| CVlm {DAAG} | R Documentation |
This function gives internal and cross-validation measures of predictive
accuracy for multiple linear regression. (For binary logistic
regression, use the CVbinary function.) The data are
randomly assigned to a number of ‘folds’.
Each fold is removed, in turn, while the remaining data is used
to re-fit the regression model and to predict at the deleted observations.
CVlm(df = houseprices, form.lm = formula(sale.price ~ area), m=3, dots =
FALSE, seed=29, plotit = c("Observed","Residual"),
main="Small symbols show cross-validation predicted values",
legend.pos="topleft", printit=TRUE)
cv.lm(df = houseprices, form.lm = formula(sale.price ~ area), m=3, dots =
FALSE, seed=29, plotit = c("Observed","Residual"),
main="Small symbols show cross-validation predicted values",
legend.pos="topleft", printit=TRUE)
df |
a data frame |
form.lm |
a formula or |
m |
the number of folds |
dots |
uses pch=16 for the plotting character |
seed |
random number generator seed |
plotit |
This can be one of the text strings |
main |
main title for graph |
legend.pos |
position of legend: one of
|
printit |
if TRUE, output is printed to the screen |
When plotit="Residual" and there is more than one explanatory
variable, the fitted lines that are shown for the individual folds
are approximations.
ss |
the cross-validation residual sum of squares |
df |
degrees of freedom |
J.H. Maindonald
CVlm()
## Not run:
CVlm(df=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),
plotit="Observed")
CVlm(df=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),
plotit="Residual")
## End(Not run)