R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("aod-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('aod')
Package aod, version 1.1-4 
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "anova.glimML"
> 
> ### * anova.glimML
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: anova-methods
> ### Title: Likelihood-Ratio Tests for Nested ML Models
> ### Aliases: anova-methods anova,ANY-method anova,glimML-method
> ###   anova.glimML-class show,anova.glimML-method
> ### Keywords: regression
> 
> ### ** Examples
> 
>   data(orob2)
>   # likelihood ratio test for the effect of root
>   fm1 <- betabin(cbind(y, n - y) ~ seed, ~ 1, data = orob2)
>   fm2 <- betabin(cbind(y, n - y) ~ seed + root, ~ 1, data = orob2)
>   anova(fm1, fm2)
Analysis of Deviance Table (beta-binomial models)

fm1: fixed = cbind(y, n - y) ~ seed; random = ~1 
fm2: fixed = cbind(y, n - y) ~ seed + root; random = ~1 

      logL k   AIC   BIC Resid. dev. Resid. Df    Test Deviance Df P(> Chi2)
fm1 -63.55 3 133.1 136.2       50.51        18                              
fm2 -55.83 4 119.7 123.8       35.07        17 fm1-fm2    15.44  1 8.509e-05
>   
> 
> 
> cleanEx(); ..nameEx <- "betabin"
> 
> ### * betabin
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: betabin
> ### Title: Beta-Binomial Model for Proportions
> ### Aliases: betabin
> ### Keywords: regression
> 
> ### ** Examples
> 
>   data(orob2)
>   fm1 <- betabin(cbind(y, n - y) ~ seed, ~ 1, data = orob2)
>   fm2 <- betabin(cbind(y, n - y) ~ seed + root, ~ 1, data = orob2)
>   fm3 <- betabin(cbind(y, n - y) ~ seed * root, ~ 1, data = orob2)
>   # show the model
>   fm1; fm2; fm3
Beta-binomial model
-------------------
betabin(formula = cbind(y, n - y) ~ seed, random = ~1, data = orob2)

Convergence was obtained after 80 iterations.

Fixed-effect coefficients:
            Estimate Std. Error z value Pr(> |z|)
(Intercept)  -0.2602     0.2262 -1.1502    0.2500
seedO75       0.4130     0.2993  1.3798    0.1676

Overdispersion coefficients:
                Estimate Std. Error z value Pr(> z)
phi.(Intercept)    0.078     0.0302  2.5885  0.0048

Log-likelihood = -63.553; nbpar = 3; df.residual = 18; Deviance = 50.509; AIC = 133.105

Beta-binomial model
-------------------
betabin(formula = cbind(y, n - y) ~ seed + root, random = ~1, 
    data = orob2)

Convergence was obtained after 167 iterations.

Fixed-effect coefficients:
             Estimate Std. Error z value Pr(> |z|)
(Intercept)   -0.7280     0.1995 -3.6489    0.0003
seedO75        0.3427     0.2106  1.6269    0.1038
rootCUCUMBER   1.0108     0.2020  5.0037    < 1e-4

Overdispersion coefficients:
                Estimate Std. Error z value Pr(> z)
phi.(Intercept)   0.0194     0.0142  1.3678  0.0857

Log-likelihood = -55.832; nbpar = 4; df.residual = 17; Deviance = 35.067; AIC = 119.664

Beta-binomial model
-------------------
betabin(formula = cbind(y, n - y) ~ seed * root, random = ~1, 
    data = orob2)

Convergence was obtained after 196 iterations.

Fixed-effect coefficients:
                     Estimate Std. Error z value Pr(> |z|)
(Intercept)           -0.4456     0.2183 -2.0411    0.0412
seedO75               -0.0961     0.2737 -0.3512    0.7255
rootCUCUMBER           0.5235     0.2968  1.7636    0.0778
seedO75:rootCUCUMBER   0.7962     0.3779  2.1068    0.0351

Overdispersion coefficients:
                Estimate Std. Error z value Pr(> z)
phi.(Intercept)   0.0124     0.0113  1.0927  0.1373

Log-likelihood = -53.767; nbpar = 5; df.residual = 16; Deviance = 30.937; AIC = 117.534

>   # AIC
>   AIC(fm1, fm2, fm3)
    df      AIC
fm1  3 133.1054
fm2  4 119.6637
fm3  5 117.5336
>   # Wald test for root effect
>   wald.test(b = coef(fm3), Sigma = vcov(fm3), Terms = 3:4)
Wald test:
----------

Chi-squared test:
X2 = 34.9, df = 2, P(> X2) = 2.6e-08
>   # likelihood ratio test for root effect
>   anova(fm1, fm3)
Analysis of Deviance Table (beta-binomial models)

fm1: fixed = cbind(y, n - y) ~ seed; random = ~1 
fm3: fixed = cbind(y, n - y) ~ seed * root; random = ~1 

      logL k   AIC   BIC Resid. dev. Resid. Df    Test Deviance Df P(> Chi2)
fm1 -63.55 3 133.1 136.2       50.51        18                              
fm3 -53.77 5 117.5 122.8       30.94        16 fm1-fm3    19.57  2 5.624e-05
>   # model predictions
>   New <- expand.grid(seed = levels(orob2$seed),
+                      root = levels(orob2$root))
>   data.frame(New, predict(fm3, New, se = TRUE, type = "response"))
  seed     root       fit     se.fit
1  O73     BEAN 0.3904148 0.05195385
2  O75     BEAN 0.3677958 0.03808118
3  O73 CUCUMBER 0.5194646 0.05118758
4  O75 CUCUMBER 0.6852506 0.03616440
>   # Djallonke sheep data
>   data(dja)
>   betabin(cbind(y, n - y) ~ group, ~ 1, dja)
Beta-binomial model
-------------------
betabin(formula = cbind(y, n - y) ~ group, random = ~1, data = dja)

Convergence was obtained after 68 iterations.

Fixed-effect coefficients:
            Estimate Std. Error z value Pr(> |z|)
(Intercept)  -0.5846     0.1861 -3.1408    0.0017
groupTREAT   -0.8522     0.2527 -3.3717    0.0007

Overdispersion coefficients:
                Estimate Std. Error z value Pr(> z)
phi.(Intercept)    0.056     0.0301  1.8594  0.0315

Log-likelihood = -107.103; nbpar = 3; df.residual = 72; Deviance = 108.394; AIC = 220.206

>   # heterogeneous phi
>   betabin(cbind(y, n - y) ~ group, ~ group, dja)
Beta-binomial model
-------------------
betabin(formula = cbind(y, n - y) ~ group, random = ~group, data = dja)

Convergence was obtained after 501 iterations.

Fixed-effect coefficients:
            Estimate Std. Error z value Pr(> |z|)
(Intercept)  -0.6072     0.2254 -2.6938    0.0071
groupTREAT   -0.8429     0.2647 -3.1838    0.0015

Overdispersion coefficients:
               Estimate Std. Error     z value Pr(> z)
phi.groupCTRL    0.1642     0.0744      2.2059  0.0137
phi.groupTREAT   0.0000     0.0000 135633.5389  < 1e-4

Log-likelihood = -102.886; nbpar = 4; df.residual = 71; Deviance = 99.960; AIC = 213.772

>   # phi fixed to zero in group TREAT
>    betabin(cbind(y, n - y) ~ group, ~ group, dja,
+     fixpar = list(4, 0))
Beta-binomial model
-------------------
betabin(formula = cbind(y, n - y) ~ group, random = ~group, data = dja, 
    fixpar = list(4, 0))

Convergence was obtained after 85 iterations.

Fixed-effect coefficients:
            Estimate Std. Error z value Pr(> |z|)
(Intercept)  -0.6081     0.2249 -2.7036    0.0069
groupTREAT   -0.8425     0.2644 -3.1868    0.0014

Overdispersion coefficients:
              Estimate Std. Error z value Pr(> z)
phi.groupCTRL   0.1624     0.0737  2.2026  0.0138

Overdispersion coefficients set to fixed values:
               Value
phi.groupTREAT     0

Log-likelihood = -102.886; nbpar = 3; df.residual = 72; Deviance = 99.960; AIC = 211.771

>   # glim without overdispersion
>   summary(glm(cbind(y, n - y) ~ group,
+     family = binomial, data = dja))

Call:
glm(formula = cbind(y, n - y) ~ group, family = binomial, data = dja)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.3440  -0.9653  -0.1476   0.6731   3.3036  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -0.5217     0.1477  -3.531 0.000414 ***
groupTREAT   -0.9289     0.2028  -4.581 4.63e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 136.68  on 74  degrees of freedom
Residual deviance: 115.57  on 73  degrees of freedom
AIC: 225.38

Number of Fisher Scoring iterations: 4

>   # phi fixed to zero in both groups
>   betabin(cbind(y, n - y) ~ group, ~ group, dja,
+     fixpar = list(c(3, 4), c(0, 0))) 
Beta-binomial model
-------------------
betabin(formula = cbind(y, n - y) ~ group, random = ~group, data = dja, 
    fixpar = list(c(3, 4), c(0, 0)))

Convergence was obtained after 41 iterations.

