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

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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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("haplo.stats-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('haplo.stats')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "Ginv"
> 
> ### * Ginv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Ginv
> ### Title: Compute Generalized Inverse of Input Matrix
> ### Aliases: Ginv
> 
> 
> ### ** Examples
> 
> # for matrix x, extract the generalized inverse and 
> # rank of x as follows
> #   > save <- Ginv(x)
> #   > ginv.x <- save$Ginv
> #   > rank.x <- save$rank
> 
> 
> 
> cleanEx(); ..nameEx <- "allele.recode"
> 
> ### * allele.recode
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: allele.recode
> ### Title: Recode allele values to integer ranks
> ### Aliases: allele.recode
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "dglm.fit"
> 
> ### * dglm.fit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dglm.fit
> ### Title: Density function for GLM fit
> ### Aliases: dglm.fit
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "geno.count.pairs"
> 
> ### * geno.count.pairs
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: geno.count.pairs
> ### Title: Counts of Total Haplotype Pairs Produced by Genotypes
> ### Aliases: geno.count.pairs
> 
> 
> ### ** Examples
> 
> setupData(hla.demo)
[1] "hla.demo"
> geno <- hla.demo[,c(17,18,21:24)]
> geno <- geno.recode(geno)$grec
> count.geno <- geno.count.pairs(geno)
> print(count.geno)
  [1]      4      4      4      2      4      2      4      2      2      1
 [11]      2      4      1      4      4      4      4      4      4      4
 [21]      4      1      4      2      4      4      4      4      4      4
 [31]      4      4      4      4      1      4      4      2      1      4
 [41]      1      2      1      4      4      4      1      4      4      4
 [51]      4      4      2      4      1      4      2      4      4      4
 [61]      2      4      2      4      2      4      4      2      4      4
 [71]      4      4      4      2      2      2      4      1      2      4
 [81]   1800      2      1      2      4      4      2      1      4      4
 [91]      4      2      4      4      4      4      4      4      4      4
[101]      4      4      4      4      4      2      2      4      4      4
[111]      4      4      2      4      4      4      4      4      4      2
[121]      4      4      2      2      2      4      4      2      4      4
[131]      4      2      4      1      4      2 129600      4      4      4
[141]      4      2      4      4      4      4      4      4      4      4
[151]      4      2      4      4      4      4      4      4      2      1
[161]      4      2      2      2      2      2      2      4      4      4
[171]      2      1      1      4      1      4      4      4      4      4
[181]      2      2      2      2      4      2      1      2      4      4
[191]      4      4      2      2      4      4      4      4      4      2
[201]      4      4      4      4      4      4      2      4      4      2
[211]      2      4      4      4      4      4      4      4      4      4
> 
> 
> 
> cleanEx(); ..nameEx <- "geno.recode"
> 
> ### * geno.recode
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: geno.recode
> ### Title: Recode Genotypes
> ### Aliases: geno.recode
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "glm.fit.nowarn"
> 
> ### * glm.fit.nowarn
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: glm.fit.nowarn
> ### Title: Modified from glm.fit function to not warn users for binomial
> ###   non-integer weights.
> ### Aliases: glm.fit.nowarn
> 
> 
> ### ** Examples
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.cc"
> 
> ### * haplo.cc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.cc
> ### Title: Haplotype Association Analysis in a Case-Control design
> ### Aliases: haplo.cc
> 
> 
> ### ** Examples
> 
> #  For a genotype matrix geno.test, case/control vector y.test
> #  The function call will be like this
> #  cc.test <- haplo.cc(y.test, geno.test, locus.label=locus.label, haplo.min.count=3, ci.prob=0.95)
> #
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.chistat"
> 
> ### * haplo.chistat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.chistat
> ### Title: Calculate a score test statistic for haplotypes
> ### Aliases: haplo.chistat
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.em"
> 
> ### * haplo.em
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.em
> ### Title: EM Computation of Haplotype Probabilities, with Progressive
> ###   Insertion of Loci
> ### Aliases: haplo.em
> 
> 
> ### ** Examples
> 
> setupData(hla.demo)
[1] "hla.demo"
> attach(hla.demo)
> geno <- hla.demo[,c(17,18,21:24)]
> label <-c("DQB","DRB","B")
> keep <- !apply(is.na(geno) | geno==0, 1, any)
> 
> save.em.keep <- haplo.em(geno=geno[keep,], locus.label=label)
> 
> # warning: output will not exactly match
> 
> print.haplo.em(save.em.keep)
================================================================================ 
                                   Haplotypes                                    
================================================================================ 

