> # test.bpairs.R:
> 
> source("test.prolog.R")
> source("check.models.equal.R")
> source("check.earth.matches.glm.R")
> library(earth)
Loading required package: Formula
Loading required package: plotmo
Loading required package: plotrix
> data(ozone1)
> data(trees)
> data(etitanic)
> options(warn=1) # print warnings as they occur
> 
> cat("\n===short and long data===\n")

===short and long data===
> x.short <- data.frame(x1=c(5,2,2,9,5), x2=c(20,20,30,20,20))
> y.short <- data.frame(true=c(1,2,0,2,2), false=c(3,3,1,0,1))
> short <- data.frame(x.short, y.short)
> cat("short:\n")
short:
> print(short)
  x1 x2 true false
1  5 20    1     3
2  2 20    2     3
3  2 30    0     1
4  9 20    2     0
5  5 20    2     1
> x.long <- data.frame(x1=c( 5, 5, 5, 5,  2, 2, 2, 2, 2,  2,  9, 9,   5,  5,  5),
+                      x2=c(20,20,20,20, 20,20,20,20,20, 30, 20,20,  20, 20, 20))
> y.long <- data.frame(true=c(1,0,0,0,  1, 1, 0, 0, 0,  0,  1, 1,   1,  1,  0))
> long <- data.frame(x.long, y.long)
> cat("long:\n")
long:
> print(long)
   x1 x2 true
1   5 20    1
2   5 20    0
3   5 20    0
4   5 20    0
5   2 20    1
6   2 20    1
7   2 20    0
8   2 20    0
9   2 20    0
10  2 30    0
11  9 20    1
12  9 20    1
13  5 20    1
14  5 20    1
15  5 20    0
> true.false <- cbind(true=short$true, false=short$false)
> weights.long <- c(4, 4, 4, 4, 5, 5, 5, 5, 5, 1, 2, 2, 3, 3, 3)
> 
> elong <- earth(true~x1+x2, data=long, glm=list(family="binomial"),
+                linpreds=TRUE, thresh=0, penalty=-1, trace=1)
x[15,2] with colnames x1 x2
y[15,1] with colname true, and values 1, 0, 0, 0, 1, 1, 0, 0, 0, 0,...
Forward pass term 1, 2, 4, 6
No new term increases RSq (perhaps reached numerical limits) at 5 terms, 3 terms used
After forward pass GRSq 0.173 RSq 0.173
Prune backward penalty -1 nprune null: selected 3 of 3 terms, and 2 of 2 preds
After pruning pass GRSq 0.173 RSq 0.173
GLM true devratio 0.15 dof 12/14 iters 16
> glong <- glm(true~x1+x2, data=long, family="binomial")
> check.earth.matches.glm(elong, glong)
check elong vs glong
> par(mfrow=c(2,2))
> plotres(elong, do.par=0, which=c(1,3), main="elong", legend.pos="topleft")
> empty.plot()
> plotres(glong, do.par=0, which=3, main="glong")
> par(mfrow=c(2,2))
> plotmo(elong, do.par=0)
 plotmo grid:    x1 x2
                  5 20
> plotmo(glong, do.par=0)
 plotmo grid:    x1 x2
                  5 20
> par(org.par)
> 
> eshort <- earth(true.false~x1+x2, data=short, glm=list(family="binomial"),
+                 linpreds=TRUE, thresh=0, penalty=-1, trace=2)
x[5,2] with colnames x1 x2
y[5,2] with colnames true false
weights used by earth internally: 4, 5, 1, 2, 3
weights passed to glm (which will adjust by rowsums): NULL
Response columns true and false are a binomial pair (15 obs in total)
yfrac[5,1] with colname true, and values 0.25, 0.4, 0, 1, 0.6667
Linear predictors 1=x1 2=x2
Forward pass: minspan 3 endspan 1   x[5,2] 80 Bytes   bx[5,21] 840 Bytes weighted

         GRSq    RSq     DeltaRSq Pred     PredName         Cut  Terms   Par Deg
1      0.0000 0.0000                    (Intercept)
2      0.5017 0.5017       0.5017    1           x1           2< 2             1 
4      0.5775 0.5775      0.07583    2           x2          20< 3             1 
6      0.5775 0.5775            0    -                                       reject (no DeltaRsq)

No new term increases RSq (perhaps reached numerical limits) at 5 terms, 3 terms used
After forward pass GRSq 0.578 RSq 0.578
Forward pass complete: 5 terms, 3 terms used
Prune backward penalty -1 nprune null: selected 3 of 3 terms, and 2 of 2 preds
After pruning pass GRSq 0.578 RSq 0.578
GLM true devratio 0.54 dof 2/4 iters 18
> gshort <- glm(true.false~x1+x2, data=short, family="binomial")
> OLD.EARTH <- FALSE # earth prior to version 5.0.0
> MAX.ARG <- if(OLD.EARTH) 1e-6 else 1e-8
> check.earth.matches.glm(eshort, gshort, max=MAX.ARG)
check eshort vs gshort
> par(mfrow=c(2,2))
> plotres(eshort, do.par=0, which=c(1,3), main="eshort", legend.pos="topleft")
> empty.plot()
> plotres(gshort, do.par=0, which=3, main="gshort")
> par(mfrow=c(2,2))
> plotmo(eshort, do.par=0)
 plotmo grid:    x1 x2
                  5 20
> plotmo(gshort, do.par=0)
 plotmo grid:    x1 x2
                  5 20
> par(org.par)
> 
> par(mfrow=c(2,2))
> plot(elong, main="elong: Model Selection", which=c(1, 3), do.par=0, legend.pos="topleft")
> plot(eshort, main="eshort: Model Selection", which=c(1, 3), do.par=0, legend.pos="topleft")
> par(org.par)
> 
> cat("\n===long data with weights ===\n")

===long data with weights ===
> elong.weights <- earth(true~x1+x2, data=long, glm=list(family="binomial"),
+                        weights=weights.long, trace=1,
+                        linpreds=TRUE, thresh=0, penalty=-1)
x[15,2] with colnames x1 x2
y[15,1] with colname true, and values 1, 0, 0, 0, 1, 1, 0, 0, 0, 0,...
earth and glm weights[15]: 4, 4, 4, 4, 5, 5, 5, 5, 5, 1, 2, 2, 3...
Forward pass term 1, 2, 4, 6
No new term increases RSq (perhaps reached numerical limits) at 5 terms, 3 terms used
After forward pass GRSq 0.064 RSq 0.064
Prune backward penalty -1 nprune null: selected 3 of 3 terms, and 2 of 2 preds
After pruning pass GRSq 0.0635 RSq 0.0635
GLM true devratio 0.05 dof 12/14 iters 14
> print(summary(elong.weights))
Call: earth(formula=true~x1+x2, data=long, weights=weights.long, trace=1,
            glm=list(family="binomial"), linpreds=TRUE, thresh=0, penalty=-1)

GLM coefficients
                  true
(Intercept) 28.6833914
x1           0.2283708
x2          -1.4902067

GLM (family binomial, link logit):
 nulldev df       dev df   devratio     AIC iters converged
 75.3529 14   71.4597 12     0.0517   77.46    14         1

Earth selected 3 of 3 terms, and 2 of 2 predictors
Termination condition: No new term increases RSq at 3 terms
Importance: x1, x2
Weights: 4, 4, 4, 4, 5, 5, 5, 5, 5, 1, 2, 2, 3, 3, 3
Number of terms at each degree of interaction: 1 2 (additive model)
Earth GCV 0.8445361    RSS 12.66804    GRSq 0.06351846    RSq 0.06351846
> glong.weights <- glm(true~x1+x2, data=long, family="binomial",
+                     weights=weights.long)
> # models match here but in general models with long and short data won't match
> check.earth.matches.glm(elong.weights, glong.weights)
check elong.weights vs glong.weights
> # compare "earth" part of earth-glm model to lm
> lm.long.weights <- lm(true~x1+x2, data=long, weights=weights.long)
> stopifnot(identical(sort(names(coef(elong.weights))), sort(names(coef(lm.long.weights)))))
> stopifnot(identical(sort(coef(elong.weights, type="earth")), sort(coef(lm.long.weights))))
> 
> cat("\n===short data with weights ===\n")

===short data with weights ===
> # add an extra row to prevent singularities in glm with a zero weight
> short6 <- rbind(short, list(x1=9, x2=10, true=1, false=1))
> true.false6 <- rbind(true.false, c(1,1))
> weights.short6 <- sqrt(1:6)
> cat("weights.short6:\n")
weights.short6:
> print(weights.short6)
[1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490
> eshort.weights6 <- earth(true.false6~x1+x2, data=short6, glm=list(family="binomial"),
+                         weights=weights.short6,
+                         trace=1,
+                         linpreds=TRUE, thresh=0, penalty=-1)
x[6,2] with colnames x1 x2
y[6,2] with colnames true false
weights used by earth internally: 4, 7.071, 1.732, 4, 6.708, 4.899
Response columns true and false are a binomial pair (17 obs in total)
yfrac[6,1] with colname true, and values 0.25, 0.4, 0, 1, 0.6667, 0.5
Forward pass term 1, 2, 4, 6
No new term increases RSq (perhaps reached numerical limits) at 5 terms, 3 terms used
After forward pass GRSq 0.417 RSq 0.417
Prune backward penalty -1 nprune null: selected 3 of 3 terms, and 2 of 2 preds
After pruning pass GRSq 0.417 RSq 0.417
GLM true devratio 0.34 dof 3/5 iters 4
> print(summary(eshort.weights6))
Call: earth(formula=true.false6~x1+x2, data=short6, weights=weights.short6,
            trace=1, glm=list(family="binomial"), linpreds=TRUE, thresh=0,
            penalty=-1)

GLM coefficients
                   true
(Intercept) -3.01560815
x1           0.32839481
x2           0.07196558

GLM (family binomial, link logit):
 nulldev df       dev df   devratio     AIC iters converged
 9.99556  5   6.62365  3      0.337   24.38     4         1

Earth selected 3 of 3 terms, and 2 of 2 predictors
Termination condition: No new term increases RSq at 3 terms
Importance: x1, x2
Weights: 1, 1.414214, 1.732051, 2, 2.236068, 2.44949
Number of terms at each degree of interaction: 1 2 (additive model)
Earth GCV 0.1876099    RSS 1.12566    GRSq 0.4166392    RSq 0.4166392
> gshort.weights6 <- glm(true.false6~x1+x2, data=short6, family="binomial",
+                       weights=weights.short6)
> print(summary(gshort.weights6))

Call:
glm(formula = true.false6 ~ x1 + x2, family = "binomial", data = short6, 
    weights = weights.short6)

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.01561    3.03667  -0.993    0.321
x1           0.32839    0.20696   1.587    0.113
x2           0.07197    0.11426   0.630    0.529

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 9.9956  on 5  degrees of freedom
Residual deviance: 6.6237  on 3  degrees of freedom
AIC: 24.377

Number of Fisher Scoring iterations: 4

> check.earth.matches.glm(eshort.weights6, gshort.weights6, max=1e-6, max.residuals=1e-10)
check eshort.weights6 vs gshort.weights6
> 
> # unweighted (because all weights equal)
> cat("weights.short6.reciprocal.of.rowsums:\n")
weights.short6.reciprocal.of.rowsums:
> eshort.weights6.reciprocal.of.rowsums <- earth(true.false6~x1+x2, data=short6, glm=list(family="binomial"),
+                         weights=1/rowSums(true.false6),
+                         trace=1,
+                         linpreds=TRUE, thresh=0, penalty=-1)
x[6,2] with colnames x1 x2
y[6,2] with colnames true false
earth and glm: unweighted (because all weights equal)
Response columns true and false are a binomial pair (17 obs in total)
yfrac[6,1] with colname true, and values 0.25, 0.4, 0, 1, 0.6667, 0.5
Forward pass term 1, 2, 4, 6
No new term increases RSq (perhaps reached numerical limits) at 5 terms, 3 terms used
After forward pass GRSq 0.512 RSq 0.512
Prune backward penalty -1 nprune null: selected 3 of 3 terms, and 2 of 2 preds
After pruning pass GRSq 0.512 RSq 0.512
GLM true devratio 0.41 dof 3/5 iters 4
> print(summary(eshort.weights6.reciprocal.of.rowsums))
Call: earth(formula=true.false6~x1+x2, data=short6,
            weights=1/rowSums(true.false6), trace=1,
            glm=list(family="binomial"), linpreds=TRUE, thresh=0, penalty=-1)

