Make it easier to add rows to the GOF block. Currently, users have to save the results in an intermediate texreg object and manipulate the gof, gof.names, and gof.decimal slots and then hand over the updated object to texreg. This could be done more easily using dedicated arguments, such as add.decimal.gof and add.integer.gof, which would expect a named list of values.

First, thank you so much for this awesome package. It's been really helpful.

The main texreg() function contains some code that checks to see if the file output is a string, e.g.:

else if (!is.character(file)) {
    stop("The 'file' argument must be a character string.")
  } 

However, on Unix systems it is possible to copy directly to the clipboard. On Mac OS, it is as simple as file=pipe('pbcopy').

Would you accept a pull request enabling direct copy of the outputted code to the clipboard?

Hi, I would like to add multiple rows to my texreg output indicating things such as fixed effects, Year-Location, and potentially many such rows. Here is an example of an output with such rows
example.pdf.

Is there an option for this?

Using example #2 from https://www.rdocumentation.org/packages/VGAM/versions/1.0-4/topics/vglm

library(VGAM)
pneumo <- transform(pneumo, let = log(exposure.time))
fit <- vglm(cbind(normal, mild, severe) ~ let, multinomial, data = pneumo)

Running texreg::extract(fit) produces the following error:
Error: $ operator is invalid for atomic vectors

sessionInfo()
R version 3.4.2 (2017-09-28)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

locale:
[1] LC_COLLATE=Swedish_Sweden.1252  LC_CTYPE=Swedish_Sweden.1252    LC_MONETARY=Swedish_Sweden.1252 LC_NUMERIC=C                   
[5] LC_TIME=Swedish_Sweden.1252    

attached base packages:
[1] splines   stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] VGAM_1.0-4

loaded via a namespace (and not attached):
[1] compiler_3.4.2 tools_3.4.2    yaml_2.1.14    texreg_1.36.23

Sometimes, it is convenient to change the rows containing the coefficients and goodness of fit statistics with the columns containing the models. This is for example interesting if you compare many models which means you are focusing first of all only on gofs and you omit all specific coefs.

Would it be possible to do produce such a table with texreg?

PS: Results from the package gnm follow the same pattern as glm. Thus you could add setMethod("extract", signature = className("gnm", "gnm"), definition = extract.glm) to extract.R. Often, one would produce some additional results using gnm's function getContrasts. I am not sure but possibly the easiest way to implement these additional results is to manually adjust the texreg object.

This would likely be a lot of work, but I'm wondering if its possible to use a package like xlsx or reporteR to print a texreg directly to a file. I can experiment with it. If anyone has an easy idea for how to implement, that would be awesome.

The code below runs correctly with texreg_1.36.18 ("logSigma" is omitted from the output), but with texreg_1.36.23 it generates this error:

Error in customnames(m, custom.coef.names) : 
  There are 3 coefficients, but you provided 4 custom names for them.

This can be sourced and run under the two versions of texreg.

library(texreg)
library(censReg)
df <-  data.frame(y=sample(0:5, replace=TRUE, size=50), x=rnorm(50),
                  z=rnorm(50))
reg <- censReg(y ~ x + z, data=df, left=0, right=10)
reg <- lm(y ~ x + z, data=df)
custom.name <- c('Intercept', 'X', 'Z', 'logSigma')
f <- texreg(reg, custom.coef.names=custom.name,
            omit.coef='logSigma')
print(f)

Sometimes, it is required for certain test to consider also significance levels beyond 5%. The stars parameter makes it easy to do this.

However, the symbol chosen by texreg's default for the 10% level - cdot is nearly invisible. It looks like dirt on the screen or toner messed up on the page. While you may think that a result at the 10% level does not deserve more recognition, consider anyway to change the symbol.

Suggestion: dagger is used by other packages and more visible

In general, I think it is a good idea to look for partial matching throughout the package.
Just run your tests with:

options(warnPartialMatchAttr   = TRUE,
        warnPartialMatchDollar = TRUE,
        warnPartialMatchArgs   = TRUE)
options(showWarnCalls = TRUE) # gives more hints where the partial matching occurs

There do not seem to be test files in the package, but I assume you have some available locally?

There is a small issue with the extract.ols() function where the option include.rsquared = F with simultaneous include.adjrs = T would throw an error because the rs object is only defined within the the if (include.rsquared== TRUE) condition. I modified the code so that if (include.adjrs == TRUE) directly pulls the R2 from the model stats & added a pull request.

Comment by user Kevin Tappe on R-Forge:

I looked around a while for an option to print SE and p values in the table (in separate rows, preferably). It seems like, it is not possible to print the p values at all (other than by indicating significance by stars or by doing some dirty substitution tricks to substitute SEs for p values)?

Would be nice to have an option for that. (stargazer has the report option, e.g. report = c*sp to print coefficients (starred), SE, and p values).

I recently came across this package and it feels way more responsive than stargazer (which seems to have been inactive in development). However there is one feature that seems missing, which is the option to automatically display dependent variables associated with each model.

Here is a minimum example:

library("tidyverse")
library("stargazer")
library("texreg")

set.seed(1234)
N = 300

data <- tibble(var1 = rnorm(N), var2 = rnorm(N),
               var3 = 0.3 * var1 + 0.4 * var2 + rnorm(N),
               var4 = 0.8 * var1 + 0.2 * var2 + rnorm(N))

model <- list(formula(var3 ~ var1 + var2),
              formula(var4 ~ var1 + var2)) %>%
  map(function(formula, ...) lm(formula, ...),
      data = data)

model %>% splice(., type = "text") %>% invoke(stargazer, .)