    DQB DRB  B hap.freq
1    21   1  8  0.00235
2    21   2  7  0.00229
3    21   2 18  0.00229
4    21   3  8  0.10515
5    21   3 18  0.00462
6    21   3 35  0.00571
7    21   3 44  0.00363
8    21   3 49  0.00229
9    21   3 57  0.00240
10   21   3 70  0.00229
11   21   4 62  0.00459
12   21   7  7  0.01430
13   21   7 13  0.01086
14   21   7 18  0.00250
15   21   7 35  0.00238
16   21   7 44  0.02186
17   21   7 45  0.00229
18   21   7 50  0.00459
19   21   7 57  0.00229
20   21   7 62  0.00774
21   21   8 18  0.00229
22   21   8 63  0.00229
23   21   9 51  0.00229
24   21  10  8  0.00229
25   31   1 27  0.00459
26   31   2 14  0.00229
27   31   2 44  0.00230
28   31   3  8  0.00459
29   31   4 13  0.00520
30   31   4 27  0.00499
31   31   4 35  0.00459
32   31   4 41  0.00229
33   31   4 44  0.02875
34   31   4 57  0.00229
35   31   4 60  0.00692
36   31   7  7  0.00000
37   31   7 44  0.00459
38   31   7 61  0.00229
39   31   8 35  0.00229
40   31   8 37  0.00229
41   31   8 45  0.00229
42   31   8 52  0.00229
43   31   8 60  0.00459
44   31  11  7  0.00229
45   31  11 18  0.00459
46   31  11 27  0.00608
47   31  11 35  0.01725
48   31  11 37  0.00688
49   31  11 38  0.00688
50   31  11 44  0.01011
51   31  11 49  0.00459
52   31  11 51  0.01095
53   31  11 56  0.00229
54   31  11 60  0.00233
55   31  11 61  0.00459
56   31  11 62  0.00603
57   31  13  8  0.00469
58   31  13 14  0.00229
59   31  13 41  0.00459
60   31  13 57  0.00219
61   31  14 58  0.00229
62   31  14 63  0.00229
63   32   1  7  0.00229
64   32   1 35  0.00224
65   32   2 44  0.00291
66   32   3 35  0.00235
67   32   3 51  0.00229
68   32   4  7  0.01714
69   32   4  8  0.00473
70   32   4 14  0.00459
71   32   4 35  0.00345
72   32   4 44  0.00243
73   32   4 45  0.00167
74   32   4 51  0.00229
75   32   4 55  0.00251
76   32   4 60  0.03090
77   32   4 62  0.02371
78   32   7 14  0.00229
79   32   7 39  0.00229
80   32   7 57  0.00459
81   32   8  7  0.00688
82   32   9 51  0.00229
83   32  10 39  0.00229
84   33   7  7  0.00459
85   33   7 57  0.00688
86   33   9  8  0.00229
87   33   9 46  0.00229
88   33   9 48  0.00229
89   33   9 60  0.00688
90   33  10 51  0.00229
91   42   4 35  0.00229
92   42   8 18  0.00229
93   42   8 55  0.00229
94   42   8 60  0.00229
95   51   1  8  0.00461
96   51   1 18  0.00241
97   51   1 27  0.01416
98   51   1 35  0.02966
99   51   1 39  0.00459
100  51   1 44  0.01743
101  51   1 47  0.00229
102  51   1 48  0.00229
103  51   1 51  0.00740
104  51   1 58  0.00229
105  51   1 60  0.00231
106  51   2 51  0.00229
107  51   4 27  0.00229
108  51   8  7  0.00459
109  51  10  7  0.00229
110  51  10 14  0.00229
111  51  10 18  0.00229
112  51  10 35  0.00229
113  51  10 37  0.00459
114  51  14 56  0.00229
115  52   2 27  0.00229
116  52   2 42  0.00229
117  52   2 56  0.00459
118  52   2 62  0.00459
119  52   4 39  0.00229
120  52   8 60  0.00229
121  53   8 57  0.00229
122  53   9 51  0.00229
123  53  14 35  0.00229
124  53  14 38  0.00459
125  53  14 44  0.00229
126  53  14 51  0.00229
127  53  14 55  0.00459
128  61   2 44  0.00229
129  61   2 60  0.00229
130  61   2 61  0.00459
131  61   2 62  0.00229
132  61   4 52  0.00229
133  61   8 35  0.00000
134  61   8 44  0.00229
135  61  13 39  0.00229
136  61  13 44  0.00229
137  62   2  7  0.05068
138  62   2  8  0.00462
139  62   2 18  0.01570
140  62   2 27  0.00459
141  62   2 35  0.01045
142  62   2 44  0.01369
143  62   2 60  0.00515
144  62   4  7  0.00230
145  62   4 45  0.00291
146  62   7 57  0.00229
147  62   8 35  0.00229
148  62   8 39  0.00229
149  62  10 51  0.00229
150  62  11 55  0.00229
151  62  13 51  0.00229
152  62  14 51  0.00229
153  63   2  7  0.01376
154  63   2 35  0.00000
155  63   2 44  0.00229
156  63   4 48  0.00229
157  63   8 18  0.00229
158  63   8 58  0.00229
159  63  10 44  0.00229
160  63  13  7  0.01606
161  63  13 35  0.00000
162  63  13 38  0.00688
163  63  13 44  0.01606
164  63  13 55  0.00437
165  63  13 60  0.00558
166  63  13 62  0.00840
167  64   2 51  0.00229
168  64  11 38  0.00229
169  64  13  7  0.00961
170  64  13 14  0.00459
171  64  13 35  0.00680
172  64  13 44  0.00238
173  64  13 51  0.00229
174  64  13 57  0.00229
175  64  13 60  0.00415
176  64  13 61  0.00229
177  64  13 63  0.00688
================================================================================ 
                                    Details                                      
================================================================================ 

lnlike =  -1837.008 
lr stat for no LD =  598.3421 , df =  123 , p-val =  0 
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.em.control"
> 
> ### * haplo.em.control
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.em.control
> ### Title: Create the Control Parameters for the EM Computation of
> ###   Haplotype Probabilities, with Progressive Insertion of Loci
> ### Aliases: haplo.em.control
> 
> 
> ### ** Examples
> 
> # This is how it is used within haplo.score
> #    > score.gauss <- haplo.score(resp, geno, trait.type="gaussian", 
> #    >           em.control=haplo.em.control(insert.batch.size = 2, n.try=1))
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.em.fitter"
> 
> ### * haplo.em.fitter
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.em.fitter
> ### Title: Compute engine for haplotype EM algorithm
> ### Aliases: haplo.em.fitter
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.enum"
> 
> ### * haplo.enum
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.enum
> ### Title: Enumerate all possible pairs of haplotypes that are consistent
> ###   with a set of un-phased multilocus markers
> ### Aliases: haplo.enum
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.glm"
> 
> ### * haplo.glm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.glm
> ### Title: GLM Regression of Trait on Ambiguous Haplotypes
> ### Aliases: haplo.glm
> 
> 
> ### ** Examples
> 
>  # FOR REGULAR USAGE, DO NOT DISCARD GENOTYPES WITH MISSING VALUES
>  # WE ONLY SUBSET BY KEEP HERE SO THE EXAMPLES RUN FASTER
> 
>  setupData(hla.demo)
[1] "hla.demo"
>  geno <- as.matrix(hla.demo[,c(17,18,21:24)])
>  keep <- !apply(is.na(geno) | geno==0, 1, any) # SKIP THESE THREE LINES
>  hla.demo <- hla.demo[keep,]                   # IN AN ANALYSIS
>  geno <- geno[keep,]                           # 
>  attach(hla.demo)
>  label <-c("DQB","DRB","B")
>  y <- hla.demo$resp
>  y.bin <- 1*(hla.demo$resp.cat=="low")
> 
> # set up a genotype array as a model.matrix for inserting into data frame
> # Note that hla.demo is a data.frame, and we need to subset to columns
> # of interest. Also also need to convert to a matrix object, so that
> # setupGeno can code alleles and convert geno to 'model.matrix' class.
> 
>  geno <- setupGeno(geno, miss.val=c(0,NA))
> 
>   # geno now has an attribute 'unique.alleles' which must be passed to
>   # haplo.glm as allele.lev=attributes(geno)$unique.alleles, see below
> 
>  my.data <- data.frame(geno=geno, age=hla.demo$age, male=hla.demo$male,
+                       y=y, y.bin=y.bin)
> 
>  fit.gaus <- haplo.glm(y ~ male + geno, family = gaussian,  na.action=
+                "na.geno.keep",allele.lev=attributes(geno)$unique.alleles, 
+                data=my.data, locus.label=label,
+                control = haplo.glm.control(haplo.freq.min=0.02))
>  fit.gaus

  Call: 
haplo.glm(formula = y ~ male + geno, family = gaussian, 
    data = my.data, na.action = "na.geno.keep", 
    locus.label = label, allele.lev = attributes(geno)$unique.alleles, 
    control = haplo.glm.control(haplo.freq.min = 0.02))

