Convert regression output to an ASCII table

Conversion of R regression output to an ASCII table for display on screen.

Usage

screenreg(
  l,
  file = NULL,
  single.row = FALSE,
  stars = c(0.001, 0.01, 0.05),
  custom.header = NULL,
  custom.model.names = NULL,
  custom.coef.names = NULL,
  custom.coef.map = NULL,
  custom.gof.names = NULL,
  custom.gof.rows = NULL,
  custom.note = NULL,
  digits = 2,
  leading.zero = TRUE,
  star.symbol = "*",
  symbol = ".",
  override.coef = 0,
  override.se = 0,
  override.pvalues = 0,
  override.ci.low = 0,
  override.ci.up = 0,
  omit.coef = NULL,
  reorder.coef = NULL,
  reorder.gof = NULL,
  ci.force = FALSE,
  ci.force.level = 0.95,
  ci.test = 0,
  groups = NULL,
  custom.columns = NULL,
  custom.col.pos = NULL,
  column.spacing = 2,
  outer.rule = "=",
  inner.rule = "-",
  ...
)

Arguments

l

A statistical model or a list of statistical models. Lists of models can be specified as l = list(model.1, model.2, ...). Different object types can also be mixed.

file

Using this argument, the resulting table is written to a file rather than to the R prompt. The file name can be specified as a character string. Writing a table to a file can be useful for working with MS Office or LibreOffice. For example, using the htmlreg function, an HTML table can be written to a file with the extension .doc and opened with MS Word. The table can then be simply copied into any Word document, retaining the formatting of the table. Note that LibreOffice can import only plain HTML; CSS decorations are not supported; the resulting tables do not retain the full formatting in LibreOffice.

single.row

By default, a model parameter takes up two lines of the table: the standard error is listed in parentheses under the coefficient. This saves a lot of horizontal space on the page and is the default table format in most academic journals. If single.row = TRUE is activated, however, both coefficient and standard error are placed in a single table cell in the same line.

stars

The significance levels to be used to draw stars. Between 0 and 4 threshold values can be provided as a numeric vector. For example, stars = numeric(0) will not print any stars and will not print any note about significance levels below the table. stars = 0.05 will attach one single star to all coefficients where the p value is below 0.05. stars = c(0.001, 0.01, 0.05, 0.1) will print one, two, or three stars, or a symbol as specified by the symbol argument depending on the p-values.

custom.header

An optional named list of multi-column headers that are placed above the model names. For example, custom.header = list("abc" = 1:3, "ef" = 4:5) will add the label "abc" to the first three models and "ef" to the fourth and fifth model. The column with coefficient names and any custom columns added by the "custom.columns" argument are not counted towards these positions. If booktabs = TRUE, \cmidrule rules are added below the respective labels; otherwise \cline lines are used.

custom.model.names

A character vector of labels for the models. By default, the models are named "Model 1", "Model 2", etc. Specifying model.names = c("My name 1", "My name 2") etc. overrides the default behavior.

custom.coef.names

By default, texreg uses the coefficient names which are stored in the models. The custom.coef.names argument can be used to replace them by other character strings in the order of appearance. For example, if a table shows a total of three different coefficients (including the intercept), the argument custom.coef.names = c("Intercept", "variable 1", "variable 2") will replace their names in this order.

Sometimes it happens that the same variable has a different name in different models. In this case, the user can use this function to assign identical names. If possible, the rows will then be merged into a single row unless both rows contain values in the same column.

Where the argument contains an NA value, the original name of the coefficient is kept. For example, custom.coef.names = c(NA, "age", NA) will only replace the second coefficient name and leave the first and third name as they are in the original model.

See also custom.coef.map for an easier and more comprehensive way to rename, omit, and reorder coefficients.

custom.coef.map

The custom.coef.map argument can be used to select, omit, rename, and reorder coefficients.

Users must supply a named list of this form: list("x" = "First variable", "y" = NA, "z" = "Third variable"). With that particular example of custom.coef.map,

coefficients will be presented in order: "x", "y", "z".
variable "x" will appear as "First variable", variable "y" will appear as "y", and variable "z" will appear as "Third variable".
all variables not named "x", "y", or "z" will be omitted from the table.

custom.gof.names

A character vector which is used to replace the names of the goodness-of-fit statistics at the bottom of the table. The vector must have the same length as the number of GOF statistics in the final table. The argument works like the custom.coef.names argument, but for the GOF values. NA values can be included where the original GOF name should be kept.

custom.gof.rows

A named list of vectors for new lines at the beginning of the GOF block of the table. For example, list("Random effects" = c("YES", "YES", "NO"), Observations = c(25, 25, 26)) would insert two new rows into the table, at the beginning of the GOF block (i.e., after the coefficients). The rows can contain integer, numeric, or character objects. Note that this argument is processed after the custom.gof.names argument (meaning custom.gof.names should not include any of the new GOF rows) and before the reorder.gof argument (meaning that the new GOF order specified there should contain values for the new custom GOF rows). Arguments for custom columns are not affected because they only insert columns into the coefficient block.

custom.note

With this argument, a replacement text for the significance note below the table can be provided. If an empty character object is provided (custom.note = ""), the note will be omitted completely. If some character string is provided (e.g., custom.note = "My note"), the significance legend is replaced by My note. The original significance legend can be included by inserting the %stars wildcard. For example, a custom note can be added right after the significance legend by providing custom.note = "%stars. My note.".