Fixed-effect coefficients:
            Estimate Std. Error z value Pr(> |z|)
(Intercept)  -0.5217     0.1477 -3.5312     4e-04
groupTREAT   -0.9289     0.2028 -4.5808    < 1e-4

Overdispersion coefficients set to fixed values:
               Value
phi.groupCTRL      0
phi.groupTREAT     0

Log-likelihood = -110.692; nbpar = 2; df.residual = 73; Deviance = 115.573; AIC = 225.384

>   
> 
> 
> cleanEx(); ..nameEx <- "donner"
> 
> ### * donner
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: donner
> ### Title: Test of Proportion Homogeneity using Donner's Adjustment
> ### Aliases: donner show,donner-class
> ### Keywords: htest
> 
> ### ** Examples
> 
>   data(rats)
>   donner(formula = cbind(y, n - y) ~ group, data = rats)

Test of proportion homogeneity (Donner, 1989) 
--------------------------------------------- 
donner(formula = cbind(y, n - y) ~ group, data = rats)
N = 32 clusters, n = 303 subjects, y = 254 cases, I = 2 groups.

Data and correction factors:
  group  N   n   y      p     C
1  CTRL 16 158 142 0.8987 3.350
2 TREAT 16 145 112 0.7724 3.115

Intra-cluster correlation (anova estimate): 0.2506 

Adjusted chi-squared test:
X2 = 2.8, df = 1, P(> X2) = 0.0966
>   donner(formula = y/n ~ group, weights = n, data = rats)

Test of proportion homogeneity (Donner, 1989) 
--------------------------------------------- 
donner(formula = y/n ~ group, weights = n, data = rats)
N = 32 clusters, n = 303 subjects, y = 254 cases, I = 2 groups.

Data and correction factors:
  group  N   n   y      p     C
1  CTRL 16 158 142 0.8987 3.350
2 TREAT 16 145 112 0.7724 3.115

Intra-cluster correlation (anova estimate): 0.2506 

Adjusted chi-squared test:
X2 = 2.8, df = 1, P(> X2) = 0.0966
>   donner(response = cbind(y, n - y), group = group, data = rats)

Test of proportion homogeneity (Donner, 1989) 
--------------------------------------------- 
donner(response = cbind(y, n - y), group = group, data = rats)
N = 32 clusters, n = 303 subjects, y = 254 cases, I = 2 groups.

Data and correction factors:
  group  N   n   y      p     C
1  CTRL 16 158 142 0.8987 3.350
2 TREAT 16 145 112 0.7724 3.115

Intra-cluster correlation (anova estimate): 0.2506 

Adjusted chi-squared test:
X2 = 2.8, df = 1, P(> X2) = 0.0966
>   donner(response = y/n, weights = n, group = group, data = rats)

Test of proportion homogeneity (Donner, 1989) 
--------------------------------------------- 
donner(response = y/n, weights = n, group = group, data = rats)
N = 32 clusters, n = 303 subjects, y = 254 cases, I = 2 groups.

Data and correction factors:
  group  N   n   y      p     C
1  CTRL 16 158 142 0.8987 3.350
2 TREAT 16 145 112 0.7724 3.115

Intra-cluster correlation (anova estimate): 0.2506 

Adjusted chi-squared test:
X2 = 2.8, df = 1, P(> X2) = 0.0966
>   # standard test
>   donner(cbind(y, n - y) ~ group, data = rats, C = c(1, 1))

Test of proportion homogeneity (Donner, 1989) 
--------------------------------------------- 
donner(formula = cbind(y, n - y) ~ group, data = rats, C = c(1, 
    1))
N = 32 clusters, n = 303 subjects, y = 254 cases, I = 2 groups.

Data and correction factors:
  group  N   n   y      p C
1  CTRL 16 158 142 0.8987 1
2 TREAT 16 145 112 0.7724 1

Intra-cluster correlation (anova estimate): 0.2506 

Adjusted chi-squared test:
X2 = 8.9, df = 1, P(> X2) = 0.0029
>   data(antibio)
>   donner(cbind(y, n - y) ~ treatment, data = antibio)

Test of proportion homogeneity (Donner, 1989) 
--------------------------------------------- 
donner(formula = cbind(y, n - y) ~ treatment, data = antibio)
N = 24 clusters, n = 542 subjects, y = 67 cases, I = 4 groups.

Data and correction factors:
  treatment N   n  y       p     C
1         1 7 144 18 0.12500 2.030
2         2 6 129  8 0.06202 1.982
3         3 5 130 24 0.18462 2.224
4         4 6 139 17 0.12230 2.129

Intra-cluster correlation (anova estimate): 0.0439 

Adjusted chi-squared test:
X2 = 4.3, df = 3, P(> X2) = 0.2318
>   
> 
> 
> cleanEx(); ..nameEx <- "icc"
> 
> ### * icc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: icc
> ### Title: Intra-Cluster Correlation
> ### Aliases: icc show,icc-method
> ### Keywords: htest
> 
> ### ** Examples
> 
>   data(rats)
>   icc(n, y, rats[rats$group == "CTRL", ])
Loading required package: nlme

Intra-cluster correlation
-------------------------
icc(n = n, y = y, data = rats[rats$group == "CTRL", ])
N = 16 clusters, n = 158 subjects, y = 142 cases.

         rho     se
REML  0.0267 0.0763
ANOVA 0.0291       

F test for ML/REML method (H0: rho = 0):
F = 1.3, df num. = 15, df denom. = 142, P(> F) = 0.2128
>   res <- icc(n, y, rats[rats$group == "TREAT", ], R = 5000)
Loading required package: MASS
>   res

Intra-cluster correlation
-------------------------
icc(n = n, y = y, data = rats[rats$group == "TREAT", ], R = 5000)
N = 16 clusters, n = 145 subjects, y = 112 cases.

         rho     se CI.low  CI.up
REML  0.3846 0.0856 0.1984 0.6166
ANOVA 0.3721                     

(Monte Carlo 95%CI based on 5000 replications)

F test for ML/REML method (H0: rho = 0):
F = 6.4, df num. = 15, df denom. = 129, P(> F) = 0
>   hist(res@rho.MC)
>   by(rats,
+      list(group = rats$group),
+      function(x) icc(n, y, data = x))
group: CTRL

Intra-cluster correlation
-------------------------
icc(n = n, y = y, data = x)
N = 16 clusters, n = 158 subjects, y = 142 cases.

         rho     se
REML  0.0267 0.0763
ANOVA 0.0291       

F test for ML/REML method (H0: rho = 0):
F = 1.3, df num. = 15, df denom. = 142, P(> F) = 0.2128
------------------------------------------------------------ 
group: TREAT

Intra-cluster correlation
-------------------------
icc(n = n, y = y, data = x)
N = 16 clusters, n = 145 subjects, y = 112 cases.

         rho     se
REML  0.3846 0.0856
ANOVA 0.3721       

F test for ML/REML method (H0: rho = 0):
F = 6.4, df num. = 15, df denom. = 129, P(> F) = 0
>   
> 
> 
> cleanEx(); ..nameEx <- "invlink"
> 
> ### * invlink
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: invlink
> ### Title: Transformation from the Link Scale to the Observation Scale
> ### Aliases: invlink
> ### Keywords: math
> 
> ### ** Examples
> 
>   x <- seq(-5, 5, length = 100)
>   plot(x, invlink(x, type = "logit"),
+        type = "l", lwd = 2, ylab = "Probability")
>   lines(x, invlink(x, type = "cloglog"), lty = 2, lwd = 2)
>   grid(col = "black")
>   legend(-5, 1, legend = c("alogit(x)", "acloglog(x)"),
+          lty = c(1, 2), bg = "white")
>   
> 
> 
> cleanEx(); ..nameEx <- "link"
> 
> ### * link
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: link
> ### Title: Transformation from the Observation Scale to the Link Scale
> ### Aliases: link
> ### Keywords: math
> 
> ### ** Examples
> 
>   x <- seq(.001, .999, length = 100)
>   plot(x, link(x, type = "logit"),
+        type = "l", lwd = 2, ylab = "link(proba.)")
>   lines(x, link(x, type = "cloglog"), lty = 2, lwd = 2)
>   grid(col = "black")
>   legend(0, 6, legend = c("logit(x)", "cloglog(x)"),
+          lty = c(1, 2), bg = "white")
>   
> 
> 
> cleanEx(); ..nameEx <- "lizards"
> 
> ### * lizards
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lizards
> ### Title: A Comparison of Site Preferences of Two Species of Lizard
> ### Aliases: lizards
> ### Keywords: datasets
> 
> ### ** Examples
> data(lizards)
> 
> 
> cleanEx(); ..nameEx <- "negbin"
> 
> ### * negbin
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: negbin
> ### Title: Negative-Binomial Model for Counts
> ### Aliases: negbin
> ### Keywords: regression
> 
> ### ** Examples
> 
>   # without offset
>   data(salmonella)
>   negbin(y ~ log(dose + 10) + dose, ~ 1, salmonella)
Negative-binomial model
-----------------------
negbin(formula = y ~ log(dose + 10) + dose, random = ~1, data = salmonella)

Convergence was obtained after 165 iterations.