## ===========================================================
##                                    Dependent variable:     
##                                ----------------------------
##                                     var3          var4     
##                                     (1)            (2)     
## -----------------------------------------------------------
## var1                              0.325***      0.842***   
##                                   (0.054)        (0.056)   
##                                                            
## var2                              0.408***      0.185***   
##                                   (0.053)        (0.055)   
##                                                            
## Constant                           -0.023        -0.055    
##                                   (0.055)        (0.057)   
##                                                            
## -----------------------------------------------------------
## Observations                        300            300     
## R2                                 0.247          0.444    
## Adjusted R2                        0.242          0.440    
## Residual Std. Error (df = 297)     0.944          0.979    
## F Statistic (df = 2; 297)        48.650***     118.400***  
## ===========================================================
## Note:                           *p<0.1; **p<0.05; ***p<0.01

model %>% invoke(screenreg, l = .)

## ===================================
##              Model 1     Model 2   
## -----------------------------------
## (Intercept)   -0.02       -0.05    
##               (0.05)      (0.06)   
## var1           0.32 ***    0.84 ***
##               (0.05)      (0.06)   
## var2           0.41 ***    0.19 ***
##               (0.05)      (0.05)   
## -----------------------------------
## R^2            0.25        0.44    
## Adj. R^2       0.24        0.44    
## Num. obs.    300         300       
## RMSE           0.94        0.98    
## ===================================
## *** p < 0.001, ** p < 0.01, * p < 0.05

I am aware that we can override model names, however it is still handy to automatically display dependent variables, when there are dozens to hundreds of candidate models to evaluate and we want to compare their performance by grouping them into several panels.

Thank you,

Yi

dcolumn is mature for replacement by siunitx. Any plans of at least adding it? In my fork I've simply replaced

with

coldef <- paste0(coldef, "S[table-format=", dl, separator, dr, "]", margin.arg, " ")

and it works in my use cases.

I also needed to replace star.prefix = "^{", with star.prefix = "\\sym{", and add

 # \sym definition
  string <- paste0(string, "\\def\\sym#1{\\ifmmode^{#1}\\else\\(^{#1}\\)\\fi}")

right after

Comment from user yennywebbv (Yenny Webb Vargas) on R-Forge:

Hi!

Thanks for the great implementation!

I have an important suggestion: add an option to use a t distribution, with appropriate degrees of freedom taken from the model. As it is implemented, the confidence intervals do not match the lm confint ones. This is due to texreg using a standard normal distribution, as opposed to a t distribution that pulls the error degrees of freedom from the model.

I live in a small-sample world, and the degrees of freedom really make a big difference.

I attach code for a possible implementation of this. You would need to update the getdata function to output the degrees of freedom of the model, and have access to it in the S4 object (models[[i]]@df.residual).

Thanks!

-Y

ciforce = function (models, ci.force = rep(FALSE, length(models)), ci.level = 0.95,tdist=FALSE) 
{
  if (class(ci.force) == "logical" && length(ci.force) == 1) {
    ci.force <- rep(ci.force, length(models))
  }
  if (class(ci.force) != "logical") {
    stop("The 'ci.force' argument must be a vector of logical values.")
  }
  if (length(ci.force) != length(models)) {
    stop(paste("There are", length(models), "models and", 
               length(ci.force), "ci.force values."))
  }
  for (i in 1:length(models)) {
    if (ci.force[i] == TRUE && length(models[[i]]@se) > 0) {
      if(tdist==TRUE){
        quant <- qt(1 - ((1 - ci.level)/2), models[[i]]@df.residual)
      }else{
        quant <- qnorm(1 - ((1 - ci.level)/2))
      }
      upper <- models[[i]]@coef + (quant * models[[i]]@se)
      lower <- models[[i]]@coef - (quant * models[[i]]@se)
      models[[i]]@ci.low <- lower
      models[[i]]@ci.up <- upper
      models[[i]]@se <- numeric(0)
      models[[i]]@pvalues <- numeric(0)
    }
  }
  return(models)
}

Comment by user snthsnht on R-Forge:

It would be great to have support for short captions in texreg. For example

\caption[short one for list of tables]{Long elaborate caption which explains everything in the table but is too long for the list of tables.}

Comment from user an ri on R-Forge:

I find myself manually adding a custom footnote after the tabular environment. The built in function custom.note puts the note into a multicolumn environment, but my annotations are often long, and the table proportions get distorted if I put it into this environment. So I add a custom note below the tabular environment using "\footnotesize". I imagine many other people do so as well.

Comment from user an ri on R-Forge:

I find myself manually adding a multicolumn-header to the output that indicates that two or more regressions belong to a certain group. For instance, if I have four regression models, I may want the word "Women" to appear above the first two regressions, and "Men" above the last two.

The documentation for 1.36.23, (texreg/omit.coef) says: "The ‘omit.coef' argument is processed after the custom.coef.names’ argument, so the regular expression should refer to the custom coefficient names."