If the threeparttable argument is used, any note should be preceded by "\\item", for example "\\item %stars. \\item Second note. \\item Third note.", and it is possible to create line breaks in the formatted table by including "\\\\" and line breaks in the LaTeX code by including "\n", for example "\n\\item %stars.\\\\\n\\item Second line.\n".

digits

Set the number of decimal places for coefficients, standard errors and goodness-of-fit statistics. Do not use negative values! The argument works like the digits argument in the round function of the base package.

leading.zero

Most journals require leading zeros of coefficients and standard errors (for example, 0.35). This is also the default texreg behavior. Some journals, however, require omission of leading zeros (for example, .35). This can be achieved by setting leading.zero = FALSE.

star.symbol

Alternative characters for the significance stars can be specified. This is useful if knitr and Markdown are used for HTML report generation. In Markdown, asterisks or stars are interpreted as special characters, so they have to be escaped. To make a HTML table compatible with Markdown, specify star.symbol = "*". Note that some other modifications are recommended for usage with knitr in combination with Markdown or HTML (see the inline.css, doctype, html.tag, head.tag, and body.tag arguments in the htmlreg function).

symbol

If four threshold values are handed over to the stars argument, p-values smaller than the largest threshold value but larger than the second-largest threshold value are denoted by this symbol. The default symbol is "\\cdot" for the LaTeX dot, "·" for the HTML dot, or simply "." for the ASCII dot. If the texreg function is used, any other mathematical LaTeX symbol or plain text symbol can be used, for example symbol = "\\circ" for a small circle (note that backslashes must be escaped). If the htmlreg function is used, any other HTML character or symbol can be used. For the screenreg function, only plain text characters can be used.

override.coef

Set custom values for the coefficients. New coefficients are provided as a list of numeric vectors. The list contains vectors of coefficients for each model. There must be as many vectors of coefficients as there are models. For example, if there are two models with three model terms each, the argument could be specified as override.coef = list(c(0.1, 0.2, 0.3), c(0.05, 0.06, 0.07)). If there is only one model, custom values can be provided as a plain vector (not embedded in a list). For example: override.coef = c(0.05, 0.06, 0.07).

override.se

Set custom values for the standard errors. New standard errors are provided as a list of numeric vectors. The list contains vectors of standard errors for each model. There must be as many vectors of standard errors as there are models. For example, if there are two models with three coefficients each, the argument could be specified as override.se = list(c(0.1, 0.2, 0.3), c(0.05, 0.06, 0.07)). If there is only one model, custom values can be provided as a plain vector (not embedded in a list).For example: override.se = c(0.05, 0.06, 0.07). Overriding standard errors can be useful for the implementation of robust SEs, for example.

override.pvalues

Set custom values for the p-values. New p-values are provided as a list of numeric vectors. The list contains vectors of p-values for each model. There must be as many vectors of p-values as there are models. For example, if there are two models with three coefficients each, the argument could be specified as override.pvalues = list(c(0.1, 0.2, 0.3), c(0.05, 0.06, 0.07)). If there is only one model, custom values can be provided as a plain vector (not embedded in a list). For example: override.pvalues = c(0.05, 0.06, 0.07). Overriding p-values can be useful for the implementation of robust SEs and p-values, for example.

override.ci.low

Set custom lower confidence interval bounds. This works like the other override arguments, with one exception: if confidence intervals are provided here and in the override.ci.up argument, the standard errors and p-values as well as the ci.force argument are ignored.

override.ci.up

Set custom upper confidence interval bounds. This works like the other override arguments, with one exception: if confidence intervals are provided here and in the override.ci.low argument, the standard errors and p values as well as the ci.force argument are ignored.

omit.coef

A character string which is used as a regular expression to remove coefficient rows from the table. For example, omit.coef = "group" deletes all coefficient rows from the table where the name of the coefficient contains the character sequence "group". More complex regular expressions can be used to filter out several kinds of model terms, for example omit.coef = "(thresh)|(ranef)" to remove all model terms matching either "thresh" or "ranef". The omit.coef argument is processed after the custom.coef.names argument, so the regular expression should refer to the custom coefficient names. To omit GOF entries instead of coefficient entries, use the custom arguments of the extract functions instead (see the help entry of the extract function.

reorder.coef

Reorder the rows of the coefficient block of the resulting table in a custom way. The argument takes a vector of the same length as the number of coefficients. For example, if there are three coefficients, reorder.coef = c(3, 2, 1) will put the third coefficient in the first row and the first coefficient in the third row. Reordering can be sensible because interaction effects are often added to the end of the model output although they were specified earlier in the model formula. Note: Reordering takes place after processing custom coefficient names and after omitting coefficients, so the custom.coef.names and omit.coef arguments should follow the original order.

