mtable produces a table of estimates for several models.mtable(...,coef.style=getOption("coef.style"),
summary.stats=TRUE,
factor.style=getOption("factor.style"),
getSummary=function(obj,...)UseMethod("getSummary"),
float.style=getOption("float.style"),
digits=min(3,getOption("digits")))format.mtable(x,
coef.title="Coefficients",
summary.title="Summaries",
colsep="",
rowsep="",
trim=TRUE,
trimleft=trim,
trimright=trim,
center.at=NULL,
align.integers=c("dot","right","left"),
topsep="",
bottomsep="",
sectionsep="",
forLaTeX=FALSE,
useDcolumn=TRUE,
colspec=if(useDcolumn) paste("D{.}{",LaTeXdec,"}{",ddigits,"}",sep="") else "r",
LaTeXdec=".",
ddigits="-1",
useBooktabs=TRUE,
toprule=if(useBooktabs) "\toprule" else "\hline\hline",
midrule=if(useBooktabs) "\midrule" else "\hline",
bottomrule=if(useBooktabs) "\bottomrule" else "\hline\hline",
interaction.sep = if(forLaTeX) " $\times$ " else " x ",
...
)
print.mtable(x,trim=FALSE,center.at=getOption("OutDec"),
colsep=" ",
topsep="=",bottomsep="=",sectionsep="-",...)
toLatex.mtable(object,...)
write.mtable(object,file="",...)
relabel.mtable(x,...)
- ...
{as argument to mtable: several model objects, e.g. of class lm; as argument to print.mtable,
toLatex.mtable, write.mtable: further arguments
passed to format.mtable; as argument to format.mtable:
further arguments passed to format.default;
as argument to relabel.mtable: further arguments
passed to dimrename.
}
- coef.style
{a character string which specifies the style of
coefficient values, whether standard errors, Wald/t-statistics,
or significance levels are reported, etc. See coef.style.}
- summary.stats
{if FALSE, no summary statistics
are repored. If TRUE, all summary statistics
produced by getSummary are reported.
This argument may also contain a character vector with
the names of the summary statistics to report}
- factor.style
{a character string that specifies the style in
which factor contrasts are labled. See factor.style.}
- getSummary
{a function that computes model-related statistics that
appear in the table. See getSummary.}
- float.style
{default format for floating point numbers if
no format is specified by coef.style; see {float.style}.
}
- digits
{number of significant digits if not specified by
the template returned from getCoefTemplate
getSummaryTemplate
}
- x, object
{an object of class mtable}
- coef.title
{a character vector, the title for the reported coefficients.}
- summary.title
{a character vector, the title for the reported model summaries.}
- colsep
{a character string which seperates the columns in the output.}
- rowsep
{a character string which seperates the rows in the output.}
- trim
{should leading and trailing spaces be trimmed?}
- trimleft
{should leading spaces be trimmed?}
- trimright
{should trailing spaces be trimmed?}
- center.at
{a character string on which resulting values are centered.
Typically equal to ".". This is the default when forLaTeX==TRUE.
If NULL, reported values are not centered.}
- align.integers
{how to align integer values.}
- topsep
{a character string that is recycled to a top rule.}
- bottomsep
{a character string that is recycled to a bottom rule.}
- sectionsep
{a character string that is recycled to seperate coefficients
from summary statistics.}
- forLaTeX
{should LaTeX code be produced?}
- useDcolumn
{should the dcolumn LaTeX package be used?}
- colspec
{LaTeX table column format specifyer(s).}
- LaTeXdec
{the decimal point in the final LaTeX output.}
- ddigits
{alignment specification or digits after the decimal point.}
- useBooktabs
{should the booktabs LaTeX package be used?}
- toprule
{appearance of the top border of the LaTeX tabular environment.}
- midrule
{how are coefficients and summary statistics
separated in the LaTeX tabular environment.}
- bottomrule
{appearance of the bottom border of the LaTeX tabular environment.}
- interaction.sep
{a character string that separates factors that are involved
in an interaction effect}
- file
{a file where to write to; defaults to console output.}mtable constructs a table of estimates for regression-type models.
format.mtable formats suitable for use with output or conversion functions
such as print.mtable, toLatex.mtable, or write.mtable.