This is incorrect as demonstrated by the following, where the omission is based on the original variable name rather than the custom name:

library(texreg)
y <- 1:20 + rnorm(20)
x1 <- rnorm(20)
x2 <- rnorm(20)
reg <- list()

reg[[1]] <- lm(y ~ x1)
reg[[2]] <- lm(y ~ x2)
reg[[3]] <- lm(y ~ x1 + x2)

texreg(reg) # three coefficients
texreg(reg, custom.coef.names=c('a', 'b3', 'b5'),
       omit.coef='2')  ## this omits "b5"
texreg(reg,  custom.coef.names=c('a', 'b3', 'b5'),
       omit.coef='b3')  ## output is unchanged 

Reported by David Hugh-Jones by e-mail on 24 June 2016:

Hi Philip

For those using knitr and rmarkdown, something like this could be quite helpful. Tell me if you are interested.

flexreg <- function(...) {
   of <- opts_knit$get('out.format')
   if (is.null(of)) {
     screenreg(...)
   } else {
     switch(of,
       'latex'  = texreg(...),
       'sweave' = texreg(...),
       'markdown' = texreg(...),
       'html'   = htmlreg(...),
       screenreg(...)
     )
   }
}

David

Update 28 June 2016:

Not many users will know the subtle opts_knit option that the code checks. I think it is quite common for r users to be using rmarkdown via rstudio, and to run chunks at the command line (screenreg needed) then knit the whole document. For ordinary tables, knitr::kable does the job but for regressions... Just my thoughts.
D

Comment by user ashenkin (Alexander Shenkin) on R-Forge:

Hi there,

The predictors in my models often have underscores. This seems to lead to illegal TEX, as the underscores don't get escaped by texreg. Is there any way to get texreg to automatically escape such characters when it produces the table, or will I have to make custom names?

thanks,
alex

my_df = data.frame(a = runif(100), b = runif(100), c_d = rep(c("a","b"),50))
my_mod = lmer(a ~ b + (1|c_d), data = my_df)
texreg(my_mod)

\begin{table}
\begin{center}
\begin{tabular}{l c }
\hline
& Model 1 \\
\hline
(Intercept) & $0.45^{***}$ \\
& $(0.05)$ \\
b & $0.12$ \\
& $(0.10)$ \\
\hline
AIC & 45.63 \\
BIC & 56.05 \\
Log Likelihood & -18.82 \\
Num. obs. & 100 \\
Num. groups: c_d & 2 \\
Variance: c_d.(Intercept) & 0.00 \\
Variance: Residual & 0.08 \\
\hline
\multicolumn{2}{l}{\scriptsize{$^{***}p<0.001$, $^{**}p<0.01$, $^*p<0.05$}}
\end{tabular}
\caption{Statistical models}
\label{table:coefficients}
\end{center}
\end{table}

Hi there,

Great package, I've been using it to do some gnls tables recently... If I have the tidyr package loaded before I define my extract.gnls method (using setMethod to define), I run into an error as extract is a standard generic already defined and exported by tidyr.

Is there a good workaround for this?

Thanks,
Dan

when outputing multinom models via texreg(), I get the same model fit statistics for all levels of the model. If you show one level at a time, then this doesn't occur, but if you want to show multiple levels of the dependent variable, then this is problematic.

In some of the extract.* functions partial matching is done due to the $ operator.
This can be checked with setting these options and running the code

options(warnPartialMatchAttr   = TRUE,
        warnPartialMatchDollar = TRUE,
        warnPartialMatchArgs   = TRUE)

A basic version was provided by Yves Croissant on 17 March 2017 by e-mail:

extract.mhurdle <- function (model, include.nobs = TRUE, ...){
  s <- summary(model, ...)
  names <- rownames(s$coefficients)
  class(names) <- "character"
  co <- s$coefficients[, 1]
  se <- s$coefficients[, 2]
  pval <- s$coefficients[, 4]
  class(co) <- class(se) <- class(pval) <- "numeric"
  n <- nobs(model)
  lik <- logLik(model)
  gof <- numeric()
  gof.names <- character()
  gof.decimal <- logical()
  gof <- c(gof, n, lik)
  gof.names <- c(gof.names, "Num. obs.", "Log Likelihood")
  gof.decimal <- c(gof.decimal, FALSE, TRUE)
  tr <- createTexreg(coef.names = names, coef = co, se = se, pvalues = pval,
                     gof.names = gof.names, gof = gof, gof.decimal = gof.decimal)
  return(tr)
}

setMethod("extract", signature = className("mhurdle", "mhurdle"), definition = extract.mhurdle)

The code needs to be checked and added, together with entries in the help files, vignette, and NAMESPACE.

Just a quick suggestion to incorporate in texreg objects of class lagImpact. Spatial lag regression models returns direct, indirect and total effects for each regression coefficient and their level of significance. It would be great if texreg could cover these models organizing the output with information about direct, indirect and total effects.

I've left a question on SO about this: https://stackoverflow.com/questions/45971419/texreg-table-for-impacts-of-spatial-durbin-model

Comment by user dan on R-Forge:

Dear Philip,

I often have models with hundreds of coefficients (many of which get thrown away). I was about to write my own functions for renaming, reordering, and omitting variables before passing them to texreg when I discovered this blog post that already contains code to do it:
http://conjugateprior.org/2013/10/call-them-what-you-will/

It has a really nice interface: just define a "name map" that gives the order and new names of any variables you want and omits anything you want to omit, and it automatically builds the parameters to pass to custom.coef.names, reorder.coef, and omit.coef. (And it's robust to including variables that aren't in the model, so it's reusable throughout a project with different models.) This is unbelievably useful and would make a great addition to texreg itself.

I didn't see texreg on github or I'd send a pull request. I'm honestly not sure how to submit a patch through R-forge, but the code is already there in the link above, so it shouldn't be hard to add.

This also satisfies a feature request from a couple years ago in this thread:
https://r-forge.r-project.org/forum/forum.php?thread_id=28268&forum_id=4325&group_id=1420

Thanks for your work on texreg!

