mtable
memisc
0.99.26.3
Comparative Table of Model Estimates¶
Description¶
mtable produces a table of estimates for several models.
Usage¶
mtable(...,coef.style=getOption("coef.style"),
summary.stats=TRUE,
signif.symbols=getOption("signif.symbols"),
factor.style=getOption("factor.style"),
show.baselevel=getOption("show.baselevel"),
baselevel.sep=getOption("baselevel.sep"),
getSummary=eval.parent(quote(getSummary)),
float.style=getOption("float.style"),
digits=min(3,getOption("digits")),
sdigits=digits,
show.eqnames=getOption("mtable.show.eqnames",NA),
gs.options=NULL
)
## S4 method for signature 'memisc_mtable'
relabel(x, ..., gsub = FALSE, fixed = !gsub, warn = FALSE)
## S4 method for signature 'memisc_mtable'
format(x,target=c("print","LaTeX","HTML","delim"),
...
)
## S4 method for signature 'memisc_mtable'
print(x,
center.at=getOption("OutDec"),
topsep="=",bottomsep="=",sectionsep="-",...)
write.mtable(object,file="",
format=c("delim","LaTeX","HTML"),...)
## S4 method for signature 'memisc_mtable'
toLatex(object,...)
Arguments¶
...-
as argument to
mtable: several model objects, e.g. of classlm; as argument toprint.memisc_mtable,toLatex.memisc_mtable,write.memisc_mtable: further arguments passed toformat.memisc_mtable; as argument toformat.memisc_mtable: further arguments passed toformat.default; as argument torelabel.memisc_mtable: further arguments passed todimrename. 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. IfTRUEthen for each object in...either all summary statistics are reported, or those specified by the option"summary.stats.<cls>", where<cls>is the class of the respective object. This argument may also contain a character vector with the names of the summary statistics to report, or a list of character vectors with names of summary statistics for each object passed as argument in.... signif.symbols-
a named numeric vector to specify the “significance levels” and corresponding symbols. The numeric elements define the significance levels, the attached names define the associated symbols.
factor.style-
a character string that specifies the style in which factor contrasts are labled. See
factor.style. show.baselevel-
logical; determines whether base levels of factors are indicated for dummy coefficients
baselevel.sep-
character that is used to separate the base level from the level that a dummy variable represents
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 digits-
number of significant digits if not specified by the template returned from
getCoefTemplategetSummaryTemplate sdigits-
integer; number of digits after decimal dot for summary statistics.
show.eqnames-
logical; if
TRUE, left-hand sides of equations are (always) shown in the table header; ifFALSE, left-hand sides of equations are not shown; ifNA, left-hand sides of equations are shown only if left-hand sides differ among models or one of the models has multiple equations. gs.options-
an optional list of arguments passed on to
getSummary -
x,object -
an object of class
mtable -
gsub,warn,fixed -
logical values, see
relabel target-
a character string which indicates the target format. Currenlty the targets “print” (see
mtable_format_print), “LaTeX” (seemtable_format_latex), “HTML” (seemtable_format_html), and “delim” (seemtable_format_delim) are supported. center.at-
a character string on which resulting values are centered. Typically equal to “.”. This is the default when
forLaTeX==TRUE. IfNULL, reported values are not centered. 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.
file-
name of the file where to write to; defaults to console output.
format-
character string that specifies the desired format.
Value¶
A call to mtable results in an object of class "mtable" with the following
components:
- coefficients
-
a list that contains the model coefficients,
- summaries
-
a matrix that contains the model summaries,
- calls
-
a list of calls that created the model estimates being summarised.
Details¶
mtable constructs a table of estimates for regression-type models.
format.memisc_mtable formats suitable for use with output or conversion functions
such as print.memisc_mtable, toLatex.memisc_mtable, or write.memisc_mtable.
