rm(list=ls(all=TRUE))
library(effects)
# preparation of data #############################################################################
dfer <- function(some.matrix) { # change a 2x2 matrix into a case-by-variable data frame
DF <- data.frame(
factor(rep(dimnames(some.matrix)[[1]], rowSums(some.matrix))),
factor(rep(rep(dimnames(some.matrix)[[2]], 2), t(some.matrix)))
); names(DF) <- names(dimnames(some.matrix))
return(DF)
}
# generate a 2x2 co=occurrence matrix representing Table 2 of the paper
addmargins(cooc <- matrix( # generate a matrix cooc (and also show its totals)
c(80, 687-80, 19, 137958), # of these frequencies (columnwise)
nrow=2, # making up 2 rows
dimnames=list(VERB=c("regard","all.other"), # use these row names & labels
CONSTR=c("aspred","all.other")))) # use these column names & labels
## CONSTR
## VERB aspred all.other Sum
## regard 80 19 99
## all.other 607 137958 138565
## Sum 687 137977 138664
# make into data frame with the case-by-variable format
summary(cbv <- dfer(cooc))
## VERB CONSTR
## all.other:138565 all.other:137977
## regard : 99 aspred : 687
head(cbv)
## VERB CONSTR
## 1 regard aspred
## 2 regard aspred
## 3 regard aspred
## 4 regard aspred
## 5 regard aspred
## 6 regard aspred
tail(cbv)
## VERB CONSTR
## 138659 all.other all.other
## 138660 all.other all.other
## 138661 all.other all.other
## 138662 all.other all.other
## 138663 all.other all.other
## 138664 all.other all.other
# check conversion was correct by comparing the table from cbv to cooc
(zxc <- table(cbv$VERB, cbv$CONSTR))
##
## all.other aspred
## all.other 137958 607
## regard 19 80
attach(cbv)
# manual computation of elementary statistics
(80/19) / (607/137958) # effect size of odds ratio: 956.9618
## [1] 956.9618
log((80/19) / (607/137958)) # logged version of the odds ration: log odds: 6.863763
## [1] 6.863763
# association in one direction: predicting VERB from ASPRED #######################################
## association as regression from the case-by-variable format
summary(model_v.from.c.cbv <- glm(VERB ~ CONSTR, family=binomial, data=cbv))
##
## Call:
## glm(formula = VERB ~ CONSTR, family = binomial, data = cbv)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.4976 -0.0166 -0.0166 -0.0166 4.2167
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.8903 0.2294 -38.75 <2e-16 ***
## CONSTRaspred 6.8638 0.2584 26.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1632.38 on 138663 degrees of freedom
## Residual deviance: 870.18 on 138662 degrees of freedom
## AIC: 874.18
##
## Number of Fisher Scoring iterations: 11
coef(model_v.from.c.cbv)[2] # = log odds / logit: how much CONSTR=="aspred" increases
## CONSTRaspred
## 6.863763
exp(coef(model_v.from.c.cbv)[2]) # = odds ratio the (log) odds of VERB=="regard"
## CONSTRaspred
## 956.9617
drop1(model_v.from.c.cbv, test="LR")$LRT[2] # = G^2 / LLR
## [1] 762.1959
model_v.from.c.cbv$null.deviance - model_v.from.c.cbv$deviance # = G^2 / LLR
## [1] 762.1959
### Delta P
plot(aspred <- effect("CONSTR", model_v.from.c.cbv), type="response",
ylab="Predicted probability of VERB being 'regard'", ylim=c(0,1), grid=TRUE)
![](data:image/png;base64,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)
as.data.frame(aspred) # the difference between the predicted probabilities
## CONSTR fit se lower upper
## 1 all.other 0.0001377041 3.158669e-05 0.00008784 0.0002158685
## 2 aspred 0.1164483261 1.223782e-02 0.09452235 0.1426590596
diff(as.data.frame(aspred)$fit) # in the column fit is Delta P cx->v : 0.1163106
## [1] 0.1163106
## association as regression from the 2x2 co-occurrence matrix: DV = matrix with the DV-levels in columns
summary(model_v.from.c <- glm(cbind(cooc[1,], cooc[2,]) ~ colnames(cooc), family=binomial)) # same as ...