Best,
Dan

mlogit would benefit from having a beside option to govern the ordering of coefficient output without as much need for manual interference:

data("Fishing", package = "mlogit")
Fish <- mlogit.data(Fishing, varying = c(2:9), shape = "wide", choice = "mode")
texreg(mlogit(mode ~ 0 | income, data = Fish))

output:

\begin{table}
\begin{center}
\begin{tabular}{l c }
\hline
 & Model 1 \\
\hline
boat:(intercept)    & $0.74^{***}$ \\
                    & $(0.20)$     \\
charter:(intercept) & $1.34^{***}$ \\
                    & $(0.19)$     \\
pier:(intercept)    & $0.81^{***}$ \\
                    & $(0.23)$     \\
boat:income         & $0.00^{*}$   \\
                    & $(0.00)$     \\
charter:income      & $-0.00$      \\
                    & $(0.00)$     \\
pier:income         & $-0.00^{**}$ \\
                    & $(0.00)$     \\
\hline
AIC                 & 2966.30      \\
Log Likelihood      & -1477.15     \\
Num. obs.           & 1182         \\
\hline
\multicolumn{2}{l}{\scriptsize{$^{***}p<0.001$, $^{**}p<0.01$, $^*p<0.05$}}
\end{tabular}
\caption{Statistical models}
\label{table:coefficients}
\end{center}
\end{table}

Prefer to have an option to produce instead:

\begin{table}
\begin{center}
\begin{tabular}{l c }
\hline
 & Model 1 \\
\hline
boat:(intercept)    & $0.74^{***}$ \\
                    & $(0.20)$     \\
boat:income         & $0.00^{*}$   \\
                    & $(0.00)$     \\
charter:(intercept) & $1.34^{***}$ \\
                    & $(0.19)$     \\
charter:income      & $-0.00$      \\
                    & $(0.00)$     \\
pier:(intercept)    & $0.81^{***}$ \\
                    & $(0.23)$     \\
pier:income         & $-0.00^{**}$ \\
                    & $(0.00)$     \\
\hline
AIC                 & 2966.30      \\
Log Likelihood      & -1477.15     \\
Num. obs.           & 1182         \\
\hline
\multicolumn{2}{l}{\scriptsize{$^{***}p<0.001$, $^{**}p<0.01$, $^*p<0.05$}}
\end{tabular}
\caption{Statistical models}
\label{table:coefficients}
\end{center}
\end{table}

This facilitates creating three groups (boat, charter, and pier).

The possibility of customizing tables in latex is nearly infinite, so it should not be expected that all the possible options will ever be added to texreg. Several open issues relate to this kind of enhancements: #2, #3, or #25.

An easier approach would be to add an option (e.g., tabular = FALSE) for exporting just what is inside the tabular environment: the coefficients and GOF. Experienced LaTeX users each have their own habits when building tables and it may be faster for them to copy/paste the table and tabular environments from previous tables than to understand from texreg options how to generate tables in their own way.

I did it for my own use in https://github.com/christophe-gouel/texreg. Let me know if you want a pull request.

At the moment, the option reorder.gof seems broken. In ?texreg, it is described with a numerical vector as input. However, this produces an error for me.

> library(texreg)
> ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
> trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
> group <- gl(2,10,20, labels = c("Ctl","Trt"))
> weight <- c(ctl, trt)
> lm.D9 <- lm(weight ~ group)
> screenreg(lm.D9)

======================
             Model 1  
----------------------
(Intercept)   5.03 ***
             (0.22)   
groupTrt     -0.37    
             (0.31)   
----------------------
R^2           0.07    
Adj. R^2      0.02    
Num. obs.    20       
RMSE          0.70    
======================
*** p < 0.001, ** p < 0.01, * p < 0.05
> screenreg(lm.D9, reorder.gof = c(4,2,3,1))
Error in matrix(nrow = nrow(gofs), ncol = ncol(gofs) + 1) : 
  non-numeric matrix extent

Comment by mdanese (Mark Danese) on R-Forge:

It would also be nice to use the single.row option to include the estimate and the standard error on the same row, and then use ci.test to report the confidence interval on the second line. That way one can provide a complete report that doesn't require the reader to choose between SE and CI. Since both arguments report two values, it should probably not hurt the table formatting. And wrapping the CI in parentheses or brackets would be good.

2.50 (0.50)
[1.50, 3.00]

[message also sent directly to p. leifeld]

`I think there may be something wrong with the
way texreg calculates stars on these objects. Here's
the result of a survreg estimation run:

Call:
survreg(formula = Surv(N_Days, censDecSummer) ~ 1 + chief + CrimX + 
    CivLibX + EconX + FirstAmX + Total_Amicus + DuePX + AttysX + 
    UnionsX + JudPowX + FedismX + TaxX + cluster(term) + percuriam + 
    nDissents + nConcurs + nCresults + nytSalience + declarationUncon, 
    data = timing, subset = nz, dist = "weibull")
                   Value Std. Err (Naive SE)       z         p
(Intercept)       4.2156  0.11722    0.07189  35.962 3.23e-283
chiefRehnquist   -0.2313  0.04735    0.02478  -4.883  1.04e-06
chiefRoberts     -0.2146  0.05736    0.03802  -3.741  1.83e-04
chiefVinson      -0.1704  0.09280    0.03114  -1.836  6.63e-02
chiefWarren      -0.3470  0.06112    0.02423  -5.677  1.37e-08
CrimXTRUE        -0.0705  0.09471    0.06245  -0.745  4.57e-01
CivLibXTRUE      -0.0609  0.08270    0.06318  -0.737  4.61e-01
EconXTRUE        -0.0878  0.08948    0.06223  -0.981  3.27e-01
FirstAmXTRUE      0.0577  0.10986    0.06924   0.525  6.00e-01
Total_Amicus      0.0195  0.00408    0.00257   4.775  1.79e-06
DuePXTRUE         0.0823  0.11426    0.07440   0.720  4.71e-01
AttysXTRUE        0.0455  0.11492    0.10697   0.396  6.92e-01
UnionsXTRUE      -0.1243  0.11907    0.07111  -1.044  2.97e-01
JudPowXTRUE      -0.0410  0.09329    0.06360  -0.440  6.60e-01
FedismXTRUE      -0.1513  0.08744    0.07121  -1.731  8.35e-02
TaxXTRUE         -0.0961  0.12706    0.07250  -0.756  4.50e-01
percuriam        -0.3020  0.07135    0.03308  -4.232  2.31e-05
nDissents         0.1637  0.00905    0.00611  18.097  3.34e-73
nConcurs          0.1084  0.01498    0.01187   7.237  4.60e-13
nCresults         0.1583  0.01771    0.01083   8.937  3.98e-19
nytSalience       0.2927  0.05540    0.02876   5.284  1.26e-07
declarationUncon  0.0743  0.02610    0.01972   2.846  4.43e-03
Log(scale)       -0.4851  0.03711    0.00985 -13.074  4.66e-39

