Comparative Table of Model Estimates
mtable.Rdmtable 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,
controls=NULL,
collapse.controls=FALSE,
control.var.indicator=getOption("control.var.indicator",c("Yes","No"))
)
# S3 method for memisc_mtable
relabel(x, ..., gsub = FALSE, fixed = !gsub, warn = FALSE)
# S3 method for memisc_mtable
format(x,target=c("print","LaTeX","HTML","delim"),
...
)
# S3 method for memisc_mtable
print(x,
center.at=getOption("OutDec"),
topsep="=",bottomsep="=",sectionsep="-",...)
write.mtable(object,file="",
format=c("delim","LaTeX","HTML"),...)
# S3 method for 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- controls
an optional formula or character vector that designates "control variables" for which no coefficients are reported, but only whether they are present in the model.
- collapse.controls
a logical values; should the report about inclusion of control variables collapsed to a single value? If yes, models should either contain none or all of the control variables.
- control.var.indicator
a character vector with to elements; the first element being used to indicate the presence of a control variable or all control variables (if
collapse.controls=TRUE), the second element being used otherwise. By default these elements are"Yes"and"No".- 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.
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.
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.
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
if (FALSE) {
# 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.
}
toLatex(mtable123)
#> \begin{tabular}{lD{.}{.}{3}D{.}{.}{3}D{.}{.}{3}}
#> \toprule
#> &
#> \multicolumn{1}{c}{Model 1} &
#> \multicolumn{1}{c}{Model 2} &
#> \multicolumn{1}{c}{Model 3}\\
#> \midrule
#> 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)\\
#> \midrule
#> 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 \\
#> \bottomrule
#> \multicolumn{4}{p{.7\linewidth}}{Significance:
#> $*** \equiv p < 0{.}001$;
#> $** \equiv p < 0{.}01$;
#> $* \equiv p < 0{.}05$}\\
#> \end{tabular}
if (FALSE) texfile123 <- "mtable123.tex"
write.mtable(mtable123,format="LaTeX",file=texfile123)
#> Error in eval(expr, envir, enclos): object 'texfile123' not found
file.show(texfile123)
#> Error in eval(expr, envir, enclos): object 'texfile123' not found
#### 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