reorder.gof

Reorder the rows of the goodness-of-fit block of the resulting table in a custom way. The argument takes a vector of the same length as the number of GOF statistics. For example, if there are three goodness-of-fit rows, reorder.gof = c(3, 2, 1) will exchange the first and the third row. Note: Reordering takes place after processing custom GOF names and after adding new custom GOF rows, so the custom.gof.names and custom.gof.rows arguments should follow the original order, and the reorder.gof argument should contain values for any rows that are added through the custom.gof.rows argument.

ci.force

Should confidence intervals be used instead of the default standard errors and p-values? Most models implemented in the texreg package report standard errors and p-values by default while few models report confidence intervals. However, the functions in the texreg package can convert standard errors and into confidence intervals using z-scores if desired. To enforce confidence intervals instead of standard errors, the ci.force argument accepts either a logical value indicating whether all models or none of the models should be forced to report confidence intervals (ci.force = TRUE for all and ci.force = FALSE for none) or a vector of logical values indicating for each model separately whether the model should be forced to report confidence intervals (e.g., ci.force = c(FALSE, TRUE, FALSE)). Confidence intervals are computed using the standard normal distribution (z-values based on the qnorm function). The t-distribution is currently not supported because this would require each extract method to have an additional argument for the degrees of freedom.

ci.force.level

If the ci.force argument is used to convert standard errors to confidence intervals, what confidence level should be used? By default, 0.95 is used (i.e., an alpha value of 0.05).

ci.test

If confidence intervals are reported, the ci.test argument specifies the reference value to establish whether a coefficient/CI is significant. The default value ci.test = 0, for example, will attach a significance star to coefficients if the confidence interval does not contain 0. A value of ci.test = 1 could be useful if coefficients are provided on the odds-ratio scale, for example. If no star should be printed at all, ci.test = NA can be used. It is possible to provide a single value for all models or a vector with a separate value for each model. The ci.test argument works both for models with native support for confidence intervals and in cases where the ci.force argument is used.

groups

This argument can be used to group the rows of the table into blocks. For example, there could be one block for hypotheses and another block for control variables. Each group has a heading, and the row labels within a group are indented. The partitions must be handed over as a list of named numeric vectors, where each number is a row index and each name is the heading of the group. Example: groups = list("first group" = 1:4, "second group" = 7:8).

custom.columns

An optional list of additional text columns to be inserted into the coefficient block of the table, for example coefficient types. The list should contain one or more character vectors with as many character or numeric elements as there are coefficients/model terms. If the vectors in the list are named, the names are used as labels in the table header. For example, custom.columns = list(type = c("a", "b", "c"), 1:3) will add two columns; the first one is labeled while the second one is not. Note that the numeric elements of the second column will be converted to character objects in this example. The consequence is that decimal alignment with the dcolumn package is switched off in these columns. Note that this argument is processed after any arguments that affect the number of rows.

custom.col.pos

An optional integer vector of positions for the columns given in the custom.columns argument. For example, if there are three custom columns, custom.col.pos = c(1, 3, 3) will insert the first custom column before the first column of the original table and the remaining two custom columns after the second column of the original table. By default, all custom columns are placed after the first column, which usually contains the coefficient names.

column.spacing

The amount of space between any two columns of a table. By default, two spaces are used. If the tables do not fit on a single page horizontally, the value can be set to 1 or 0.

outer.rule

The character which is used to draw the outer horizontal line above and below a table. If an empty character object is provided (i.e., outer.rule = ""), there will be no outer horizontal lines. Recommended values are "", "=", "-", "_", or "#".

inner.rule

The character used to draw the inner horizontal line above and below a table. If an empty character object is provided (i.e., outer.rule = ""), there will be no inner horizontal lines. Recommended values are "", "-", or "_".

...

Custom options to be passed on to the extract function. For example, most extract methods provide custom options for the inclusion or exclusion of specific goodness-of-fit statistics. See the help entries of extract for more information.

Details

The screenreg function creates text representations of tables and prints them to the R console. This is an alternative to the summary function and serves easy model comparison. Moreover, once a table has been prepared in the R console, it can be later exported to LaTeX or HTML with little extra effort because the majority of arguments of the different functions are identical.

References

Leifeld, Philip (2013). texreg: Conversion of Statistical Model Output in R to LaTeX and HTML Tables. Journal of Statistical Software 55(8): 1-24. doi:10.18637/jss.v055.i08 .

Author

Philip Leifeld

Examples

# Display models from ?lm:
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)
lm.D90 <- lm(weight ~ group - 1)
screenreg(list(lm.D9, lm.D90))
#> 
#> =================================
#>              Model 1    Model 2  
#> ---------------------------------
#> (Intercept)   5.03 ***           
#>              (0.22)              
#> groupTrt     -0.37       4.66 ***
#>              (0.31)     (0.22)   
#> groupCtl                 5.03 ***
#>                         (0.22)   
#> ---------------------------------
#> R^2           0.07       0.98    
#> Adj. R^2      0.02       0.98    
#> Num. obs.    20         20       
#> =================================
#> *** p < 0.001; ** p < 0.01; * p < 0.05