Scale= 0.616 

Weibull distribution
Loglik(model)= -28011.6   Loglik(intercept only)= -29009.1
	Chisq= 1995.02 on 21 degrees of freedom, p= 0 
(Loglikelihood assumes independent observations)
Number of Newton-Raphson Iterations: 5 
n=7387 (9 observations deleted due to missingness)

and here's what happens when I run this through screenreg:
(etr1 is the result of extract.survreg)

> screenreg(etr1,single.row=T,digits=3,custom.model.names="Model 1")

========================================
                  Model 1               
----------------------------------------
(Intercept)            4.216 (0.117)    
chiefRehnquist        -0.231 (0.047) ***
chiefRoberts          -0.215 (0.057) ***
chiefVinson           -0.170 (0.093) ***
chiefWarren           -0.347 (0.061) ***
CrimXTRUE             -0.071 (0.095) ***
CivLibXTRUE           -0.061 (0.083) ***
EconXTRUE             -0.088 (0.089) ***
FirstAmXTRUE           0.058 (0.110)    
Total_Amicus           0.019 (0.004)    
DuePXTRUE              0.082 (0.114)    
AttysXTRUE             0.045 (0.115)    
UnionsXTRUE           -0.124 (0.119) ***
JudPowXTRUE           -0.041 (0.093) ***
FedismXTRUE           -0.151 (0.087) ***
TaxXTRUE              -0.096 (0.127) ***
percuriam             -0.302 (0.071) ***
nDissents              0.164 (0.009)    
nConcurs               0.108 (0.015)    
nCresults              0.158 (0.018)    
nytSalience            0.293 (0.055)    
declarationUncon       0.074 (0.026)    
Log(scale)            -0.485 (0.037) ***
----------------------------------------
AIC                56069.214            
Log Likelihood    -28011.607            
========================================
*** p < 0.001, ** p < 0.01, * p < 0.05

The stars look wrong to me, at least if they're being
calcuated as coeff/std-error, which appears to be represented
as pvalue in the extract.survreg result. To check this, I did:

> c1<-etr1@coef	   # extract coeffs
> se1<-etr1@se     # extract se s
> t1<-c1/se1	   # calculate tstats
> p1<-etr@pvalues  # extract pvalues, for comparison
> cbind(c1,se1,t1,p1)
                          c1         se1          t1          p1
(Intercept)       4.21563707 0.117223289  35.9624535  35.9624535
chiefRehnquist   -0.23125223 0.047354249  -4.8834526  -4.8834526
chiefRoberts     -0.21459294 0.057360414  -3.7411331  -3.7411331
chiefVinson      -0.17039978 0.092795972  -1.8362842  -1.8362842
chiefWarren      -0.34695761 0.061116736  -5.6769655  -5.6769655
CrimXTRUE        -0.07052040 0.094711720  -0.7445795  -0.7445795
CivLibXTRUE      -0.06094230 0.082695655  -0.7369468  -0.7369468
EconXTRUE        -0.08775338 0.089477503  -0.9807313  -0.9807313
FirstAmXTRUE      0.05768771 0.109862080   0.5250921   0.5250921
Total_Amicus      0.01948112 0.004079518   4.7753480   4.7753480
DuePXTRUE         0.08227394 0.114257493   0.7200748   0.7200748
AttysXTRUE        0.04548361 0.114924076   0.3957710   0.3957710
UnionsXTRUE      -0.12430357 0.119071832  -1.0439377  -1.0439377
JudPowXTRUE      -0.04100121 0.093290039  -0.4395026  -0.4395026
FedismXTRUE      -0.15133097 0.087437920  -1.7307247  -1.7307247
TaxXTRUE         -0.09606226 0.127062616  -0.7560230  -0.7560230
percuriam        -0.30197049 0.07136025  -4.2324781  -4.2324781
nDissents         0.16370191 0.009045561  18.0974865  18.0974865
nConcurs          0.10842433 0.014982691   7.2366394   7.2366394
nCresults         0.15831111 0.017713210   8.9374602   8.9374602
nytSalience       0.29272654 0.055398539   5.2840120   5.2840120
declarationUncon  0.07429407 0.026103381   2.8461475   2.8461475
Log(scale)       -0.48509865 0.037105383 -13.0735386 -13.0735386

Note that the last two columns are the same. What I don't see
is why chiefRehnquist, with a pvalue of 4.88 gets 3 stars, while
nConcurs, with a pvalue of 8.94 gets none. Or why nDissents,
with a pvalue of 18.10 also gets none. Am I missing something
here? (Now that I look at it, it seems that only negative
pvalues are being correctly starred: could there be a problem
with positive values?).
`

It is a great pleasure to use texreg package, but recently I need use the speedglm to run Poisson regression since it is developed recently thus sadly not supported by by texreg, would you consider to add it? Thank you！

Bug report by e-mail from Christopher Gandrud on 17 March 2016:

Hi Philip

I noticed in texreg 1.36.4 that users basically can’t (or can’t easily) omit coefficients and provide custom coefficient names for the remaining coefficients.