Fixed-effect coefficients:
               Estimate Std. Error z value Pr(> |z|)
(Intercept)      2.1976     0.3566  6.1618    < 1e-4
log(dose + 10)   0.3125     0.0994  3.1440    0.0017
dose            -0.0010     0.0005 -2.0369    0.0417

Overdispersion coefficients:
                Estimate Std. Error z value Pr(> z)
phi.(Intercept)   0.0488     0.0324  1.5062   0.066

Log-likelihood = -62.890; nbpar = 4; df.residual = 14; Deviance = 33.243; AIC = 133.779

>   library(MASS) # function glm.nb in MASS
>   fm.nb <- glm.nb(y ~ log(dose + 10) + dose,
+                   link = log, data = salmonella)
>   coef(fm.nb)
   (Intercept) log(dose + 10)           dose 
  2.1976273870   0.3125097501  -0.0009802996 
>   1 / fm.nb$theta # theta = 1 / phi
[1] 0.0487684
>   c(logLik(fm.nb), AIC(fm.nb))
[1] -62.88959 133.77918
>   # with offset
>   data(dja)
>   negbin(y ~ group + offset(log(trisk)), ~ group, dja)
Negative-binomial model
-----------------------
negbin(formula = y ~ group + offset(log(trisk)), random = ~group, 
    data = dja)

Convergence was obtained after 243 iterations.

Fixed-effect coefficients:
            Estimate Std. Error z value Pr(> |z|)
(Intercept)  -0.5404     0.2297 -2.3530    0.0186
groupTREAT   -1.0355     0.2616 -3.9584    < 1e-4

Overdispersion coefficients:
               Estimate Std. Error     z value Pr(> z)
phi.groupCTRL    0.8388      0.418      2.0067  0.0224
phi.groupTREAT   0.0000      0.000 616167.5865  < 1e-4

Log-likelihood = -121.150; nbpar = 4; df.residual = 71; Deviance = 111.829; AIC = 250.301

>   # phi fixed to zero in group TREAT
>   negbin(y ~ group + offset(log(trisk)), ~ group, dja,
+     fixpar = list(4, 0))
Negative-binomial model
-----------------------
negbin(formula = y ~ group + offset(log(trisk)), random = ~group, 
    data = dja, fixpar = list(4, 0))

Convergence was obtained after 113 iterations.

Fixed-effect coefficients:
            Estimate Std. Error z value Pr(> |z|)
(Intercept)  -0.5526     0.2277 -2.4267    0.0152
groupTREAT   -1.0205     0.2598 -3.9287    < 1e-4

Overdispersion coefficients:
              Estimate Std. Error z value Pr(> z)
phi.groupCTRL   0.8287      0.412  2.0117  0.0221

Overdispersion coefficients set to fixed values:
               Value
phi.groupTREAT     0

Log-likelihood = -121.149; nbpar = 3; df.residual = 72; Deviance = 111.826; AIC = 248.297

>   # glim without overdispersion
>   summary(glm(y ~ group + offset(log(trisk)),
+     family = poisson, data = dja))

Call:
glm(formula = y ~ group + offset(log(trisk)), family = poisson, 
    data = dja)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.3888  -1.0801  -0.1201   0.6566   3.7450  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -0.6975     0.1170  -5.960 2.53e-09 ***
groupTREAT   -0.8754     0.1712  -5.112 3.19e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 162.67  on 74  degrees of freedom
Residual deviance: 136.86  on 73  degrees of freedom
AIC: 271.33

Number of Fisher Scoring iterations: 5

>   # phi fixed to zero in both groups
>   negbin(y ~ group + offset(log(trisk)), ~ group, dja,
+     fixpar = list(c(3, 4), c(0, 0))) 
Negative-binomial model
-----------------------
negbin(formula = y ~ group + offset(log(trisk)), random = ~group, 
    data = dja, fixpar = list(c(3, 4), c(0, 0)))

Convergence was obtained after 41 iterations.

Fixed-effect coefficients:
            Estimate Std. Error z value Pr(> |z|)
(Intercept)  -0.6975     0.1170 -5.9596    < 1e-4
groupTREAT   -0.8754     0.1712 -5.1121    < 1e-4

Overdispersion coefficients set to fixed values:
               Value
phi.groupCTRL      0
phi.groupTREAT     0

Log-likelihood = -133.664; nbpar = 2; df.residual = 73; Deviance = 136.856; AIC = 271.328

>   
> 
> 
> cleanEx(); ..nameEx <- "quasibin"
> 
> ### * quasibin
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: quasibin
> ### Title: Quasi-Likelihood Model for Proportions
> ### Aliases: quasibin
> ### Keywords: regression
> 
> ### ** Examples
> 
>   data(orob2) 
>   fm1 <- glm(cbind(y, n - y) ~ seed * root,
+              family = binomial, data = orob2)
>   fm2 <- quasibin(cbind(y, n - y) ~ seed * root,
+                   data = orob2, phi = 0)
>   fm3 <- quasibin(cbind(y, n - y) ~ seed * root,
+                   data = orob2)
>   rbind(fm1 = coef(fm1), fm2 = coef(fm2), fm3 = coef(fm3))
    (Intercept)     seedO75 rootCUCUMBER seedO75:rootCUCUMBER
fm1  -0.4122448 -0.14592695    0.5400782            0.7781037
fm2  -0.4122448 -0.14592695    0.5400782            0.7781037
fm3  -0.4653218 -0.07008965    0.5102360            0.8195562
>   # show the model
>   fm3
Quasi-likelihood generalized linear model
-----------------------------------------
quasibin(formula = cbind(y, n - y) ~ seed * root, data = orob2)

Fixed-effect coefficients:
                     Estimate Std. Error z value Pr(>|z|)
(Intercept)           -0.4653     0.2439 -1.9081   0.0564
seedO75               -0.0701     0.3115 -0.2250   0.8219
rootCUCUMBER           0.5102     0.3347  1.5244   0.1274
seedO75:rootCUCUMBER   0.8196     0.4352  1.8831   0.0597

Overdispersion parameter:
   phi 
0.0249 

Pearson's chi-squared goodness-of-fit statistic = 17.0007 

>   # dispersion parameter and goodness-of-fit statistic
>   c(phi = fm3@phi, 
+     X2 = sum(residuals(fm3, type = "pearson")^2))
        phi          X2 
 0.02493559 17.00065130 
>   # model predictions
>   predfm1 <- predict(fm1, type = "response", se = TRUE)
>   predfm3 <- predict(fm3, type = "response", se = TRUE)
>   New <- expand.grid(seed = levels(orob2$seed),
+                      root = levels(orob2$root))
>   predict(fm3, New, se = TRUE, type = "response")
$fit
        1         2         3         4 
0.3857241 0.3692557 0.5112267 0.6887712 

$se.fit
         1          2          3          4 
0.05778166 0.04512309 0.05728607 0.04278711 

$residual.scale
[1] 1

>   data.frame(orob2, p1 = predfm1$fit, 
+                     se.p1 = predfm1$se.fit,
+                     p3 = predfm3$fit,
+                     se.p3 = predfm3$se.fit)
   seed     root  n  y        p1      se.p1        p3      se.p3
1   O75     BEAN 39 10 0.3639706 0.02917342 0.3692557 0.04512309
2   O75     BEAN 62 23 0.3639706 0.02917342 0.3692557 0.04512309
3   O75     BEAN 81 23 0.3639706 0.02917342 0.3692557 0.04512309
4   O75     BEAN 51 26 0.3639706 0.02917342 0.3692557 0.04512309
5   O75     BEAN 39 17 0.3639706 0.02917342 0.3692557 0.04512309
6   O75 CUCUMBER  6  5 0.6813559 0.02712870 0.6887712 0.04278711
7   O75 CUCUMBER 74 53 0.6813559 0.02712870 0.6887712 0.04278711
8   O75 CUCUMBER 72 55 0.6813559 0.02712870 0.6887712 0.04278711
9   O75 CUCUMBER 51 32 0.6813559 0.02712870 0.6887712 0.04278711
10  O75 CUCUMBER 79 46 0.6813559 0.02712870 0.6887712 0.04278711
11  O75 CUCUMBER 13 10 0.6813559 0.02712870 0.6887712 0.04278711
12  O73     BEAN 16  8 0.3983740 0.04414243 0.3857241 0.05778166
13  O73     BEAN 30 10 0.3983740 0.04414243 0.3857241 0.05778166
14  O73     BEAN 28  8 0.3983740 0.04414243 0.3857241 0.05778166
15  O73     BEAN 45 23 0.3983740 0.04414243 0.3857241 0.05778166
16  O73     BEAN  4  0 0.3983740 0.04414243 0.3857241 0.05778166
17  O73 CUCUMBER 12  3 0.5319149 0.04202173 0.5112267 0.05728607
18  O73 CUCUMBER 41 22 0.5319149 0.04202173 0.5112267 0.05728607
19  O73 CUCUMBER 30 15 0.5319149 0.04202173 0.5112267 0.05728607
20  O73 CUCUMBER 51 32 0.5319149 0.04202173 0.5112267 0.05728607
21  O73 CUCUMBER  7  3 0.5319149 0.04202173 0.5112267 0.05728607
>   fm4 <- quasibin(cbind(y, n - y) ~ seed + root,
+                   data = orob2, phi = fm3@phi)
>   # Pearson's chi-squared goodness-of-fit statistic
>   # compare with fm3's X2
>   sum(residuals(fm4, type = "pearson")^2)
[1] 20.72527
>   
> 
> 
> cleanEx(); ..nameEx <- "quasipois"
> 
> ### * quasipois
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: quasipois
> ### Title: Quasi-Likelihood Model for Counts
> ### Aliases: quasipois
> ### Keywords: regression
> 
> ### ** Examples
> 
>   # without offset
>   data(salmonella)
>   quasipois(y ~ log(dose + 10) + dose,
+             data = salmonella)
Quasi-likelihood generalized linear model
-----------------------------------------
quasipois(formula = y ~ log(dose + 10) + dose, data = salmonella)

