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("meta-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('meta')
load meta:  /CRANPkg/check/meta.Rcheck ...
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "Fleiss93"
> 
> ### * Fleiss93
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Fleiss93
> ### Title: Aspirin after Myocardial Infarction
> ### Aliases: Fleiss93
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(Fleiss93)
> metabin(event.e, n.e, event.c, n.c, data=Fleiss93)
      RR            95%-CI %W(fixed) %W(random)
1 0.7420  [0.5223; 1.0543]      2.89       7.84
2 0.6993  [0.4828; 1.0129]      2.76       7.18
3 0.8270  [0.6487; 1.0545]      5.42      13.59
4 0.8209  [0.5269; 1.2789]      1.67       5.29
5 0.8193  [0.5927; 1.1326]      3.01       8.92
6 1.1183  [0.9411; 1.3289]      9.54      20.41
7 0.9142  [0.8596; 0.9722]     74.71      36.79

Number of trials combined: 7 

                         RR            95%-CI       z  p.value
Fixed effects model  0.9136  [0.8657; 0.9642] -3.2878   0.001 
Random effects model 0.8929  [0.8006; 0.9959] -2.0347   0.0419

Quantifying heterogeneity:
tau^2 = 0.0074; H = 1.29 [1; 1.98]; I^2 = 39.6% [0%; 74.6%]

Test of heterogeneity:
    Q d.f.  p.value
 9.93    6   0.1277

Method: Mantel-Haenszel method 
> 
> 
> 
> cleanEx(); ..nameEx <- "Fleiss93cont"
> 
> ### * Fleiss93cont
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Fleiss93cont
> ### Title: Mental Health Treatment
> ### Aliases: Fleiss93cont
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(Fleiss93cont)
> metacont(n.e, mean.e, sd.e,
+          n.c, mean.c, sd.c,
+          data=Fleiss93cont)
    WMD              95%-CI %W(fixed) %W(random)
1 -1.50  [-4.7855;  1.7855]      2.79       4.50
2 -1.20  [-2.0837; -0.3163]     38.61      34.40
3 -2.40  [-6.1078;  1.3078]      2.19       3.58
4  0.20  [-0.7718;  1.1718]     31.93      31.03
5 -0.88  [-1.9900;  0.2300]     24.47      26.48

Number of trials combined: 5 

                         WMD              95%-CI       z  p.value
Fixed effects model  -0.7094  [-1.2585; -0.1603] -2.5321   0.0113
Random effects model -0.7373  [-1.4577; -0.0170] -2.0061   0.0448

Quantifying heterogeneity:
tau^2 = 0.1894; H = 1.19 [1; 1.91]; I^2 = 29.3% [0%; 72.6%]

Test of heterogeneity:
    Q d.f. p.value
 5.66    4   0.226

Method: Inverse variance method 
> 
> 
> 
> cleanEx(); ..nameEx <- "Olkin95"
> 
> ### * Olkin95
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Olkin95
> ### Title: Thrombolytic Therapy after Acute Myocardial Infarction
> ### Aliases: Olkin95
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(Olkin95)
> summary(metabin(event.e, n.e, event.c, n.c, data=Olkin95))
Warning in metabin(event.e, n.e, event.c, n.c, data = Olkin95) : 
	 0.5 added to each cell in 2x2 tables with zero cell frequencies
Number of trials combined: 70 

                         RR            95%-CI       z  p.value
Fixed effects model  0.7728  [0.7342; 0.8135] -9.8421 < 0.0001
Random effects model 0.7667  [0.7048; 0.8341] -6.1783 < 0.0001

Quantifying heterogeneity:
tau^2 = 0.0125; H = 1.1 [1; 1.28]; I^2 = 17.2% [0%; 39.1%]