Coefficients:
               coef    se t.stat    pval
(Intercept)  1.0672 0.343  3.114 0.00211
male         0.0815 0.156  0.524 0.60100
geno.16      0.2509 0.453  0.553 0.58053
geno.33     -0.3160 0.343 -0.920 0.35863
geno.76      0.2243 0.361  0.621 0.53501
geno.77      1.1386 0.384  2.967 0.00335
geno.98      0.5882 0.362  1.625 0.10572
geno.137     0.9825 0.304  3.234 0.00142
geno.rare    0.4068 0.182  2.235 0.02647

Haplotypes:
           DQB DRB  B hap.freq
geno.16     21   7 44   0.0214
geno.33     31   4 44   0.0288
geno.76     32   4 60   0.0305
geno.77     32   4 62   0.0241
geno.98     51   1 35   0.0297
geno.137    62   2  7   0.0504
geno.rare    *   *  *   0.7099
haplo.base  21   3  8   0.1052
>  
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.glm.control"
> 
> ### * haplo.glm.control
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.glm.control
> ### Title: Create list of control parameters for haplo.glm
> ### Aliases: haplo.glm.control
> 
> 
> ### ** Examples
> 
> # using the data set up in the example for haplo.glm,
> # the control function is used in haplo.glm as follows
> #  > fit <- haplo.glm(y ~ male + geno, family = gaussian,  
> #  >          na.action="na.geno.keep",
> #  >          data=my.data, locus.label=locus.label,
> #  >          control = haplo.glm.control(haplo.min.count=5,
> #  >          em.c=haplo.em.control(n.try=1)))
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.group"
> 
> ### * haplo.group
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.group
> ### Title: Frequencies for Haplotypes by Grouping Variable
> ### Aliases: haplo.group
> 
> 
> ### ** Examples
> 
>   setupData(hla.demo)
[1] "hla.demo"
>   geno <- as.matrix(hla.demo[,c(17,18,21:24)])
> 
> # remove any subjects with missing alleles for faster examples, 
> # but you may keep them in practice
>   keep <- !apply(is.na(geno) | geno==0, 1, any)
>   hla.demo <- hla.demo[keep,]
>   geno <- geno[keep,]
>   attach(hla.demo)
>   
>   y.ord <- as.numeric(resp.cat)
>   y.bin <-ifelse(y.ord==1,1,0)
>   group.bin <- haplo.group(y.bin, geno, miss.val=0)
>   print.haplo.group(group.bin)

-------------------------------------------------------------------------------- 
                       Counts per Grouping Variable Value                        
-------------------------------------------------------------------------------- 

group
  0   1 
156  62 


-------------------------------------------------------------------------------- 
                        Haplotype Frequencies By Group                           
-------------------------------------------------------------------------------- 

