline
#> x2 & 2.165 & -0.887 \tabularnewline
#> &( 3.465)&( 0.568)\tabularnewline
#> x3 & 0.490 &\multicolumn{1}{l}{\_ }\tabularnewline
#> &( 0.507) &\tabularnewline
#> x4 & -0.730 &\multicolumn{1}{l}{\_ }\tabularnewline
#> &( 0.457) &\tabularnewline
#> x5 & 0.170 &\multicolumn{1}{l}{\_ }\tabularnewline
#> &( 0.533) &\tabularnewline
#> x6 & 1.028 &\multicolumn{1}{l}{\_ }\tabularnewline
#> &( 0.609) &\tabularnewline
#> x1:x2 & -0.063 &\multicolumn{1}{l}{\_ }\tabularnewline
#> &( 0.065) &\tabularnewline
#> \hline
#> N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline
#> RMSE&50.572 &51.886\tabularnewline
#> $R^2$&0.138 &0.043\tabularnewline
#> adj $R^2$&0.072 &0.024\tabularnewline
#> F($df_{num}$,$df_{denom}$)&\multicolumn{1}{c}{2.11(7,92)} &\multicolumn{1}{c}{2.2(2,97)}\tabularnewline
#> BIC&\multicolumn{1}{c}{1101.58} &\multicolumn{1}{c}{1088.97}\tabularnewline
#> \hline
#> \hline
#>
#> \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.01$}\tabularnewline
#> \end{tabular}
outreg2HTML(m2out)
#> <table>
#> <tr><td> </td><td colspan = '1'> Great Regression </td><td colspan = '1'> Small Regression \tabularnewline
#> <tr><td></td><td colspan = '1'> Estimate</td><td colspan = '1'> Estimate\tabularnewline
#> <tr><td></td><td colspan = '1'> (S.E.)</td><td colspan = '1'> (S.E.)\tabularnewline
#> <tr><td> (Intercept) </td><td> -150.845 </td><td> 30.068 \tabularnewline
#> <tr><td></td><td>(163.967)</td><td>(36.741)\tabularnewline
#> <tr><td> x1 </td><td> 3.561 </td><td> 0.773 \tabularnewline
#> <tr><td></td><td>( 3.154)</td><td>( 0.502)\tabularnewline
#> <tr><td> x2 </td><td> 2.165 </td><td> -0.887 \tabularnewline
#> <tr><td></td><td>( 3.465)</td><td>( 0.568)\tabularnewline
#> <tr><td> x3 </td><td> 0.490 </td><td colspan = '1'> \_ \tabularnewline
#> <tr><td></td><td>( 0.507) </td><td>\tabularnewline
#> <tr><td> x4 </td><td> -0.730 </td><td colspan = '1'> \_ \tabularnewline
#> <tr><td></td><td>( 0.457) </td><td>\tabularnewline
#> <tr><td> x5 </td><td> 0.170 </td><td colspan = '1'> \_ \tabularnewline
#> <tr><td></td><td>( 0.533) </td><td>\tabularnewline
#> <tr><td> x6 </td><td> 1.028 </td><td colspan = '1'> \_ \tabularnewline
#> <tr><td></td><td>( 0.609) </td><td>\tabularnewline
#> <tr><td> x1:x2 </td><td> -0.063 </td><td colspan = '1'> \_ \tabularnewline
#> <tr><td></td><td>( 0.065) </td><td>\tabularnewline
#> <tr><td>N</td><td colspan = '1'> 100</td><td colspan = '1'> 100 \tabularnewline
#> <tr><td>RMSE</td><td>50.572 </td><td>51.886\tabularnewline
#> <tr><td>R<sup>2</sup></td><td>0.138 </td><td>0.043\tabularnewline
#> <tr><td>adj R<sup>2</sup></td><td>0.072 </td><td>0.024\tabularnewline
#> <tr><td>F( df_{num , df_{denom )</td><td colspan = '1'> 2.11(7,92) </td><td colspan = '1'> 2.2(2,97)\tabularnewline
#> <tr><td>BIC</td><td colspan = '1'> 1101.58 </td><td colspan = '1'> 1088.97\tabularnewline
#> <tr><td></tr></td>
#> <tr><td colspan = '3'>* p≤ 0.05 *\!\!* p≤ 0.01 *\!\!*\!\!* p≤ 0.01 \tabularnewline
#> </table>
## Run this for yourself, it will create the output file funky2.html
## outreg2HTML(m2out, filename = "funky2.html")
## Please inspect the file "funky2.html