This is because in:

m <- customnames(m, custom.coef.names)  #rename coefficients
m <- rearrangeMatrix(m)  #resort matrix and conflate duplicate entries
m <- as.data.frame(m)
m <- omitcoef(m, omit.coef)  #remove coefficient rows matching regex

omitcoef comes after customnames have been applied. In the attached file I simply moved the omitcoef call before customnames. Now I can omit coefficients and rename them.

Best
Christopher

Attached revised version of texreg: texreg.R

/cc @christophergandrud

Further explanation by Shahram Amini via e-mail on 11 November 2016:

Thank you for replying my email and your help. Sometimes the user wants to stretch out the table to fit the width of the text. This task is accomplished by tabularx environment. This environment takes all the regular "l","r", and "c" arguments but it has extra column type called "X". When "X" is used, the table gets stretched out according to a scale passed to the table. The environment looks like this:

\begin{tabularx}{\textwidth}{XXX}

......

\end{tabularx}

The same task can be accomplished by tabular* environment but i did not see a support for that one either.

I have a use case where I batch submit regressions and then save the serialized output (rds of the returned value of extract(lm) ). This is much better than stargazer (need to save lm, which carries around residuals) or tidy (cannot be entered into an outreg).

So, when I need to look through hundreds of robustness checks, I simply call a function that filters regressions on dependent variables and independent variables. Maybe a strange use case. Wondering if it would have general value to add something like this to the package. The beneficiaries would be anyone who has to run batch analysis they need to store for later review.

Also, in one set of interactions between client and server, I can only receive textual output. I have had to save a string representation of the list that contains a call to tidy and glance from broom, and then write my own bootleg texreg function. Perhaps it would aid batch analysis as well to have a string representation of an extract object. This might violate S4 principles, so it would require creating something like toString for texreg which could then be parsed and converted back.

Comment by user sdaza (Sebastian Daza) on R-Forge:

I get a weird error with texreg but not with screenreg:

This works perfectly:

screenreg(models,
custom.model.names = mnames ,
custom.coef.names = ror$ccn,
groups = groups,
omit.coef = ror$oc,
reorder.coef = ror$rc
)

But, when I use texreg I get:

texreg(models,
custom.model.names = mnames,
custom.coef.names = ror$ccn,
omit.coef = ror$oc,
reorder.coef = ror$rc,
groups = groups)

Error in reorder(m, reorder.coef) :
Error when reordering matrix: there are 20 rows, but you provided 9 numbers.

So, the problem is when omitting variables.This is what I am trying to omit:

"(Intercept)|rfemale|z_rage|rrace_2|rrace_3|rmstatus_1|rmstatus_3|rmstatus_4|rworking_2|rworking_3|z_radla|z_nkids|kfemale|z_kage|stepchild|parent|kworking|chwealth"

Any ideas about what could be the problem?
Thanks, Sebastian

If I try to get an output from the table command with texreg I get an error

Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘extract’ for signature ‘"table"’

texreg(table(c(2,5,5,5,5,7,7,7,7,NA),c(1,5,2,2,2,2,7,7,NA,NA)))

How can I get it?
I would like to get the left title rotated, as in this picture:

Comment by user ignacio82 (Ignacio Martinez) on R-Forge:

Thanks for the great work!

I’m reproducing this stata example:

regress api00 acs_k3 acs_46 full enroll, cluster(dnum)

Regression with robust standard errors                 Number of obs =     395
                                                       F(  4,    36) =   31.18
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.3849
Number of clusters (dnum) = 37                         Root MSE      =  112.20

------------------------------------------------------------------------------
         |               Robust
   api00 |      Coef.   Std. Err.       t     P>|t|       [95% Conf. Interval]
---------+--------------------------------------------------------------------
  acs_k3 |   6.954381   6.901117      1.008   0.320      -7.041734     20.9505
  acs_46 |   5.966015   2.531075      2.357   0.024       .8327565    11.09927
    full |   4.668221   .7034641      6.636   0.000        3.24153    6.094913
  enroll |  -.1059909   .0429478     -2.468   0.018      -.1930931   -.0188888
   _cons |  -5.200407   121.7856     -0.043   0.966       -252.193    241.7922
------------------------------------------------------------------------------

In R:

library(readstata13)
library(texreg)
library(sandwich)
library(lmtest)

clustered.se <- function(model_result, data, cluster, level=0.95) {
  model_variables   <-
  intersect(colnames(data), c(colnames(model_result$model), cluster))
  model_rows <- rownames(model_result$model)
  data <- data[model_rows, model_variables]
  cl <- data[[cluster]]
  M <- length(unique(cl))
  N <- length(model_result$residuals)
  K <- model_result$rank
  dfc <- (M / (M - 1)) * ((N - 1) / (N - K))
  uj  <-
  apply(estfun(model_result), 2, function(x)
  tapply(x, cl, sum))
  vcovCL <- dfc * sandwich(model_result, meat = crossprod(uj) / N)
  standard.errors <- coeftest(model_result, vcov. = vcovCL)[, 2]
  p.values <- coeftest(model_result, vcov. = vcovCL)[, 4]
  a <- 1-(1 - level)/2
  df <- M - 1
  coeff <- model_result$coefficients
  lb <- coeff - qt(p = a, df = df)*standard.errors
  ub <- coeff + qt(p = a, df = df)*standard.errors
  clustered.se <-
  list(vcovCL = vcovCL,
  standard.errors = standard.errors,
  p.values = p.values,
  lb = lb, ub = ub)
  return(clustered.se)
}

