Description Usage Arguments Details Author(s) See Also Examples

Create coefficient plots from statistical model output.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
plotreg(l, file = NULL, custom.model.names = NULL,
custom.coef.names = NULL, custom.note = NULL,
override.coef = 0, override.se = 0, override.pval = 0,
override.ci.low = 0, override.ci.up = 0,
omit.coef = NULL, reorder.coef = NULL, ci.level = 0.95,
use.se = FALSE, mfrow = TRUE, xlim = NULL, cex = 2.5,
lwd.zerobar = 4, lwd.vbars = 1, lwd.inner = 7,
lwd.outer = 5, ylab.cex = 1.0, signif.light = "#fbc9b9",
signif.medium = "#f7523a", signif.dark = "#bd0017",
insignif.light = "#c5dbe9", insignif.medium = "#5a9ecc",
insignif.dark = "#1c5ba6", ...)
coefplot(labels, estimates, lower.inner = NULL,
upper.inner = NULL, lower.outer = NULL,
upper.outer = NULL, signif.outer = TRUE,
xlab = "Coefficients and confidence intervals",
main = "Coefficient plot", xlim = NULL,
cex = 2.5, lwd.zerobar = 4, lwd.vbars = 1,
lwd.inner = 7, lwd.outer = 5, ylab.cex = 1.0,
signif.light = "#fbc9b9", signif.medium = "#f7523a",
signif.dark = "#bd0017", insignif.light = "#c5dbe9",
insignif.medium = "#5a9ecc", insignif.dark = "#1c5ba6",
...)
``` |

`l` |
A statistical model or a list of statistical models. Lists of models can be specified as |

`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. The file extension is automatically recognized. |

`custom.model.names` |
A character vector of labels for the models. By default, the models are named Model 1, Model 2, etc. Specifying |

`custom.coef.names` |
By default, |

`custom.note` |
With this argument, a replacement text for the |

`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.se` |
Set custom values for the standard errors. This only has an effect where standard errors are converted into confidence intervals because no other CIs are present. 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.pval` |
Set custom values for the p values. This only has an effect where standard errors are converted into confidence intervals because no other CIs are present. In this case, significance is derived from the p values rather than the confidence intervals. 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.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` |
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 |

`omit.coef` |
A character string which is used as a regular expression to remove coefficient rows from the table. For example, |

`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, |

`ci.level` |
If standard errors are converted to confidence intervals (because a model does not natively support CIs), what confidence level should be used for the outer confidence interval? By default, |

`use.se` |
Use one standard error for the inner horizontal bar and two standard errors from the estimate for the outer horizontal bar (instead of confidence intervals). Only available if standard errors can be extracted from the model using the respective |

`mfrow` |
If multiple models are handed over as the |

`xlim` |
Horizontal limits. In the |

`lwd.zerobar` |
Line width of the vertical gray bar at the |

`lwd.vbars` |
Line width of the thin vertical gray bars. To remove them completely, set |

`labels` |
The names of the model terms. They are arranged on the left axis. |

`estimates` |
The coefficients (point estimates) of the model terms. They are depicted as bold dots in the coefficient plot. |

`lower.inner` |
The lower bounds of the inner confidence intervals, provided as a vector. Inner CI means more relaxed (lower confidence level, higher alpha) because fewer observations have to fall into the CI, therefore the CI gets smaller. |

`upper.inner` |
The upper bounds of the inner confidence intervals, provided as a vector. Inner CI means more relaxed (lower confidence level, higher alpha) because fewer observations have to fall into the CI, therefore the CI gets smaller. |

`lower.outer` |
The lower bounds of the outer confidence intervals, provided as a vector. Outer CI means stricter or narrower (higher confidence level, lower alpha) because more observations have to fall into the CI, therefore the CI gets larger. |

`upper.outer` |
The upper bounds of the outer confidence intervals, provided as a vector. Outer CI means stricter or narrower (higher confidence level, lower alpha) because more observations have to fall into the CI, therefore the CI gets larger. |

`signif.outer` |
Different colors are used for significant estimates and confidence intervals. If |

`xlab` |
The label of the x axis. |

`main` |
The main title or heading of the plot. |

`cex` |
Size of the point representing the estimate. |

`lwd.inner` |
Line width of the inner confidence interval. |

`lwd.outer` |
Line width of the outer confidence interval. |

`ylab.cex` |
Size of the coefficient labels. The size of the x axis labels can be adjusted by using argument |

`signif.light` |
Color of outer confidence intervals for significant model terms. |

`signif.medium` |
Color of inner confidence intervals for significant model terms. |

`signif.dark` |
Color of point estimates and labels for significant model terms. |

`insignif.light` |
Color of outer confidence intervals for insignificant model terms. |

`insignif.medium` |
Color of inner confidence intervals for insignificant model terms. |

`insignif.dark` |
Color of point estimates and labels for insignificant model terms. |

`...` |
Custom options to be passed on to the extract function or the graphics device. See the help entries of extract and extract-methods for more information. |

The `coefplot`

function produces coefficient plots (i.e., forest plots applied to point estimates and confidence intervals). It accepts raw data (the lower and upper bounds of inner and outer confidence intervals as well as the point estimates and their names) as input data. Significant coefficients and intervals can be plotted in a different color.

The `plotreg`

function is a wrapper for the `coefplot`

function and works much like the
`screenreg`

, `texreg`

, and `htmlreg`

functions. It accepts a single or multiple statistical models as input and internally extracts the relevant data from the models. If confidence intervals are not defined in the extract method of a statistical model (see extract and extract-methods), the default standard errors are converted to confidence intervals. Most of the arguments work either like in the `screenreg`

, `texreg`

, and `htmlreg`

functions, or they work like in the `coefplot`

function.

Philip Leifeld (http://www.philipleifeld.com)

`texreg-package extract extract-methods texreg`

1 2 3 4 5 6 7 8 | ```
#example from the 'lm' help file:
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) # print model output to the R console
plotreg(lm.D9) # plot model output as a diagram
``` |

```
Version: 1.36.23
Date: 2017-03-03
Author: Philip Leifeld (University of Glasgow)
Please cite the JSS article in your publications -- see citation("texreg").
======================
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
Model 1: bars denote 0.5 (inner) resp. 0.95 (outer) confidence intervals (computed from standard errors).
```

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