GLM coefficients
                   true
(Intercept) -2.75047847
x1           0.39024757
x2           0.02659503

GLM (family binomial, link logit):
 nulldev df       dev df   devratio     AIC iters converged
 3.16533  5   1.87393  3      0.408   9.964     4         1

Earth selected 3 of 3 terms, and 2 of 2 predictors
Termination condition: No new term increases RSq at 3 terms
Importance: x1, x2
Number of terms at each degree of interaction: 1 2 (additive model)
Earth GCV 0.04836798    RSS 0.2902079    GRSq 0.5119899    RSq 0.5119899
> gshort.weights6.reciprocal.of.rowsums <- glm(true.false6~x1+x2, data=short6, family="binomial",
+                          weights=1/rowSums(true.false6))
> print(summary(gshort.weights6.reciprocal.of.rowsums))

Call:
glm(formula = true.false6 ~ x1 + x2, family = "binomial", data = short6, 
    weights = 1/rowSums(true.false6))

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)  -2.7505     6.9971  -0.393    0.694
x1            0.3902     0.4964   0.786    0.432
x2            0.0266     0.2387   0.111    0.911

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 3.1653  on 5  degrees of freedom
Residual deviance: 1.8739  on 3  degrees of freedom
AIC: 9.9642

Number of Fisher Scoring iterations: 4

> check.earth.matches.glm(eshort.weights6.reciprocal.of.rowsums, gshort.weights6.reciprocal.of.rowsums, max=1e-6, max.residuals=1e-10)
check eshort.weights6.reciprocal.of.rowsums vs gshort.weights6.reciprocal.of.rowsums
> 
> weights.short6zero <- sqrt(1:6)
> weights.short6zero[3] <- 0
> cat("weights.short6zero:\n")
weights.short6zero:
> print(weights.short6zero)
[1] 1.000000 1.414214 0.000000 2.000000 2.236068 2.449490
> eshort.weights6zero <- earth(true.false6~x1+x2, data=short6, glm=list(family="binomial"),
+                         weights=weights.short6zero,
+                         trace=1,
+                         linpreds=TRUE, thresh=0, penalty=-1)
x[6,2] with colnames x1 x2
y[6,2] with colnames true false
weights used by earth internally: 4, 7.071, 0, 4, 6.708, 4.899
Response columns true and false are a binomial pair (17 obs in total)
yfrac[6,1] with colname true, and values 0.25, 0.4, 0, 1, 0.6667, 0.5
Forward pass term 1, 2, 4, 6
No new term increases RSq (perhaps reached numerical limits) at 5 terms, 3 terms used
After forward pass GRSq 0.616 RSq 0.616
Prune backward penalty -1 nprune null: selected 3 of 3 terms, and 2 of 2 preds
After pruning pass GRSq 0.616 RSq 0.616
GLM true devratio 0.54 dof 2/4 iters 4
> print(summary(eshort.weights6zero))
Call: earth(formula=true.false6~x1+x2, data=short6, weights=weights.short6zero,
            trace=1, glm=list(family="binomial"), linpreds=TRUE, thresh=0,
            penalty=-1)

GLM coefficients
                  true
(Intercept) -5.7222363
x1           0.4061112
x2           0.2067235

GLM (family binomial, link logit):
 nulldev df       dev df   devratio     AIC iters converged
 7.33721  4    3.4012  2      0.536   21.15     4         1

Earth selected 3 of 3 terms, and 2 of 2 predictors
Termination condition: No new term increases RSq at 3 terms
Importance: x1, x2
Weights: 1, 1.414214, 0, 2, 2.236068, 2.44949
Number of terms at each degree of interaction: 1 2 (additive model)
Earth GCV 0.09169144    RSS 0.5501486    GRSq 0.6159334    RSq 0.6159334
> gshort.weights6zero <- glm(true.false6~x1+x2, data=short6, family="binomial",
+                       weights=weights.short6zero)
> print(summary(gshort.weights6zero))

Call:
glm(formula = true.false6 ~ x1 + x2, family = "binomial", data = short6, 
    weights = weights.short6zero)

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept)  -5.7222     3.7137  -1.541   0.1234  
x1            0.4061     0.2349   1.729   0.0838 .
x2            0.2067     0.1497   1.381   0.1673  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 7.3372  on 4  degrees of freedom
Residual deviance: 3.4012  on 2  degrees of freedom
AIC: 21.155

Number of Fisher Scoring iterations: 4

> # max.residuals has to be big because of the way earth handles zero weights
> check.earth.matches.glm(eshort.weights6zero, gshort.weights6zero)
check eshort.weights6zero vs gshort.weights6zero
> 
> cat("\n===short and long data with hinges===\n")

===short and long data with hinges===
> # test without linpreds=TRUE (to avoid int-only model, need thresh=0, penalty=-1)
> elong.hinge <- earth(true~x1+x2, data=long, glm=list(family="binomial"),
+                      thresh=0, penalty=-1)
> print(summary(elong.hinge))
Call: earth(formula=true~x1+x2, data=long, glm=list(family="binomial"),
            thresh=0, penalty=-1)

GLM coefficients
                 true
(Intercept) 36.033525
x2          -1.816060
h(5-x1)     -0.039261
h(x1-5)      4.713438

GLM (family binomial, link logit):
 nulldev df       dev df   devratio     AIC iters converged
 20.7277 14   16.2908 11      0.214   24.29    17         1

Earth selected 4 of 4 terms, and 2 of 2 predictors
Termination condition: No new term increases RSq at 4 terms
Importance: x1, x2
Number of terms at each degree of interaction: 1 3 (additive model)
Earth GCV 0.1942857    RSS 2.914286    GRSq 0.2193878    RSq 0.2193878
> eshort.hinge <- earth(true.false~x1+x2, data=short, glm=list(family="binomial"),
+                       thresh=0, penalty=-1)
> print(summary(eshort.hinge))
Call: earth(formula=true.false~x1+x2, data=short, glm=list(family="binomial"),
            thresh=0, penalty=-1)

GLM coefficients
                 true
(Intercept) 40.033525
x2          -2.016060
h(5-x1)     -0.039261
h(x1-5)      5.297520

GLM (family binomial, link logit):
 nulldev df       dev df   devratio     AIC iters converged
 5.67982  4   1.24295  1      0.781   14.72    19         1

Earth selected 4 of 4 terms, and 2 of 2 predictors
Termination condition: No new term increases RSq at 4 terms
Importance: x1, x2
Number of terms at each degree of interaction: 1 3 (additive model)
Earth GCV 0.05952381    RSS 0.297619    GRSq 0.7334755    RSq 0.7334755
> eshort.hinge2 <- earth(true+false~x1+x2, data=short, glm=list(family="binomial"),
+                       thresh=0, penalty=-1)
> check.models.equal(eshort.hinge, eshort.hinge2, "eshort.hinge, eshort.hinge2, ", newdata=short[2:3,])
eshort.hinge, eshort.hinge2, : models not identical

Formulas differ: true.false ~ x1 + x2
and:             ~true + false + (x1 + x2)

eshort.hinge, eshort.hinge2, : glm submodel formula strings are identical: yarg ~ `h(x1-5)` + `h(5-x1)` + x2
eshort.hinge, eshort.hinge2, : but the actual glm submodel formulas differ (classes are "formula" and "formula")
eshort.hinge, eshort.hinge2, : glm submodels not identical (but coefs, residuals, fitted.values are the same)
eshort.hinge, eshort.hinge2, : Models are equivalent, within numerical tolerances

> if(OLD.EARTH) {
+     stopifnot(identical(eshort.hinge$dirs[order(rownames(eshort.hinge$dirs)),],
+                         elong.hinge$dirs [order(rownames(elong.hinge$dirs)),]))
+ } else
+     stopifnot(identical(eshort.hinge$dirs, elong.hinge$dirs))
> 
> par(mfrow=c(2,2))
> plotres(elong.hinge, do.par=0, which=c(1,3), main="elong.hinge", legend.pos="topleft")
> plotres(eshort.hinge, do.par=0, which=c(1,3), main="eshort.hinge", legend.pos="topleft")
> 
> par(mfrow=c(2,2))
> plotmo(elong.hinge, do.par=0, ndiscrete=0)
 plotmo grid:    x1 x2
                  5 20
> plotmo(eshort.hinge, do.par=0, ndiscrete=0)
 plotmo grid:    x1 x2
                  5 20
> par(org.par)
> 
> # test with a y with a binomial pair row with both entries equal to 0
> x.short.with.zeros <- data.frame(x1=c(5,2,2,9,5,9), x2=c(20,20,30,20,20,30))
> y.short.with.zeros <- data.frame(true=c(1,2,0,2,2,0), false=c(3,3,1,0,1,0))
> short.with.zeros <- data.frame(x.short.with.zeros, y.short.with.zeros)
> true.false.with.zeros <- cbind(true=short.with.zeros$true, false=short.with.zeros$false)
> eshort.with.zeros <- earth(true.false.with.zeros~x1+x2, data=short.with.zeros, glm=list(family="binomial"),
+                            linpreds=TRUE, thresh=0, penalty=-1)
> gshort.with.zeros <- glm(true.false.with.zeros~x1+x2, data=short.with.zeros, family="binomial")
> check.earth.matches.glm(eshort.with.zeros, gshort.with.zeros)
check eshort.with.zeros vs gshort.with.zeros
> par(mfrow=c(2,2))
> plotres(eshort.with.zeros, do.par=0, which=c(1,3), main="eshort.with.zeros", legend.pos="topleft")
> empty.plot()
> plotres(gshort.with.zeros, do.par=0, which=3, main="gshort.with.zeros")
> par(mfrow=c(2,2))
> plotmo(eshort.with.zeros, do.par=0, ndiscrete=0)
 plotmo grid:    x1 x2
                  5 20
> plotmo(gshort.with.zeros, do.par=0, ndiscrete=0)
 plotmo grid:    x1 x2
                  5 20
> par(org.par)
> eshort.with.zeros.plus <- earth(true+false~x1+x2, data=short.with.zeros, glm=list(family="binomial"),
+                                 linpreds=TRUE, thresh=0, penalty=-1, trace=1)
Using class "Formula" because lhs of formula has terms separated by "+"
x[6,2] with colnames x1 x2
y[6,2] with colnames true false
Note: Both entries in row 6 of the true and false response are zero
weights used by earth internally: 4, 5, 1, 2, 3, 0
Response columns true and false are a binomial pair (15 obs in total)
yfrac[6,1] with colname true, and values 0.25, 0.4, 0, 1, 0.6667, 0
Forward pass term 1, 2, 4, 6
No new term increases RSq (perhaps reached numerical limits) at 5 terms, 3 terms used
After forward pass GRSq 0.578 RSq 0.578
Prune backward penalty -1 nprune null: selected 3 of 3 terms, and 2 of 2 preds
After pruning pass GRSq 0.578 RSq 0.578
GLM true devratio 0.54 dof 2/4 iters 18
> check.models.equal(eshort.with.zeros, eshort.with.zeros.plus, "eshort.with.zeros, eshort.with.zeros.plus", newdata=short.with.zeros[2:3,])
eshort.with.zeros, eshort.with.zeros.plus: models not identical