    loc.1 loc.2 loc.3   Total y.bin.0 y.bin.1
1      21     1     8 0.00235 0.00337      NA
2      21    10     8 0.00229 0.00321      NA
3      21     2    18 0.00229 0.00321      NA
4      21     2     7 0.00229 0.00321      NA
5      21     3    18 0.00462 0.00675      NA
6      21     3    35 0.00571 0.00651      NA
7      21     3    44 0.00363 0.00335 0.01613
8      21     3    49 0.00229      NA      NA
9      21     3    57 0.00240      NA      NA
10     21     3    70 0.00229      NA      NA
11     21     3     8 0.10515 0.06976 0.19355
12     21     4    62 0.00459 0.00641      NA
13     21     7    13 0.01086      NA 0.02419
14     21     7    18 0.00250      NA      NA
15     21     7    35 0.00238      NA 0.00806
16     21     7    44 0.02186 0.01764 0.04839
17     21     7    45 0.00229 0.00321      NA
18     21     7    50 0.00459 0.00321 0.00806
19     21     7    57 0.00229 0.00641      NA
20     21     7    62 0.00774 0.00742      NA
21     21     7     7 0.01430 0.01981      NA
22     21     8    18 0.00229 0.00286      NA
23     21     8    63 0.00229      NA      NA
24     21     9    51 0.00229      NA      NA
25     31     1    27 0.00459 0.00641      NA
26     31    11    18 0.00459      NA 0.00806
27     31    11    27 0.00608 0.00507 0.00806
28     31    11    35 0.01725 0.01923 0.01613
29     31    11    37 0.00688 0.00641 0.00806
30     31    11    38 0.00688 0.00641 0.00806
31     31    11    44 0.01011 0.00943      NA
32     31    11    49 0.00459      NA 0.01613
33     31    11    51 0.01095 0.01215      NA
34     31    11    56 0.00229      NA 0.00806
35     31    11    60 0.00233      NA 0.00806
36     31    11    61 0.00459 0.00641      NA
37     31    11    62 0.00603 0.00860      NA
38     31    11     7 0.00229 0.00321      NA
39     31    13    14 0.00229 0.00321      NA
40     31    13    41 0.00459 0.00641      NA
41     31    13    57 0.00219      NA 0.00806
42     31    13     8 0.00469      NA      NA
43     31    14    58 0.00229      NA      NA
44     31    14    63 0.00229 0.00321      NA
45     31     2    14 0.00229      NA      NA
46     31     2    44 0.00230      NA 0.01613
47     31     3     8 0.00459 0.00321 0.00806
48     31     4    13 0.00520 0.00321      NA
49     31     4    27 0.00499 0.00758      NA
50     31     4    35 0.00459      NA 0.01613
51     31     4    41 0.00229 0.00321      NA
52     31     4    44 0.02875 0.01458 0.06452
53     31     4    57 0.00229      NA      NA
54     31     4    60 0.00692 0.01310      NA
55     31     7    44 0.00459 0.00641      NA
56     31     7    61 0.00229      NA      NA
57     31     7     7 0.00000      NA 0.00806
58     31     8    35 0.00229 0.00321      NA
59     31     8    37 0.00229 0.00321      NA
60     31     8    45 0.00229 0.00321      NA
61     31     8    52 0.00229 0.00321      NA
62     31     8    60 0.00459 0.00321      NA
63     32     1    35 0.00224 0.00304      NA
64     32     1     7 0.00229 0.00000      NA
65     32    10    39 0.00229 0.00321      NA
66     32     2    44 0.00291 0.00321      NA
67     32     3    35 0.00235 0.00337      NA
68     32     3    51 0.00229 0.00321      NA
69     32     4    14 0.00459      NA      NA
70     32     4    35 0.00345 0.00565      NA
71     32     4    44 0.00243 0.00333 0.01613
72     32     4    45 0.00167      NA      NA
73     32     4    51 0.00229 0.00321      NA
74     32     4    55 0.00251      NA 0.00000
75     32     4    60 0.03090 0.03173 0.04032
76     32     4    62 0.02371 0.01923      NA
77     32     4     7 0.01714 0.02645 0.00806
78     32     4     8 0.00473 0.00335 0.00806
79     32     7    14 0.00229      NA 0.00806
80     32     7    39 0.00229 0.00321      NA
81     32     7    57 0.00459      NA      NA
82     32     8     7 0.00688 0.00962      NA
83     32     9    51 0.00229 0.00321      NA
84     33    10    51 0.00229 0.00321      NA
85     33     7    57 0.00688      NA 0.01613
86     33     7     7 0.00459 0.00321      NA
87     33     9    46 0.00229 0.00321      NA
88     33     9    48 0.00229 0.00321      NA
89     33     9    60 0.00688 0.00321 0.01613
90     33     9     8 0.00229      NA      NA
91     42     4    35 0.00229      NA      NA
92     42     8    18 0.00229 0.00321      NA
93     42     8    55 0.00229      NA      NA
94     42     8    60 0.00229      NA      NA
95     51     1    18 0.00241 0.00331      NA
96     51     1    27 0.01416 0.01619 0.00806
97     51     1    35 0.02966 0.03779 0.00806
98     51     1    39 0.00459      NA 0.00806
99     51     1    44 0.01743 0.01791      NA
100    51     1    47 0.00229      NA      NA
101    51     1    48 0.00229      NA 0.00806
102    51     1    51 0.00740 0.01029      NA
103    51     1    58 0.00229      NA 0.00806
104    51     1    60 0.00231 0.00325      NA
105    51     1     8 0.00461 0.00323 0.00806
106    51    10    14 0.00229 0.00321      NA
107    51    10    18 0.00229 0.00321      NA
108    51    10    35 0.00229 0.00321      NA
109    51    10    37 0.00459      NA 0.00806
110    51    10     7 0.00229 0.00000      NA
111    51    14    56 0.00229 0.00321      NA
112    51     2    51 0.00229 0.00321      NA
113    51     4    27 0.00229 0.00321      NA
114    51     8     7 0.00459      NA 0.00806
115    52     2    27 0.00229 0.00321      NA
116    52     2    42 0.00229 0.00321      NA
117    52     2    56 0.00459 0.00321 0.00806
118    52     2    62 0.00459 0.00641      NA
119    52     4    39 0.00229 0.00321      NA
120    52     8    60 0.00229 0.00321      NA
121    53    14    35 0.00229      NA 0.00806
122    53    14    38 0.00459 0.00641      NA
123    53    14    44 0.00229      NA 0.00806
124    53    14    51 0.00229 0.00321      NA
125    53    14    55 0.00459      NA 0.00806
126    53     8    57 0.00229 0.00321      NA
127    53     9    51 0.00229 0.00321      NA
128    61    13    39 0.00229 0.00321      NA
129    61    13    44 0.00229 0.00321      NA
130    61     2    44 0.00229 0.00321      NA
131    61     2    60 0.00229 0.00321      NA
132    61     2    61 0.00459 0.00321      NA
133    61     2    62 0.00229 0.00321      NA
134    61     4    52 0.00229      NA      NA
135    61     8    35 0.00000      NA      NA
136    61     8    44 0.00229 0.00321      NA
137    62    10    51 0.00229 0.00321      NA
138    62    11    55 0.00229 0.00321      NA
139    62    13    51 0.00229 0.00321      NA
140    62    14    51 0.00229 0.00321      NA
141    62     2    18 0.01570 0.01272 0.02419
142    62     2    27 0.00459      NA      NA
143    62     2    35 0.01045 0.00491 0.00806
144    62     2    44 0.01369 0.01340      NA
145    62     2    60 0.00515      NA 0.00806
146    62     2     7 0.05068 0.06833 0.01613
147    62     2     8 0.00462 0.00641      NA
148    62     4    45 0.00291 0.00321      NA
149    62     4     7 0.00230 0.00321      NA
150    62     7    57 0.00229 0.00321      NA
151    62     8    35 0.00229      NA 0.00806
152    62     8    39 0.00229 0.00321      NA
153    63    10    44 0.00229 0.00321      NA
154    63    13    35 0.00000      NA      NA
155    63    13    38 0.00688 0.00641 0.00806
156    63    13    44 0.01606 0.01650 0.00806
157    63    13    55 0.00437      NA 0.00806
158    63    13    60 0.00558      NA 0.01613
159    63    13    62 0.00840 0.01282      NA
160    63    13     7 0.01606 0.02196      NA
161    63     2    35 0.00000 0.00246      NA
162    63     2    44 0.00229      NA      NA
163    63     2     7 0.01376 0.01036 0.01613
164    63     4    48 0.00229 0.00321      NA
165    63     8    18 0.00229      NA 0.00806
166    63     8    58 0.00229 0.00321      NA
167    64    11    38 0.00229 0.00321      NA
168    64    13    14 0.00459      NA 0.01613
169    64    13    35 0.00680 0.01319      NA
170    64    13    44 0.00238      NA 0.00806
171    64    13    51 0.00229 0.00321      NA
172    64    13    57 0.00229      NA      NA
173    64    13    60 0.00415 0.00641      NA
174    64    13    61 0.00229 0.00321      NA
175    64    13    63 0.00688 0.00962      NA
176    64    13     7 0.00961 0.00918      NA
177    64     2    51 0.00229 0.00321      NA
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.hash"
> 
> ### * haplo.hash
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.hash
> ### Title: Integer Rank Codes for Haplotypes
> ### Aliases: haplo.hash
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.model.frame"
> 
> ### * haplo.model.frame
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.model.frame
> ### Title: Sets up a model frame for haplo.glm
> ### Aliases: haplo.model.frame
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.scan"
> 
> ### * haplo.scan
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.scan
> ### Title: Search for a trait-locus by sliding a fixed-width window over
> ###   each marker locus and scanning all possible haplotype lengths within
> ###   the window
> ### Aliases: haplo.scan
> 
> 
> ### ** Examples
> 
>   # create a random genotype matrix with 10 loci, 50 cases, 50 controls
>   set.seed(1)
>   tmp <- ifelse(runif(2000)>.3, 1, 2)
>   geno <- matrix(tmp, ncol=20)
>   y <- rep(c(0,1),c(50,50))
> 
>   # search 10-locus region, typically don't limit the number of
>   # simulations, but run time can get long with many simulations
> 
>   scan.obj <- haplo.scan(y, geno, width=3,
+                 sim.control = score.sim.control(min.sim=10, max.sim=20))
> 
>   print(scan.obj)

  Call: 
haplo.scan(y = y, geno = geno, width = 3, 
    sim.control = score.sim.control(min.sim = 10, 
        max.sim = 20))