Examples¶
#### Basic workflow
lm0 <- lm(sr ~ pop15 + pop75, data = LifeCycleSavings)
lm1 <- lm(sr ~ dpi + ddpi, data = LifeCycleSavings)
lm2 <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
options(summary.stats.lm=c("R-squared","N"))
mtable("Model 1"=lm0,"Model 2"=lm1,"Model 3"=lm2)
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
------------------------------------------------
(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)
------------------------------------------------
R-squared 0.262 0.162 0.338
N 50 50 50
================================================
Significance: *** = p < 0.001;
** = p < 0.01; * = p < 0.05
options(summary.stats.lm=c("sigma","R-squared","N"))
mtable("Model 1"=lm0,"Model 2"=lm1,"Model 3"=lm2)
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
------------------------------------------------
(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)
------------------------------------------------
sigma 3.931 4.189 3.803
R-squared 0.262 0.162 0.338
N 50 50 50
================================================
Significance: *** = p < 0.001;
** = p < 0.01; * = p < 0.05
options(summary.stats.lm=NULL)
mtable123 <- mtable("Model 1"=lm0,"Model 2"=lm1,"Model 3"=lm2,
summary.stats=c("sigma","R-squared","F","p","N"))
(mtable123 <- relabel(mtable123,
"(Intercept)" = "Constant",
pop15 = "Percentage of population under 15",
pop75 = "Percentage of population over 75",
dpi = "Real per-capita disposable income",
ddpi = "Growth rate of real per-capita disp. income"
))
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
--------------------------------------------------------------------------------
Constant 30.628*** 6.360*** 28.566***
(7.409) (1.252) (7.355)
Percentage of population under 15 -0.471** -0.461**
(0.147) (0.145)
Percentage of population over 75 -1.934 -1.691
(1.041) (1.084)
Real per-capita disposable income 0.001 -0.000
(0.001) (0.001)
Growth rate of real per-capita disp. income 0.529* 0.410*
(0.210) (0.196)
--------------------------------------------------------------------------------
sigma 3.931 4.189 3.803
R-squared 0.262 0.162 0.338
F 8.332 4.528 5.756
p 0.001 0.016 0.001
N 50 50 50
================================================================================
Significance: *** = p < 0.001; ** = p < 0.01; * = p < 0.05
# This produces output in tab-delimited format:
write.mtable(mtable123)
Model 1 Model 2 Model 3
Constant 30.628*** 6.360*** 28.566***
(7.409) (1.252) (7.355)
Percentage of population under 15 -0.471** -0.461**
(0.147) (0.145)
Percentage of population over 75 -1.934 -1.691
(1.041) (1.084)
Real per-capita disposable income 0.001 -0.000
(0.001) (0.001)
Growth rate of real per-capita disp. income 0.529* 0.410*
(0.210) (0.196)
sigma 3.931 4.189 3.803
R-squared 0.262 0.162 0.338
F 8.332 4.528 5.756
p 0.001 0.016 0.001
N 50 50 50
# This produces output in tab-delimited format:
file123 <- "mtable123.txt"
write.mtable(mtable123,file=file123)
file.show(file123)
# The contents of this file can be pasted into Word
# and converted into a Word table.
texfile123 <- "mtable123.tex"
write.mtable(mtable123,format="LaTeX",file=texfile123)
file.show(texfile123)
#### Examples with UC Berkeley data
berkeley <- Aggregate(Table(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,summary.stats=c("Deviance","N"))
Calls:
berk0: glm(formula = cbind(Admitted, Rejected) ~ 1, family = "binomial",
data = berkeley)
============================
(Intercept) -0.457***
(0.031)
----------------------------
Deviance 877.056
N 4526
============================
Significance:
*** = p < 0.001;
** = p < 0.01;
* = p < 0.05
mtable(berk1,summary.stats=c("Deviance","N"))
Calls:
berk1: glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial",
data = berkeley)
====================================
(Intercept) -0.220***
(0.039)
Gender: Female/Male -0.610***
(0.064)
------------------------------------
Deviance 783.607
N 4526
====================================
Significance: *** = p < 0.001;
** = p < 0.01;
* = p < 0.05
mtable(berk0,berk1,berk2,summary.stats=c("Deviance","N"))
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
--------------------------------------------------------------
(Intercept) -0.457*** -0.220*** 0.582***