Formulas differ: true.false.with.zeros ~ x1 + x2
and:             ~true + false + (x1 + x2)

eshort.with.zeros, eshort.with.zeros.plus: glm submodel formula strings are identical: yarg ~ x1 + x2
eshort.with.zeros, eshort.with.zeros.plus: but the actual glm submodel formulas differ (classes are "formula" and "formula")
eshort.with.zeros, eshort.with.zeros.plus: glm submodels not identical (but coefs, residuals, fitted.values are the same)
eshort.with.zeros, eshort.with.zeros.plus: Models are equivalent, within numerical tolerances

> 
> eshort.with.zeros.plus.quasibinomial <- earth(true+false~x1+x2, data=short.with.zeros, glm=list(family="quasibinomial"),
+                                 linpreds=TRUE, thresh=0, penalty=-1, trace=1)
Using class "Formula" because lhs of formula has terms separated by "+"
x[6,2] with colnames x1 x2
y[6,2] with colnames true false
Note: Both entries in row 6 of the true and false response are zero
weights used by earth internally: 4, 5, 1, 2, 3, 0
Response columns true and false are a binomial pair (15 obs in total)
yfrac[6,1] with colname true, and values 0.25, 0.4, 0, 1, 0.6667, 0
Forward pass term 1, 2, 4, 6
No new term increases RSq (perhaps reached numerical limits) at 5 terms, 3 terms used
After forward pass GRSq 0.578 RSq 0.578
Prune backward penalty -1 nprune null: selected 3 of 3 terms, and 2 of 2 preds
After pruning pass GRSq 0.578 RSq 0.578
GLM true devratio 0.54 dof 2/4 iters 18
> check.models.equal(eshort.with.zeros.plus, eshort.with.zeros.plus.quasibinomial, "eshort.with.zeros.plus eshort.with.zeros.plus.quasibinomial", newdata=short.with.zeros[1:3,])
eshort.with.zeros.plus eshort.with.zeros.plus.quasibinomial: models not identical

eshort.with.zeros.plus eshort.with.zeros.plus.quasibinomial: glm submodel formula strings are identical: yarg ~ x1 + x2
eshort.with.zeros.plus eshort.with.zeros.plus.quasibinomial: but the actual glm submodel formulas differ (classes are "formula" and "formula")
eshort.with.zeros.plus eshort.with.zeros.plus.quasibinomial: glm submodels not identical (but coefs, residuals, fitted.values are the same)
eshort.with.zeros.plus eshort.with.zeros.plus.quasibinomial: Models are equivalent, within numerical tolerances

> # print(summary(eshort.with.zeros.plus))
> # print(summary(eshort.with.zeros.plus.quasibinomial))
> # print(summary(eshort.with.zeros.plus$glm.list[[1]]))
> # print(summary(eshort.with.zeros.plus.quasibinomial$glm.list[[1]]))
> 
> cat("\n===compare with model where yfrac is generated manually===\n")

===compare with model where yfrac is generated manually===
> bpairs.frac <- function(y)
+ {
+     stopifnot(NCOL(y) == 2)         # binomial pairs y has two columns
+     stopifnot(is.numeric(y[,1]) ||is.logical(y[,1]))
+     stopifnot(is.numeric(y[,2]) ||is.logical(y[,2]))
+     stopifnot(all(y >= 0))          # all y values non-negative
+     stopifnot(round(y) == y)        # all y values integers
+     weights <- y[,1] + y[,2]
+     if(length(weights > 1) == 0)
+         warning("no rows of y sum to greater than 1 (earth will not consider y to be a binomial pair")
+     y[weights == 0, 2] <- 1         # so all-zero rows will be treated as fraction=0
+     # we return y as a one column mat (not a vector) so we can give it a colname
+     frac <- matrix(y[, 1] / (y[,1] + y[,2]), ncol=1) # fraction true
+     colnames(frac) <- colnames(y)[1]
+     nchar <- nchar(colnames(frac))
+     if(length(nchar) == 0 || nchar == 0)
+         colnames(frac) <- "frac"
+     list(frac=frac, weights=weights)
+ }
> ret <- bpairs.frac(cbind(short.with.zeros$true, short.with.zeros$false))
> print(ret)
$frac
          frac
[1,] 0.2500000
[2,] 0.4000000
[3,] 0.0000000
[4,] 1.0000000
[5,] 0.6666667
[6,] 0.0000000

$weights
[1] 4 5 1 2 3 0

> stopifnot(identical(colnames(ret$frac), "frac")) # column name added automatically
> ret <- bpairs.frac(short.with.zeros[,c("true", "false")])
> print(ret)
$frac
          true
[1,] 0.2500000
[2,] 0.4000000
[3,] 0.0000000
[4,] 1.0000000
[5,] 0.6666667
[6,] 0.0000000

$weights
[1] 4 5 1 2 3 0

> stopifnot(identical(colnames(ret$frac), "true"))
> frac <- ret$frac
> weights <- ret$weights
> options(warn=2)
> # expect warning: non-integer #successes in a binomial glm
> expect.err(try(earth(frac~x1+x2, data=short.with.zeros, glm=list(family="binomial"),
+                linpreds=TRUE, thresh=0, penalty=-1)), "non-integer #successes in a binomial glm")
Error in eval(family$initialize) : 
  (converted from warning) non-integer #successes in a binomial glm!
Got expected error from try(earth(frac ~ x1 + x2, data = short.with.zeros, glm = list(family = "binomial"),     linpreds = TRUE, thresh = 0, penalty = -1))
> # warning goes away if we use quasibinomial
> eshort.with.zeros.frac.quasibinomial <- earth(frac~x1+x2, data=short.with.zeros, weights=weights, glm=list(family="quasibinomial"),
+                                 linpreds=TRUE, thresh=0, penalty=-1, trace=1)
x[6,2] with colnames x1 x2
y[6,1] with colname frac, and values 0.25, 0.4, 0, 1, 0.6667, 0
earth and glm weights[6]: 4, 5, 1, 2, 3, 0
Forward pass term 1, 2, 4, 6
No new term increases RSq (perhaps reached numerical limits) at 5 terms, 3 terms used
After forward pass GRSq 0.578 RSq 0.578
Prune backward penalty -1 nprune null: selected 3 of 3 terms, and 2 of 2 preds
After pruning pass GRSq 0.578 RSq 0.578
GLM frac devratio 0.54 dof 2/4 iters 18
> options(warn=1)
> check.models.equal(eshort.with.zeros.frac.quasibinomial, eshort.with.zeros.plus, "eshort.frac, eshort.with.zeros.plus", newdata=short.with.zeros[2:3,], allow.different.names=TRUE)
eshort.frac, eshort.with.zeros.plus: models not identical

mod1 coefficients 
[1] "frac"
mod2 coefficients 
[1] "true"

Warning: coefficients has different column names but is otherwise identical, see above messages

mod1 residuals 
[1] "frac"
mod2 residuals 
[1] "true"

Warning: residuals has different column names but is otherwise identical, see above messages

mod1 predict with no newdata, default type 
[1] "frac"
mod2 predict with no newdata, default type 
[1] "true"

Warning: predict with no newdata, default type has different column names but is otherwise identical, see above messages

mod1 predict with no newdata, type="link" 
[1] "frac"
mod2 predict with no newdata, type="link" 
[1] "true"

Warning: predict with no newdata, type="link" has different column names but is otherwise identical, see above messages

mod1 predict with no newdata, type="response" 
[1] "frac"
mod2 predict with no newdata, type="response" 
[1] "true"

Warning: predict with no newdata, type="response" has different column names but is otherwise identical, see above messages

mod1 predict with no newdata, type="earth" 
[1] "frac"
mod2 predict with no newdata, type="earth" 
[1] "true"

Warning: predict with no newdata, type="earth" has different column names but is otherwise identical, see above messages

mod1 predict with newdata, default type 
[1] "frac"
mod2 predict with newdata, default type 
[1] "true"

Warning: predict with newdata, default type has different column names but is otherwise identical, see above messages

mod1 predict with newdata, , type="link" 
[1] "frac"
mod2 predict with newdata, , type="link" 
[1] "true"

Warning: predict with newdata, , type="link" has different column names but is otherwise identical, see above messages

mod1 predict with newdata, , type="response" 
[1] "frac"
mod2 predict with newdata, , type="response" 
[1] "true"

Warning: predict with newdata, , type="response" has different column names but is otherwise identical, see above messages

mod1 predict with newdata, , type="earth" 
[1] "frac"
mod2 predict with newdata, , type="earth" 
[1] "true"

Warning: predict with newdata, , type="earth" has different column names but is otherwise identical, see above messages

Formulas differ: frac ~ x1 + x2
and:             ~true + false + (x1 + x2)

eshort.frac, eshort.with.zeros.plus: glm submodel formula strings are identical: yarg ~ x1 + x2
eshort.frac, eshort.with.zeros.plus: but the actual glm submodel formulas differ (classes are "formula" and "formula")
eshort.frac, eshort.with.zeros.plus: glm submodels not identical (but coefs, residuals, fitted.values are the same)
eshort.frac, eshort.with.zeros.plus: Models are equivalent, within numerical tolerances

> eshort.with.zeros.frac.binomial <- earth(frac~x1+x2, data=short.with.zeros, weights=weights, glm=list(family="binomial"),
+                                          linpreds=TRUE, thresh=0, penalty=-1)
> # # compare stats like deviance etc (all identical here except no AIC for quasibinomial,
> # #                                  and standard deviations of glm submodels differ)
> # cat("eshort.with.zeros.frac.binomial:\n")
> # print(summary(eshort.with.zeros.frac.binomial))
> # cat("eshort.with.zeros.frac.quasibinomial:\n")
> # print(summary(eshort.with.zeros.frac.quasibinomial))
> # cat("---------------------------------------------------\n")
> # print(summary(eshort.with.zeros.frac.binomial$glm.list[[1]]))
> # print(summary(eshort.with.zeros.frac.quasibinomial$glm.list[[1]]))
> 
> # lizard data used in McCullagh and Nelder GLM book (2nd ed)
> # this has an entry with both responses equal to zero (similar to the above data):
> #    site.shade diameter.wide height.tall  time grahami opalinus
> # 11      FALSE          TRUE        TRUE   Mid       0        0
> 
> cat("\n===lizards===\n")