Test of heterogeneity:
     Q d.f. p.value
 83.33   69   0.115

Method: Mantel-Haenszel method 
> 
> 
> 
> cleanEx(); ..nameEx <- "ci"
> 
> ### * ci
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ci
> ### Title: Calculation of confidence intervals (normal approximation)
> ### Aliases: ci
> ### Keywords: htest
> 
> ### ** Examples
> 
> as.data.frame(ci(170, 10))
   TE seTE    lower    upper  z p level
1 170   10 150.4004 189.5996 17 0  0.95
> as.data.frame(ci(170, 10, 0.99))
   TE seTE    lower    upper  z p level
1 170   10 144.2417 195.7583 17 0  0.99
> 
> 
> 
> cleanEx(); ..nameEx <- "funnel"
> 
> ### * funnel
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: funnel
> ### Title: Plot to assess funnel plot asymmetry
> ### Aliases: funnel radial
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(Olkin95)
> meta1 <- metabin(event.e, n.e, event.c, n.c,
+                  data=Olkin95, subset=c(41,47,51,59),
+                  sm="RR", meth="I")
> 
> ##
> ## Same results:
> ##
> oldpar <- par(mfrow=c(2,2))
> funnel(meta1)
> funnel(meta1$TE, meta1$seTE, sm="RR")
> par(oldpar)
> 
> oldpar <- par(mfrow=c(2,2))
> funnel(meta1)
> funnel(meta1, yaxis="inv")
> funnel(meta1, yaxis="size")
> par(oldpar)
> 
> funnel(meta1, comb.f=TRUE, xlim=c(0.1, 10), axes=FALSE)
> box()
> axis(1, at=c(0.1, 0.5, 1, 2, 10))
> axis(2)
> 
> radial(meta1, level=0.95)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "metabias"
> 
> ### * metabias
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: metabias
> ### Title: Test for funnel plot asymmetry
> ### Aliases: metabias
> ### Keywords: htest
> 
> ### ** Examples
> 
> data(Olkin95)
> meta1 <- metabin(event.e, n.e, event.c, n.c,
+                  data=Olkin95, subset=c(41,47,51,59),
+                  sm="RR", meth="I")
> 
> metabias(meta1)

	Rank correlation test of funnel plot asymmetry

data:  meta1 
z = 0, p-value = 1
alternative hypothesis: true is 0 
sample estimates:
     ks   se.ks 
0.00000 2.94392 

> metabias(meta1, correct=TRUE)

	Rank correlation test of funnel plot asymmetry (with continuity
	correction)

data:  meta1 
z = 0, p-value = 1
alternative hypothesis: true is 0 
sample estimates:
     ks   se.ks 
0.00000 2.94392 

> 
> metabias(meta1, method="linreg")

	Linear regression test of funnel plot asymmetry

data:  meta1 
t = 0.0685, df = 2, p-value = 0.9516
alternative hypothesis: true is 0 
sample estimates:
      bias    se.bias      slope 
 0.1234649  1.8023334 -0.8890871 

> metabias(meta1, method="linreg", plotit=TRUE)

	Linear regression test of funnel plot asymmetry

data:  meta1 
t = 0.0685, df = 2, p-value = 0.9516
alternative hypothesis: true is 0 
sample estimates:
      bias    se.bias      slope 
 0.1234649  1.8023334 -0.8890871 

> 
> metabias(meta1, method="count")

	New test of funnel plot asymmetry

data:  meta1 
z = 0, p-value = 1
alternative hypothesis: true is 0 
sample estimates:
     ks   se.ks 
0.00000 2.94392 

> 
> ##
> ## Same result:
> ##
> metabias(meta1, method="linreg")$p.value
[1] 0.951618
> metabias(meta1$TE, meta1$seTE, method="linreg")$p.value
[1] 0.951618
> 
> 
> 
> cleanEx(); ..nameEx <- "metabin"
> 
> ### * metabin
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: metabin
> ### Title: Meta-analysis of binary outcome data
> ### Aliases: metabin
> ### Keywords: htest
> 
> ### ** Examples
> 
> metabin(10, 20, 15, 20, sm="OR")
Warning in metabin(10, 20, 15, 20, sm = "OR") : 
	 For single trials, inverse variance method used instead of Mantel Haenszel method.
     OR            95%-CI       z  p.value
 0.3333  [0.0874; 1.2716] -1.6082   0.1078

Method: Inverse variance method
> 
> ##
> ## Different results:
> ##
> metabin(0, 10, 0, 10, sm="OR")
Warning in metabin(0, 10, 0, 10, sm = "OR") : 
	 For single trials, inverse variance method used instead of Mantel Haenszel method.
   OR 95%-CI  z  p.value
 <NA>        NA       --