##
## Call:
## glm(formula = cbind(cooc[1, ], cooc[2, ]) ~ colnames(cooc), family = binomial)
##
## Deviance Residuals:
## [1] 0 0
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.8903 0.2294 -38.75 <2e-16 ***
## colnames(cooc)aspred 6.8638 0.2584 26.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 7.6220e+02 on 1 degrees of freedom
## Residual deviance: -2.4758e-13 on 0 degrees of freedom
## AIC: 14.889
##
## Number of Fisher Scoring iterations: 3
summary(model_v.from.c <- glm(t(cooc) ~ colnames(cooc), family=binomial)) # ... this one
##
## Call:
## glm(formula = t(cooc) ~ colnames(cooc), family = binomial)
##
## Deviance Residuals:
## [1] 0 0
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.8903 0.2294 -38.75 <2e-16 ***
## colnames(cooc)aspred 6.8638 0.2584 26.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 7.6220e+02 on 1 degrees of freedom
## Residual deviance: -2.4758e-13 on 0 degrees of freedom
## AIC: 14.889
##
## Number of Fisher Scoring iterations: 3
coef(model_v.from.c)[2] # = log odds / logit: how much CONSTR=="aspred" increases
## colnames(cooc)aspred
## 6.863763
exp(coef(model_v.from.c)[2]) # = odds ratio the (log) odds of VERB=="regard"
## colnames(cooc)aspred
## 956.9618
drop1(model_v.from.c, test="Chisq")$LRT[2] # = G^2 / LLR
## [1] 762.1959
model_v.from.c$null.deviance # = G^2 / LLR
## [1] 762.1959
summary(null.model_v.from.c.cbv <- glm(VERB ~ 1, family=binomial, data=cbv))
##
## Call:
## glm(formula = VERB ~ 1, family = binomial, data = cbv)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.0378 -0.0378 -0.0378 -0.0378 3.8065
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.2440 0.1005 -72.05 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1632.4 on 138663 degrees of freedom
## Residual deviance: 1632.4 on 138663 degrees of freedom
## AIC: 1634.4
##
## Number of Fisher Scoring iterations: 10
coef(null.model_v.from.c.cbv) # logit of the relative frequency of regard in the corpus,
## (Intercept)
## -7.243975
# i.e. of sum(VERB=="regard") / sum(VERB!="")
# association in other direction: predicting ASPRED from VERB #####################################
## association as regression from the case-by-variable format
summary(model_c.from.v.cbv <- glm(CONSTR ~ VERB, family=binomial, data=cbv))
##
## Call:
## glm(formula = CONSTR ~ VERB, family = binomial, data = cbv)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8170 -0.0937 -0.0937 -0.0937 3.2956
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.42618 0.04068 -133.39 <2e-16 ***
## VERBregard 6.86376 0.25843 26.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 8663.1 on 138663 degrees of freedom
## Residual deviance: 7900.9 on 138662 degrees of freedom
## AIC: 7904.9
##
## Number of Fisher Scoring iterations: 8
coef(model_c.from.v.cbv)[2] # = log odds / logit: how much VERB=="regard" increases
## VERBregard
## 6.863763
exp(coef(model_c.from.v.cbv)[2]) # = odds ratio the (log) odds of CONSTR=="aspred"
## VERBregard
## 956.9618
drop1(model_c.from.v.cbv, test="LR")$LRT[2] # = G^2 / LLR
## [1] 762.1959
model_c.from.v.cbv$null.deviance - model_c.from.v.cbv$deviance # = G^2 / LLR
## [1] 762.1959
### Delta P
plot(regard <- effect("VERB", model_c.from.v.cbv), type="response",
ylab="Predicted probability of CONSTRUCTION being 'as-predicative'", ylim=c(0,1), grid=TRUE)
![](data:image/png;base64,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)
as.data.frame(regard) # the difference between the predicted probabilities
## VERB fit se lower upper
## 1 all.other 0.004380616 0.0001774135 0.00404628 0.004742445
## 2 regard 0.808080808 0.0395793814 0.71857163 0.874108919
diff(as.data.frame(regard)$fit) # in the column fit is Delta P v->cx : 0.8037002
## [1] 0.8037002
## association as regression from the 2x2 co-occurrence matrix: DV = matrix with the DV-levels in columns
summary(model_c.from.v <- glm(cbind(cooc[,1], cooc[,2]) ~ rownames(cooc), family=binomial)) # same as ...