================================================================================ 
                    Locus Scan-statistic Simulated P-values                      
================================================================================ 

          loc-1 loc-2 loc-3 loc-4 loc-5 loc-6 loc-7 loc-8 loc-9 loc-10
sim.p-val  0.25  0.05  0.05  0.05   0.3  0.45   0.4   0.8  0.95      1


      Loci with max scan statistic:   2 3 4 
 Max-Stat Simulated Global p-value:   0.2 
             Number of Simulations:   20 
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.scan.obs"
> 
> ### * haplo.scan.obs
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.scan.obs
> ### Title: For observed data, slide a fixed-width window over each marker
> ###   locus and scan all possible haplotypes within the window
> ### Aliases: haplo.scan.obs
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.scan.sim"
> 
> ### * haplo.scan.sim
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.scan.sim
> ### Title: For simulated data, slide a fixed-width window over each marker
> ###   locus and scan all possible haplotypes within the window
> ### Aliases: haplo.scan.sim
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.score"
> 
> ### * haplo.score
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.score
> ### Title: Score Statistics for Association of Traits with Haplotypes
> ### Aliases: haplo.score
> 
> 
> ### ** Examples
> 
>   # establish all hla.demo data, 
>   # remove genotypes with missing alleles just so haplo.score runs faster 
>   # with missing values included, this example takes 2-4 minutes
>   # FOR REGULAR USAGE, DO NOT DISCARD GENOTYPES WITH MISSING VALUES
> 
>   setupData(hla.demo)
[1] "hla.demo"
>   geno <- as.matrix(hla.demo[,c(17,18,21:24)])
>   keep <- !apply(is.na(geno) | geno==0, 1, any)
>   hla.demo <- hla.demo[keep,]
>   geno <- geno[keep,]
>   attach(hla.demo)
>   label <- c("DQB","DRB","B")
>  
> # For quantitative, normally distributed trait:
> 
>   score.gaus <- haplo.score(resp, geno, locus.label=label, 
+                             trait.type = "gaussian")
>   print(score.gaus)
-------------------------------------------------------------------------------- 
                            Global Score Statistics                              
-------------------------------------------------------------------------------- 

global-stat = 30.79521, df = 17, p-val = 0.02115



-------------------------------------------------------------------------------- 
                           Haplotype-specific Scores                             
-------------------------------------------------------------------------------- 

      DQB DRB B  Hap-Freq Hap-Score p-val  
 [1,] 21  3   8  0.10515  -2.4485   0.01435
 [2,] 31  4   44 0.02875  -2.26997  0.02321
 [3,] 51  1   44 0.01743  -0.93081  0.35195
 [4,] 63  13  44 0.01606  -0.74713  0.45498
 [5,] 63  2   7  0.01376  -0.55206  0.58091
 [6,] 21  7   44 0.02186  -0.49537  0.62034
 [7,] 32  4   60 0.0309   -0.49136  0.62317
 [8,] 62  2   44 0.01369  -0.25836  0.79613
 [9,] 62  2   18 0.0157   -0.2548   0.79888
[10,] 51  1   27 0.01416  0.05697   0.95457
[11,] 31  11  35 0.01725  0.49671   0.6194 
[12,] 51  1   35 0.02966  0.79313   0.4277 
[13,] 32  4   7  0.01714  1.00837   0.31328
[14,] 21  7   7  0.0143   1.19616   0.23163
[15,] 63  13  7  0.01606  2.19393   0.02824
[16,] 32  4   62 0.02371  2.35151   0.0187 
[17,] 62  2   7  0.05068  2.397     0.01653


> 
> # For ordinal trait:
>   y.ord <- as.numeric(resp.cat)
>   score.ord <- haplo.score(y.ord, geno, locus.label=label,
+                            trait.type="ordinal")
>   print(score.ord)
-------------------------------------------------------------------------------- 
                            Global Score Statistics                              
-------------------------------------------------------------------------------- 

global-stat = 34.46701, df = 17, p-val = 0.0073



-------------------------------------------------------------------------------- 
                           Haplotype-specific Scores                             
-------------------------------------------------------------------------------- 

      DQB DRB B  Hap-Freq Hap-Score p-val  
 [1,] 21  3   8  0.10502  -2.83517  0.00458
 [2,] 31  4   44 0.02875  -2.6415   0.00825
 [3,] 63  13  44 0.01555  -0.56699  0.57072
 [4,] 62  2   18 0.01573  -0.55686  0.57763
 [5,] 51  1   44 0.01744  -0.5409   0.58857
 [6,] 21  7   44 0.0219   -0.33723  0.73594
 [7,] 32  4   60 0.0309   -0.20674  0.83622
 [8,] 63  2   7  0.01348  -0.07091  0.94347
 [9,] 62  2   44 0.01369  0.02745   0.9781 
[10,] 51  1   27 0.01416  0.32061   0.74851
[11,] 31  11  35 0.01728  0.64425   0.51942
[12,] 51  1   35 0.0297   0.92883   0.35298
[13,] 21  7   7  0.0143   1.42418   0.1544 
[14,] 32  4   7  0.01691  1.47411   0.14045
[15,] 32  4   62 0.02371  1.89113   0.05861
[16,] 63  13  7  0.01605  1.89743   0.05777
[17,] 62  2   7  0.0512   2.40725   0.01607


> 
> # For a  binary trait and simulations,
> # limit simulations to 500 in score.sim.control, default is 20000
>   y.bin <-ifelse(y.ord==1,1,0)
>   score.bin.sim <- haplo.score(y.bin, geno, trait.type = "binomial",
+                      locus.label=label, simulate=TRUE, sim.control=
+                      score.sim.control(min.sim=200,max.sim=500))
> 
>   print(score.bin.sim)
-------------------------------------------------------------------------------- 
                            Global Score Statistics                              
-------------------------------------------------------------------------------- 

global-stat = 34.99167, df = 17, p-val = 0.00624

-------------------------------------------------------------------------------- 
                       Global Simulation p-value Results                         
-------------------------------------------------------------------------------- 

Global sim. p-val =  0.006 
Max-Stat sim. p-val =  0.008 
Number of Simulations, Global:  500 , Max-Stat: 500

-------------------------------------------------------------------------------- 
                           Haplotype-specific Scores                             
-------------------------------------------------------------------------------- 