===lizards===
> 
> shade <- factor(x=c(
+     "sun", "sun", "sun", "sun", "sun", "sun", "sun", "sun", "sun", "sun", "sun", "sun",
+     "shade", "shade", "shade", "shade", "shade", "shade", "shade", "shade", "shade", "shade", "shade", "shade"),
+     levels=c("sun", "shade"))
> diameter.wide <- as.logical(c(
+     0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
+     0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1))
> height.tall <- as.logical(c(
+     0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1,
+     0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1))
> time <- factor(x=c(
+       "Early", "Mid", "Late",
+       "Early", "Mid", "Late",
+       "Early", "Mid", "Late",
+       "Early", "Mid", "Late",
+       "Early", "Mid", "Late",
+       "Early", "Mid", "Late",
+       "Early", "Mid", "Late",
+       "Early", "Mid", "Late"),
+     levels=c("Early", "Mid", "Late"), ordered=FALSE)
> grahami <- c(
+     20, 8, 4, 13, 8, 12, 8, 4, 5, 6, 0, 1,
+     34, 69, 18, 31, 55, 13, 17, 60, 8, 12, 21, 4)
> opalinus <- c(
+     2, 1, 4, 0, 0, 0, 3, 1, 3, 0, 0, 1, 11,
+     20, 10, 5, 4, 3, 15, 32, 8, 1, 5, 4)
> lizards <- data.frame(
+     shade=shade,
+     wide=diameter.wide,
+     tall=height.tall,
+     time=time,
+     grahami=grahami,
+     opalinus=opalinus)
> 
> grahami.opalinus <- cbind(grahami=lizards$grahami, opalinus=lizards$opalinus)
> eliz <- earth(grahami.opalinus~as.numeric(shade)+wide+tall*time,
+               data=lizards, glm=list(family="binomial"),
+               linpreds=TRUE, thresh=0, penalty=-1, trace=1)
x[24,7] with colnames as.numeric(shade) wideTRUE tallTRUE timeMid timeLate tall...
y[24,2] with colnames grahami opalinus
Note: Both entries in row 11 of the grahami and opalinus response are zero
weights used by earth internally: 22, 9, 8, 13, 8, 12, 11, 5, 8, 6, 0, 2, 45, 89,...
Response columns grahami and opalinus are a binomial pair (564 obs in total)
yfrac[24,1] with colname grahami, and values 0.9091, 0.8889, 0.5, 1, 1, 1,...
Forward pass term 1, 2, 4, 6, 8, 10, 12, 14, 16
No new term increases RSq (perhaps reached numerical limits) at 15 terms, 8 terms used
After forward pass GRSq 0.852 RSq 0.852
Prune backward penalty -1 nprune null: selected 8 of 8 terms, and 7 of 7 preds
After pruning pass GRSq 0.852 RSq 0.852
GLM grahami devratio 0.80 dof 15/22 iters 4
> print(summary(eliz))
Call: earth(formula=grahami.opalinus~as.numeric(shade)+wide+tall*time,
            data=lizards, trace=1, glm=list(family="binomial"), linpreds=TRUE,
            thresh=0, penalty=-1)

GLM coefficients
                     grahami
(Intercept)        2.7497470
as.numeric(shade) -0.8524139
wideTRUE          -0.7615229
tallTRUE           1.3647511
timeMid            0.2788588
timeLate          -0.6100012
tallTRUE:timeMid  -0.2352590
tallTRUE:timeLate -0.4958645

GLM (family binomial, link logit):
 nulldev df       dev df   devratio     AIC iters converged
 70.1018 22    13.682 15      0.805   86.51     4         1

Earth selected 8 of 8 terms, and 7 of 7 predictors
Termination condition: No new term increases RSq at 8 terms
Importance: tallTRUE, wideTRUE, timeLate, as.numeric(shade), timeMid, ...
Number of terms at each degree of interaction: 1 7 (additive model)
Earth GCV 0.06803856    RSS 1.632925    GRSq 0.852299    RSq 0.852299
> eliz.Formula <- earth(grahami+opalinus~as.numeric(shade)+wide+tall*time,
+               data=lizards, glm=list(family="binomial"),
+               linpreds=TRUE, thresh=0, penalty=-1, trace=1)
Using class "Formula" because lhs of formula has terms separated by "+"
x[24,7] with colnames as.numeric(shade) wideTRUE tallTRUE timeMid timeLate tall...
y[24,2] with colnames grahami opalinus
Note: Both entries in row 11 of the grahami and opalinus response are zero
weights used by earth internally: 22, 9, 8, 13, 8, 12, 11, 5, 8, 6, 0, 2, 45, 89,...
Response columns grahami and opalinus are a binomial pair (564 obs in total)
yfrac[24,1] with colname grahami, and values 0.9091, 0.8889, 0.5, 1, 1, 1,...
Forward pass term 1, 2, 4, 6, 8, 10, 12, 14, 16
No new term increases RSq (perhaps reached numerical limits) at 15 terms, 8 terms used
After forward pass GRSq 0.852 RSq 0.852
Prune backward penalty -1 nprune null: selected 8 of 8 terms, and 7 of 7 preds
After pruning pass GRSq 0.852 RSq 0.852
GLM grahami devratio 0.80 dof 15/22 iters 4
> print(summary(eliz.Formula))
Call: earth(formula=grahami+opalinus~as.numeric(shade)+wide+tall*time,
            data=lizards, trace=1, glm=list(family="binomial"), linpreds=TRUE,
            thresh=0, penalty=-1)

GLM coefficients
                     grahami
(Intercept)        2.7497470
as.numeric(shade) -0.8524139
wideTRUE          -0.7615229
tallTRUE           1.3647511
timeMid            0.2788588
timeLate          -0.6100012
tallTRUE:timeMid  -0.2352590
tallTRUE:timeLate -0.4958645

GLM (family binomial, link logit):
 nulldev df       dev df   devratio     AIC iters converged
 70.1018 22    13.682 15      0.805   86.51     4         1

Earth selected 8 of 8 terms, and 7 of 7 predictors
Termination condition: No new term increases RSq at 8 terms
Importance: tallTRUE, wideTRUE, timeLate, as.numeric(shade), timeMid, ...
Number of terms at each degree of interaction: 1 7 (additive model)
Earth GCV 0.06803856    RSS 1.632925    GRSq 0.852299    RSq 0.852299
> gliz <- glm(grahami.opalinus~as.numeric(shade)+wide+tall*time,
+             data=lizards, family="binomial")
> print(summary(gliz))

Call:
glm(formula = grahami.opalinus ~ as.numeric(shade) + wide + tall * 
    time, family = "binomial", data = lizards)

Coefficients:
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)         2.7497     0.6226   4.416    1e-05 ***
as.numeric(shade)  -0.8524     0.3219  -2.648 0.008095 ** 
wideTRUE           -0.7615     0.2115  -3.601 0.000317 ***
tallTRUE            1.3648     0.4842   2.819 0.004820 ** 
timeMid             0.2789     0.2789   1.000 0.317437    
timeLate           -0.6100     0.3467  -1.759 0.078494 .  
tallTRUE:timeMid   -0.2353     0.6215  -0.379 0.705032    
tallTRUE:timeLate  -0.4959     0.6885  -0.720 0.471368    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 70.102  on 22  degrees of freedom
Residual deviance: 13.682  on 15  degrees of freedom
AIC: 86.506

Number of Fisher Scoring iterations: 4

> 
> check.earth.matches.glm(eliz, gliz, newdata=lizards[c(2:5),], max=1e-12)
check eliz vs gliz
> check.earth.matches.glm(eliz.Formula, gliz, newdata=lizards[c(2:5),], max=1e-12)
check eliz.Formula vs gliz
> print(evimp(eliz))
                  nsubsets   gcv    rss
tallTRUE                 7 100.0  100.0
wideTRUE                 6  72.7   72.7
timeLate                 5  53.5   53.5
as.numeric(shade)        4  35.8   35.8
timeMid                  3  13.0   13.0
tallTRUE:timeMid         2   5.5    5.5
tallTRUE:timeLate        1   0.7    0.7
> par(mfrow=c(3,2))
> plotres(eliz,         do.par=0, which=c(1,3), main="eliz",         legend.pos="topleft")
> plotres(eliz.Formula, do.par=0, which=c(1,3), main="eliz.Formula", legend.pos="topleft")
> empty.plot()
> plotres(gliz,         do.par=0, which=3, main="gliz")
> par(org.par)
> plotmo(eliz, ndiscrete=0, SHOWCALL=TRUE)
 plotmo grid:    shade  wide  tall  time
                   sun FALSE FALSE Early
> plotmo(eliz.Formula, ndiscrete=0, SHOWCALL=TRUE)
 plotmo grid:    shade  wide  tall  time
                   sun FALSE FALSE Early
> plotmo(gliz, ndiscrete=0, SHOWCALL=TRUE)
 plotmo grid:    shade  wide  tall  time
                   sun FALSE FALSE Early
> 
> cat("\n===incorrect bpairs (error handling for bad data)===\n")

===incorrect bpairs (error handling for bad data)===
> test.incorrect.bpairs <- function(msg, expect.err, trace, y.short)
+ {
+     printf("\ntest.incorrect.bpairs: %s\n", msg)
+     x.short <- data.frame(x1=as.double(1:5))
+     short <- data.frame(x.short, y.short)
+     true.false <- cbind(true=short$true, false=short$false)
+     if(expect.err)
+         expect.err(try(earth(true.false~x1, data=short, glm=list(family="binomial"), trace=trace)), "Binomial response (see above): all values should be between 0 and 1, or a binomial pair")
+     else
+         earth(true.false~x1, data=short, glm=list(family="binomial"), trace=trace)
+ }
> test.incorrect.bpairs("non integral, greater than 1", expect.err=TRUE, trace=1,
+                      data.frame(true=as.double(c(0,1,0,1,0)), false=as.double(c(1,0,1,0,1.1))))

test.incorrect.bpairs: non integral, greater than 1
x[5,1] with colname x1, and values 1, 2, 3, 4, 5
y[5,2] with colnames true false

print(head(y)):
     true false
[1,]    0   1.0
[2,]    1   0.0
[3,]    0   1.0
[4,]    1   0.0
[5,]    0   1.1

Response has two columns but is not a binomial pair because not all values are integers
Earth will build two GLM models with responses "true" and "false"


print(head(y)):
     true false
[1,]    0   1.0
[2,]    1   0.0
[3,]    0   1.0
[4,]    1   0.0
[5,]    0   1.1

Error : Binomial response (see above): all values should be between 0 and 1, or a binomial pair
       Response has two columns but is not a binomial pair because not all values are integers
Got expected error from try(earth(true.false ~ x1, data = short, glm = list(family = "binomial"),     trace = trace))
> test.incorrect.bpairs("non integral but in range 0...1", expect.err=FALSE, trace=1,
+                      data.frame(true=as.double(c(0,1,0,1,0)), false=as.double(c(1,0,1,0,.1))))

test.incorrect.bpairs: non integral but in range 0...1
x[5,1] with colname x1, and values 1, 2, 3, 4, 5
y[5,2] with colnames true false

print(head(y)):
     true false
[1,]    0   1.0
[2,]    1   0.0
[3,]    0   1.0
[4,]    1   0.0
[5,]    0   0.1

Response has two columns but is not a binomial pair because not all values are integers
Earth will build two GLM models with responses "true" and "false"

Forward pass term 1, 2
GRSq -Inf at 1 term
After forward pass GRSq -in RSq 0.363
Prune backward penalty 2 nprune null: selected 1 of 1 terms, and 0 of 1 preds
After pruning pass GRSq 0 RSq 0
earth_glm: intercept-only earth model
GLM true devratio 0.00 dof 4/4 iters 4
Warning in eval(family$initialize) :
  non-integer #successes in a binomial glm!
GLM false devratio 0.00 dof 4/4 iters 4
GLM (family binomial, link logit):
      nulldev df       dev df   devratio     AIC iters converged
true  6.73012  4   6.73012  4          0   8.730     4         1
false 6.15275  4   6.15275  4          0   8.738     4         1

Earth selected 1 of 1 terms, and 0 of 1 predictors
Termination condition: GRSq -Inf at 1 term
Importance: x1-unused
Number of terms at each degree of interaction: 1 (intercept only model)

Earth
         GCV   RSS GRSq RSq
true  0.3750 1.200    0   0
false 0.3525 1.128    0   0
All   0.7275 2.328    0   0
> test.incorrect.bpairs("non integral but in range 0...1", expect.err=FALSE, trace=0,
+                      data.frame(true=as.double(c(0,1,0,1,0)), false=as.double(c(1,0,1,0,.1))))

test.incorrect.bpairs: non integral but in range 0...1

print(head(y)):
     true false
[1,]    0   1.0
[2,]    1   0.0
[3,]    0   1.0
[4,]    1   0.0
[5,]    0   0.1

Response has two columns but is not a binomial pair because not all values are integers
Earth will build two GLM models with responses "true" and "false"