Run the code:

elemapi2 <- read.dta13(file = 'elemapi2.dta')

lm1 <-
  lm(formula = api00 ~ acs_k3 + acs_46 + full + enroll,
  data = elemapi2)

clustered_se <-
  clustered.se(model_result = lm1,
  data = elemapi2,
  cluster = "dnum")

screenreg works as expected:

screenreg(
  lm1,
  override.se = clustered_se$standard.errors,
  override.p = clustered_se$p.value,
  override.ci.low = clustered_se$lb,
  override.ci.up = clustered_se$ub,
  digits = 7
  )

## 
## ========================================
##              Model 1                    
## ----------------------------------------
## (Intercept)     -5.2004067              
##              [-252.1930390; 241.7922255]
## acs_k3           6.9543811              
##              [  -7.0417337;  20.9504960]
## acs_46           5.9660147 *            
##              [   0.8327565;  11.0992729]
## full             4.6682211 *            
##              [   3.2415297;   6.0949125]
## enroll          -0.1059909 *            
##              [  -0.1930931;  -0.0188888]
## ----------------------------------------
## R^2              0.3848830              
## Adj. R^2         0.3785741              
## Num. obs.      395                      
## RMSE           112.1983218              
## ========================================
## * 0 outside the confidence interval

htmlreg outputs the se instead of the ci

htmlreg(
  lm1,
  override.se = clustered_se$standard.errors,
  override.p = clustered_se$p.value,
  override.ci.low = clustered_se$lb,
  override.ci.up = clustered_se$ub,
  star.symbol = "\\*",
  digits = 7
  ) 

ci.force=T produces the wrong CI

htmlreg(
  lm1,
  override.se = clustered_se$standard.errors,
  override.p = clustered_se$p.value,
  ci.force = TRUE,
  override.ci.low = clustered_se$lb,
  override.ci.up = clustered_se$ub,
  star.symbol = "\\*",
  digits = 7
  ) 

For example, the CI for acs_k3 goes is [-6.5715605; 20.4803228] but it should be [-7.0417337; 20.9504960]

Thanks for you great work!

Hi

I would like to show the point estimate and CI on the odds ratio scale, but cannot locate this option. I am currently using an own customized model extraction for R2mlwin (Mlwin), where the original models are logistic multilevel. The current extraction mode collects and transforms the log-odds coefficient and SE in the following manner:

> # model summary (model is a R2mlwin object)
> s <- summary(model)
> 
> # Convert coefficients to odds ratios (just remove this part if you want to stay on the log-odds scale)
>   coef <- exp(s@FP)
>   se <- coef*se # This is the converstion of the log-odds scale standard error to the odds scale standard error, 
>   # It is an approximate method: take the exponentiate of the log-odds and multiply it by its log-odds standard error
>   # se.odds.scale = exp(log-odds)*se.log.odds.scale
>   # http://stats.stackexchange.com/questions/158481/how-to-convert-the-standard-error-of-the-log-odds-ratio-to-the-odds-ratio-standa?rq=1

However, when I use the ci.force option it seems to assumes that the SE of the odds ratio is additive, and therefore, producing incorrect upper and lower bounds:

htmlreg(tr, file = "mytable1.Std.doc", 
        ci.force = TRUE,
        ci.force.level = 0.95, bold = 0.05, ci.test = 1,
        inline.css = FALSE, doctype = TRUE, html.tag = TRUE, head.tag = TRUE, body.tag = TRUE)

Any ideas about for a workaround to get correct CI?

Here is an example that reproduces the problem

library(nlme)
library(texreg)
library(multilevel)

data <- univbct[,c(1,8,18:22)]
data <- data[complete.cases(data),]
data$BTN <- as.factor(data$BTN)
data$SUBNUM <- as.factor(data$SUBNUM)

mlm1 <- lme(JSAT ~ 1,
            random = ~ 1 | BTN/SUBNUM,
            data = data,  method = "REML")

screenreg(list(mlm1))

The problem itself is that the extract.lme function does not properly recognize that "number of groups" can receive multiple values if there are more than one grouping level in the model

Here is a log that demonstrates the issue. In the data, there are 16 level 3 groups and 486 level 2 groups.

> extract(mlm1)
Tracing createTexreg(coef.names = coefficient.names, coef = coefficients,  .... on entry 
Called from: eval(expr, p)
Browse[1]> n
debug: {
    new("texreg", coef.names = coef.names, coef = coef, se = se, 
        pvalues = pvalues, ci.low = ci.low, ci.up = ci.up, gof.names = gof.names, 
        gof = gof, gof.decimal = gof.decimal, model.name = model.name)
}
Browse[2]> gof.names
[1] "AIC"            "BIC"            "Log Likelihood" "Num. obs."     
[5] "Num. groups"   
Browse[2]> gof
                                           SUBNUM       BTN 
 3352.390  3373.219 -1672.195  1350.000   486.000    16.000 

Comment by user jonkeane (Jonathan Keane) on R-Forge:

I noticed that there is a typo in the stars note for html format when there are 4 levels. This results in an extra tag being shown in the html output.

line 976 in texreg.R currently is :
star.symbol, star.symbol, "</sup", css.sup, ">p &lt; ", st[2],
but it looks like it should be:
star.symbol, star.symbol, "</sup>p &lt; ", st[2],

I've tried this change, and the output looks good to me.

Comment from user an ri on R-Forge:

I find myself manually stacking different texreg-tables "on top of each other". That is, I split the table with strsplit, "grep" it from the beginning to the "Num Obs" field, and place the content of a second texreg-table directly below this. I wish there was an easier way of doing this. Something along the lines of an rbind-method for texreg-tables.