      DQB DRB B  Hap-Freq Hap-Score p-val   sim p-val
 [1,] 62  2   7  0.05067  -2.14348  0.03207 0.042    
 [2,] 51  1   35 0.02967  -1.70404  0.08837 0.064    
 [3,] 63  13  7  0.01606  -1.69539  0.09    0.094    
 [4,] 21  7   7  0.01431  -1.55298  0.12043 0.1      
 [5,] 32  4   7  0.01714  -1.02178  0.30689 0.282    
 [6,] 51  1   27 0.01416  -0.69704  0.48578 0.466    
 [7,] 32  4   62 0.02371  -0.66703  0.50475 0.466    
 [8,] 31  11  35 0.01728  -0.5236   0.60056 0.66     
 [9,] 51  1   44 0.01744  -0.13408  0.89334 0.788    
[10,] 32  4   60 0.0309   0.04749   0.96213 0.958    
[11,] 63  13  44 0.01555  0.06218   0.95042 1        
[12,] 62  2   44 0.0139   0.18204   0.85555 0.924    
[13,] 63  2   7  0.01376  0.26941   0.78762 0.942    
[14,] 62  2   18 0.01553  0.80075   0.42327 0.636    
[15,] 21  7   44 0.02167  1.04709   0.29506 0.322    
[16,] 31  4   44 0.02875  2.53818   0.01114 0.008    
[17,] 21  3   8  0.10502  3.82255   0.00013 0        


> 
> # For a binary trait, adjusted for sex and age:
> 
>   x <- cbind(male, age)
>   score.bin.adj <- haplo.score(y.bin, geno, trait.type = "binomial", 
+                                locus.label=label, x.adj=x)
>   print(score.bin.adj)
-------------------------------------------------------------------------------- 
                            Global Score Statistics                              
-------------------------------------------------------------------------------- 

global-stat = 36.17382, df = 18, p-val = 0.0067



-------------------------------------------------------------------------------- 
                           Haplotype-specific Scores                             
-------------------------------------------------------------------------------- 

      DQB DRB B  Hap-Freq Hap-Score p-val  
 [1,] 62  2   7  0.04948  -2.1712   0.02992
 [2,] 51  1   35 0.02968  -1.76135  0.07818
 [3,] 63  13  7  0.01309  -1.60828  0.10777
 [4,] 21  7   7  0.01426  -1.56454  0.11769
 [5,] 32  4   7  0.02296  -1.26014  0.20762
 [6,] 51  1   27 0.014    -0.74114  0.45861
 [7,] 31  11  35 0.01728  -0.55781  0.57697
 [8,] 32  4   62 0.02135  -0.51999  0.60307
 [9,] 62  2   35 0.0116   -0.44104  0.65918
[10,] 51  1   44 0.01746  -0.16896  0.86583
[11,] 62  2   44 0.0137   0.09197   0.92672
[12,] 63  13  44 0.01555  0.19142   0.8482 
[13,] 32  4   60 0.03094  0.20878   0.83462
[14,] 63  2   7  0.01376  0.39965   0.68941
[15,] 62  2   18 0.01569  0.72257   0.46995
[16,] 21  7   44 0.02192  1.01114   0.31195
[17,] 31  4   44 0.02874  2.62702   0.00861
[18,] 21  3   8  0.105    3.81016   0.00014


> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.score.glm"
> 
> ### * haplo.score.glm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.score.glm
> ### Title: Compute haplotype score statistics for GLM
> ### Aliases: haplo.score.glm
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.score.merge"
> 
> ### * haplo.score.merge
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.score.merge
> ### Title: Merge haplo.score And haplo.group Objects
> ### Aliases: haplo.score.merge
> 
> 
> ### ** Examples
> 
>   setupData(hla.demo)
[1] "hla.demo"
>   geno <- as.matrix(hla.demo[,c(17,18,21:24)])
>   keep <- !apply(is.na(geno) | geno==0, 1, any)
>   hla.demo <- hla.demo[keep,]
>   geno <- geno[keep,]
>   attach(hla.demo)
>   y.ord <- as.numeric(resp.cat)
>   y.bin <-ifelse(y.ord==1,1,0)
>   
>   group.bin <- haplo.group(y.bin, geno, miss.val=0)
>   score.bin <- haplo.score(y.bin, geno, trait.type="binomial")
>   score.merged <- haplo.score.merge(score.bin, group.bin)
> 
>   print(score.merged)


-------------------------------------------------------------------------------- 
              Haplotype Scores, p-values, and Frequencies By Group               
-------------------------------------------------------------------------------- 

   loc.1 loc.2 loc.3 Hap.Score   p.val Hap.Freq y.bin.0 y.bin.1
1     62     2     7  -2.08863 0.03674  0.05068 0.06833 0.01613
2     51     1    35  -1.70404 0.08837  0.02966 0.03779 0.00806
3     21     7     7  -1.56010 0.11874  0.01430 0.01981      NA
4     63    13     7  -1.53912 0.12377  0.01606 0.02196      NA
5     32     4     7  -1.24093 0.21463  0.01714 0.02645 0.00806
6     51     1    27  -0.69715 0.48571  0.01416 0.01619 0.00806
7     32     4    62  -0.66705 0.50474  0.02371 0.01923      NA
8     31    11    35  -0.53177 0.59488  0.01725 0.01923 0.01613
9     62     2    35  -0.43653 0.66245  0.01045 0.00491 0.00806
10    51     1    44  -0.13397 0.89343  0.01743 0.01791      NA
11    63    13    44   0.00781 0.99377  0.01606 0.01650 0.00806
12    32     4    60   0.04802 0.96170  0.03090 0.03173 0.04032
13    62     2    44   0.05072 0.95955  0.01369 0.01340      NA
14    63     2     7   0.26941 0.78762  0.01376 0.01036 0.01613
15    62     2    18   0.87289 0.38272  0.01570 0.01272 0.02419
16    21     7    44   1.11398 0.26529  0.02186 0.01764 0.04839
17    31     4    44   2.53701 0.01118  0.02875 0.01458 0.06452
18    21     3     8   3.81983 0.00013  0.10515 0.06976 0.19355
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.score.podds"
> 
> ### * haplo.score.podds
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.score.podds
> ### Title: Compute Haplotype Score Statistics for Ordinal Traits with
> ###   Proportional Odds Model
> ### Aliases: haplo.score.podds
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "haplo.score.slide"
> 
> ### * haplo.score.slide
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: haplo.score.slide
> ### Title: Score Statistics for Association of Traits with Haplotypes
> ### Aliases: haplo.score.slide
> 
> 
> ### ** Examples
> 
>   setupData(hla.demo)
[1] "hla.demo"
> 
> # Continuous trait slide by 2 loci on all 11 loci, uncomment to run it.
> # Takes > 20 minutes to run
> #  geno.11 <- hla.demo[,-c(1:4)]
> #  label.11 <- c("DPB","DPA","DMA","DMB","TAP1","TAP2","DQB","DQA","DRB","B","A")
> #  slide.gaus <- haplo.score.slide(resp, geno.11, trait.type = "gaussian",
> #                                  locus.label=label.11, n.slide=2)
> 
> #  print(slide.gaus)
> #  plot(slide.gaus)
> 
> # Run shortened example on 9 loci 
> # For an ordinal trait, slide by 3 loci, and simulate p-values:
> #  geno.9 <- hla.demo[,-c(1:6,15,16)]
> #  label.9 <- c("DPA","DMA","DMB","TAP1","DQB","DQA","DRB","B","A")
> 
> #  y.ord <- as.numeric(hla.demo$resp.cat)
> 
> # data is set up, to run, run these lines of code on the data that was
> # set up in this example. It takes > 15 minutes to run
> #  slide.ord.sim <-  haplo.score.slide(y.ord, geno.9, trait.type = "ordinal",
> #                      n.slide=3, locus.label=label.9, simulate=TRUE,
> #                      sim.control=score.sim.control(min.sim=200, max.sim=500))
> 
>   # note, results will vary due to simulations
> #  print(slide.ord.sim)
> #  plot(slide.ord.sim)
> #  plot(slide.ord.sim, pval="global.sim")
> #  plot(slide.ord.sim, pval="max.sim")
> 
> 
> 
> cleanEx(); ..nameEx <- "locator.haplo"
> 
> ### * locator.haplo
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: locator.haplo
> ### Title: Find Location from Mouse Clicks and Print Haplotypes on Plot
> ### Aliases: locator.haplo
> 
> 
> ### ** Examples
> 
> # follow the pseudo-code
> #  score.out <-  haplo.score(y, geno, trait.type = "gaussian")
> 
> #  plot(score.out)
> 
> #  locator.haplo(score.out)
> 
> 
> 
> cleanEx(); ..nameEx <- "loci"
> 
> ### * loci
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: loci
> ### Title: Create a group of locus objects from a genotype matrix, assign
> ###   to 'model.matrix' class.
> ### Aliases: loci
> 
> 
> ### ** Examples
> 
> # Create some loci to work with
> a1 <- 1:6
> a2 <- 7:12
> 
> b1 <- c("A","A","B","C","E","D")
> b2 <-c("A","A","C","E","F","G")
> 
> c1 <- c("101","10","115","132","21","112")
> c2 <- c("100","101","0","100","21","110")
> 
> myloci <- data.frame(a1,a2,b1,b2,c1,c2)
> myloci <- loci(myloci, locus.names=c("A","B","C"),miss.val=c(0,NA))
> myloci
     A.a1 A.a2 B.a1 B.a2 C.a1 C.a2
[1,]    1    7    1    1    3    2
[2,]    2    8    1    1    1    3
[3,]    3    9    2    3    6   NA
[4,]    4   10    3    5    7    2
[5,]    5   11    5    6    8    8
[6,]    6   12    4    7    5    4
attr(,"class")
[1] "model.matrix"
attr(,"locus.names")
[1] "A" "B" "C"
attr(,"map")
[1] NA
attr(,"x.linked")
[1] FALSE
attr(,"unique.alleles")
attr(,"unique.alleles")[[1]]
 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12"