Warning in eval(family$initialize) :
  non-integer #successes in a binomial glm!
GLM (family binomial, link logit):
      nulldev df       dev df   devratio     AIC iters converged
true  6.73012  4   6.73012  4          0   8.730     4         1
false 6.15275  4   6.15275  4          0   8.738     4         1

Earth selected 1 of 1 terms, and 0 of 1 predictors
Termination condition: GRSq -Inf at 1 term
Importance: x1-unused
Number of terms at each degree of interaction: 1 (intercept only model)

Earth
         GCV   RSS GRSq RSq
true  0.3750 1.200    0   0
false 0.3525 1.128    0   0
All   0.7275 2.328    0   0
> test.incorrect.bpairs("negative value", expect.err=TRUE, trace=1,
+                      data.frame(true=as.double(c(0,1,0,1,0)), false=as.double(c(1,0,1,0,-2))))

test.incorrect.bpairs: negative value
x[5,1] with colname x1, and values 1, 2, 3, 4, 5
y[5,2] with colnames true false
Response has two columns but is not a binomial pair because some values are negative

print(head(y)):
     true false
[1,]    0     1
[2,]    1     0
[3,]    0     1
[4,]    1     0
[5,]    0    -2

Error : Binomial response (see above): all values should be between 0 and 1, or a binomial pair
       Response has two columns but is not a binomial pair because some values are negative
Got expected error from try(earth(true.false ~ x1, data = short, glm = list(family = "binomial"),     trace = trace))
> test.incorrect.bpairs("no rows sum to greater than 1", expect.err=FALSE, trace=1,
+                      data.frame(true=as.double(c(0,1,0,1,0)), false=as.double(c(1,0,1,0,0))))

test.incorrect.bpairs: no rows sum to greater than 1
x[5,1] with colname x1, and values 1, 2, 3, 4, 5
y[5,2] with colnames true false
Response has two columns but is not a binomial pair because no rows sum to greater than 1
Earth will build two GLM models with responses "true" and "false"

Forward pass term 1, 2
GRSq -Inf at 1 term
After forward pass GRSq -in RSq 0.375
Prune backward penalty 2 nprune null: selected 1 of 1 terms, and 0 of 1 preds
After pruning pass GRSq 0 RSq 0
earth_glm: intercept-only earth model
GLM true devratio 0.00 dof 4/4 iters 4
GLM false devratio 0.00 dof 4/4 iters 4
GLM (family binomial, link logit):
      nulldev df       dev df   devratio     AIC iters converged
true  6.73012  4   6.73012  4          0    8.73     4         1
false 6.73012  4   6.73012  4          0    8.73     4         1

Earth selected 1 of 1 terms, and 0 of 1 predictors
Termination condition: GRSq -Inf at 1 term
Importance: x1-unused
Number of terms at each degree of interaction: 1 (intercept only model)

Earth
        GCV RSS GRSq RSq
true  0.375 1.2    0   0
false 0.375 1.2    0   0
All   0.750 2.4    0   0
> printf("\n")

> 
> #--------------------------------------------------------
> 
> ldose <- rep(0:5, 2) - 2 # Venables and Ripley 4th edition page 191
> sex <- factor(rep(c("male", "female"), times=c(6,6)))
> numdead <- c(1,4,9,13,18,20,0,2,6,10,12,16)
> numalive = 20 - numdead
> pair <- cbind(numalive, numdead)
> # following uses formula not Formula
> pairmod <- earth(pair ~ sex + ldose, trace=1, pmethod="none",
+                   glm=list(family=binomial))
x[12,2] with colnames sexmale ldose
y[12,2] with colnames numalive numdead
earth and glm: unweighted
Response columns numalive and numdead are a binomial pair (240 obs in total)
yfrac[12,1] with colname numalive, and values 0.95, 0.8, 0.55, 0.35, 0.1, 0...
Forward pass term 1, 2, 4, 6
RSq changed by less than 0.001 at 5 terms, 3 terms used (DeltaRSq 0)
After forward pass GRSq 0.907 RSq 0.981
Prune none penalty 2 nprune null: selected 3 of 3 terms, and 2 of 2 preds
After pruning pass GRSq 0.952 RSq 0.981
GLM numalive devratio 0.95 dof 9/11 iters 4
> stopifnot(attr(terms(pairmod), "response") == 1)
> stopifnot(is.null(attr(terms(pairmod), "Response")))
> glm.weights <- 1 * c(.8,1,1,.5,1,1,1,1,1,1,1,1) # will change model slightly
> pairmod.weights <- earth(pair ~ sex + ldose, weights=glm.weights,
+                          trace=0, pmethod="none",
+                          glm=list(family=binomial))
> # build a model using a global variables
> # following uses Formula not formula because of "+"
> pairmod2 <- earth(numalive + numdead ~ sex + ldose, trace=1, pmethod="none",
+                    glm=list(family=binomial))
Using class "Formula" because lhs of formula has terms separated by "+"
x[12,2] with colnames sexmale ldose
y[12,2] with colnames numalive numdead
earth and glm: unweighted
Response columns numalive and numdead are a binomial pair (240 obs in total)
yfrac[12,1] with colname numalive, and values 0.95, 0.8, 0.55, 0.35, 0.1, 0...
Forward pass term 1, 2, 4, 6
RSq changed by less than 0.001 at 5 terms, 3 terms used (DeltaRSq 0)
After forward pass GRSq 0.907 RSq 0.981
Prune none penalty 2 nprune null: selected 3 of 3 terms, and 2 of 2 preds
After pruning pass GRSq 0.952 RSq 0.981
GLM numalive devratio 0.95 dof 9/11 iters 4
> stopifnot(attr(terms(pairmod2), "response") == 0)
> stopifnot(attr(terms(pairmod2), "Response") == c(1,2))
> check.models.equal(pairmod2, pairmod, "pairmod2, pairmod", newdata=data.frame(sex="male", ldose=3))
pairmod2, pairmod: models not identical

Formulas differ: ~numalive + numdead + (sex + ldose)
and:             pair ~ sex + ldose

pairmod2, pairmod: glm submodel formula strings are identical: yarg ~ ldose + sexmale
pairmod2, pairmod: but the actual glm submodel formulas differ (classes are "formula" and "formula")
pairmod2, pairmod: glm submodels not identical (but coefs, residuals, fitted.values are the same)
pairmod2, pairmod: Models are equivalent, within numerical tolerances

> plot(pairmod2, info=TRUE, SHOWCALL=TRUE)
> pairmod2.weights <- earth(numalive + numdead ~ sex + ldose, weights=glm.weights,
+                          trace=0, pmethod="none",
+                          glm=list(family=binomial))
> plot(pairmod2.weights, info=TRUE, SHOWCALL=TRUE)
> check.models.equal(pairmod2.weights, pairmod.weights, "pairmod2.weights, pairmod.weights", newdata=data.frame(sex="male", ldose=3))
pairmod2.weights, pairmod.weights: models not identical

Formulas differ: ~numalive + numdead + (sex + ldose)
and:             pair ~ sex + ldose

pairmod2.weights, pairmod.weights: glm submodel formula strings are identical: yarg ~ `h(ldose-0)` + `h(0-ldose)` + sexmale
pairmod2.weights, pairmod.weights: but the actual glm submodel formulas differ (classes are "formula" and "formula")
pairmod2.weights, pairmod.weights: glm submodels not identical (but coefs, residuals, fitted.values are the same)
pairmod2.weights, pairmod.weights: Models are equivalent, within numerical tolerances

> plotmo(pairmod, SHOWCALL=TRUE)
 plotmo grid:    sex ldose
              female   0.5
> plotmo(pairmod2, SHOWCALL=TRUE)
 plotmo grid:    sex ldose
              female   0.5
> 
> # build a model using a combo of global and data.frame data
> df.except.numdead <- data.frame(ldose=ldose, numalive=numalive, sex=sex)
> # change global data to invalid values so we can see if we use it by mistake
> ldose <- rep(90:95, 2) - 2 # Venables and Ripley 4th edition page 191
> sex <- factor(rep(c("a", "be"), times=c(6,6)))
> numalive = NA
> # following uses Formula not formula because of "+"
> pairmod3 <- earth(numalive + numdead ~ sex + ldose, data=df.except.numdead, trace=0, pmethod="none",
+                    glm=list(family=binomial))
> check.models.equal(pairmod3, pairmod2, "pairmod3, pairmod2", newdata=df.except.numdead[3:4,])
pairmod3, pairmod2: models not identical

pairmod3, pairmod2: glm submodel formula strings are identical: yarg ~ ldose + sexmale
pairmod3, pairmod2: but the actual glm submodel formulas differ (classes are "formula" and "formula")
pairmod3, pairmod2: glm submodels not identical (but coefs, residuals, fitted.values are the same)
pairmod3, pairmod2: Models are equivalent, within numerical tolerances

> plot(pairmod3, info=TRUE)
> plotmo(pairmod3, SHOWCALL=TRUE)
 plotmo grid:    sex ldose
              female   0.5
> 
> # build a model using only data from a data.frame
> df <- data.frame(df.except.numdead, numdead=numdead)
> numdead <- 991:992 # invalidate the global data
> # following uses Formula not formula because of "+"
> pairmod_Formula <- earth(numalive + numdead ~ sex + ldose, data=df, trace=0, pmethod="none",
+                    glm=list(family=binomial))
> plot(pairmod_Formula, info=TRUE)
> check.models.equal(pairmod_Formula, pairmod2, "pairmod_Formula, pairmod2", newdata=df[5:6,])
pairmod_Formula, pairmod2: models not identical

pairmod_Formula, pairmod2: glm submodel formula strings are identical: yarg ~ ldose + sexmale
pairmod_Formula, pairmod2: but the actual glm submodel formulas differ (classes are "formula" and "formula")
pairmod_Formula, pairmod2: glm submodels not identical (but coefs, residuals, fitted.values are the same)
pairmod_Formula, pairmod2: Models are equivalent, within numerical tolerances

> 
> expect.err(try(earth(20-numdead+numdead ~ sex + ldose, data=df, glm=list(family=binomial))), "invalid model formula in ExtractVars")
Error in terms.formula(paste_formula(NULL, attr(Formula, "lhs"), rsep = "+")) : 
  invalid model formula in ExtractVars
Got expected error from try(earth(20 - numdead + numdead ~ sex + ldose, data = df, glm = list(family = binomial)))
> 
> # following uses formula not Formula
> pairmod_formula <- earth(pair ~ sex + ldose, data=df, trace=0, pmethod="none",
+                    glm=list(family=binomial))
> stopifnot(attr(terms(pairmod_formula), "response") == 1)
> stopifnot(is.null(attr(terms(pairmod_formula), "Response")))
> check.models.equal(pairmod_formula, pairmod_Formula, "pairmod_Formula, pairmod2", newdata=df[1:3,])
pairmod_Formula, pairmod2: models not identical

Formulas differ: pair ~ sex + ldose
and:             ~numalive + numdead + (sex + ldose)

pairmod_Formula, pairmod2: glm submodel formula strings are identical: yarg ~ ldose + sexmale
pairmod_Formula, pairmod2: but the actual glm submodel formulas differ (classes are "formula" and "formula")
pairmod_Formula, pairmod2: glm submodels not identical (but coefs, residuals, fitted.values are the same)
pairmod_Formula, pairmod2: Models are equivalent, within numerical tolerances

> 
> # subset
> # build a model using only data from a data.frame
> # following uses Formula not formula because of "+"
> subset.middle <- seq(from=2, to=nrow(df)-2)
> pairmod_Formula_subset <- earth(numalive + numdead ~ sex + ldose, data=df, subset=subset.middle, trace=0, pmethod="none",
+                    glm=list(family=binomial))
> plot(pairmod_Formula_subset, info=TRUE)
> 
> # following uses formula not Formula
> pairmod_formula_subset <- earth(pair ~ sex + ldose, data=df, subset=subset.middle, trace=0, pmethod="none",
+                    glm=list(family=binomial))
> stopifnot(attr(terms(pairmod_formula_subset), "response") == 1)
> stopifnot(is.null(attr(terms(pairmod_formula_subset), "Response")))
> check.models.equal(pairmod_formula_subset, pairmod_Formula_subset, "pairmod_Formula_subset, pairmod2", newdata=df[1:3,])
pairmod_Formula_subset, pairmod2: models not identical