Fixed-effect coefficients:
               Estimate Std. Error z value Pr(>|z|)
(Intercept)      2.2031     0.3636  6.0591   < 1e-4
log(dose + 10)   0.3110     0.0991  3.1394   0.0017
dose            -0.0010     0.0004 -2.2284   0.0259

Overdispersion parameter:
   phi 
0.0718 

Pearson's chi-squared goodness-of-fit statistic = 15.0004 

>   quasipois(y ~ log(dose + 10) + dose, 
+             data = salmonella, phi = 0.07180449)
Quasi-likelihood generalized linear model
-----------------------------------------
quasipois(formula = y ~ log(dose + 10) + dose, data = salmonella, 
    phi = 0.07180449)

Fixed-effect coefficients:
               Estimate Std. Error z value Pr(>|z|)
(Intercept)      2.2031     0.3636  6.0591   < 1e-4
log(dose + 10)   0.3110     0.0991  3.1394   0.0017
dose            -0.0010     0.0004 -2.2284   0.0259

Overdispersion parameter:
   phi 
0.0718 

Pearson's chi-squared goodness-of-fit statistic = 15.0004 

>   summary(glm(y ~ log(dose + 10) + dose,
+           family = poisson, data = salmonella))

Call:
glm(formula = y ~ log(dose + 10) + dose, family = poisson, data = salmonella)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5173  -1.1731  -0.4032   0.7995   3.7041  

Coefficients:
                 Estimate Std. Error z value Pr(>|z|)    
(Intercept)     2.1727730  0.2184269   9.947  < 2e-16 ***
log(dose + 10)  0.3198250  0.0570014   5.611 2.01e-08 ***
dose           -0.0010130  0.0002452  -4.131 3.61e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 78.358  on 17  degrees of freedom
Residual deviance: 43.716  on 15  degrees of freedom
AIC: 142.25

Number of Fisher Scoring iterations: 4

>   quasipois(y ~ log(dose + 10) + dose,
+           data = salmonella, phi = 0)
Quasi-likelihood generalized linear model
-----------------------------------------
quasipois(formula = y ~ log(dose + 10) + dose, data = salmonella, 
    phi = 0)

Fixed-effect coefficients:
               Estimate Std. Error z value Pr(>|z|)
(Intercept)      2.1728     0.2184  9.9474   < 1e-4
log(dose + 10)   0.3198     0.0570  5.6108   < 1e-4
dose            -0.0010     0.0002 -4.1311   < 1e-4

Overdispersion parameter:
phi 
  0 

Pearson's chi-squared goodness-of-fit statistic = 46.2707 

>   # with offset
>   data(cohorts)
>   i <- cohorts$age ; levels(i) <- 1:7
>   j <- cohorts$period ; levels(j) <- 1:7
>   i <- as.numeric(i); j <- as.numeric(j)
>   cohorts$cohort <- j + max(i) - i
>   cohorts$cohort <- as.factor(1850 + 5 * cohorts$cohort)
>   fm1 <- quasipois(y ~ age + period + cohort + offset(log(n)),
+                    data = cohorts)
>   fm1
Quasi-likelihood generalized linear model
-----------------------------------------
quasipois(formula = y ~ age + period + cohort + offset(log(n)), 
    data = cohorts)

Fixed-effect coefficients:
            Estimate Std. Error   z value Pr(>|z|)
(Intercept)  -8.0269     0.0750 -106.9987   < 1e-4
age55-        0.7222     0.0432   16.7251   < 1e-4
age60-        1.3511     0.0463   29.1893   < 1e-4
age65-        1.8291     0.0523   35.0026   < 1e-4
age70-        2.2917     0.0604   37.9489   < 1e-4
age75-        2.6606     0.0698   38.0943   < 1e-4
age80-        2.8568     0.0750   38.0816   < 1e-4
period1940-   0.1134     0.0448    2.5337   0.0113
period1945-   0.2490     0.0473    5.2633   < 1e-4
period1950-   0.3920     0.0529    7.4086   < 1e-4
period1955-   0.4630     0.0606    7.6418   < 1e-4
period1960-   0.5186     0.0695    7.4648   < 1e-4
period1965-   0.6386     0.0750    8.5130   < 1e-4
cohort1860    0.2453     0.1249    1.9643   0.0495
cohort1865    0.2928     0.1099    2.6644   0.0077
cohort1870    0.4258     0.0996    4.2732   < 1e-4
cohort1875    0.5545     0.0919    6.0317   < 1e-4
cohort1880    0.5954     0.0857    6.9451   < 1e-4
cohort1885    0.6436     0.0798    8.0611   < 1e-4
cohort1890    0.7819     0.0793    9.8547   < 1e-4
cohort1895    0.7082     0.0796    8.8992   < 1e-4
cohort1900    0.6599     0.0818    8.0692   < 1e-4
cohort1905    0.4080     0.0870    4.6919   < 1e-4
cohort1910    0.2246     0.0955    2.3511   0.0187
cohort1915        NA         NA        NA     <NA>

Overdispersion parameter:
   phi 
0.0033 

Pearson's chi-squared goodness-of-fit statistic = 25.0004 

>   quasipois(y ~ age + cohort + offset(log(n)),
+             data = cohorts, phi = fm1@phi)
Quasi-likelihood generalized linear model
-----------------------------------------
quasipois(formula = y ~ age + cohort + offset(log(n)), data = cohorts, 
    phi = fm1@phi)

Fixed-effect coefficients:
            Estimate Std. Error  z value Pr(>|z|)
(Intercept)  -8.6441     0.1236 -69.9115   < 1e-4
age55-        0.8225     0.0436  18.8858   < 1e-4
age60-        1.5490     0.0441  35.1408   < 1e-4
age65-        2.1276     0.0450  47.2789   < 1e-4
age70-        2.6960     0.0466  57.8405   < 1e-4
age75-        3.1715     0.0487  65.0778   < 1e-4
age80-        3.4741     0.0520  66.8309   < 1e-4
cohort1860    0.3550     0.1312   2.7055   0.0068
cohort1865    0.5194     0.1237   4.1982   < 1e-4
cohort1870    0.7743     0.1208   6.4082   < 1e-4
cohort1875    1.0121     0.1196   8.4596   < 1e-4
cohort1880    1.1508     0.1190   9.6676   < 1e-4
cohort1885    1.2996     0.1187  10.9467   < 1e-4
cohort1890    1.5449     0.1206  12.8121   < 1e-4
cohort1895    1.5749     0.1217  12.9435   < 1e-4
cohort1900    1.6271     0.1232  13.2088   < 1e-4
cohort1905    1.4643     0.1260  11.6223   < 1e-4
cohort1910    1.3720     0.1314  10.4435   < 1e-4
cohort1915    1.2559     0.1479   8.4940   < 1e-4

Overdispersion parameter:
   phi 
0.0033 

Pearson's chi-squared goodness-of-fit statistic = 31.9015 

>   
> 
> 
> cleanEx(); ..nameEx <- "raoscott"
> 
> ### * raoscott
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: raoscott
> ### Title: Test of Proportion Homogeneity using Rao and Scott's Adjustment
> ### Aliases: raoscott show,raoscott-class
> ### Keywords: htest
> 
> ### ** Examples
> 
>   data(rats)
>   # deff by group
>   raoscott(cbind(y, n - y) ~ group, data = rats)

Test of proportion homogeneity (Rao and Scott, 1993) 
---------------------------------------------------- 
raoscott(formula = cbind(y, n - y) ~ group, data = rats)
N = 32 clusters, n = 303 subjects, y = 254 cases, I = 2 groups.

Data and design effects:
  group  N   n   y      p     vbin   vratio  deff
1  CTRL 16 158 142 0.8987 0.000576 0.000710 1.232
2 TREAT 16 145 112 0.7724 0.001212 0.004792 3.953

Adjusted chi-squared test:
X2 = 4, df = 1, P(> X2) = 0.0444
>   raoscott(y/n ~ group, weights = n, data = rats)

Test of proportion homogeneity (Rao and Scott, 1993) 
---------------------------------------------------- 
raoscott(formula = y/n ~ group, weights = n, data = rats)
N = 32 clusters, n = 303 subjects, y = 254 cases, I = 2 groups.