Method: Inverse variance method
> metabin(0, 10, 0, 10, sm="OR", allstudies=TRUE)
Warning in metabin(0, 10, 0, 10, sm = "OR", allstudies = TRUE) : 
	 For single trials, inverse variance method used instead of Mantel Haenszel method.
Warning in metabin(0, 10, 0, 10, sm = "OR", allstudies = TRUE) : 
	 0.5 added to each cell in 2x2 tables with zero cell frequencies
Warning in metabin(x$event.e, x$n.e, x$event.c, x$n.c, studlab = x$studlab,  : 
	 0.5 added to each cell in 2x2 tables with zero cell frequencies
 OR             95%-CI z p.value
  1  [0.0181; 55.2669] 0       1

Method: Inverse variance method
> 
> data(Olkin95)
> 
> meta1 <- metabin(event.e, n.e, event.c, n.c,
+                  data=Olkin95, subset=c(41,47,51,59),
+                  sm="RR", meth="I")
> summary(meta1)
Number of trials combined: 4 

                         RR            95%-CI       z  p.value
Fixed effects model  0.4407  [0.2416; 0.8039] -2.6716   0.0075
Random effects model 0.4434  [0.2038; 0.9648] -2.0503   0.0403

Quantifying heterogeneity:
tau^2 = 0.1685; H = 1.16 [1; 1.86]; I^2 = 25.1% [0%; 71.1%]

Test of heterogeneity:
 Q d.f.  p.value
 4    3   0.2611

Method: Inverse variance method 
> funnel(meta1)
> 
> meta2 <- metabin(event.e, n.e, event.c, n.c,
+                  data=Olkin95, subset=Olkin95$year<1970,
+                  sm="RR", meth="I")
> summary(meta2)
Number of trials combined: 4 

                         RR           95%-CI       z  p.value
Fixed effects model  0.9591  [0.611; 1.5056] -0.1814   0.8561
Random effects model 0.8618  [0.480; 1.5474] -0.4981   0.6184

Quantifying heterogeneity:
tau^2 = 0.1026; H = 1.18 [1; 1.93]; I^2 = 27.9% [0%; 73.2%]

Test of heterogeneity:
    Q d.f.  p.value
 4.16    3   0.2446

Method: Inverse variance method 
> 
> 
> 
> cleanEx(); ..nameEx <- "metacont"
> 
> ### * metacont
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: metacont
> ### Title: Meta-analysis of continuous outcome data
> ### Aliases: metacont
> ### Keywords: htest
> 
> ### ** Examples
> 
> data(Fleiss93cont)
> meta1 <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=Fleiss93cont, sm="SMD")
> meta1
      SMD              95%-CI %W(fixed) %W(random)
1 -0.3399  [-1.1152;  0.4354]     11.54      11.54
2 -0.5659  [-1.0274; -0.1044]     32.58      32.58
3 -0.2999  [-0.7712;  0.1714]     31.23      31.23
4  0.1250  [-0.4954;  0.7455]     18.02      18.02
5 -0.7346  [-1.7575;  0.2883]      6.63       6.63

Number of trials combined: 5 

                         SMD            95%-CI       z  p.value
Fixed effects model  -0.3434  [-0.6068; -0.08] -2.5555   0.0106
Random effects model -0.3434  [-0.6068; -0.08] -2.5555   0.0106

Quantifying heterogeneity:
tau^2 = 0; H = 1 [1; 2.1]; I^2 = 0% [0%; 77.4%]

Test of heterogeneity:
    Q d.f.  p.value
 3.68    4   0.4515

Method: Inverse variance method 
> 
> meta2 <- metacont(Fleiss93cont$n.e, Fleiss93cont$mean.e,
+                   Fleiss93cont$sd.e,
+                   Fleiss93cont$n.c, Fleiss93cont$mean.c,
+                   Fleiss93cont$sd.c,
+                   sm="SMD")
> meta2
      SMD              95%-CI %W(fixed) %W(random)
1 -0.3399  [-1.1152;  0.4354]     11.54      11.54
2 -0.5659  [-1.0274; -0.1044]     32.58      32.58
3 -0.2999  [-0.7712;  0.1714]     31.23      31.23
4  0.1250  [-0.4954;  0.7455]     18.02      18.02
5 -0.7346  [-1.7575;  0.2883]      6.63       6.63

Number of trials combined: 5 

                         SMD            95%-CI       z  p.value
Fixed effects model  -0.3434  [-0.6068; -0.08] -2.5555   0.0106
Random effects model -0.3434  [-0.6068; -0.08] -2.5555   0.0106

Quantifying heterogeneity:
tau^2 = 0; H = 1 [1; 2.1]; I^2 = 0% [0%; 77.4%]

