Lfx: Hypothesis matrix generated by expressions for each column or...

In gmonette/spida15: Collection of miscellaneous functions for mixed models etc. prepared for SPIDA 2009+ (development version)

Description

Creates an L matrix using expressions evaluated in data for each column or multi-column term of the L matrix

Usage

Lfx(fit, expr.list, data = getData(fit), wrap = FALSE, debug = FALSE)

Lfx(fit)

M(formula)

M(expr)

M.factor(fac)

fac1 < fac2

Arguments

fit	a fitted model with a 'getFix' method.
expr.list	a list of expressions with one component for each column (or groups of columns) of the hypothesis matrix corresponding to each term of the model. A term with multiple degrees of freedom can either be generated as separate single terms or with an expression that evaluates to a suitable matrix.
data	the data frame in which expressions are evaluated.
wrap
debug
formula	as an argument of M, a one-sided formula defining a main effect or an interaction term involving factors.
expr	as an argument of M, an expression that can be evaluated in each row of data to form element(s) of the corresponding row of L. formula defining a main effect or an interaction term involving factors.

Examples

##
## The estimated effect of an additional year of education
##

data(Prestige)   # from car
fit <- lm( income ~ (education+I(education^2)) * type, Prestige)
summary(fit)
Lfx(fit)  # geneates expression to cut and paste and differentiate

pred <- expand.grid( education = 3:16, type = levels(Prestige$type))

Lf.ed <- Lfx( fit, 
              list( 0,
                    1 + 0* education,
                    2 * M(I(education)),
                    0 * M(type),
                    1 * 1 * M(type),
                    2 * M(I(education)) * M(type) 
              ),pred)
zw <- as.data.frame(wald(fit,Lf.ed))
head(zw)
xyplot( coef ~ education , zw, groups = type, type = 'l', auto.key = T)

fit2 <- lm( income ~ (education+I(education^2)) * type*women, Prestige)
pred <- expand.grid( education = 3:16, type = levels(Prestige$type), women=c(0,50,100))
Lfx(fit2)
zw <- Lfx(fit2,
    list( 0,   # differentiated wrt education
          1 * 1,
          1 * M(I(education)),
          0 * M(type),
          0 * women,
          1 * 1 * M(type),
          2 * M(I(education)) * M(type),
          1 * 1 * women,
          2 * M(I(education)) * women,
          0 * M(type) * women,
          1 * 1 * M(type) * women,
          2 * M(I(education)) * M(type) * women 
    ),pred)
zw    
zw <- as.data.frame( wald(fit2, zw))
summary(fit2)
xyplot( coef ~ education|women , zw, groups = type, type = 'l', auto.key = T,
        ylim = c(-30000,30000))

###
###  Differencing of factor
###

pred <- expand.grid( education = 3:18, women = c(0,50,100), 
                     type = levels(Prestige$type),
                     type0 = levels(Prestige$type))

Lfx(fit2)

Lcomp <- Lfx(fit2,
  list( 0,
      0 * education,
      0 * M(I(education^2)),
      1 * M(type) - M(type0),
      0 * women,
      1 * education * (M(type)-M(type0)),
      1 * M(I(education^2)) * (M(type) - M(type0)),
      0 * education * women,
      0 * M(I(education^2)) * women,
      1 * (M(type)-M(type0)) * women,
      1 * education * (M(type) - M(type0)) * women,
      1 * M(I(education^2)) * (M(type) - M(type0)) * women 
  ), pred)

zw <- as.data.frame( wald( fit2, Lcomp))
zw

gmonette/spida15 documentation built on May 17, 2019, 7:26 a.m.