Data and design effects:
  group + n  N   n   y      p     vbin   vratio  deff
1      CTRL 16 158 142 0.8987 0.000576 0.000710 1.232
2     TREAT 16 145 112 0.7724 0.001212 0.004792 3.953

Adjusted chi-squared test:
X2 = 4, df = 1, P(> X2) = 0.0444
>   raoscott(response = cbind(y, n - y), group = group, data = rats)

Test of proportion homogeneity (Rao and Scott, 1993) 
---------------------------------------------------- 
raoscott(response = cbind(y, n - y), group = group, data = rats)
N = 32 clusters, n = 303 subjects, y = 254 cases, I = 2 groups.

Data and design effects:
  group + NULL  N   n   y      p     vbin   vratio  deff
1         CTRL 16 158 142 0.8987 0.000576 0.000710 1.232
2        TREAT 16 145 112 0.7724 0.001212 0.004792 3.953

Adjusted chi-squared test:
X2 = 4, df = 1, P(> X2) = 0.0444
>   raoscott(response = y/n, weights = n, group = group, data = rats)

Test of proportion homogeneity (Rao and Scott, 1993) 
---------------------------------------------------- 
raoscott(response = y/n, weights = n, group = group, data = rats)
N = 32 clusters, n = 303 subjects, y = 254 cases, I = 2 groups.

Data and design effects:
  group + n  N   n   y      p     vbin   vratio  deff
1      CTRL 16 158 142 0.8987 0.000576 0.000710 1.232
2     TREAT 16 145 112 0.7724 0.001212 0.004792 3.953

Adjusted chi-squared test:
X2 = 4, df = 1, P(> X2) = 0.0444
>   # pooled deff
>   raoscott(cbind(y, n - y) ~ group, data = rats, pooled = TRUE)

Test of proportion homogeneity (Rao and Scott, 1993) 
---------------------------------------------------- 
raoscott(formula = cbind(y, n - y) ~ group, data = rats, pooled = TRUE)
N = 32 clusters, n = 303 subjects, y = 254 cases, I = 2 groups.

Data and design effects:
  group  N   n   y      p     vbin   vratio  deff
1  CTRL 16 158 142 0.8987 0.000576 0.000710 3.069
2 TREAT 16 145 112 0.7724 0.001212 0.004792 3.069

Adjusted chi-squared test:
X2 = 2.9, df = 1, P(> X2) = 0.0886
>   # standard test
>   raoscott(cbind(y, n - y) ~ group, data = rats, deff = c(1, 1))

Test of proportion homogeneity (Rao and Scott, 1993) 
---------------------------------------------------- 
raoscott(formula = cbind(y, n - y) ~ group, data = rats, deff = c(1, 
    1))
N = 32 clusters, n = 303 subjects, y = 254 cases, I = 2 groups.

Data and design effects:
  group  N   n   y      p     vbin   vratio deff
1  CTRL 16 158 142 0.8987 0.000576 0.000710    1
2 TREAT 16 145 112 0.7724 0.001212 0.004792    1

Adjusted chi-squared test:
X2 = 8.9, df = 1, P(> X2) = 0.0029
>   data(antibio)
>   raoscott(cbind(y, n - y) ~ treatment, data = antibio)

Test of proportion homogeneity (Rao and Scott, 1993) 
---------------------------------------------------- 
raoscott(formula = cbind(y, n - y) ~ treatment, data = antibio)
N = 24 clusters, n = 542 subjects, y = 67 cases, I = 4 groups.

Data and design effects:
  treatment N   n  y       p      vbin    vratio  deff
1         1 7 144 18 0.12500 0.0007595 0.0028676 3.775
2         2 6 129  8 0.06202 0.0004509 0.0007568 1.678
3         3 5 130 24 0.18462 0.0011579 0.0014880 1.285
4         4 6 139 17 0.12230 0.0007723 0.0020771 2.690