Formulas differ: pair ~ sex + ldose
and:             ~numalive + numdead + (sex + ldose)

pairmod_Formula_subset, pairmod2: glm submodel formula strings are identical: yarg ~ `h(ldose-0)` + `h(0-ldose)` + sexmale
pairmod_Formula_subset, pairmod2: but the actual glm submodel formulas differ (classes are "formula" and "formula")
pairmod_Formula_subset, pairmod2: glm submodels not identical (but coefs, residuals, fitted.values are the same)
pairmod_Formula_subset, pairmod2: Models are equivalent, within numerical tolerances

> plot(pairmod_formula_subset, info=TRUE)
> plotmo(pairmod_Formula_subset, SHOWCALL=TRUE)
 plotmo grid:    sex ldose
                male     0
> plotmo(pairmod_formula_subset, SHOWCALL=TRUE)
 plotmo grid:    sex ldose
                male     0
> 
> # Terms on lhs like I(20-numdead) are not supported in multiple response Formulas
> # (else `log(O3)` is included in model matrix if log(O3) is used on lhs of the Formula)
> # Tested Sep 2020, problem in Formula package?
> expect.err(try(earth(I(20-numdead) + numdead ~ sex + ldose, data=df, trace=1, pmethod="none",
+                glm=list(family=binomial))),
+            "terms like 'I(20 - numdead)' are not allowed on the LHS of a multiple-response formula")
Using class "Formula" because lhs of formula has terms separated by "+"
Error : terms like 'I(20 - numdead)' are not allowed on the LHS of a multiple-response formula
Got expected error from try(earth(I(20 - numdead) + numdead ~ sex + ldose, data = df,     trace = 1, pmethod = "none", glm = list(family = binomial)))
> 
> pairmod6a <- earth(numalive + numdead ~ sex + ldose - sex, data=df, trace=1, pmethod="none")
Using class "Formula" because lhs of formula has terms separated by "+"
x[12,1] with colname ldose, and values -2, -1, 0, 1, 2, 3, -2, -1, 0...
y[12,2] with colnames numalive numdead
Forward pass term 1, 2, 4
RSq changed by less than 0.001 at 3 terms, 2 terms used (DeltaRSq 0)
After forward pass GRSq 0.805 RSq 0.921
Prune none penalty 2 nprune null: selected 2 of 2 terms, and 1 of 1 preds
After pruning pass GRSq 0.882 RSq 0.921
> pairmod6b <- earth(numalive + numdead ~ ldose, data=df, trace=1, pmethod="none")
Using class "Formula" because lhs of formula has terms separated by "+"
x[12,1] with colname ldose, and values -2, -1, 0, 1, 2, 3, -2, -1, 0...
y[12,2] with colnames numalive numdead
Forward pass term 1, 2, 4
RSq changed by less than 0.001 at 3 terms, 2 terms used (DeltaRSq 0)
After forward pass GRSq 0.805 RSq 0.921
Prune none penalty 2 nprune null: selected 2 of 2 terms, and 1 of 1 preds
After pruning pass GRSq 0.882 RSq 0.921
> print(summary(pairmod6a))
Call: earth(formula=numalive+numdead~sex+ldose-sex, data=df, pmethod="none",
            trace=1)

             numalive  numdead
(Intercept) 12.571429 7.428571
ldose       -3.642857 3.642857

Selected 2 of 2 terms, and 1 of 1 predictors (pmethod="none")
Termination condition: RSq changed by less than 0.001 at 2 terms
Importance: ldose
Number of terms at each degree of interaction: 1 1 (additive model)

              GCV      RSS      GRSq       RSq
numalive  5.89418 39.78571 0.8821359 0.9210992
numdead   5.89418 39.78571 0.8821359 0.9210992
All      11.78836 79.57143 0.8821359 0.9210992
> plot(pairmod6a, nresponse=1)
> plotmo(pairmod6a, nresponse=1)
 plotmo grid:    sex ldose
              female   0.5
> check.models.equal(pairmod6a, pairmod6b, "pairmod6a, pairmod6b", newdata=df[5:6,])
pairmod6a, pairmod6b: models not identical

Formulas differ: ~numalive + numdead + (sex + ldose - sex)
and:             ~numalive + numdead + ldose

pairmod6a, pairmod6b: Models are equivalent, within numerical tolerances

> 
> pairmod7 <- earth(numalive + numdead ~ sex * ldose, data=df, trace=1, pmethod="none")
Using class "Formula" because lhs of formula has terms separated by "+"
x[12,3] with colnames sexmale ldose sexmale:ldose
y[12,2] with colnames numalive numdead
Forward pass term 1, 2, 4, 6, 8
RSq changed by less than 0.001 at 7 terms, 5 terms used (DeltaRSq 0)
After forward pass GRSq -0.057 RSq 0.991
Prune none penalty 2 nprune null: selected 5 of 5 terms, and 3 of 3 preds
After pruning pass GRSq 0.883 RSq 0.991
> print(summary(pairmod7))
Call: earth(formula=numalive+numdead~sex*ldose, data=df, pmethod="none",
            trace=1)

                     numalive    numdead
(Intercept)        13.9619048  6.0380952
sexmale            -2.9794486  2.9794486
ldose              -3.2571429  3.2571429
h(0-sexmale:ldose)  0.9533835 -0.9533835
h(sexmale:ldose-0) -0.6639098  0.6639098

Selected 5 of 5 terms, and 3 of 3 predictors (pmethod="none")
Termination condition: RSq changed by less than 0.001 at 5 terms
Importance: ldose, sexmale, sexmale:ldose
Number of terms at each degree of interaction: 1 4 (additive model)

               GCV      RSS      GRSq       RSq
numalive  5.872348 4.404261 0.8825725 0.9912657
numdead   5.872348 4.404261 0.8825725 0.9912657
All      11.744695 8.808521 0.8825725 0.9912657
> plot(pairmod7, nresponse=1)
> plotmo(pairmod7, nresponse=1)
 plotmo grid:    sex ldose
              female   0.5
> 
> pairmod8 <- earth(numalive + numdead ~ ., data=df, trace=1, pmethod="none",
+                    glm=list(family=binomial))
Using class "Formula" because lhs of formula has terms separated by "+"
x[12,2] with colnames ldose sexmale
y[12,2] with colnames numalive numdead
earth and glm: unweighted
Response columns numalive and numdead are a binomial pair (240 obs in total)
yfrac[12,1] with colname numalive, and values 0.95, 0.8, 0.55, 0.35, 0.1, 0...
Forward pass term 1, 2, 4, 6
RSq changed by less than 0.001 at 5 terms, 3 terms used (DeltaRSq 0)
After forward pass GRSq 0.907 RSq 0.981
Prune none penalty 2 nprune null: selected 3 of 3 terms, and 2 of 2 preds
After pruning pass GRSq 0.952 RSq 0.981
GLM numalive devratio 0.95 dof 9/11 iters 4
> print(summary(pairmod8))
Call: earth(formula=numalive+numdead~., data=df, pmethod="none", trace=1,
            glm=list(family=binomial))

GLM coefficients
             numalive
(Intercept)  1.344727
ldose       -1.064214
sexmale     -1.100743

GLM (family binomial, link logit):
 nulldev df       dev df   devratio     AIC iters converged
 124.876 11   6.75706  9      0.946   42.87     4         1

Earth selected 3 of 3 terms, and 2 of 2 predictors (pmethod="none")
Termination condition: RSq changed by less than 0.001 at 3 terms
Importance: ldose, sexmale
Number of terms at each degree of interaction: 1 2 (additive model)
Earth GCV 0.005940233    RSS 0.02425595    GRSq 0.952486    RSq 0.9807588
> plot(pairmod8, nresponse=1)
> plotmo(pairmod8, nresponse=1)
 plotmo grid:    ldose    sex
                   0.5 female
> # following fails because predictors are in a different order in dirs, ok
> try(check.models.equal(pairmod8, pairmod2, "pairmod8, pairmod2", newdata=df[5:6,]))
pairmod8, pairmod2: models not identical


m1 dirs 
            ldose sexmale
(Intercept)     0       0
ldose           2       0
sexmale         0       2

m2 dirs 
            sexmale ldose
(Intercept)       0     0
ldose             0     2
sexmale           2     0

difference mod1-mod2 dirs 
            ldose sexmale
(Intercept)     0       0
ldose           2      -2
sexmale        -2       2

Error : dirs don't match, see above messages (max=0)
> stopifnot(all.equal(sort(coef(pairmod8)), sort(coef(pairmod2)))) # ok
> set.seed(2019)
> pairmod.cv <- earth(numalive + numdead ~ ., data=df, nfold=2, trace=1, pmethod="none",
+                     keepxy=TRUE,
+                     glm=list(family=binomial))
Using class "Formula" because lhs of formula has terms separated by "+"
x[12,2] with colnames ldose sexmale
y[12,2] with colnames numalive numdead
earth and glm: unweighted
Response columns numalive and numdead are a binomial pair (240 obs in total)
yfrac[12,1] with colname numalive, and values 0.95, 0.8, 0.55, 0.35, 0.1, 0...
Forward pass term 1, 2, 4, 6
RSq changed by less than 0.001 at 5 terms, 3 terms used (DeltaRSq 0)
After forward pass GRSq 0.907 RSq 0.981
Prune none penalty 2 nprune null: selected 3 of 3 terms, and 2 of 2 preds
After pruning pass GRSq 0.952 RSq 0.981
GLM numalive devratio 0.95 dof 9/11 iters 4

CV fold 1  CVRSq  0.778   n.oof  6 50%   n.infold.nz  5 83%   n.oof.nz  6 100%
CV fold 2  CVRSq  0.707   n.oof  6 50%   n.infold.nz  6 100%   n.oof.nz  5 83%
CV all     CVRSq  0.743                  n.infold.nz 11 92%

> check.models.equal(pairmod.cv, pairmod8, "pairmod.cv, pairmod9", newdata=df[3:5,])
pairmod.cv, pairmod9: models not identical

pairmod.cv, pairmod9: glm submodel formula strings are identical: yarg ~ ldose + sexmale
pairmod.cv, pairmod9: but the actual glm submodel formulas differ (classes are "formula" and "formula")
pairmod.cv, pairmod9: glm submodels not identical (but coefs, residuals, fitted.values are the same)
pairmod.cv, pairmod9: Models are equivalent, within numerical tolerances

> 
> # TODO following fails, it shouldn't (the minus sign on the rhs messes things up), cf pairmod6a
> try(earth(numalive + numdead ~ . - ldose, data=df))
Warning in terms.formula(form, data = data) :
  'varlist' has changed (from nvar=2) to new 3 after EncodeVars() -- should no longer happen!
Error in model.matrix.default(mt, data = data, ...) : 
  model frame and formula mismatch in model.matrix()
> 
> newdata.dataframe <- df[1,,drop=FALSE] # data.frame
> print(newdata.dataframe)
  ldose numalive  sex numdead
1    -2       19 male       1
> predict.pairmod  <- predict(pairmod, newdata.dataframe)
> predict.pairmod2 <- predict(pairmod2, newdata.dataframe)
> predict.pairmod_Formula <- predict(pairmod_Formula, newdata.dataframe)
> # predict.pairmod5 <- predict(pairmod5, newdata.dataframe)
> check.same(predict.pairmod, 2.372412, max=1e-4)
> check.same(predict.pairmod2, predict.pairmod, "predict pairmod2,pairmod with newdata.dataframe")
> check.same(predict.pairmod_Formula, predict.pairmod, "predict pairmod_Formula,pairmod2 with newdata.dataframe")
> # check.same(predict.pairmod5, predict.pairmod, "predict pairmod5,pairmod2 with newdata.dataframe", allow.different.names=TRUE)
> 
> newdata.vector <- df[1,,drop=TRUE] # list
> print(newdata.vector)
$ldose
[1] -2

$numalive
[1] 19

$sex
[1] male
Levels: female male

$numdead
[1] 1

> predict.pairmodv  <- predict(pairmod, newdata.vector)
> predict.pairmod2v <- predict(pairmod2, newdata.vector)
> predict.pairmod_Formulav <- predict(pairmod_Formula, newdata.vector)
> # predict.pairmod5v <- predict(pairmod5, newdata.vector)
> check.same(predict.pairmodv, 2.372412, max=1e-4)
> check.same(predict.pairmod2v, predict.pairmodv, "predict pairmod2,pairmod with newdata.vector")
> check.same(predict.pairmod_Formulav, predict.pairmodv, "predict pairmod_Formula,pairmod2 with newdata.vector")
> # check.same(predict.pairmod5v, predict.pairmodv, "predict pairmod5,pairmod2 with newdata.vector", allow.different.names=TRUE)
> 
> plotmo(pairmod_Formula, SHOWCALL=TRUE)
 plotmo grid:    sex ldose
              female   0.5
> # plotmo(pairmod5, SHOWCALL=TRUE)
> expect.err(try(plotmo(pairmod2)), "cannot get the original model predictors") # because we deleted ldose, numalive, etc.