Test of heterogeneity:
    Q d.f.  p.value
 3.68    4   0.4515

Method: Inverse variance method 
> 
> 
> 
> cleanEx(); ..nameEx <- "metacum"
> 
> ### * metacum
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: metacum
> ### Title: Cumulative meta-analysis
> ### Aliases: metacum
> ### Keywords: htest
> 
> ### ** Examples
> 
> data(Fleiss93)
> meta1 <- metabin(event.e, n.e, event.c, n.c,
+                  data=Fleiss93, studlab=study,
+                  sm="RR", meth="I")
> meta1
           RR            95%-CI %W(fixed) %W(random)
MRC-1  0.7420  [0.5223; 1.0543]      2.35       7.84
CDP    0.6993  [0.4828; 1.0129]      2.11       7.18
MRC-2  0.8270  [0.6487; 1.0545]      4.92      13.59
GASP   0.8209  [0.5269; 1.2789]      1.48       5.29
PARIS  0.8193  [0.5927; 1.1326]      2.77       8.92
AMIS   1.1183  [0.9411; 1.3289]      9.75      20.41
ISIS-2 0.9142  [0.8596; 0.9722]     76.63      36.79

Number of trials combined: 7 

                         RR            95%-CI       z  p.value
Fixed effects model  0.9137  [0.8658; 0.9643] -3.2822   0.001 
Random effects model 0.8929  [0.8006; 0.9959] -2.0347   0.0419

Quantifying heterogeneity:
tau^2 = 0.0074; H = 1.29 [1; 1.98]; I^2 = 39.6% [0%; 74.6%]

Test of heterogeneity:
    Q d.f.  p.value
 9.93    6   0.1277

Method: Inverse variance method 
> 
> metacum(meta1)

Cumulative meta-analysis (Fixed effect model)

                        RR            95%-CI
Adding MRC-1  (k=1) 0.7420  [0.5223; 1.0543]
Adding CDP    (k=2) 0.7215  [0.5592; 0.9310]
Adding MRC-2  (k=3) 0.7750  [0.6500; 0.9240]
Adding GASP   (k=4) 0.7811  [0.6633; 0.9198]
Adding PARIS  (k=5) 0.7887  [0.6816; 0.9126]
Adding AMIS   (k=6) 0.9124  [0.8162; 1.0199]
Adding ISIS-2 (k=7) 0.9137  [0.8658; 0.9643]
                                            
Pooled estimate     0.9137  [0.8658; 0.9643]

Method: Inverse variance method 
> metacum(meta1, pooled="random")

Cumulative meta-analysis (Random effects model)

                        RR            95%-CI
Adding MRC-1  (k=1) 0.7420  [0.5223; 1.0543]
Adding CDP    (k=2) 0.7215  [0.5592; 0.9310]
Adding MRC-2  (k=3) 0.7750  [0.6500; 0.9240]
Adding GASP   (k=4) 0.7811  [0.6633; 0.9198]
Adding PARIS  (k=5) 0.7887  [0.6816; 0.9126]
Adding AMIS   (k=6) 0.8596  [0.7249; 1.0194]
Adding ISIS-2 (k=7) 0.8929  [0.8006; 0.9959]
                                            
Pooled estimate     0.8929  [0.8006; 0.9959]

Method: Inverse variance method 
> plot(metacum(meta1, pooled="random"))
> 
> 
> 
> cleanEx(); ..nameEx <- "metagen"
> 
> ### * metagen
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: metagen
> ### Title: Generic inverse variance meta-analysis
> ### Aliases: metagen
> ### Keywords: htest
> 
> ### ** Examples
> 
> data(Fleiss93)
> meta1 <- metabin(event.e, n.e, event.c, n.c, data=Fleiss93, sm="RR", meth="I")
> meta1
      RR            95%-CI %W(fixed) %W(random)
1 0.7420  [0.5223; 1.0543]      2.35       7.84
2 0.6993  [0.4828; 1.0129]      2.11       7.18
3 0.8270  [0.6487; 1.0545]      4.92      13.59
4 0.8209  [0.5269; 1.2789]      1.48       5.29
5 0.8193  [0.5927; 1.1326]      2.77       8.92
6 1.1183  [0.9411; 1.3289]      9.75      20.41
7 0.9142  [0.8596; 0.9722]     76.63      36.79

