Fast (Weighted) F-test for Linear Models (with Factors)
fFtest computes an R-squared based F-test for the exclusion of the variables in exc, where the full (unrestricted) model is defined by variables supplied to both exc and X. The test is efficient and designed for cases where both exc and X may contain multiple factors and continuous variables.
fFtest(y, exc, X = NULL, w = NULL, full.df = TRUE, ...)
y |
a numeric vector: The dependent variable. |
exc |
a numeric vector, factor, numeric matrix or list / data frame of numeric vectors and/or factors: Variables to test / exclude. |
X |
a numeric vector, factor, numeric matrix or list / data frame of numeric vectors and/or factors: Covariates to include in both the restricted (without |
w |
numeric. A vector of (frequency) weights. |
full.df |
logical. If |
... |
other arguments passed to |
Factors and continuous regressors are efficiently projected out using fHDwithin, and the option full.df regulates whether a degree of freedom is subtracted for each used factor level (equivalent to dummy-variable estimator / expanding factors), or only one degree of freedom per factor (treating factors as variables). The test automatically removes missing values and considers only the complete cases of y, exc and X. Unused factor levels in exc and X are dropped.
Note that an intercept is always added by fHDwithin, so it is not necessary to include an intercept in data supplied to exc / X.
A 5 x 3 numeric matrix of statistics. The columns contain statistics:
the R-squared of the model
the numerator degrees of freedom i.e. the number of variables (k) and used factor levels if full.df = TRUE
the denominator degrees of freedom: N - k - 1.
the F-statistic
the corresponding P-value
The rows show these statistics for:
the Full (unrestricted) Model (y ~ exc + X)
the Restricted Model (y ~ X)
the Exclusion Restriction of exc. The R-squared shown is simply the difference of the full and restricted R-Squared's, not the R-Squared of the model y ~ exc.
If X = NULL, only a vector of the same 5 statistics testing the model (y ~ exc) is shown.
## We could use fFtest as a seasonality test:
fFtest(AirPassengers, qF(cycle(AirPassengers))) # Testing for level-seasonality
fFtest(AirPassengers, qF(cycle(AirPassengers)), # Seasonality test around a cubic trend
poly(seq_along(AirPassengers), 3))
fFtest(fdiff(AirPassengers), qF(cycle(AirPassengers))) # Seasonality in first-difference
## A more classical example with only continuous variables
fFtest(mtcars$mpg, mtcars[c("cyl","vs")], mtcars[c("hp","carb")])
## Now encoding cyl and vs as factors
fFtest(mtcars$mpg, dapply(mtcars[c("cyl","vs")], qF), mtcars[c("hp","carb")])
## Using iris data: A factor and a continuous variable excluded
fFtest(iris$Sepal.Length, iris[4:5], iris[2:3])
## Testing the significance of country-FE in regression of GDP on life expectancy
fFtest(wlddev$PCGDP, wlddev$iso3c, wlddev$LIFEEX)
## Ok, country-FE are significant, what about adding time-FE
fFtest(wlddev$PCGDP, qF(wlddev$year), wlddev[c("iso3c","LIFEEX")])
# Same test done using lm:
data <- na_omit(get_vars(wlddev, c("iso3c","year","PCGDP","LIFEEX")))
full <- lm(PCGDP ~ LIFEEX + iso3c + qF(year), data)
rest <- lm(PCGDP ~ LIFEEX + iso3c, data)
anova(rest, full)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.