It would be great to have an option (or default) to put commas in between numbers that are integers.

4,234,332

Comment by user felix_h (Felix Haas) on R-Forge:

My second relates to custom additional rows. Sometimes it is desirable to indicate whether some set of variables was included in a model but not shown (e.g. country fixed-effects), see e.g. the "Time Dummies" row in this table:

Is it possible to add a similar functionality to texreg?

Again thanks for such a great package and keep up the good work!

Best
Felix

Often in published work we need to write 12345 as 12,345.

The way I do this by hand is with prettyNum: prettyNum(12345, big.mark = ','). But as it stands I have to clean this up in the post-up when using texreg. I suppose it's possible to add a prettyNum argument to extract methods to handle this.

Allow coefficients to be re-ordered by a vector of regex:

Eg:
reorder.coef = c("^log", "^GDP", "^Population")
would put all coefficients that start with "log" on top, then "GDP" and then "Population" coefficients.

Motivation: I routinely have model with some fixed effects and interactions, then I would have to calculate the number of created dummy variables and based on these I could reorder them.

Thanks for this nice package!

If I use a text font that is different from the math font, my texreg fonts are inconsistent:

I can gsub-out the math stuff like $ and ^ from the texreg output but I wonder if there is an option to get this out of the box, something like a 'no-math' mode?

I'm trying to use texreg to create outputs for results from the Johansen test results generated by the urca package. The problem I run into is that the function from the package returns an object of class summary.lm rather than the lm itself.

Since essentially all the information needed to render the table is contained in the summary object, it should be easy to use the summary object. However, I had a little bit of difficulty and it may be possible to change the code to make it easier.

Workaround: How to render summary.lm objects using texreg

The native extract.lm method uses almost exclusively data from the summary objects, so we are almost there. However, there is one bit missing in the summary: the number of observations. To get this information, the easiest way appears to be to simply count the length of the residuals (which are contained in the summary). This leads to the following extract.summary.lm function which is almost identical to the original one:

extract.summary.lm <- function (model, include.rsquared = TRUE, include.adjrs = TRUE, 
                                include.nobs = TRUE, include.fstatistic = FALSE, include.rmse = TRUE, 
                                ...) 
{
  s <- model;
  names <- rownames(s$coef)
  co <- s$coef[, 1]
  se <- s$coef[, 2]
  pval <- s$coef[, 4]
  rs <- s$r.squared
  adj <- s$adj.r.squared
  n <- length(s$residuals)
  gof <- numeric()
  gof.names <- character()
  gof.decimal <- logical()
  if (include.rsquared == TRUE) {
    gof <- c(gof, rs)
    gof.names <- c(gof.names, "R$^2$")
    gof.decimal <- c(gof.decimal, TRUE)
  }
  if (include.adjrs == TRUE) {
    gof <- c(gof, adj)
    gof.names <- c(gof.names, "Adj. R$^2$")
    gof.decimal <- c(gof.decimal, TRUE)
  }
  if (include.nobs == TRUE) {
    gof <- c(gof, n)
    gof.names <- c(gof.names, "Num. obs.")
    gof.decimal <- c(gof.decimal, FALSE)
  }
  if (include.fstatistic == TRUE) {
    fstat <- s$fstatistic[[1]]
    gof <- c(gof, fstat)
    gof.names <- c(gof.names, "F statistic")
    gof.decimal <- c(gof.decimal, TRUE)
  }
  if (include.rmse == TRUE && !is.null(s$sigma[[1]])) {
    rmse <- s$sigma[[1]]
    gof <- c(gof, rmse)
    gof.names <- c(gof.names, "RMSE")
    gof.decimal <- c(gof.decimal, TRUE)
  }
  tr <- createTexreg(coef.names = names, coef = co, se = se, 
                     pvalues = pval, gof.names = gof.names, gof = gof, gof.decimal = gof.decimal)
  return(tr)
}

setMethod("extract",  signature = 'summary.lm', definition = extract.summary.lm)

After this modification, it is possible to use the summary method to render the table:

> model <- lm(mpg ~ disp , data = mtcars)
> screenreg(list(model, summary(model)))

=================================
             Model 1    Model 2  
---------------------------------
(Intercept)  29.60 ***  29.60 ***
             (1.23)     (1.23)   
disp         -0.04 ***  -0.04 ***
             (0.00)     (0.00)   
---------------------------------
R^2           0.72       0.72    
Adj. R^2      0.71       0.71    
Num. obs.    32         32       
RMSE          3.25       3.25    
=================================
*** p < 0.001, ** p < 0.01, * p < 0.05

Feature Request: Consider handling summary.lm objects natively

It would be a great help if the extract.lm function would also accept summary.lm objects - then this entire workaround would not be needed. The change would also appear to be quite minor.

Thanks for awesome package!

<<copy-pasted from e-mail>>

I use the program LyX for formatting my documents with LaTeX. I find it very convenient for taking care of most of the annoying formatting bits that can be a pain to handle in raw LaTeX.

The problem is that copy-pasting single-spaced TeX into LyX screws up the formatting.

A workaround that I've discovered is simply double-spacing the code to be copy-pasted, a workaround which can be accomplished in R by simply changing cat(...,collapse="\n") to cat(...,collapse="\n\n") (basically, doubling the newline separator whenever it comes up works).

As such it should be simple to integrate into the function itself by allowing a lyx option with default FALSE, used like: cat(...,collapse=if (lyx) "\n\n" else "\n").

<<end>>

I don't use LyX so much any more, but I do think it can be a useful tool, and the fix is simple.

I'll file a PR sometime today.