Number of trials combined: 7 

                         RR            95%-CI       z  p.value
Fixed effects model  0.9137  [0.8658; 0.9643] -3.2822   0.001 
Random effects model 0.8929  [0.8006; 0.9959] -2.0347   0.0419

Quantifying heterogeneity:
tau^2 = 0.0074; H = 1.29 [1; 1.98]; I^2 = 39.6% [0%; 74.6%]

Test of heterogeneity:
    Q d.f.  p.value
 9.93    6   0.1277

Method: Inverse variance method 
> 
> ##
> ## Identical results by using the following commands:
> ##
> meta1
      RR            95%-CI %W(fixed) %W(random)
1 0.7420  [0.5223; 1.0543]      2.35       7.84
2 0.6993  [0.4828; 1.0129]      2.11       7.18
3 0.8270  [0.6487; 1.0545]      4.92      13.59
4 0.8209  [0.5269; 1.2789]      1.48       5.29
5 0.8193  [0.5927; 1.1326]      2.77       8.92
6 1.1183  [0.9411; 1.3289]      9.75      20.41
7 0.9142  [0.8596; 0.9722]     76.63      36.79

Number of trials combined: 7 

                         RR            95%-CI       z  p.value
Fixed effects model  0.9137  [0.8658; 0.9643] -3.2822   0.001 
Random effects model 0.8929  [0.8006; 0.9959] -2.0347   0.0419

Quantifying heterogeneity:
tau^2 = 0.0074; H = 1.29 [1; 1.98]; I^2 = 39.6% [0%; 74.6%]

Test of heterogeneity:
    Q d.f.  p.value
 9.93    6   0.1277

Method: Inverse variance method 
> metagen(meta1$TE, meta1$seTE, sm="RR")
      RR            95%-CI %W(fixed) %W(random)
1 0.7420  [0.5223; 1.0543]      2.35       7.84
2 0.6993  [0.4828; 1.0129]      2.11       7.18
3 0.8270  [0.6487; 1.0545]      4.92      13.59
4 0.8209  [0.5269; 1.2789]      1.48       5.29
5 0.8193  [0.5927; 1.1326]      2.77       8.92
6 1.1183  [0.9411; 1.3289]      9.75      20.41
7 0.9142  [0.8596; 0.9722]     76.63      36.79

Number of trials combined: 7 

                         RR            95%-CI       z  p.value
Fixed effects model  0.9137  [0.8658; 0.9643] -3.2822   0.001 
Random effects model 0.8929  [0.8006; 0.9959] -2.0347   0.0419

Quantifying heterogeneity:
tau^2 = 0.0074; H = 1.29 [1; 1.98]; I^2 = 39.6% [0%; 74.6%]

Test of heterogeneity:
    Q d.f.  p.value
 9.93    6   0.1277

Method: Inverse variance method 
> 
> 
> 
> cleanEx(); ..nameEx <- "metainf"
> 
> ### * metainf
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: metainf
> ### Title: Influence analysis in meta-analysis
> ### Aliases: metainf
> ### Keywords: htest
> 
> ### ** Examples
> 
> data(Fleiss93)
> meta1 <- metabin(event.e, n.e, event.c, n.c,
+                  data=Fleiss93, studlab=study,
+                  sm="RR", meth="I")
> meta1
           RR            95%-CI %W(fixed) %W(random)
MRC-1  0.7420  [0.5223; 1.0543]      2.35       7.84
CDP    0.6993  [0.4828; 1.0129]      2.11       7.18
MRC-2  0.8270  [0.6487; 1.0545]      4.92      13.59
GASP   0.8209  [0.5269; 1.2789]      1.48       5.29
PARIS  0.8193  [0.5927; 1.1326]      2.77       8.92
AMIS   1.1183  [0.9411; 1.3289]      9.75      20.41
ISIS-2 0.9142  [0.8596; 0.9722]     76.63      36.79

Number of trials combined: 7 

                         RR            95%-CI       z  p.value
Fixed effects model  0.9137  [0.8658; 0.9643] -3.2822   0.001 
Random effects model 0.8929  [0.8006; 0.9959] -2.0347   0.0419

Quantifying heterogeneity:
tau^2 = 0.0074; H = 1.29 [1; 1.98]; I^2 = 39.6% [0%; 74.6%]

Test of heterogeneity:
    Q d.f.  p.value
 9.93    6   0.1277

Method: Inverse variance method 
> 
> metainf(meta1)