Adjusted chi-squared test:
X2 = 5.9, df = 3, P(> X2) = 0.1174
>   
> 
> 
> cleanEx(); ..nameEx <- "residuals"
> 
> ### * residuals
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: residuals-methods
> ### Title: Residuals for Maximum-Likelihood and Quasi-Likelihood Models
> ### Aliases: residuals-methods residuals,ANY-method residuals,glimML-method
> ###   residuals,glimQL-method
> ### Keywords: regression
> 
> ### ** Examples
> 
>   data(orob2)
>   fm <- betabin(cbind(y, n - y) ~ seed, ~ 1,
+                  link = "logit", data = orob2)
>   #Pearson's chi-squared goodness-of-fit statistic
>   sum(residuals(fm, type = "pearson")^2)
[1] 21.85501
>   
> 
> 
> cleanEx(); ..nameEx <- "splitbin"
> 
> ### * splitbin
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: splitbin
> ### Title: Splits Binomial Data into Bernoulli Data
> ### Aliases: splitbin
> ### Keywords: datagen
> 
> ### ** Examples
> 
>   data(orob1)
>   splitbin(cbind(y, n - y) ~ 1, orob1)
    id y
1    1 0
2    1 0
3    1 0
4    1 0
5    1 0
6    1 0
7    1 0
8    1 0
9    1 0
10   1 0
11   1 0
12   1 0
13   1 0
14   1 0
15   1 0
16   1 0
17   1 0
18   1 0
19   1 0
20   1 0
21   1 0
22   1 0
23   1 0
24   1 0
25   1 0
26   1 0
27   1 0
28   1 0
29   1 0
30   1 0
31   1 0
32   1 0
33   1 0
34   1 0
35   1 0
36   1 0
37   1 0
38   1 0
39   1 0
40   1 0
41   1 0
42   1 1
43   1 1
44   2 0
45   2 0
46   2 0
47   2 0
48   2 0
49   2 0
50   2 0
51   2 0
52   2 0
53   2 0
54   2 0
55   2 0
56   2 0
57   2 0
58   2 0
59   2 0
60   2 0
61   2 0
62   2 0
63   2 0
64   2 0
65   2 0
66   2 0
67   2 0
68   2 0
69   2 0
70   2 0
71   2 0
72   2 0
73   2 0
74   2 0
75   2 0
76   2 0
77   2 0
78   2 0
79   2 0
80   2 0
81   2 0
82   2 0
83   2 0
84   2 0
85   2 0
86   2 1
87   2 1
88   2 1
89   2 1
90   2 1
91   2 1
92   2 1
93   2 1
94   2 1
95   3 0
96   3 0
97   3 0
98   3 0
99   3 0
100  3 0
101  3 0
102  3 0
103  3 0
104  3 0
105  3 0
106  3 0
107  3 0
108  3 0
109  3 0
110  3 0
111  3 0
112  3 0
113  3 0
114  3 0
115  3 0
116  3 0
117  3 0
118  3 0
119  3 0
120  3 0
121  3 0
122  3 0
123  3 0
124  3 0
125  3 0
126  3 0
127  3 0
128  3 0
129  3 0
130  3 0
131  3 0
132  3 0
133  3 0
134  3 1
135  3 1
136  3 1
137  3 1
138  3 1
139  4 0
140  4 0
141  4 0
142  4 0
143  4 0
144  4 0
145  4 0
146  4 0
147  4 0
148  4 0
149  4 0
150  4 0
151  4 0
152  4 0
153  4 0
154  4 0
155  4 0
156  4 0
157  4 0
158  4 0
159  4 0
160  4 0
161  4 0
162  4 0
163  4 0
164  4 0
165  4 0
166  4 0
167  4 0
168  4 0
169  4 0
170  4 0
171  4 0
172  4 0
173  4 0
174  4 0
175  4 0
176  4 0
177  4 0
178  4 0
179  4 0
180  4 0
181  4 0
182  4 0
183  4 0
184  4 0
185  4 0
186  4 0
187  4 0
188  4 0
189  4 0
190  4 0
191  4 0
192  4 0
193  4 0
194  4 1
195  4 1
196  4 1
197  4 1
198  4 1
199  4 1
200  4 1
201  4 1
202  4 1
203  4 1
204  4 1
205  4 1
206  4 1
207  4 1
208  4 1
209  4 1
210  5 0
211  5 0
212  5 0
213  5 0
214  5 0
215  5 0
216  5 0
217  5 0
218  5 0
219  5 0
220  5 0
221  5 0
222  5 0
223  5 0
224  5 0
225  5 0
226  5 0
227  5 0
228  5 0
229  5 0
230  5 0
231  5 0
232  5 1
233  5 1
234  6 0
235  6 0
236  6 0
237  6 0
238  6 0
239  6 0
240  6 0
241  7 0
242  7 0
243  7 1
244  7 1
245  7 1
246  7 1
247  7 1
248  7 1
249  7 1
250  7 1
251  7 1
252  7 1
253  7 1
254  7 1
255  7 1
256  7 1
257  7 1
258  7 1
259  7 1
260  8 0
261  8 0
262  8 0
263  8 0
264  8 0
265  8 0
266  8 0
267  8 0
268  8 0
269  8 0
270  8 0
271  8 0
272  8 0
273  8 1
274  8 1
275  8 1
276  8 1
277  8 1
278  8 1
279  8 1
280  8 1
281  8 1
282  8 1
283  8 1
284  8 1
285  8 1
286  8 1
287  8 1
288  8 1
289  8 1
290  8 1
291  8 1
292  8 1
293  8 1
294  8 1
295  8 1
296  8 1
297  8 1
298  8 1
299  8 1
300  8 1
301  8 1
302  8 1
303  8 1
304  8 1
305  8 1
306  8 1
307  8 1
308  8 1
309  8 1
310  8 1
311  8 1
312  8 1
313  8 1
314  8 1
315  8 1
316  9 0
317  9 0
318  9 0
319  9 0
320  9 0
321  9 0
322  9 0
323  9 0
324  9 1
325  9 1
326  9 1
327  9 1
328  9 1
329  9 1
330  9 1
331  9 1
332  9 1
333  9 1
334  9 1
335  9 1
336  9 1
337  9 1
338  9 1
339  9 1
340  9 1
341  9 1
342  9 1
343  9 1
344  9 1
345  9 1
346  9 1
347  9 1
348  9 1
349  9 1
350  9 1
351  9 1
352  9 1
353  9 1
354  9 1
355  9 1
356  9 1
357  9 1
358  9 1
359  9 1
360  9 1
361  9 1
362  9 1
363  9 1
364  9 1
365  9 1
366  9 1
367  9 1
368  9 1
369  9 1
370  9 1
371  9 1
372  9 1
373  9 1
374  9 1
375  9 1
376  9 1
377  9 1
378  9 1
379  9 1
380  9 1
381  9 1
382  9 1
383  9 1
384  9 1
385  9 1
386  9 1
387  9 1
388  9 1
389  9 1
390  9 1
391  9 1
392  9 1
393  9 1
394  9 1
395  9 1
396  9 1
397  9 1
398  9 1
399  9 1
400  9 1
401  9 1
402  9 1
403 10 0
404 10 0
405 10 0
406 10 0
407 10 0
408 10 1
409 10 1
410 10 1
411 10 1
412 10 1
413 10 1
414 10 1
415 10 1
416 10 1
417 10 1
418 10 1
419 10 1
420 10 1
421 10 1
422 10 1
423 10 1
424 10 1
425 10 1
426 10 1
427 10 1
428 10 1
429 10 1
430 10 1
431 10 1
432 10 1
433 10 1
434 10 1
435 10 1
436 10 1
437 10 1
438 10 1
439 10 1
440 10 1
441 10 1
442 10 1
443 10 1
444 10 1
445 10 1
446 10 1
447 10 1
448 10 1
449 10 1
450 10 1
451 10 1
452 10 1
453 10 1
454 10 1
455 10 1
456 10 1
457 10 1
458 11 0
459 11 1
460 11 1
461 11 1
462 11 1
463 11 1
464 11 1
465 11 1
466 11 1
467 11 1
468 12 0
469 12 0
470 12 1
471 12 1
472 12 1
473 12 1
474 12 1
475 12 1
476 12 1
477 12 1
478 12 1
479 12 1
480 12 1
481 13 0
482 13 0
483 13 0
484 13 0
485 13 0
486 13 0
487 13 0
488 13 0
489 13 0
490 13 0
491 13 0
492 13 0
493 13 0
494 13 0
495 13 0
496 13 1
497 13 1
498 13 1
499 13 1
500 13 1
501 13 1
502 13 1
503 13 1
504 13 1
505 13 1
506 13 1
507 13 1
508 13 1
509 13 1
510 13 1
511 13 1
512 13 1
513 13 1
514 13 1
515 13 1
516 13 1
517 13 1
518 13 1
519 13 1
520 13 1
521 13 1
522 13 1
523 13 1
524 13 1
525 13 1
526 13 1
527 13 1
528 13 1
529 13 1
530 13 1
531 13 1
532 13 1
533 13 1
534 13 1
535 13 1
536 13 1
537 13 1
538 13 1
539 13 1
540 13 1
541 13 1
542 13 1
543 14 0
544 14 0
545 14 0
546 14 0
547 14 0
548 14 0
549 14 0
550 14 0
551 14 0
552 14 0
553 14 0
554 14 0
555 14 0
556 14 0
557 14 1
558 14 1
559 14 1
560 14 1
561 14 1
562 14 1
563 14 1
564 14 1
565 14 1
566 14 1
567 14 1
568 14 1
569 14 1
570 14 1
571 14 1
572 14 1
573 14 1
574 14 1
575 14 1
576 14 1
577 14 1
578 14 1
579 14 1
580 14 1
581 14 1
582 14 1
583 14 1
584 14 1
585 14 1
586 14 1
587 14 1
588 14 1
589 14 1
590 14 1
591 14 1
592 14 1
593 14 1
594 14 1
595 14 1
596 14 1
597 14 1
598 14 1
599 14 1
600 14 1
601 14 1
602 14 1
603 14 1
604 14 1
605 14 1
606 14 1
607 14 1
608 14 1
609 14 1
610 14 1
611 14 1
612 14 1
613 14 1
614 14 1
615 14 1
616 14 1
617 14 1
618 14 1
619 14 1
620 14 1
621 14 1
622 14 1
623 14 1
624 14 1
625 14 1
626 14 1
627 14 1
628 14 1
629 14 1
630 14 1
631 14 1
632 14 1
633 14 1
634 14 1
635 14 1
636 14 1
637 14 1
638 14 1
639 14 1
640 14 1
641 14 1
642 14 1
643 14 1
644 14 1
645 14 1
646 14 1
647 15 0
648 15 0
649 15 0
650 15 0
651 15 0
652 15 1
653 15 1
654 15 1
655 15 1
656 15 1
657 15 1
658 15 1
659 15 1
660 15 1
661 15 1
662 15 1
663 15 1
664 15 1
665 15 1
666 15 1
667 15 1
668 15 1
669 15 1
670 15 1
671 15 1
672 15 1
673 15 1
674 15 1
675 15 1
676 15 1
677 15 1
678 15 1
679 15 1
680 15 1
681 15 1
682 15 1
683 15 1
684 15 1
685 15 1
686 15 1
687 15 1
688 15 1
689 15 1
690 15 1
691 15 1
692 15 1
693 15 1
694 15 1
695 15 1
696 15 1
697 15 1
698 16 0
699 16 0
700 16 1
701 16 1
702 16 1
703 16 1
704 16 1
705 16 1
706 16 1
707 16 1
708 16 1
>   splitbin(cbind(y, n - y) ~ dilution, orob1)
    id y dilution
1    1 0      1/1
2    1 0      1/1
3    1 0      1/1
4    1 0      1/1
5    1 0      1/1
6    1 0      1/1
7    1 0      1/1
8    1 0      1/1
9    1 0      1/1
10   1 0      1/1
11   1 0      1/1
12   1 0      1/1
13   1 0      1/1
14   1 0      1/1
15   1 0      1/1
16   1 0      1/1
17   1 0      1/1
18   1 0      1/1
19   1 0      1/1
20   1 0      1/1
21   1 0      1/1
22   1 0      1/1
23   1 0      1/1
24   1 0      1/1
25   1 0      1/1
26   1 0      1/1
27   1 0      1/1
28   1 0      1/1
29   1 0      1/1
30   1 0      1/1
31   1 0      1/1
32   1 0      1/1
33   1 0      1/1
34   1 0      1/1
35   1 0      1/1
36   1 0      1/1
37   1 0      1/1
38   1 0      1/1
39   1 0      1/1
40   1 0      1/1
41   1 0      1/1
42   1 1      1/1
43   1 1      1/1
44   2 0      1/1
45   2 0      1/1
46   2 0      1/1
47   2 0      1/1
48   2 0      1/1
49   2 0      1/1
50   2 0      1/1
51   2 0      1/1
52   2 0      1/1
53   2 0      1/1
54   2 0      1/1
55   2 0      1/1
56   2 0      1/1
57   2 0      1/1
58   2 0      1/1
59   2 0      1/1
60   2 0      1/1
61   2 0      1/1
62   2 0      1/1
63   2 0      1/1
64   2 0      1/1
65   2 0      1/1
66   2 0      1/1
67   2 0      1/1
68   2 0      1/1
69   2 0      1/1
70   2 0      1/1
71   2 0      1/1
72   2 0      1/1
73   2 0      1/1
74   2 0      1/1
75   2 0      1/1
76   2 0      1/1
77   2 0      1/1
78   2 0      1/1
79   2 0      1/1
80   2 0      1/1
81   2 0      1/1
82   2 0      1/1
83   2 0      1/1
84   2 0      1/1
85   2 0      1/1
86   2 1      1/1
87   2 1      1/1
88   2 1      1/1
89   2 1      1/1
90   2 1      1/1
91   2 1      1/1
92   2 1      1/1
93   2 1      1/1
94   2 1      1/1
95   3 0      1/1
96   3 0      1/1
97   3 0      1/1
98   3 0      1/1
