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) ModSummary(x, ...) ## S3 method for class 'lm' ModSummary(x, conf.level = 0.95, ...) ## S3 method for class 'glm' ModSummary(x, conf.level = 0.95, ...) ## S3 method for class 'TMod' plot(x, terms = NULL, intercept = FALSE, ...) ## S3 method for class 'TMod' print(x, digits = 3, na.form = "-", ...)
x |
a (general) linear model object. |
... |
a list of (general) linear models. |
conf.level |
the level for the confidence intervals. |
FUN |
function with arguments |
terms |
a vector with the terms of the model formula to be plotted. By default this will be all of them. |
intercept |
logical, defining whether the intercept should be plotted (default is |
digits |
integer, the desired (fixed) number of digits after the decimal point. Unlike |
na.form |
character, string specifying how |
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 <andri@signorell.net>
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(as.data.frame(Titanic),
# 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"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.