A call to mtable results in an object that inherits from mtable
with the following components:
- coefficients
{an array that contains the model coefficients.}
- summaries
{a matrix that contains the model summaries.}
In addition the mtable object has attributes as.row,
as.col, kill.col, kill.header,
and coef.dim necessary for formatting.
lm0 <- lm(sr ~ pop15 + pop75, data = LifeCycleSavings)
lm1 <- lm(sr ~ dpi + ddpi, data = LifeCycleSavings)
lm2 <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
(mtable123 <- mtable("Model 1"=lm0,"Model 2"=lm1,"Model 3"=lm2))
#
# This should result in the following output:
#
# Calls:
# Model 1: lm(formula = sr ~ pop15 + pop75, data = LifeCycleSavings)
# Model 2: lm(formula = sr ~ dpi + ddpi, data = LifeCycleSavings)
# Model 3: lm(formula = sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
#
# ==================================================
# Model 1 Model 2 Model 3
# --------------------------------------------------
# Coefficients
# (Intercept) 30.628*** 6.360*** 28.566***
# (7.409) (1.252) (7.355)
# pop15 -0.471** -0.461**
# (0.147) (0.145)
# pop75 -1.934 -1.691
# (1.041) (1.084)
# dpi 0.001 -0.000
# (0.001) (0.001)
# ddpi 0.529* 0.410*
# (0.210) (0.196)
# --------------------------------------------------
# Summaries
# R-squared 0.262 0.162 0.338
# adj. R-squared 0.230 0.126 0.280
# sigma 3.931 4.189 3.803
# F 8.3 4.5 5.8
# p 0.001 0.016 0.001
# Log-likelihood -137.8 -141.0 -135.1
# Deviance 726.2 824.7 650.7
# AIC 283.7 290.0 282.2
# BIC 291.3 297.7 293.7
# N 50 50 50
# ==================================================
#
#
# Some other output formats
write.mtable(mtable123)
file123 <- "mtable123.txt"
write.mtable(mtable123,file=file123)
file.show(file123)
toLatex(mtable123)
texfile123 <- "mtable123.tex"
cat(toLatex(mtable123),sep="\n",file=texfile123)
file.show(texfile123)
#
# You should see:
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %
# % Calls:
# % Model 1: lm(formula = sr ~ pop15 + pop75, data = LifeCycleSavings)
# % Model 2: lm(formula = sr ~ dpi + ddpi, data = LifeCycleSavings)
# % Model 3: lm(formula = sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
# %
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# tabular {lD{.}{.}{-1}D{.}{.}{-1}D{.}{.}{-1}}
# # & 1 {c}{Model 1}
# & 1 {c}{Model 2}
# & 1 {c}{Model 3}
# \\
# # Coefficients & & & \\
# (Intercept) & 30.628^{***} & 6.360^{***} & 28.566^{***} \\
# & (7.409) & (1.252) & (7.355) \\
# pop15 & -0.471^{**} & & -0.461^{**} \\
# & (0.147) & & (0.145) \\
# pop75 & -1.934 & & -1.691 \\
# & (1.041) & & (1.084) \\
# dpi & & 0.001 & -0.000 \\
# & & (0.001) & (0.001) \\
# ddpi & & 0.529^{*} & 0.410^{*} \\
# & & (0.210) & (0.196) \\
# # Summaries & & & \\
# R-squared & 0.262 & 0.162 & 0.338 \\
# adj. R-squared & 0.230 & 0.126 & 0.280 \\
# sigma & 3.931 & 4.189 & 3.803 \\
# F & 8.3 & 4.5 & 5.8 \\
# p & 0.001 & 0.016 & 0.001 \\
# Log-likelihood & -137.8 & -141.0 & -135.1 \\
# Deviance & 726.2 & 824.7 & 650.7 \\
# AIC & 283.7 & 290.0 & 282.2 \\
# BIC & 291.3 & 297.7 & 293.7 \\
# N & 50 & 50 & 50 \\
# # tabular
#
berkeley <- aggregate(wtable(Admit,Freq)~.,data=UCBAdmissions)
berk0 <- glm(cbind(Admitted,Rejected)~1,data=berkeley,family="binomial")
berk1 <- glm(cbind(Admitted,Rejected)~Gender,data=berkeley,family="binomial")
berk2 <- glm(cbind(Admitted,Rejected)~Gender+Dept,data=berkeley,family="binomial")
mtable(berk0,berk1,berk2)