99   3 0      1/1
100  3 0      1/1
101  3 0      1/1
102  3 0      1/1
103  3 0      1/1
104  3 0      1/1
105  3 0      1/1
106  3 0      1/1
107  3 0      1/1
108  3 0      1/1
109  3 0      1/1
110  3 0      1/1
111  3 0      1/1
112  3 0      1/1
113  3 0      1/1
114  3 0      1/1
115  3 0      1/1
116  3 0      1/1
117  3 0      1/1
118  3 0      1/1
119  3 0      1/1
120  3 0      1/1
121  3 0      1/1
122  3 0      1/1
123  3 0      1/1
124  3 0      1/1
125  3 0      1/1
126  3 0      1/1
127  3 0      1/1
128  3 0      1/1
129  3 0      1/1
130  3 0      1/1
131  3 0      1/1
132  3 0      1/1
133  3 0      1/1
134  3 1      1/1
135  3 1      1/1
136  3 1      1/1
137  3 1      1/1
138  3 1      1/1
139  4 0      1/1
140  4 0      1/1
141  4 0      1/1
142  4 0      1/1
143  4 0      1/1
144  4 0      1/1
145  4 0      1/1
146  4 0      1/1
147  4 0      1/1
148  4 0      1/1
149  4 0      1/1
150  4 0      1/1
151  4 0      1/1
152  4 0      1/1
153  4 0      1/1
154  4 0      1/1
155  4 0      1/1
156  4 0      1/1
157  4 0      1/1
158  4 0      1/1
159  4 0      1/1
160  4 0      1/1
161  4 0      1/1
162  4 0      1/1
163  4 0      1/1
164  4 0      1/1
165  4 0      1/1
166  4 0      1/1
167  4 0      1/1
168  4 0      1/1
169  4 0      1/1
170  4 0      1/1
171  4 0      1/1
172  4 0      1/1
173  4 0      1/1
174  4 0      1/1
175  4 0      1/1
176  4 0      1/1
177  4 0      1/1
178  4 0      1/1
179  4 0      1/1
180  4 0      1/1
181  4 0      1/1
182  4 0      1/1
183  4 0      1/1
184  4 0      1/1
185  4 0      1/1
186  4 0      1/1
187  4 0      1/1
188  4 0      1/1
189  4 0      1/1
190  4 0      1/1
191  4 0      1/1
192  4 0      1/1
193  4 0      1/1
194  4 1      1/1
195  4 1      1/1
196  4 1      1/1
197  4 1      1/1
198  4 1      1/1
199  4 1      1/1
200  4 1      1/1
201  4 1      1/1
202  4 1      1/1
203  4 1      1/1
204  4 1      1/1
205  4 1      1/1
206  4 1      1/1
207  4 1      1/1
208  4 1      1/1
209  4 1      1/1
210  5 0      1/1
211  5 0      1/1
212  5 0      1/1
213  5 0      1/1
214  5 0      1/1
215  5 0      1/1
216  5 0      1/1
217  5 0      1/1
218  5 0      1/1
219  5 0      1/1
220  5 0      1/1
221  5 0      1/1
222  5 0      1/1
223  5 0      1/1
224  5 0      1/1
225  5 0      1/1
226  5 0      1/1
227  5 0      1/1
228  5 0      1/1
229  5 0      1/1
230  5 0      1/1
231  5 0      1/1
232  5 1      1/1
233  5 1      1/1
234  6 0      1/1
235  6 0      1/1
236  6 0      1/1
237  6 0      1/1
238  6 0      1/1
239  6 0      1/1
240  6 0      1/1
241  7 0     1/25
242  7 0     1/25
243  7 1     1/25
244  7 1     1/25
245  7 1     1/25
246  7 1     1/25
247  7 1     1/25
248  7 1     1/25
249  7 1     1/25
250  7 1     1/25
251  7 1     1/25
252  7 1     1/25
253  7 1     1/25
254  7 1     1/25
255  7 1     1/25
256  7 1     1/25
257  7 1     1/25
258  7 1     1/25
259  7 1     1/25
260  8 0     1/25
261  8 0     1/25
262  8 0     1/25
263  8 0     1/25
264  8 0     1/25
265  8 0     1/25
266  8 0     1/25
267  8 0     1/25
268  8 0     1/25
269  8 0     1/25
270  8 0     1/25
271  8 0     1/25
272  8 0     1/25
273  8 1     1/25
274  8 1     1/25
275  8 1     1/25
276  8 1     1/25
277  8 1     1/25
278  8 1     1/25
279  8 1     1/25
280  8 1     1/25
281  8 1     1/25
282  8 1     1/25
283  8 1     1/25
284  8 1     1/25
285  8 1     1/25
286  8 1     1/25
287  8 1     1/25
288  8 1     1/25
289  8 1     1/25
290  8 1     1/25
291  8 1     1/25
292  8 1     1/25
293  8 1     1/25
294  8 1     1/25
295  8 1     1/25
296  8 1     1/25
297  8 1     1/25
298  8 1     1/25
299  8 1     1/25
300  8 1     1/25
301  8 1     1/25
302  8 1     1/25
303  8 1     1/25
304  8 1     1/25
305  8 1     1/25
306  8 1     1/25
307  8 1     1/25
308  8 1     1/25
309  8 1     1/25
310  8 1     1/25
311  8 1     1/25
312  8 1     1/25
313  8 1     1/25
314  8 1     1/25
315  8 1     1/25
316  9 0     1/25
317  9 0     1/25
318  9 0     1/25
319  9 0     1/25
320  9 0     1/25
321  9 0     1/25
322  9 0     1/25
323  9 0     1/25
324  9 1     1/25
325  9 1     1/25
326  9 1     1/25
327  9 1     1/25
328  9 1     1/25
329  9 1     1/25
330  9 1     1/25
331  9 1     1/25
332  9 1     1/25
333  9 1     1/25
334  9 1     1/25
335  9 1     1/25
336  9 1     1/25
337  9 1     1/25
338  9 1     1/25
339  9 1     1/25
340  9 1     1/25
341  9 1     1/25
342  9 1     1/25
343  9 1     1/25
344  9 1     1/25
345  9 1     1/25
346  9 1     1/25
347  9 1     1/25
348  9 1     1/25
349  9 1     1/25
350  9 1     1/25
351  9 1     1/25
352  9 1     1/25
353  9 1     1/25
354  9 1     1/25
355  9 1     1/25
356  9 1     1/25
357  9 1     1/25
358  9 1     1/25
359  9 1     1/25
360  9 1     1/25
361  9 1     1/25
362  9 1     1/25
363  9 1     1/25
364  9 1     1/25
365  9 1     1/25
366  9 1     1/25
367  9 1     1/25
368  9 1     1/25
369  9 1     1/25
370  9 1     1/25
371  9 1     1/25
372  9 1     1/25
373  9 1     1/25
374  9 1     1/25
375  9 1     1/25
376  9 1     1/25
377  9 1     1/25
378  9 1     1/25
379  9 1     1/25
380  9 1     1/25
381  9 1     1/25
382  9 1     1/25
383  9 1     1/25
384  9 1     1/25
385  9 1     1/25
386  9 1     1/25
387  9 1     1/25
388  9 1     1/25
389  9 1     1/25
390  9 1     1/25
391  9 1     1/25
392  9 1     1/25
393  9 1     1/25
394  9 1     1/25
395  9 1     1/25
396  9 1     1/25
397  9 1     1/25
398  9 1     1/25
399  9 1     1/25
400  9 1     1/25
401  9 1     1/25
402  9 1     1/25
403 10 0     1/25
404 10 0     1/25
405 10 0     1/25
406 10 0     1/25
407 10 0     1/25
408 10 1     1/25
409 10 1     1/25
410 10 1     1/25
411 10 1     1/25
412 10 1     1/25
413 10 1     1/25
414 10 1     1/25
415 10 1     1/25
416 10 1     1/25
417 10 1     1/25
418 10 1     1/25
419 10 1     1/25
420 10 1     1/25
421 10 1     1/25
422 10 1     1/25
423 10 1     1/25
424 10 1     1/25
425 10 1     1/25
426 10 1     1/25
427 10 1     1/25
428 10 1     1/25
429 10 1     1/25
430 10 1     1/25
431 10 1     1/25
432 10 1     1/25
433 10 1     1/25
434 10 1     1/25
435 10 1     1/25
436 10 1     1/25
437 10 1     1/25
438 10 1     1/25
439 10 1     1/25
440 10 1     1/25
441 10 1     1/25
442 10 1     1/25
443 10 1     1/25
444 10 1     1/25
445 10 1     1/25
446 10 1     1/25
447 10 1     1/25
448 10 1     1/25
449 10 1     1/25
450 10 1     1/25
451 10 1     1/25
452 10 1     1/25
453 10 1     1/25
454 10 1     1/25
455 10 1     1/25
456 10 1     1/25
457 10 1     1/25
458 11 0     1/25
459 11 1     1/25
460 11 1     1/25
461 11 1     1/25
462 11 1     1/25
463 11 1     1/25
464 11 1     1/25
465 11 1     1/25
466 11 1     1/25
467 11 1     1/25
468 12 0    1/625
469 12 0    1/625
470 12 1    1/625
471 12 1    1/625
472 12 1    1/625
473 12 1    1/625
474 12 1    1/625
475 12 1    1/625
476 12 1    1/625
477 12 1    1/625
478 12 1    1/625
479 12 1    1/625
480 12 1    1/625
481 13 0    1/625
482 13 0    1/625
483 13 0    1/625
484 13 0    1/625
485 13 0    1/625
486 13 0    1/625
487 13 0    1/625
488 13 0    1/625
489 13 0    1/625
490 13 0    1/625
491 13 0    1/625
492 13 0    1/625
493 13 0    1/625
494 13 0    1/625
495 13 0    1/625
496 13 1    1/625
497 13 1    1/625
498 13 1    1/625
499 13 1    1/625
500 13 1    1/625
501 13 1    1/625
502 13 1    1/625
503 13 1    1/625
504 13 1    1/625
505 13 1    1/625
506 13 1    1/625
507 13 1    1/625
508 13 1    1/625
509 13 1    1/625
510 13 1    1/625
511 13 1    1/625
512 13 1    1/625
513 13 1    1/625
514 13 1    1/625
515 13 1    1/625
516 13 1    1/625
517 13 1    1/625
518 13 1    1/625
519 13 1    1/625
520 13 1    1/625
521 13 1    1/625
522 13 1    1/625
523 13 1    1/625
524 13 1    1/625
525 13 1    1/625
526 13 1    1/625
527 13 1    1/625
528 13 1    1/625
529 13 1    1/625
530 13 1    1/625
531 13 1    1/625
532 13 1    1/625
533 13 1    1/625
534 13 1    1/625
535 13 1    1/625
536 13 1    1/625
537 13 1    1/625
538 13 1    1/625
539 13 1    1/625
540 13 1    1/625
541 13 1    1/625
542 13 1    1/625
543 14 0    1/625
544 14 0    1/625
545 14 0    1/625
546 14 0    1/625
547 14 0    1/625
548 14 0    1/625
549 14 0    1/625
550 14 0    1/625
551 14 0    1/625
552 14 0    1/625
553 14 0    1/625
554 14 0    1/625
555 14 0    1/625
556 14 0    1/625
557 14 1    1/625
558 14 1    1/625
559 14 1    1/625
560 14 1    1/625
561 14 1    1/625
562 14 1    1/625
563 14 1    1/625
564 14 1    1/625
565 14 1    1/625
566 14 1    1/625
567 14 1    1/625
568 14 1    1/625
569 14 1    1/625
570 14 1    1/625
571 14 1    1/625
572 14 1    1/625
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>   
> 
> 
> cleanEx(); ..nameEx <- "summary.glimML-class"
> 
> ### * summary.glimML-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.glimML-class
> ### Title: Summary of Objects of Class "summary.glimML"
> ### Aliases: summary.glimML-class show,glimML-class summary,glimML-method
> ###   show,summary.glimML-method
> ### Keywords: classes
> 
> ### ** Examples
> 
>   data(orob2)
>   fm1 <- betabin(cbind(y, n - y) ~ seed, ~ 1, data = orob2)
>   # show objects of class "glimML"
>   fm1
Beta-binomial model
-------------------
betabin(formula = cbind(y, n - y) ~ seed, random = ~1, data = orob2)