Looked unsuccessfully for the original predictors in the following places:

(1) object$x: NULL

(2) model.frame: variable lengths differ (found for 'numdead')

(3) getCall(object)$x: NULL

Error : cannot get the original model predictors
Got expected error from try(plotmo(pairmod2))
> expect.err(try(plotmo(pairmod2.weights)), "cannot get the original model predictors") # because we deleted ldose, numalive, etc.

Looked unsuccessfully for the original predictors in the following places:

(1) object$x: NULL

(2) model.frame: variable lengths differ (found for 'numdead')

(3) getCall(object)$x: NULL

Error : cannot get the original model predictors
Got expected error from try(plotmo(pairmod2.weights))
> 
> expect.err(try(earth(numalive + 20 - numdead ~ sex + ldose, data=df, glm=list(family=binomial))), "Binomial response (see above): all values should be between 0 and 1, or a binomial pair")

print(head(y)):
     numalive + 20 - numdead
[1,]                      38
[2,]                      32
[3,]                      22
[4,]                      14
[5,]                       4
[6,]                       0

Error : Binomial response (see above): all values should be between 0 and 1, or a binomial pair
Got expected error from try(earth(numalive + 20 - numdead ~ sex + ldose, data = df, glm = list(family = binomial)))
> 
> cat("\n===vignette short/long data example===\n")

===vignette short/long data example===
> ldose    <- rep(0:5, 2) - 2 # Venables and Ripley 4th edition page 191
> sex      <- factor(rep(c("male", "female"), times=c(6,6)))
> numdead  <- c(1,4,9,13,18,20,0,2,6,10,12,16)
> numalive <- 20 - numdead
> 
> glm.short       <- glm(cbind(numalive,numdead) ~ ldose + sex, family=binomial)
> 
> earth.short     <- earth(cbind(numalive,numdead)  ~ ldose + sex,
+                          glm=list(family=binomial))
> 
> earth.short.lin <- earth(cbind(numalive,numdead)  ~ ldose + sex,
+                          glm=list(family=binomial),
+                          # coerce earth to build a linear (no hinge) model with all vars
+                          # (generated model matches the glm.short model above)
+                          linpreds=TRUE, thresh=0, penalty=-1)
> 
> data.short <- data.frame(numalive, numdead, ldose, sex)
> 
> data.long <- expand.bpairs(data.short, c("numalive", "numdead"))
>                          # data.long$num.alive will be a fraction 0...1
> print(data.long)
         numalive ldose    sex
row1.1      FALSE    -2   male
row1.2       TRUE    -2   male
row1.3       TRUE    -2   male
row1.4       TRUE    -2   male
row1.5       TRUE    -2   male
row1.6       TRUE    -2   male
row1.7       TRUE    -2   male
row1.8       TRUE    -2   male
row1.9       TRUE    -2   male
row1.10      TRUE    -2   male
row1.11      TRUE    -2   male
row1.12      TRUE    -2   male
row1.13      TRUE    -2   male
row1.14      TRUE    -2   male
row1.15      TRUE    -2   male
row1.16      TRUE    -2   male
row1.17      TRUE    -2   male
row1.18      TRUE    -2   male
row1.19      TRUE    -2   male
row1.20      TRUE    -2   male
row2.1      FALSE    -1   male
row2.2      FALSE    -1   male
row2.3      FALSE    -1   male
row2.4      FALSE    -1   male
row2.5       TRUE    -1   male
row2.6       TRUE    -1   male
row2.7       TRUE    -1   male
row2.8       TRUE    -1   male
row2.9       TRUE    -1   male
row2.10      TRUE    -1   male
row2.11      TRUE    -1   male
row2.12      TRUE    -1   male
row2.13      TRUE    -1   male
row2.14      TRUE    -1   male
row2.15      TRUE    -1   male
row2.16      TRUE    -1   male
row2.17      TRUE    -1   male
row2.18      TRUE    -1   male
row2.19      TRUE    -1   male
row2.20      TRUE    -1   male
row3.1      FALSE     0   male
row3.2      FALSE     0   male
row3.3      FALSE     0   male
row3.4      FALSE     0   male
row3.5      FALSE     0   male
row3.6      FALSE     0   male
row3.7      FALSE     0   male
row3.8      FALSE     0   male
row3.9      FALSE     0   male
row3.10      TRUE     0   male
row3.11      TRUE     0   male
row3.12      TRUE     0   male
row3.13      TRUE     0   male
row3.14      TRUE     0   male
row3.15      TRUE     0   male
row3.16      TRUE     0   male
row3.17      TRUE     0   male
row3.18      TRUE     0   male
row3.19      TRUE     0   male
row3.20      TRUE     0   male
row4.1      FALSE     1   male
row4.2      FALSE     1   male
row4.3      FALSE     1   male
row4.4      FALSE     1   male
row4.5      FALSE     1   male
row4.6      FALSE     1   male
row4.7      FALSE     1   male
row4.8      FALSE     1   male
row4.9      FALSE     1   male
row4.10     FALSE     1   male
row4.11     FALSE     1   male
row4.12     FALSE     1   male
row4.13     FALSE     1   male
row4.14      TRUE     1   male
row4.15      TRUE     1   male
row4.16      TRUE     1   male
row4.17      TRUE     1   male
row4.18      TRUE     1   male
row4.19      TRUE     1   male
row4.20      TRUE     1   male
row5.1      FALSE     2   male
row5.2      FALSE     2   male
row5.3      FALSE     2   male
row5.4      FALSE     2   male
row5.5      FALSE     2   male
row5.6      FALSE     2   male
row5.7      FALSE     2   male
row5.8      FALSE     2   male
row5.9      FALSE     2   male
row5.10     FALSE     2   male
row5.11     FALSE     2   male
row5.12     FALSE     2   male
row5.13     FALSE     2   male
row5.14     FALSE     2   male
row5.15     FALSE     2   male
row5.16     FALSE     2   male
row5.17     FALSE     2   male
row5.18     FALSE     2   male
row5.19      TRUE     2   male
row5.20      TRUE     2   male
row6.1      FALSE     3   male
row6.2      FALSE     3   male
row6.3      FALSE     3   male
row6.4      FALSE     3   male
row6.5      FALSE     3   male
row6.6      FALSE     3   male
row6.7      FALSE     3   male
row6.8      FALSE     3   male
row6.9      FALSE     3   male
row6.10     FALSE     3   male
row6.11     FALSE     3   male
row6.12     FALSE     3   male
row6.13     FALSE     3   male
row6.14     FALSE     3   male
row6.15     FALSE     3   male
row6.16     FALSE     3   male
row6.17     FALSE     3   male
row6.18     FALSE     3   male
row6.19     FALSE     3   male
row6.20     FALSE     3   male
row7.1       TRUE    -2 female
row7.2       TRUE    -2 female
row7.3       TRUE    -2 female
row7.4       TRUE    -2 female
row7.5       TRUE    -2 female
row7.6       TRUE    -2 female
row7.7       TRUE    -2 female
row7.8       TRUE    -2 female
row7.9       TRUE    -2 female
row7.10      TRUE    -2 female
row7.11      TRUE    -2 female
row7.12      TRUE    -2 female
row7.13      TRUE    -2 female
row7.14      TRUE    -2 female
row7.15      TRUE    -2 female
row7.16      TRUE    -2 female
row7.17      TRUE    -2 female
row7.18      TRUE    -2 female
row7.19      TRUE    -2 female
row7.20      TRUE    -2 female
row8.1      FALSE    -1 female
row8.2      FALSE    -1 female
row8.3       TRUE    -1 female
row8.4       TRUE    -1 female
row8.5       TRUE    -1 female
row8.6       TRUE    -1 female
row8.7       TRUE    -1 female
row8.8       TRUE    -1 female
row8.9       TRUE    -1 female
row8.10      TRUE    -1 female
row8.11      TRUE    -1 female
row8.12      TRUE    -1 female
row8.13      TRUE    -1 female
row8.14      TRUE    -1 female
row8.15      TRUE    -1 female
row8.16      TRUE    -1 female
row8.17      TRUE    -1 female
row8.18      TRUE    -1 female
row8.19      TRUE    -1 female
row8.20      TRUE    -1 female
row9.1      FALSE     0 female
row9.2      FALSE     0 female
row9.3      FALSE     0 female
row9.4      FALSE     0 female
row9.5      FALSE     0 female
row9.6      FALSE     0 female
row9.7       TRUE     0 female
row9.8       TRUE     0 female
row9.9       TRUE     0 female
row9.10      TRUE     0 female
row9.11      TRUE     0 female
row9.12      TRUE     0 female
row9.13      TRUE     0 female
row9.14      TRUE     0 female
row9.15      TRUE     0 female
row9.16      TRUE     0 female
row9.17      TRUE     0 female
row9.18      TRUE     0 female
row9.19      TRUE     0 female
row9.20      TRUE     0 female
row10.1     FALSE     1 female
row10.2     FALSE     1 female
row10.3     FALSE     1 female
row10.4     FALSE     1 female
row10.5     FALSE     1 female
row10.6     FALSE     1 female
row10.7     FALSE     1 female
row10.8     FALSE     1 female
row10.9     FALSE     1 female
row10.10    FALSE     1 female
row10.11     TRUE     1 female
row10.12     TRUE     1 female
row10.13     TRUE     1 female
row10.14     TRUE     1 female
row10.15     TRUE     1 female
row10.16     TRUE     1 female
row10.17     TRUE     1 female
row10.18     TRUE     1 female
row10.19     TRUE     1 female
row10.20     TRUE     1 female
row11.1     FALSE     2 female
row11.2     FALSE     2 female
row11.3     FALSE     2 female
row11.4     FALSE     2 female
row11.5     FALSE     2 female
row11.6     FALSE     2 female
row11.7     FALSE     2 female
row11.8     FALSE     2 female
row11.9     FALSE     2 female
row11.10    FALSE     2 female
row11.11    FALSE     2 female
row11.12    FALSE     2 female
row11.13     TRUE     2 female
row11.14     TRUE     2 female
row11.15     TRUE     2 female
row11.16     TRUE     2 female
row11.17     TRUE     2 female
row11.18     TRUE     2 female
row11.19     TRUE     2 female
row11.20     TRUE     2 female
row12.1     FALSE     3 female
row12.2     FALSE     3 female
row12.3     FALSE     3 female
row12.4     FALSE     3 female
row12.5     FALSE     3 female
row12.6     FALSE     3 female
row12.7     FALSE     3 female
row12.8     FALSE     3 female
row12.9     FALSE     3 female
row12.10    FALSE     3 female
row12.11    FALSE     3 female
row12.12    FALSE     3 female
row12.13    FALSE     3 female
row12.14    FALSE     3 female
row12.15    FALSE     3 female
row12.16    FALSE     3 female
row12.17     TRUE     3 female
row12.18     TRUE     3 female
row12.19     TRUE     3 female
row12.20     TRUE     3 female
> 
> glm.long       <- glm(numalive ~ ldose + sex, data=data.long, family=binomial)
> 
> earth.long     <- earth(numalive ~ ldose + sex, data=data.long,
+                         glm=list(family=binomial))
> 
> earth.long.lin <- earth(numalive ~ ldose + sex, data=data.long,
+                         glm=list(family=binomial),
+                         linpreds=TRUE, thresh=0, penalty=-1)
> print(summary(glm.short))