attr(,"unique.alleles")[[2]]
[1] "A" "B" "C" "D" "E" "F" "G"

attr(,"unique.alleles")[[3]]
[1] "10"  "100" "101" "110" "112" "115" "132" "21" 

attr(,"male.code")
[1] "M"
attr(,"female.code")
[1] "F"
> 
> attributes(myloci)
$dim
[1] 6 6

$dimnames
$dimnames[[1]]
NULL

$dimnames[[2]]
[1] "A.a1" "A.a2" "B.a1" "B.a2" "C.a1" "C.a2"


$class
[1] "model.matrix"

$locus.names
[1] "A" "B" "C"

$map
[1] NA

$x.linked
[1] FALSE

$unique.alleles
$unique.alleles[[1]]
 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12"

$unique.alleles[[2]]
[1] "A" "B" "C" "D" "E" "F" "G"

$unique.alleles[[3]]
[1] "10"  "100" "101" "110" "112" "115" "132" "21" 


$male.code
[1] "M"

$female.code
[1] "F"

> 
> 
> 
> cleanEx(); ..nameEx <- "locus"
> 
> ### * locus
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: locus
> ### Title: Creates an object of class "locus"
> ### Aliases: locus
> ### Keywords: classes
> 
> ### ** Examples
> 
> b1 <- c("A","A","B","C","E","D")
> b2 <- c("A","A","C","E","F","G")
> loc1 <- locus(b1,b2,chrom=4,locus.alias="D4S1111")
> 
> loc1
     a1 a2
[1,]  1  1
[2,]  1  1
[3,]  2  3
[4,]  3  5
[5,]  5  6
[6,]  4  7
attr(,"chrom.label")
[1] 4
attr(,"locus.alias")
[1] "D4S1111"
attr(,"x.linked")
[1] FALSE
attr(,"class")
[1] "model.matrix"
attr(,"allele.labels")
[1] "A" "B" "C" "D" "E" "F" "G"
> 
> # a second example which uses more parameters, some may not be supported.
> # c1 <- c("101","10","115","132","21","112")
> # c2 <- c("100","101","0","100","21","110")
> 
> # gender <- rep(c("M","F"),3)
> # loc2 <- locus(c2,c2,chrom="X",locus.alias="DXS1234",x.linked=T,sex=gender)
> 
> 
> 
> cleanEx(); ..nameEx <- "louis.info"
> 
> ### * louis.info
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: louis.info
> ### Title: Louis Information for haplo.glm
> ### Aliases: louis.info
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "mf.gindx"
> 
> ### * mf.gindx
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mf.gindx
> ### Title: Model Frame Genotype Index to Account for Missing Data in
> ###   haplo.glm
> ### Aliases: mf.gindx
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "na.geno.keep"
> 
> ### * na.geno.keep
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: na.geno.keep
> ### Title: Remove rows with NA in covariates, but keep genotypes with NAs
> ### Aliases: na.geno.keep
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.haplo.score"
> 
> ### * plot.haplo.score
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.haplo.score
> ### Title: Plot Haplotype Frequencies versus Haplotype Score Statistics
> ### Aliases: plot.haplo.score
> 
> 
> ### ** Examples
> 
>   setupData(hla.demo)
[1] "hla.demo"
>   geno <- as.matrix(hla.demo[,c(17,18,21:24)])
>   keep <- !apply(is.na(geno) | geno==0, 1, any)
>   hla.demo <- hla.demo[keep,]
>   geno <- geno[keep,]
>   attach(hla.demo)
>   label <- c("DQB","DRB","B")
>  
> # For quantitative, normally distributed trait:
> 
>   score.gaus <- haplo.score(resp, geno, locus.label=label, 
+                             trait.type = "gaussian")
> 
>   plot.haplo.score(score.gaus)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.haplo.score.slide"
> 
> ### * plot.haplo.score.slide
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.haplo.score.slide
> ### Title: Plot a haplo.score.slide Object
> ### Aliases: plot.haplo.score.slide
> 
> 
> ### ** Examples
> 
> # This example has a long run time, therefore it is commented
> 
> #   setupData(hla.demo)
> #   attach(hla.demo)
> #   geno.11 <- hla.demo[,-c(1:4)]
> #   label.11 <- c("DPB","DPA","DMA","DMB","TAP1","TAP2","DQB","DQA","DRB","B","A")
> 
> #For an ordinal trait, slide by 3 loci, and simulate p-values:
> #   y.ord <- as.numeric(resp.cat)
> #   slide.ord.sim <-  haplo.score.slide(y.ord, geno.11, trait.type = "ordinal",
> #                              n.slide=3, locus.label=label.11, simulate=TRUE,
> #                              sim.control=score.sim.control(min.sim=500))
> 
> #   print(slide.ord.sim)
> #   plot(slide.ord.sim)
> #   plot(slide.ord.sim, pval="global.sim")
> #   plot(slide.ord.sim, pval="max.sim")
> 
> 
> 
> cleanEx(); ..nameEx <- "print.haplo.cc"
> 
> ### * print.haplo.cc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.haplo.cc
> ### Title: Print a haplo.cc object
> ### Aliases: print.haplo.cc
> 
> 
> ### ** Examples
> 
> ## for a haplo.cc object named cc.test, 
> ##    order results by haplotype 
> # print.haplo.cc(cc.test, order.by="haplotype")
> 
> 
> 
> cleanEx(); ..nameEx <- "print.haplo.em"
> 
> ### * print.haplo.em
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.haplo.em
> ### Title: Print contents of a haplo.em object
> ### Aliases: print.haplo.em
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "print.haplo.glm"
> 
> ### * print.haplo.glm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.haplo.glm
> ### Title: Print a contents of a haplo.glm object
> ### Aliases: print.haplo.glm
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "print.haplo.group"
> 
> ### * print.haplo.group
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.haplo.group
> ### Title: Print a haplo.group object
> ### Aliases: print.haplo.group