Convergence was obtained after 80 iterations.

Fixed-effect coefficients:
            Estimate Std. Error z value Pr(> |z|)
(Intercept)  -0.2602     0.2262 -1.1502    0.2500
seedO75       0.4130     0.2993  1.3798    0.1676

Overdispersion coefficients:
                Estimate Std. Error z value Pr(> z)
phi.(Intercept)    0.078     0.0302  2.5885  0.0048

Log-likelihood = -63.553; nbpar = 3; df.residual = 18; Deviance = 50.509; AIC = 133.105

>   # summary for objects of class "glimML"
>   res <- summary(fm1)
>   res@Coef
              Estimate Std. Error   z value Pr(> |z|)
(Intercept) -0.2601803  0.2261948 -1.150248 0.2500415
seedO75      0.4130146  0.2993271  1.379810 0.1676452
>   # show objects of class "summary.glimML"
>   res
Beta-binomial model
-------------------
betabin(formula = cbind(y, n - y) ~ seed, random = ~1, data = orob2)

Convergence was obtained after 80 iterations.

Fixed-effect coefficients:
            Estimate Std. Error z value Pr(> |z|)
(Intercept)  -0.2602     0.2262 -1.1502    0.2500
seedO75       0.4130     0.2993  1.3798    0.1676

Overdispersion coefficients:
                Estimate Std. Error z value Pr(> z)
phi.(Intercept)    0.078     0.0302  2.5885  0.0048

Log-likelihood = -63.553; nbpar = 3; df.residual = 18; Deviance = 50.509; AIC = 133.105

>   
> 
> 
> cleanEx(); ..nameEx <- "varbin"
> 
> ### * varbin
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: varbin
> ### Title: Mean, Variance and Confidence Interval of a Proportion
> ### Aliases: varbin show,varbin-class
> ### Keywords: htest
> 
> ### ** Examples
> 
>   data(rabbits)
>   varbin(n, y, rabbits[rabbits$group == "M", ])
varbin(n = n, y = y, data = rabbits[rabbits$group == "M", ])
N = 21 clusters, n = 151 subjects, y = 52 cases.

Proportion
----------
                p     se  lower  upper
Binomial   0.3444 0.0388 0.2683 0.4204
Ratio      0.3444 0.0603 0.2263 0.4625
Arithmetic 0.3453 0.0706 0.2069 0.4837
Jackknife  0.3444 0.0601 0.2267 0.4621
Bootstrap  0.3456 0.0593 0.2294 0.4619

alpha = 0.05 for the CIs; R = 5000 samples for the bootstrap estimates.
>   by(rabbits,
+      list(group = rabbits$group),
+      function(x) varbin(n = n, y = y, data = x, R = 1000))
group: C
varbin(n = n, y = y, data = x, R = 1000)
N = 27 clusters, n = 215 subjects, y = 29 cases.

Proportion
----------
                p     se  lower  upper
Binomial   0.1349 0.0234 0.0891 0.1807
Ratio      0.1349 0.0356 0.0651 0.2046
Arithmetic 0.1480 0.0407 0.0683 0.2277
Jackknife  0.1346 0.0356 0.0650 0.2043
Bootstrap  0.1340 0.0343 0.0668 0.2013

alpha = 0.05 for the CIs; R = 1000 samples for the bootstrap estimates.
------------------------------------------------------------ 
group: H
varbin(n = n, y = y, data = x, R = 1000)
N = 17 clusters, n = 101 subjects, y = 23 cases.

Proportion
----------
                p     se  lower  upper
Binomial   0.2277 0.0419 0.1455 0.3099
Ratio      0.2277 0.0545 0.1210 0.3345
Arithmetic 0.2512 0.0603 0.1330 0.3694
Jackknife  0.2263 0.0549 0.1187 0.3338
Bootstrap  0.2307 0.0548 0.1233 0.3382

alpha = 0.05 for the CIs; R = 1000 samples for the bootstrap estimates.
------------------------------------------------------------ 
group: L
varbin(n = n, y = y, data = x, R = 1000)
N = 19 clusters, n = 133 subjects, y = 18 cases.

Proportion
----------
                p     se  lower  upper
Binomial   0.1353 0.0298 0.0770 0.1937
Ratio      0.1353 0.0412 0.0546 0.2160
Arithmetic 0.1135 0.0358 0.0433 0.1836
Jackknife  0.1363 0.0417 0.0546 0.2180
Bootstrap  0.1344 0.0403 0.0554 0.2133

alpha = 0.05 for the CIs; R = 1000 samples for the bootstrap estimates.
------------------------------------------------------------ 
group: M
varbin(n = n, y = y, data = x, R = 1000)
N = 21 clusters, n = 151 subjects, y = 52 cases.

Proportion
----------
                p     se  lower  upper
Binomial   0.3444 0.0388 0.2683 0.4204
Ratio      0.3444 0.0603 0.2263 0.4625
Arithmetic 0.3453 0.0706 0.2069 0.4837
Jackknife  0.3444 0.0601 0.2267 0.4621
Bootstrap  0.3451 0.0579 0.2317 0.4586

alpha = 0.05 for the CIs; R = 1000 samples for the bootstrap estimates.
>   
> 
> 
> cleanEx(); ..nameEx <- "wald.test"
> 
> ### * wald.test
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: wald.test
> ### Title: Wald Test for Model Coefficients
> ### Aliases: wald.test print.wald.test
> ### Keywords: htest
> 
> ### ** Examples
> 
>   data(orob2)
>   fm <- quasibin(cbind(y, n - y) ~ seed * root, data = orob2)
>   # Wald test for the effect of root
>   wald.test(b = coef(fm), Sigma = vcov(fm), Terms = 3:4)
Wald test:
----------

Chi-squared test:
X2 = 25.2, df = 2, P(> X2) = 3.4e-06
>   
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  12.85 0.27 13.24 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')