(0.031) (0.039) (0.069)
Gender: Female/Male -0.610*** 0.100
(0.064) (0.081)
Dept: B/A -0.043
(0.110)
Dept: C/A -1.263***
(0.107)
Dept: D/A -1.295***
(0.106)
Dept: E/A -1.739***
(0.126)
Dept: F/A -3.306***
(0.170)
--------------------------------------------------------------
Deviance 877.056 783.607 20.204
N 4526 4526 4526
==============================================================
Significance: *** = p < 0.001; ** = p < 0.01; * = p < 0.05
mtable(berk0,berk1,berk2,
coef.style="horizontal",
summary.stats=c("Deviance","AIC","N"))
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
--------------------------------------------------------------------------------------
(Intercept) -0.457*** (0.031) -0.220*** (0.039) 0.582*** (0.069)
Gender: Female/Male -0.610*** (0.064) 0.100 (0.081)
Dept: B/A -0.043 (0.110)
Dept: C/A -1.263*** (0.107)
Dept: D/A -1.295*** (0.106)
Dept: E/A -1.739*** (0.126)
Dept: F/A -3.306*** (0.170)
--------------------------------------------------------------------------------------
Deviance 877.056 783.607 20.204
AIC 947.996 856.547 103.144
N 4526 4526 4526
======================================================================================
Significance: *** = p < 0.001; ** = p < 0.01; * = p < 0.05
mtable(berk0,berk1,berk2,
coef.style="stat",
summary.stats=c("Deviance","AIC","N"))
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
--------------------------------------------------------------
(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)
--------------------------------------------------------------
Deviance 877.056 783.607 20.204
AIC 947.996 856.547 103.144
N 4526 4526 4526
==============================================================
Significance: *** = p < 0.001; ** = p < 0.01; * = p < 0.05
mtable(berk0,berk1,berk2,
coef.style="ci",
summary.stats=c("Deviance","AIC","N"))
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
--------------------------------------------------------
(Intercept) -0.457 -0.220 0.582
[-0.517 [-0.296 [0.447
-0.397] -0.144] 0.717]
Gender: Female/Male -0.610 0.100
[-0.736 [-0.059
-0.485] 0.258]
Dept: B/A -0.043
[-0.259
0.172]
Dept: C/A -1.263
[-1.472
-1.054]
Dept: D/A -1.295
[-1.502
-1.087]
Dept: E/A -1.739
[-1.986
-1.492]
Dept: F/A -3.306
[-3.640
-2.973]
--------------------------------------------------------
Deviance 877.056 783.607 20.204
AIC 947.996 856.547 103.144
N 4526 4526 4526
========================================================
mtable(berk0,berk1,berk2,
coef.style="ci.se",
summary.stats=c("Deviance","AIC","N"))
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
--------------------------------------------------------
(Intercept) -0.457 -0.220 0.582
(0.031) (0.039) (0.069)
[-0.517 [-0.296 [0.447
-0.397] -0.144] 0.717]
Gender: Female/Male -0.610 0.100
(0.064) (0.081)
[-0.736 [-0.059
-0.485] 0.258]
Dept: B/A -0.043
(0.110)
[-0.259
0.172]
Dept: C/A -1.263
(0.107)
[-1.472
-1.054]
Dept: D/A -1.295
(0.106)
[-1.502
-1.087]
Dept: E/A -1.739
(0.126)
[-1.986
-1.492]
Dept: F/A -3.306
(0.170)
[-3.640
-2.973]
--------------------------------------------------------
Deviance 877.056 783.607 20.204
AIC 947.996 856.547 103.144
N 4526 4526 4526
========================================================
mtable(berk0,berk1,berk2,
coef.style="ci.se.horizontal",
summary.stats=c("Deviance","AIC","N"))
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
-----------------------------------------------------------------------------
(Intercept) -0.457 (0.031) -0.220 (0.039) 0.582 (0.069)
[-0.517 -0.397] [-0.296 -0.144] [0.447 0.717]
Gender: Female/Male -0.610 (0.064) 0.100 (0.081)
[-0.736 -0.485] [-0.059 0.258]
Dept: B/A -0.043 (0.110)
[-0.259 0.172]
Dept: C/A -1.263 (0.107)
[-1.472 -1.054]
Dept: D/A -1.295 (0.106)
[-1.502 -1.087]
Dept: E/A -1.739 (0.126)
[-1.986 -1.492]
Dept: F/A -3.306 (0.170)
[-3.640 -2.973]
-----------------------------------------------------------------------------
Deviance 877.056 783.607 20.204
AIC 947.996 856.547 103.144
N 4526 4526 4526
=============================================================================
mtable(berk0,berk1,berk2,
coef.style="ci.p.horizontal",
summary.stats=c("Deviance","AIC","N"))
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
