Discrete Variables Copula

Incorporating discrete variables

We can also fit models in which some of the covariates and/or outcomes are binary. For example, suppose we assume that and X ∣ Z₁ = z₁, Z₂ = z₂ ∼ Bernoulli(q), and we assume a Gaussian copula over Z₁, Z₂, Y with correlation R, where ρ_Z₁Z₂ = 0.5, ρ_Z₁Y = 0.3 and ρ_Z₂Y = 0.4.

Then we can set up this model in the usual way:

forms <- list(list(Z1 ~ 1, Z2 ~ 1), X ~ Z1*Z2, Y ~ X, ~ 1)
fams <- list(c(1,5), 5, 1, 1)
pars <- list(Z1 = list(beta=0, phi=1),
             Z2 = list(beta=0),
             X = list(beta=c(-0.3,0.1,0.2,0)),
             Y = list(beta=c(-0.25, 0.5), phi=0.5),
             cop = list(beta=matrix(c(0.5,0.3,0.4), nrow=1)))
cm <- causl_model(formulas = forms, family = fams, pars = pars)

Now call rfrugal as usual.

set.seed(1234)
n <- 1e4
dat <- rfrugal(n, causl_model = cm)

## Inversion method selected: using pair-copula parameterization

Then we can check that the distributions follow the specification that we asked for.

ks.test(dat$Z1, pnorm)

## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  dat$Z1
## D = 0.01, p-value = 0.2
## alternative hypothesis: two-sided

binom.test(sum(dat$Z2), n=n, p = 0.5)

## 
##  Exact binomial test
## 
## data:  sum(dat$Z2) and n
## number of successes = 5099, number of trials = 10000, p-value = 0.05
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
##  0.50 0.52
## sample estimates:
## probability of success 
##                   0.51

glmX <- glm(X ~ Z1*Z2, family=binomial, data=dat)
summary(glmX)$coefficients

##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  -0.3056     0.0289 -10.564 4.36e-26
## Z1            0.0757     0.0293   2.586 9.70e-03
## Z2            0.1623     0.0403   4.024 5.73e-05
## Z1:Z2         0.0323     0.0409   0.788 4.31e-01

These are all consistent with the values we provided. We can finally check the outcome model.

ps <- predict(glmX, type="response")
wt <- dat$X/ps + (1-dat$X)/(1-ps)
glmY <- svyglm(Y ~ X, design = svydesign(~1, weights=wt, data=dat))
summary(glmY)$coefficients

##             Estimate Std. Error t value  Pr(>|t|)
## (Intercept)   -0.244    0.00966   -25.3 1.31e-136
## X              0.493    0.01430    34.5 2.85e-246

Categorical and Ordinal Variables

We can also extend this to variables that are categorical or ordered categorical. These options correspond to the family indicators 11 and 10 respectively, or they can be accessed by passing the strings "categorical" and "ordinal" to the family argument. Both types of variable are stored as a factor, with labels for the objects 1, 2, up to ℓ, where ℓ is the number of levels.

These methods respectively use a contrast with the baseline level, or a contrast with the category immediately below the current one in the ordering. For example, if we have a three variable categorical variable using the formula Z ~ A then the parameters are of the form: For a categorical variable the contrasts become log {P(Z = i)/P(Z = i − 1)} for the same range of i.

The regression parameters can be passed either as a vector or a matrix, but in either case they must be ordered so that all the coefficients for one contrast precede all those for another. For example, if we want we could put Z = list(beta = c(0.5,0.1,-0.5,0.4), nlevel = 3) in the pars argument to require that for an unordered categorical variable, and the same for an ordinal variable but replacing the second quantity with log {P(Z = 3)/P(Z = 2)}. Equivalently, we could put Z = list(beta = matrix(c(0.5,0.1,-0.5,0.4), nrow=2), nlevel = 3), and the same model would be fitted.

forms <- list(list(Z0 ~ A0, Z1 ~ A0),
              list(A0 ~ 1, A1 ~ A0*Z0),
              Y ~ A0*A1,
              ~ A0)
fams <- list(c("categorical","categorical"),c(5,5),1,1)
pars <- list(A0 = list(beta = 0),
             Z0 = list(beta = c(0.3,-0.2,0.4,0.1), nlevel=3),
             Z1 = list(beta = c(0.3,-0.2,0.4,0.1), nlevel=3),
             A1 = list(beta = 2*c(-0.3,0.4,0.3,0.5,0.3,0.5)),
             Y = list(beta = c(-0.5,0.2,0.3,0), phi=1),
             cop = list(beta=c(2,0.5)))
cm <- causl_model(formulas=forms, pars=pars, family=fams)

In this case we set both covariate variables to be categorical.

set.seed(123)
n <- 5e4
dat <- rfrugal(n=n, causl_model=cm)

## Inversion method selected: using pair-copula parameterization

Now we can check that the parameters being simulated match those input.

glm(I(Z0==2) ~ A0, family=binomial, data=dat[dat$Z0 != 3,])$coefficients

## (Intercept)          A0 
##       0.307      -0.185

glm(I(Z0==3) ~ A0, family=binomial, data=dat[dat$Z0 != 2,])$coefficients

## (Intercept)          A0 
##      0.4175      0.0963

glm(I(Z1==2) ~ A0, family=binomial, data=dat[dat$Z1 != 3,])$coefficients

## (Intercept)          A0 
##       0.306      -0.197

glm(I(Z1==3) ~ A0, family=binomial, data=dat[dat$Z1 != 2,])$coefficients

## (Intercept)          A0 
##       0.378       0.132

Finally, we can check that the outcome model works as it should.

ps <- predict(glm(A1 ~ A0*Z0, family=binomial, data=dat), type="response")
wt <- dat$A1/ps + (1-dat$A1)/(1-ps)
summary(svyglm(Y ~ A0*A1, design = svydesign(~1,data=dat,weights=wt)))$coef

##             Estimate Std. Error t value  Pr(>|t|)
## (Intercept)  -0.5058    0.00906 -55.819  0.00e+00
## A0            0.2010    0.01786  11.253  2.43e-29
## A1            0.3053    0.01284  23.770 3.38e-124
## A0:A1         0.0105    0.02138   0.492  6.23e-01

Comparing with a naïve (unweighted) estimate.

summary(svyglm(Y ~ A0*A1, design = svydesign(~1,data=dat,weights=~1)))$coef

##             Estimate Std. Error t value  Pr(>|t|)
## (Intercept)  -0.5863    0.00897  -65.33  0.00e+00
## A0            0.0484    0.01600    3.02  2.51e-03
## A1            0.4671    0.01254   37.25 1.62e-299
## A0:A1         0.1506    0.01959    7.68  1.57e-14

- Incorporating discrete variables
- Categorical and Ordinal Variables