#
# This should result in the following output:
#
# Calls:
# berk0: glm(formula = cbind(Admitted, Rejected) ~ 1, family = "binomial",
# data = berkeley)
# berk1: glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial",
# data = berkeley)
# berk2: glm(formula = cbind(Admitted, Rejected) ~ Gender + Dept, family = "binomial",
# data = berkeley)
#
# =======================================================
# berk0 berk1 berk2
# -------------------------------------------------------
# Coefficients
# (Intercept) -0.457*** -0.220*** 0.582***
# (-14.972) (-5.675) (8.436)
# Gender: Female-Male -0.610*** 0.100
# (-9.553) (1.235)
# Dept: B-A -0.043
# (-0.395)
# Dept: C-A -1.263***
# (-11.841)
# Dept: D-A -1.295***
# (-12.234)
# Dept: E-A -1.739***
# (-13.792)
# Dept: F-A -3.306***
# (-19.452)
# -------------------------------------------------------
# Summaries
# McFadden R-sq. 0.107 0.977
# Cox-Snell R-sq. 0.020 0.172
# Nagelkerke R-sq. 0.116 0.979
# phi 1.000 1.000 1.000
# Likelihood-ratio 93.4 856.9
# p 0.000 0.000
# Log-likelihood -473.0 -426.3 -44.6
# Deviance 877.1 783.6 20.2
# AIC 948.0 856.5 103.1
# BIC 954.4 869.4 148.1
# N 4526 4526 4526
# =======================================================
mtable(berk0,berk1,berk2,coef.style="stat")
mtable(berk0,berk1,berk2,coef.style="ci")
mtable(berk0,berk1,berk2,coef.style="ci.vertical")
mtable(berk0,berk1,berk2,coef.style="ci.horizontal")
mtable(berk0,berk1,berk2,coef.style="all")
mtable(berk0,berk1,berk2,coef.style="all.nostar")
mtable(by(berkeley,berkeley$Dept,function(x)glm(cbind(Admitted,Rejected)~Gender,data=x,family="binomial")))
#
# This should result in the following output:
#
# Calls:
# A: glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial",
# data = x)
# B: glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial",
# data = x)
# C: glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial",
# data = x)
# D: glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial",
# data = x)
# E: glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial",
# data = x)
# F: glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial",
# data = x)
#
# =====================================================================================
# A B C D E F
# -------------------------------------------------------------------------------------
# Coefficients
# (Intercept) 0.492*** 0.534*** -0.536*** -0.704*** -0.957*** -2.770***
# (0.072) (0.088) (0.115) (0.104) (0.162) (0.220)
# Gender: Female-Male 1.052*** 0.220 -0.125 0.082 -0.200 0.189
# (0.263) (0.438) (0.144) (0.150) (0.200) (0.305)
# -------------------------------------------------------------------------------------
# Summaries
# McFadden R-sq. 1.000 1.000 1.000 1.000 1.000 1.000
# Cox-Snell R-sq. 0.020 0.000 0.001 0.000 0.002 0.001
# Nagelkerke R-sq. 1.000 1.000 1.000 1.000 1.000 1.000
# phi 1.000 1.000 1.000 1.000 1.000 1.000
# Likelihood-ratio 19.1 0.3 0.8 0.3 1.0 0.4
# p 0.000 0.611 0.386 0.585 0.320 0.536
# Log-likelihood -5.9 -5.1 -6.4 -6.3 -5.8 -4.9
# Deviance 0.0 0.0 -0.0 0.0 0.0 -0.0
# AIC 15.7 14.3 16.9 16.6 15.6 13.8
# BIC 25.4 23.0 26.5 26.0 24.3 23.0
# N 933 585 918 792 584 714
# =====================================================================================
mtable(By(~Gender,glm(cbind(Admitted,Rejected)~Dept,family="binomial"),data=berkeley))
#
# This should result in the following output:
#
# Male: glm(formula = cbind(Admitted, Rejected) ~ Dept, family = "binomial")
# Female: glm(formula = cbind(Admitted, Rejected) ~ Dept, family = "binomial")
#
# ========================================
# Male Female
# ----------------------------------------
# Coefficients
# (Intercept) 0.492*** 1.544***
# (0.072) (0.253)
# Dept: B-A 0.042 -0.790
# (0.113) (0.498)
# Dept: C-A -1.028*** -2.205***
# (0.135) (0.267)
# Dept: D-A -1.196*** -2.166***
# (0.126) (0.275)
# Dept: E-A -1.449*** -2.701***
# (0.177) (0.279)
# Dept: F-A -3.262*** -4.125***
# (0.231) (0.330)
# ----------------------------------------
# Summaries
# McFadden R-sq. 1.000 1.000
# Cox-Snell R-sq. 0.174 0.136
# Nagelkerke R-sq. 1.000 1.000
# phi 1.000 1.000
# Likelihood-ratio 514.8 268.9
# p 0.000 0.000
# Log-likelihood -18.4 -16.1
# Deviance -0.0 0.0
# AIC 48.7 44.2
# BIC 84.1 77.3
# N 2691 1835
# ========================================
misc