-----------------------------------------------------------------------------
(Intercept) -0.457 (0.000) -0.220 (0.000) 0.582 (0.000)
[-0.517 -0.397] [-0.296 -0.144] [0.447 0.717]
Gender: Female/Male -0.610 (0.000) 0.100 (0.217)
[-0.736 -0.485] [-0.059 0.258]
Dept: B/A -0.043 (0.693)
[-0.259 0.172]
Dept: C/A -1.263 (0.000)
[-1.472 -1.054]
Dept: D/A -1.295 (0.000)
[-1.502 -1.087]
Dept: E/A -1.739 (0.000)
[-1.986 -1.492]
Dept: F/A -3.306 (0.000)
[-3.640 -2.973]
-----------------------------------------------------------------------------
Deviance 877.056 783.607 20.204
AIC 947.996 856.547 103.144
N 4526 4526 4526
=============================================================================
Significance: *** = p < 0.001; ** = p < 0.01; * = p < 0.05
mtable(berk0,berk1,berk2,
coef.style="ci.horizontal",
summary.stats=c("Deviance","AIC","N"))
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
-----------------------------------------------------------------------------------------------------
(Intercept) -0.457 [-0.517 -0.397] -0.220 [-0.296 -0.144] 0.582 [0.447 0.717]
Gender: Female/Male -0.610 [-0.736 -0.485] 0.100 [-0.059 0.258]
Dept: B/A -0.043 [-0.259 0.172]
Dept: C/A -1.263 [-1.472 -1.054]
Dept: D/A -1.295 [-1.502 -1.087]
Dept: E/A -1.739 [-1.986 -1.492]
Dept: F/A -3.306 [-3.640 -2.973]
-----------------------------------------------------------------------------------------------------
Deviance 877.056 783.607 20.204
AIC 947.996 856.547 103.144
N 4526 4526 4526
=====================================================================================================
mtable(berk0,berk1,berk2,
coef.style="all",
summary.stats=c("Deviance","AIC","N"))
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
--------------------------------------------------------------
(Intercept) -0.457*** -0.220*** 0.582***
(0.031) (0.039) (0.069)
(-14.972) (-5.675) (8.436)
(0.000) (0.000) (0.000)
Gender: Female/Male -0.610*** 0.100
(0.064) (0.081)
(-9.553) (1.235)
(0.000) (0.217)
Dept: B/A -0.043
(0.110)
(-0.395)
(0.693)
Dept: C/A -1.263***
(0.107)
(-11.841)
(0.000)
Dept: D/A -1.295***
(0.106)
(-12.234)
(0.000)
Dept: E/A -1.739***
(0.126)
(-13.792)
(0.000)
Dept: F/A -3.306***
(0.170)
(-19.452)
(0.000)
--------------------------------------------------------------
Deviance 877.056 783.607 20.204
AIC 947.996 856.547 103.144
N 4526 4526 4526
==============================================================
Significance: *** = p < 0.001; ** = p < 0.01; * = p < 0.05
mtable(berk0,berk1,berk2,
coef.style="all.nostar",
summary.stats=c("Deviance","AIC","N"))
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
--------------------------------------------------------
(Intercept) -0.457 -0.220 0.582
(0.031) (0.039) (0.069)
(-14.972) (-5.675) (8.436)
(0.000) (0.000) (0.000)
Gender: Female/Male -0.610 0.100
(0.064) (0.081)
(-9.553) (1.235)
(0.000) (0.217)
Dept: B/A -0.043
(0.110)
(-0.395)
(0.693)
Dept: C/A -1.263
(0.107)
(-11.841)
(0.000)
Dept: D/A -1.295
(0.106)
(-12.234)
(0.000)
Dept: E/A -1.739
(0.126)
(-13.792)
(0.000)
Dept: F/A -3.306
(0.170)
(-19.452)
(0.000)
--------------------------------------------------------
Deviance 877.056 783.607 20.204
AIC 947.996 856.547 103.144
N 4526 4526 4526
========================================================
Significance: *** = p < 0.001; ** = p < 0.01;
* = p < 0.05
mtable(by(berkeley,berkeley$Dept,
function(x)glm(cbind(Admitted,Rejected)~Gender,
data=x,family="binomial")),
summary.stats=c("Likelihood-ratio","N"))
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
-----------------------------------------------------------------------------------------------
(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)
-----------------------------------------------------------------------------------------------
Likelihood-ratio 19.054 0.259 0.751 0.298 0.990 0.384
N 933 585 918 792 584 714
===============================================================================================
Significance: *** = p < 0.001; ** = p < 0.01; * = p < 0.05
mtable(By(~Gender,
glm(cbind(Admitted,Rejected)~Dept,
family="binomial"),
data=berkeley),
summary.stats=c("Likelihood-ratio","N"))
Calls:
Male: glm(formula = cbind(Admitted, Rejected) ~ Dept, family = "binomial")