##
## Call:
## glm(formula = cbind(cooc[, 1], cooc[, 2]) ~ rownames(cooc), family = binomial)
##
## Deviance Residuals:
## [1] 0 0
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.42618 0.04068 -133.39 <2e-16 ***
## rownames(cooc)regard 6.86376 0.25843 26.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 7.6220e+02 on 1 degrees of freedom
## Residual deviance: 1.3591e-12 on 0 degrees of freedom
## AIC: 16.821
##
## Number of Fisher Scoring iterations: 3
summary(model_c.from.v <- glm(cooc ~ rownames(cooc), family=binomial)) # ... this one
##
## Call:
## glm(formula = cooc ~ rownames(cooc), family = binomial)
##
## Deviance Residuals:
## [1] 0 0
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.42618 0.04068 -133.39 <2e-16 ***
## rownames(cooc)regard 6.86376 0.25843 26.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 7.6220e+02 on 1 degrees of freedom
## Residual deviance: 1.3591e-12 on 0 degrees of freedom
## AIC: 16.821
##
## Number of Fisher Scoring iterations: 3
coef(model_c.from.v)[2] # = log odds / logit: how much VERB=="regard" increases
## rownames(cooc)regard
## 6.863763
exp(coef(model_c.from.v)[2]) # = odds ratio the (log) odds of CONSTR=="aspred"
## rownames(cooc)regard
## 956.9618
drop1(model_c.from.v, test="Chisq")$LRT[2] # = G^2 / LLR
## [1] 762.1959
model_c.from.v$null.deviance # = G^2 / LLR
## [1] 762.1959
summary(null.model_c.from.v.cbv <- glm(CONSTR ~ 1, family=binomial, data=cbv))
##
## Call:
## glm(formula = CONSTR ~ 1, family = binomial, data = cbv)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.0997 -0.0997 -0.0997 -0.0997 3.2581
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.30251 0.03825 -138.6 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 8663.1 on 138663 degrees of freedom
## Residual deviance: 8663.1 on 138663 degrees of freedom
## AIC: 8665.1
##
## Number of Fisher Scoring iterations: 8
coef(null.model_c.from.v.cbv) # logit of the relative frequency of as-predicatives in the corpus,
## (Intercept)
## -5.302508
# i.e. of sum(CONSTR=="aspred") / sum(CONSTR!="")
# traditional and widely-used measures ############################################################
## obs.a and exp.a computed in the traditional way from the 2x2 co-occurrence matrix cooc
addmargins(cooc)
## CONSTR
## VERB aspred all.other Sum
## regard 80 19 99
## all.other 607 137958 138565
## Sum 687 137977 138664
(obs.a <- cooc["regard","aspred"]) # observed co-occurrence freq
## [1] 80
(exp.a <- chisq.test(cooc, correct=FALSE)$exp["regard","aspred"]) # expected co-occurrence freq
## Warning in chisq.test(cooc, correct = FALSE): Chi-squared approximation may
## be incorrect
## [1] 0.4904878
### from this we can compute
log2(obs.a/exp.a) # 7.349639, = pointwise MI
## [1] 7.349639
(obs.a - exp.a)/sqrt(obs.a) # 8.889434, = t
## [1] 8.889434
(obs.a - exp.a)/sqrt(exp.a) # 113.5285 , = z (same as chisq.test(cooc, correct=FALSE)$residuals["regard","aspred"])
## [1] 113.5285
## obs.a and exp.a computed from the data frame cbv and the regression models
### from model_v.from.c.cbv
(obs.a <- sum(CONSTR=="aspred") * # = how often CONSTR=="aspred" *
ilogit(sum(coef(model_v.from.c.cbv)))) # = predicted probability of VERB=="regard"
## [1] 80
### from model_c.from.v.cbv
(obs.a <- sum(VERB=="regard") * # = how often VERB=="regard" *
ilogit(sum(coef(model_c.from.v.cbv)))) # = predicted probability of CONSTR=="aspred"
## [1] 80
(exp.a <- ilogit(coef(null.model_c.from.v.cbv)) * ilogit(coef(null.model_v.from.c.cbv)) *
nrow(null.model_c.from.v.cbv$data))
## (Intercept)
## 0.4904878
### from this we can compute (as above)
log2(obs.a/exp.a) # 7.349639, = pointwise MI
## (Intercept)
## 7.349639
(obs.a - exp.a)/sqrt(obs.a) # 8.889434, = t
## (Intercept)
## 8.889434
(obs.a - exp.a)/sqrt(exp.a) # 113.5285 , = z
## (Intercept)
## 113.5285