Call:
glm(formula = cbind(numalive, numdead) ~ ldose + sex, family = binomial)

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)   1.3447     0.2797   4.809 1.52e-06 ***
ldose        -1.0642     0.1311  -8.119 4.70e-16 ***
sexmale      -1.1007     0.3558  -3.093  0.00198 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 124.8756  on 11  degrees of freedom
Residual deviance:   6.7571  on  9  degrees of freedom
AIC: 42.867

Number of Fisher Scoring iterations: 4

> print(summary(earth.short))
Call: earth(formula=cbind(numalive,numdead)~ldose+sex,
            glm=list(family=binomial))

GLM coefficients
             numalive
(Intercept)  1.344727
ldose       -1.064214
sexmale     -1.100743

GLM (family binomial, link logit):
 nulldev df       dev df   devratio     AIC iters converged
 124.876 11   6.75706  9      0.946   42.87     4         1

Earth selected 3 of 3 terms, and 2 of 2 predictors
Termination condition: RSq changed by less than 0.001 at 3 terms
Importance: ldose, sexmale
Number of terms at each degree of interaction: 1 2 (additive model)
Earth GCV 0.005940233    RSS 0.02425595    GRSq 0.952486    RSq 0.9807588
> print(summary(earth.short.lin))
Call: earth(formula=cbind(numalive,numdead)~ldose+sex,
            glm=list(family=binomial), linpreds=TRUE, thresh=0, penalty=-1)

GLM coefficients
             numalive
(Intercept)  1.344727
ldose       -1.064214
sexmale     -1.100743

GLM (family binomial, link logit):
 nulldev df       dev df   devratio     AIC iters converged
 124.876 11   6.75706  9      0.946   42.87     4         1

Earth selected 3 of 3 terms, and 2 of 2 predictors
Termination condition: No new term increases RSq at 3 terms
Importance: ldose, sexmale
Number of terms at each degree of interaction: 1 2 (additive model)
Earth GCV 0.002021329    RSS 0.02425595    GRSq 0.9807588    RSq 0.9807588
> print(summary(glm.long))

Call:
glm(formula = numalive ~ ldose + sex, family = binomial, data = data.long)

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)   1.3447     0.2797   4.809 1.52e-06 ***
ldose        -1.0642     0.1311  -8.119 4.70e-16 ***
sexmale      -1.1007     0.3558  -3.093  0.00198 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 331.36  on 239  degrees of freedom
Residual deviance: 213.24  on 237  degrees of freedom
AIC: 219.24

Number of Fisher Scoring iterations: 5

> print(summary(earth.long))
Call: earth(formula=numalive~ldose+sex, data=data.long,
            glm=list(family=binomial))

GLM coefficients
             numalive
(Intercept) -1.107855
sexmale     -1.087416
h(2-ldose)   1.196265

GLM (family binomial, link logit):
 nulldev  df       dev  df   devratio     AIC iters converged
 331.359 239   215.878 237      0.349   221.9     5         1

Earth selected 3 of 5 terms, and 2 of 2 predictors
Termination condition: RSq changed by less than 0.001 at 5 terms
Importance: ldose, sexmale
Number of terms at each degree of interaction: 1 2 (additive model)
Earth GCV 0.1541512    RSS 35.47083    GRSq 0.3850639    RSq 0.4054752
> print(summary(earth.long.lin))
Call: earth(formula=numalive~ldose+sex, data=data.long,
            glm=list(family=binomial), linpreds=TRUE, thresh=0, penalty=-1)

GLM coefficients
             numalive
(Intercept)  1.344727
ldose       -1.064214
sexmale     -1.100743

GLM (family binomial, link logit):
 nulldev  df       dev  df   devratio     AIC iters converged
 331.359 239   213.241 237      0.356   219.2     5         1

Earth selected 3 of 3 terms, and 2 of 2 predictors
Termination condition: No new term increases RSq at 3 terms
Importance: ldose, sexmale
Number of terms at each degree of interaction: 1 2 (additive model)
Earth GCV 0.145563    RSS 34.93512    GRSq 0.4144543    RSq 0.4144543
> 
> print(coef(glm.short))
(Intercept)       ldose     sexmale 
   1.344727   -1.064214   -1.100743 
> stopifnot(max(coef(earth.short.lin) - coef(glm.short)) < 1e-12)  # same
> stopifnot(max(coef(glm.long) - coef(glm.short)) < 1e-12)         # same
> stopifnot(max(coef(earth.long.lin) - coef(glm.short)) < 1e-12)   # same
> coef(earth.short)     # different
(Intercept)       ldose     sexmale 
   1.344727   -1.064214   -1.100743 
> coef(earth.long)      # different
(Intercept)  h(2-ldose)     sexmale 
  -1.107855    1.196265   -1.087416 
> 
> cat("\n===cross validated binomial pair model===\n")

===cross validated binomial pair model===
> # use a big enough data set for cross validation without negative GRSqs
> n2 <- 20
> set.seed(2019)
> good <- pmax(round(c((1:n2),(n2:1)) + rnorm(2*n2)), 0)
> bad  <- pmax(n2 - good, 0)
> data <- data.frame(good, bad, x=1:(2 * n2))
> set.seed(2020)
> earth_cv <- earth(good+bad~., data=data, glm=list(family=binomial), trace=1, nfold=2, keepxy=TRUE)
Using class "Formula" because lhs of formula has terms separated by "+"
x[40,1] with colname x, and values 1, 2, 3, 4, 5, 6, 7, 8, 9, 10...
y[40,2] with colnames good bad
earth and glm: unweighted
Response columns good and bad are a binomial pair (800 obs in total)
yfrac[40,1] with colname good, and values 0.1, 0.05, 0.05, 0.25, 0.2, 0...
Forward pass term 1, 2, 4, 6, 8, 10, 12
RSq changed by less than 0.001 at 11 terms, 7 terms used (DeltaRSq 0.00011)
After forward pass GRSq 0.923 RSq 0.968
Prune backward penalty 2 nprune null: selected 4 of 7 terms, and 1 of 1 preds
After pruning pass GRSq 0.949 RSq 0.963
GLM good devratio 0.93 dof 36/39 iters 4

CV fold 1  CVRSq  0.923   n.oof 21 48%   n.infold.nz 21 100%   n.oof.nz 19 100%
CV fold 2  CVRSq  0.928   n.oof 19 52%   n.infold.nz 19 100%   n.oof.nz 21 100%
CV all     CVRSq  0.926                  n.infold.nz 40 100%

> cat("cross validated model:\n")
cross validated model:
> print(summary(earth_cv))
Call: earth(formula=good+bad~., data=data, keepxy=TRUE, trace=1,
            glm=list(family=binomial), nfold=2)

GLM coefficients
                  good
(Intercept)  2.6553682
h(20-x)     -0.2657911
h(x-23)     -0.5420035
h(x-26)      0.3168845

GLM (family binomial, link logit):
 nulldev df       dev df   devratio     AIC iters converged
 324.951 39   23.0641 36      0.929     145     4         1

Earth selected 4 of 7 terms, and 1 of 1 predictors
Termination condition: RSq changed by less than 0.001 at 7 terms
Importance: x
Number of terms at each degree of interaction: 1 3 (additive model)
Earth GCV 0.004633736  RSS 0.1261535  GRSq 0.9488392  RSq 0.9633701  CVRSq 0.9256185

Note: the cross-validation sd's below are standard deviations across folds

Cross validation:   nterms 3.50 sd 0.71    nvars 1.00 sd 0.00

 CVRSq    sd MaxErr    sd   AUC    sd MeanDev    sd CalibInt    sd CalibSlope
 0.926 0.004 -0.208 0.289 0.994 0.008    1.04 0.132   -0.052 0.401       1.08
    sd
 0.243
> cat("first fold model:\n")
first fold model:
> print(summary(earth_cv$cv.list[[1]]))
Call: earth(x=infold.x, y=infold.y, weights=infold.weights, wp=wp,
            subset=subset, pmethod=if(pmethod=="cv")"backward"elsepmethod,
            keepxy=(keepxy==2), trace=trace, glm=glm.arg, degree=degree,
            nfold=0, ncross=0, varmod.method="none", Scale.y=Scale.y)

GLM coefficients
                  good
(Intercept)  3.0769321
h(21-x)     -0.2770319
h(x-21)     -0.2732809

GLM (family binomial, link logit):
 nulldev df       dev df   devratio     AIC iters converged
 206.311 20   10.5272 18      0.949   72.58     4         1

Earth selected 3 of 3 terms, and 1 of 1 predictors
Termination condition: RSq changed by less than 0.001 at 3 terms
Importance: x
Number of terms at each degree of interaction: 1 2 (additive model)
Earth GCV 0.004101511    RSS 0.04999937    GRSq 0.9637956    RSq 0.9768292
> par(mfrow = c(2, 2), mar = c(3, 3, 3, 1), mgp = c(1.5, 0.5, 0))
> set.seed(2019)
> plotmo(earth_cv, type="earth", pt.col=2, do.par=0)
> empty.plot()
> plot.earth.models(list(earth_cv, earth_cv$cv.list[[1]], earth_cv$cv.list[[2]]), which=1:2, do.par=0)
> 
> # try plotmo on one of the fold models
> expect.err(try(plotmo(earth_cv$cv.list[[1]])), "cannot get the original model predictors (use keepxy=2 in the call to earth)")

Looked unsuccessfully for the original predictors in the following places:

(1) object$x: NULL

(2) model.frame: no formula in getCall(object)

(3) getCall(object)$x: object 'infold.x' not found

Error : cannot get the original model predictors (use keepxy=2 in the call to earth)
Got expected error from try(plotmo(earth_cv$cv.list[[1]]))
> # can plotmo on a fold model if we use keepxy=2 in call to earth
> set.seed(2020)
> earth_cv.keepxy2 <- earth(good+bad~., data=data, glm=list(family=binomial), trace=.5, nfold=2, keepxy=2)
Model with pmethod="backward": GRSq 0.949 RSq 0.963 nterms 4
CV fold 1  CVRSq  0.923   n.oof 21 48%   n.infold.nz 21 100%   n.oof.nz 19 100%
CV fold 2  CVRSq  0.928   n.oof 19 52%   n.infold.nz 19 100%   n.oof.nz 21 100%
CV all     CVRSq  0.926                  n.infold.nz 40 100%

> plotmo(earth_cv.keepxy2$cv.list[[1]], type="earth", SHOWCALL=TRUE)
> 
> source("test.epilog.R")