Female: glm(formula = cbind(Admitted, Rejected) ~ Dept, family = "binomial")
==============================================
Male Female
----------------------------------------------
(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)
----------------------------------------------
Likelihood-ratio 514.756 268.851
N 2691 1835
==============================================
Significance: *** = p < 0.001;
** = p < 0.01; * = p < 0.05
berkfull <- glm(cbind(Admitted,Rejected)~Dept/Gender - 1,
data=berkeley,family="binomial")
relabel(mtable(berkfull),Dept="Department",gsub=TRUE)
Calls:
berkfull: glm(formula = cbind(Admitted, Rejected) ~ Dept/Gender - 1, family =
"binomial",
data = berkeley)
====================================================
Department: A 0.492***
(0.072)
Department: B 0.534***
(0.088)
Department: C -0.536***
(0.115)
Department: D -0.704***
(0.104)
Department: E -0.957***
(0.162)
Department: F -2.770***
(0.220)
Department: A x Gender: Female/Male 1.052***
(0.263)
Department: B x Gender: Female/Male 0.220
(0.438)
Department: C x Gender: Female/Male -0.125
(0.144)
Department: D x Gender: Female/Male 0.082
(0.150)
Department: E x Gender: Female/Male -0.200
(0.200)
Department: F x Gender: Female/Male 0.189
(0.305)
----------------------------------------------------
Log-likelihood -34.470
N 4526
====================================================
Significance: *** = p < 0.001; ** = p < 0.01;
* = p < 0.05
#### Array-like semantics
mtable123 <- mtable("Model 1"=lm0,"Model 2"=lm1,"Model 3"=lm2,
summary.stats=c("sigma","R-squared","F","p","N"))
dim(mtable123)
[1] 5 1 3
dimnames(mtable123)
[[1]]
[1] "(Intercept)" "pop15" "pop75" "dpi" "ddpi"
[[2]]
[1] "sr"
[[3]]
[1] "Model 1" "Model 2" "Model 3"
mtable123[c("dpi","ddpi"),
c("Model 2","Model 3")]
Calls:
Model 2: lm(formula = sr ~ dpi + ddpi, data = LifeCycleSavings)
Model 3: lm(formula = sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
===============================
Model 2 Model 3
-------------------------------
dpi 0.001 -0.000
(0.001) (0.001)
ddpi 0.529* 0.410*
(0.210) (0.196)
-------------------------------
sigma 4.189 3.803
R-squared 0.162 0.338
F 4.528 5.756
p 0.016 0.001
N 50 50
===============================
Significance:
*** = p < 0.001;
** = p < 0.01;
* = p < 0.05
#### Concatention
mt01 <- mtable(lm0,lm1,summary.stats=c("R-squared","N"))
mt12 <- mtable(lm1,lm2,summary.stats=c("R-squared","F","N"))
c(mt01,mt12) # not that this makes sense, but ...
Calls:
lm0: lm(formula = sr ~ pop15 + pop75, data = LifeCycleSavings)
lm1: lm(formula = sr ~ dpi + ddpi, data = LifeCycleSavings)
lm1: lm(formula = sr ~ dpi + ddpi, data = LifeCycleSavings)
lm2: lm(formula = sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
===========================================================
lm0 lm1 lm1 lm2
-----------------------------------------------------------
(Intercept) 30.628*** 6.360*** 6.360*** 28.566***
(7.409) (1.252) (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 0.001 -0.000
(0.001) (0.001) (0.001)
ddpi 0.529* 0.529* 0.410*
(0.210) (0.210) (0.196)
-----------------------------------------------------------
R-squared 0.262 0.162 0.162 0.338
N 50 50 50 50
F 4.528 5.756
===========================================================
Significance: *** = p < 0.001; ** = p < 0.01;
* = p < 0.05
c("Group 1"=mt01,
"Group 2"=mt12)
Calls:
lm0: lm(formula = sr ~ pop15 + pop75, data = LifeCycleSavings)
lm1: lm(formula = sr ~ dpi + ddpi, data = LifeCycleSavings)
lm1: lm(formula = sr ~ dpi + ddpi, data = LifeCycleSavings)
lm2: lm(formula = sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
=========================================================
Group 1 Group 2
------------------- -------------------
lm0 lm1 lm1 lm2
---------------------------------------------------------
(Intercept) 30.628*** 6.360*** 6.360*** 28.566***
(7.409) (1.252) (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 0.001 -0.000
(0.001) (0.001) (0.001)
ddpi 0.529* 0.529* 0.410*
(0.210) (0.210) (0.196)
---------------------------------------------------------
R-squared 0.262 0.162 0.162 0.338
N 50 50 50 50
F 4.528 5.756
=========================================================
Significance: *** = p < 0.001; ** = p < 0.01;
* = p < 0.05