Influential analysis (Fixed effect model)

                    RR            95%-CI
Omitting MRC-1  0.9183  [0.8696; 0.9698]
Omitting CDP    0.9190  [0.8703; 0.9705]
Omitting MRC-2  0.9185  [0.8691; 0.9706]
Omitting GASP   0.9152  [0.8669; 0.9663]
Omitting PARIS  0.9166  [0.8679; 0.9680]
Omitting AMIS   0.8940  [0.8447; 0.9462]
Omitting ISIS-2 0.9124  [0.8162; 1.0199]
                                        
Pooled estimate 0.9137  [0.8658; 0.9643]

Method: Inverse variance method 
> metainf(meta1, pooled="random")

Influential analysis (Random effects model)

                    RR            95%-CI
Omitting MRC-1  0.9071  [0.8098; 1.0162]
Omitting CDP    0.9124  [0.8201; 1.0151]
Omitting MRC-2  0.8983  [0.7907; 1.0204]
Omitting GASP   0.8931  [0.7922; 1.0068]
Omitting PARIS  0.8963  [0.7929; 1.0131]
Omitting AMIS   0.8940  [0.8447; 0.9462]
Omitting ISIS-2 0.8596  [0.7249; 1.0194]
                                        
Pooled estimate 0.8929  [0.8006; 0.9959]

Method: Inverse variance method 
> plot(metainf(meta1, pooled="random"))
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.meta"
> 
> ### * plot.meta
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.meta
> ### Title: Plot function for objects of class meta
> ### Aliases: plot.meta
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(Olkin95)
> meta1 <- metabin(event.e, n.e, event.c, n.c,
+                  data=Olkin95, subset=c(41,47,51,59),
+                  sm="RR", meth="I")
> 
> plot(meta1)
> plot(meta1, byvar=c(1,2,1,2), bylab="label")
> plot(meta1, byvar=1:4, xlim=c(0.02, 10))
> 
> 
> 
> cleanEx(); ..nameEx <- "print.meta"
> 
> ### * print.meta
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.meta
> ### Title: Print and summary method for objects of class meta
> ### Aliases: print.meta summary.meta print.summary.meta
> ### Keywords: print
> 
> ### ** Examples
> 
> data(Fleiss93cont)
> meta1 <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=Fleiss93cont, sm="SMD")
> summary(meta1)
Number of trials combined: 5 

                         SMD            95%-CI       z  p.value
Fixed effects model  -0.3434  [-0.6068; -0.08] -2.5555   0.0106
Random effects model -0.3434  [-0.6068; -0.08] -2.5555   0.0106

Quantifying heterogeneity:
tau^2 = 0; H = 1 [1; 2.1]; I^2 = 0% [0%; 77.4%]

Test of heterogeneity:
    Q d.f.  p.value
 3.68    4   0.4515

Method: Inverse variance method 
> summary(meta1, byvar=c(1,2,1,1,2), bylab="label")
Number of trials combined: 5 

                         SMD            95%-CI       z  p.value
Fixed effects model  -0.3434  [-0.6068; -0.08] -2.5555   0.0106
Random effects model -0.3434  [-0.6068; -0.08] -2.5555   0.0106

Quantifying heterogeneity:
tau^2 = 0; H = 1 [1; 2.1]; I^2 = 0% [0%; 77.4%]

Test of heterogeneity:
                    Q d.f.     SMD              95%-CI  p.value
Total            3.68    4      --                  --   0.4515
Between groups   2.25    1      --                  --   0.1336
Within groups    1.43    3      --                  --   0.6992
label = 1        1.34    2 -0.1815  [-0.5194;  0.1563]       --
label = 2        0.09    1 -0.5944  [-1.0151; -0.1738]       --

Method: Inverse variance method 
> 
> 
> 
> cleanEx(); ..nameEx <- "read.mtv"
> 
> ### * read.mtv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: read.mtv
> ### Title: Import RevMan data files (.mtv)
> ### Aliases: read.mtv
> ### Keywords: datagen
> 
> ### ** Examples
> 
> ## Locate MTV-data file "FLEISS93.MTV" in sub-directory of package "meta"
> ##
> filename <- paste(searchpaths()[seq(along=search())[search()==
+                   "package:meta"]], "/data/FLEISS93.MTV", sep="")
> ##
> fleiss93.cc <- read.mtv(filename)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  1.8 0.06 1.88 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')