TMod {DescTools}R Documentation

Comparison Table For Linear Models


Collect the coefficients and some qualifying statistics of linear models and organize it in a table for comparison and reporting. The function supports linear and general linear models.


TMod(..., FUN = NULL, order = NA)

ModSummary(x, ...)

## S3 method for class 'lm'
ModSummary(x, conf.level = 0.95, ...)
## S3 method for class 'glm'
ModSummary(x, conf.level = 0.95, use.profile = TRUE, ...)

## S3 method for class 'TMod'
plot(x, terms = NULL, intercept = FALSE, ...)
## S3 method for class 'TMod'
print(x, digits = 3, na.form = "-", ...)



a (general) linear model object.


a list of (general) linear models.


the level for the confidence intervals.


function with arguments est, se, tval, pval, lci, uci to display the coefficients. The default function will display the coefficient and significance stars for the p-values.


row of the results table to be used as order for the models (as typically "AIC"). Can be any label in the first column of the results table. Default is NA for no special order.


a vector with the terms of the model formula to be plotted. By default this will be all of them.


logical. Defines if profile approach should be used, which normally is a good choice for small datasets. Calculating profile can however take ages for large datasets and not be necessary there. So we can fallback to normal confidence intervals.


logical, defining whether the intercept should be plotted (default is FALSE).


integer, the desired (fixed) number of digits after the decimal point. Unlike formatC you will always get this number of digits even if the last digit is 0.


character, string specifying how NAs should be specially formatted. If set to NULL (default) no special action will be taken.


In order to compare the coefficients of linear models, the user is left to his own devices. R offers no support in this respect. TMod() jumps into the breach and displays the coefficients of several models in tabular form. For this purpose, different quality indicators for the models are displayed, so that a comprehensive comparison of the models is possible. In particular, it is easy to see the effect that adding or omitting variables has on forecast quality.

A plot function for a TMod object will produce a dotchart with the coefficients and their confidence intervals.


character table


Andri Signorell <>

See Also



r.full <- lm(Fertility ~ . , swiss)
r.nox <- lm(Fertility ~ . -Examination - Catholic, swiss)
r.grp <- lm(Fertility ~ . -Education - Catholic + CutQ(Catholic), swiss)
r.gam <- glm(Fertility ~ . , swiss, family=Gamma(link="identity"))
r.gama <- glm(Fertility ~ .- Agriculture , swiss, family=Gamma(link="identity"))
r.gaml <- glm(Fertility ~ . , swiss, family=Gamma(link="log"))

TMod(r.full, r.nox, r.grp, r.gam, r.gama, r.gaml)

# display confidence intervals
TMod(r.full, r.nox, r.gam, FUN = function(est, se, tval, pval, lci, uci){
  gettextf("%s [%s, %s]",
           Format(est, fmt=Fmt("num")),
           Format(lci, digits=3),
           Format(uci, digits=2)

# cbind interface is not supported!!
# d.titanic <- reshape(,
#                       idvar = c("Class","Sex","Age"),
#                       timevar="Survived",
#                       direction = "wide")
# r.glm0 <- glm(cbind(Freq.Yes, Freq.No) ~ 1, data=d.titanic, family="binomial")
# r.glm1 <- glm(cbind(Freq.Yes, Freq.No) ~ Class, data=d.titanic, family="binomial")
# r.glm2 <- glm(cbind(Freq.Yes, Freq.No) ~ ., data=d.titanic, family="binomial")

d.titanic <- Untable(Titanic)

r.glm0 <- glm(Survived ~ 1, data=d.titanic, family="binomial")
r.glm1 <- glm(Survived ~ Class, data=d.titanic, family="binomial")
r.glm2 <- glm(Survived ~ ., data=d.titanic, family="binomial")

TMod(r.glm0, r.glm1, r.glm2)

# plot OddsRatios
d.pima <- MASS::Pima.tr2

r.a <- glm(type ~ npreg + bp + skin + bmi + ped + age, data=d.pima, family=binomial)
r.b <- glm(type ~ npreg + glu + bp + skin, data=d.pima, family=binomial)
r.c <- glm(type ~ npreg + age, data=d.pima, family=binomial)

or.a <- OddsRatio(r.a)
or.b <- OddsRatio(r.b)
or.c <- OddsRatio(r.c)

# create the model table
tm <- TMod(m_A=or.a, m_B=or.b, m_C=or.c)
# .. and plotit
plot(tm, main="ORs for Models A, B, C", intercept=FALSE,
     pch=15, col=c(hred, hblue, horange), 
     panel.first=abline(v=1, col="grey30"))

[Package DescTools version 0.99.51 Index]