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "print.haplo.scan"
> 
> ### * print.haplo.scan
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.haplo.scan
> ### Title: Print a haplo.scan object
> ### Aliases: print.haplo.scan
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "print.haplo.score"
> 
> ### * print.haplo.score
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.haplo.score
> ### Title: Print a haplo.score object
> ### Aliases: print.haplo.score
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "print.haplo.score.merge"
> 
> ### * print.haplo.score.merge
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.haplo.score.merge
> ### Title: Print a haplo.score.merge object
> ### Aliases: print.haplo.score.merge
> 
> 
> ### ** Examples
> 
> #see example for haplo.score.merge
> 
> 
> 
> cleanEx(); ..nameEx <- "print.haplo.score.slide"
> 
> ### * print.haplo.score.slide
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.haplo.score.slide
> ### Title: Print the contents of a haplo.score.slide object
> ### Aliases: print.haplo.score.slide
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "printBanner"
> 
> ### * printBanner
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: printBanner
> ### Title: Print a nice banner
> ### Aliases: printBanner
> 
> 
> ### ** Examples
> 
> printBanner("This is a pretty banner", banner.width=40, char.perline=30)
======================================== 
        This is a pretty banner          
======================================== 

> 
> # the output looks like this:
> # ========================================
> #         This is a pretty banner
> # ========================================
> 
> 
> 
> cleanEx(); ..nameEx <- "residScaledGlmFit"
> 
> ### * residScaledGlmFit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: residScaledGlmFit
> ### Title: Scaled Residuals for GLM fit
> ### Aliases: residScaledGlmFit
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "score.sim.control"
> 
> ### * score.sim.control
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: score.sim.control
> ### Title: Create the list of control parameters for simulations in
> ###   haplo.score
> ### Aliases: score.sim.control
> 
> 
> ### ** Examples
> 
> # it would be used in haplo.score as appears below
> #
> # score.sim.500 <- haplo.score(y, geno, trait.type="gaussian", simulate=T, 
> #                sim.control=score.sim.control(min.sim=500, max.sim=2000)
> 
> 
> 
> cleanEx(); ..nameEx <- "setupData"
> 
> ### * setupData
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: setupData
> ### Title: Set up an example dataset provided within the library.
> ### Aliases: setupData
> 
> 
> ### ** Examples
> 
> ## for a data set named my.data load it by
> # setupData(my.data)
> 
> ## check the names of my.data to see if it is loaded
> # names(my.data)
> 
> 
> 
> cleanEx(); ..nameEx <- "setupGeno"
> 
> ### * setupGeno
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: setupGeno
> ### Title: Create a group of locus objects from a genotype matrix, assign
> ###   to 'model.matrix' class.
> ### Aliases: setupGeno
> 
> 
> ### ** Examples
> 
> # Create some loci to work with
> a1 <- 1:6
> a2 <- 7:12
> 
> b1 <- c("A","A","B","C","E","D")
> b2 <-c("A","A","C","E","F","G")
> 
> c1 <- c("101","10","115","132","21","112")
> c2 <- c("100","101","0","100","21","110")
> 
> myGeno <- data.frame(a1,a2,b1,b2,c1,c2)
> myGeno <- setupGeno(myGeno)
> myGeno
     loc-1.a1 loc-1.a2 loc-2.a1 loc-2.a2 loc-3.a1 loc-3.a2
[1,]        1        7        1        1        3        2
[2,]        2        8        1        1        1        3
[3,]        3        9        2        3        6       NA
[4,]        4       10        3        5        7        2
[5,]        5       11        5        6        8        8
[6,]        6       12        4        7        5        4
attr(,"class")
[1] "model.matrix"
attr(,"unique.alleles")
attr(,"unique.alleles")[[1]]
 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12"

attr(,"unique.alleles")[[2]]
[1] "A" "B" "C" "D" "E" "F" "G"

attr(,"unique.alleles")[[3]]
[1] "10"  "100" "101" "110" "112" "115" "132" "21" 

> 
> attributes(myGeno)$unique.alleles
[[1]]
 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12"

[[2]]
[1] "A" "B" "C" "D" "E" "F" "G"

[[3]]
[1] "10"  "100" "101" "110" "112" "115" "132" "21" 

> 
> 
> 
> cleanEx(); ..nameEx <- "summary.haplo.em"
> 
> ### * summary.haplo.em
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.haplo.em
> ### Title: Summarize contents of a haplo.em object
> ### Aliases: summary.haplo.em
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "summaryGeno"
> 
> ### * summaryGeno
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summaryGeno
> ### Title: Summarize Full Haplotype Enumeration on Genotype Matrix
> ### Aliases: summaryGeno
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "varfunc.glm.fit"
> 
> ### * varfunc.glm.fit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: varfunc.glm.fit
> ### Title: Variance Function for GLM
> ### Aliases: varfunc.glm.fit
> 
> 
> ### ** Examples
> 
> 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  12.17 0.9 13.15 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')