# detect.lindep

From plm v2.2-5
0th

Percentile

##### Functions to detect linear dependence

Little helper functions to aid users to detect linear dependent columns in a two-dimensional data structure, especially in a (transformed) model matrix - typically useful in interactive mode during model building phase.

Keywords
manip, array
##### Usage
detect.lindep(object, ...)# S3 method for matrix
detect.lindep(object, suppressPrint = FALSE, ...)# S3 method for data.frame
detect.lindep(object, suppressPrint = FALSE, ...)# S3 method for plm
detect.lindep(object, suppressPrint = FALSE, ...)# S3 method for plm
alias(object, ...)# S3 method for pdata.frame
alias(
object,
model = c("pooling", "within", "Between", "between", "mean", "random", "fd"),
effect = c("individual", "time", "twoways"),
...
)
##### Arguments
object

for detect.lindep: an object which should be checked for linear dependence (of class "matrix", "data.frame", or "plm"); for alias: either an estimated model of class "plm" or a "pdata.frame". Usually, one wants to input a model matrix here or check an already estimated plm model,

further arguments.

suppressPrint

for detect.lindep only: logical indicating whether a message shall be printed; defaults to printing the message, i. e. to suppressPrint = FALSE,

model

(see plm),

effect

(see plm),

##### Details

Linear dependence of columns/variables is (usually) readily avoided when building one's model. However, linear dependence is sometimes not obvious and harder to detect for less experienced applied statisticians. The so called "dummy variable trap" is a common and probably the best--known fallacy of this kind (see e. g. Wooldridge (2016), sec. 7-2.). When building linear models with lm or plm's pooling model, linear dependence in one's model is easily detected, at times post hoc.

However, linear dependence might also occur after some transformations of the data, albeit it is not present in the untransformed data. The within transformation (also called fixed effect transformation) used in the "within" model can result in such linear dependence and this is harder to come to mind when building a model. See Examples for two examples of linear dependent columns after the within transformation: ex. 1) the transformed variables have the opposite sign of one another; ex. 2) the transformed variables are identical.

During plm's model estimation, linear dependent columns and their corresponding coefficients in the resulting object are silently dropped, while the corresponding model frame and model matrix still contain the affected columns. The plm object contains an element aliased which indicates any such aliased coefficients by a named logical.

Both functions, detect.lindep and alias, help to detect linear dependence and accomplish almost the same: detect.lindep is a stand alone implementation while alias is a wrapper around stats::alias.lm(), extending the alias generic to classes "plm" and "pdata.frame". alias hinges on the availability of the package MASS on the system. Not all arguments of alias.lm are supported. Output of alias is more informative as it gives the linear combination of dependent columns (after data transformations, i. e. after (quasi)-demeaning) while detect.lindep only gives columns involved in the linear dependence in a simple format (thus being more suited for automatic post--processing of the information).

##### Value

For detect.lindep: A named numeric vector containing column numbers of the linear dependent columns in the object after data transformation, if any are present. NULL if no linear dependent columns are detected.

For alias: return value of stats::alias.lm() run on the (quasi-)demeaned model, i. e. the information outputted applies to the transformed model matrix, not the original data.

##### Note

function detect.lindep was called detect_lin_dep initially but renamed for naming consistency later with a back-compatible solution.

##### References

WOOL:13plm

stats::alias(), stats::model.matrix() and especially plm's model.matrix() for (transformed) model matrices, plm's model.frame().

##### Aliases
• detect.lindep
• detect.lindep.matrix
• detect.lindep.data.frame
• detect.lindep.plm
• alias.plm
• alias.pdata.frame
##### Examples
# NOT RUN {
### Example 1 ###
# prepare the data
data("Cigar" , package = "plm")
Cigar[ , "fact1"] <- c(0,1)
Cigar[ , "fact2"] <- c(1,0)
Cigar.p <- pdata.frame(Cigar)

# setup a formula and a model frame
form <- price ~ 0 + cpi + fact1 + fact2
mf <- model.frame(Cigar.p, form)
# no linear dependence in the pooling model's model matrix
# (with intercept in the formula, there would be linear depedence)
detect.lindep(model.matrix(mf, model = "pooling"))
# linear dependence present in the FE transformed model matrix
modmat_FE <- model.matrix(mf, model = "within")
detect.lindep(modmat_FE)
mod_FE <- plm(form, data = Cigar.p, model = "within")
detect.lindep(mod_FE)
alias(mod_FE) # => fact1 == -1*fact2
plm(form, data = mf, model = "within")$aliased # "fact2" indicated as aliased # look at the data: after FE transformation fact1 == -1*fact2 head(modmat_FE) all.equal(modmat_FE[ , "fact1"], -1*modmat_FE[ , "fact2"]) ### Example 2 ### # Setup the data: # Assume CEOs stay with the firms of the Grunfeld data # for the firm's entire lifetime and assume some fictional # data about CEO tenure and age in year 1935 (first observation # in the data set) to be at 1 to 10 years and 38 to 55 years, respectively. # => CEO tenure and CEO age increase by same value (+1 year per year). data("Grunfeld", package = "plm") set.seed(42) # add fictional data Grunfeld$CEOtenure <- c(replicate(10, seq(from=s<-sample(1:10,  1), to=s+19, by=1)))
Grunfeld\$CEOage    <- c(replicate(10, seq(from=s<-sample(38:65, 1), to=s+19, by=1)))

# look at the data

form <- inv ~ value + capital + CEOtenure + CEOage
mf <- model.frame(pdata.frame(Grunfeld), form)
# no linear dependent columns in original data/pooling model
modmat_pool <- model.matrix(mf, model="pooling")
detect.lindep(modmat_pool)
mod_pool <- plm(form, data = Grunfeld, model = "pooling")
alias(mod_pool)

# CEOtenure and CEOage are linear dependent after FE transformation
# (demeaning per individual)
modmat_FE <- model.matrix(mf, model="within")
detect.lindep(modmat_FE)
mod_FE <- plm(form, data = Grunfeld, model = "within")
detect.lindep(mod_FE)
alias(mod_FE)

# look at the transformed data: after FE transformation CEOtenure == 1*CEOage