Calculate Fisher's method p-value and meta-analysis statistics

Usage

fishp(df, vars, df_sigma, sum_sigma)

Arguments

df: data frame with "markname" and study names as column names.
vars: character vector of study names to include in the meta-analysis.
df_sigma: data frame of tetrachoric correlations.
sum_sigma: sum of tetrachoric correlations.

Value

A data frame with columns 'markname', 'sum_chisq', 'sum_z', 'sum_sigma_var', 'pvalue', 'meta_z', 'meta_p', 'meta_nlog10p'

Examples

  data(snp_example)
  head(snp_example)
#>         markname    trt1   trt2   trt3
#> 1 c01b000015585s 0.35580 0.7356 0.6920
#> 2 c01b000015644s 0.58850 0.4539 0.7164
#> 3 c01b000015647s 0.18840 0.3029 0.2111
#> 4 c01b000015717s 0.99820 0.2474 0.2029
#> 5 c01b000015721s 0.74750 0.2206 0.1954
#> 6 c01b000016805s 0.08051 0.1532 0.7910
  varlist <- c("trt1","trt2","trt3")
  tc <- tetracorr(snp_example, varlist)
  fishp(snp_example, varlist, tc$sigma, tc$sum_sigma)
#>          markname    trt1   trt2    trt3 num_obs sum_sigma_var sum_chisq
#> 1  c01b000015585s 0.35580 0.7356 0.69200       3      3.253552  3.417249
#> 2  c01b000015644s 0.58850 0.4539 0.71640       3      3.253552  3.307147
#> 3  c01b000015647s 0.18840 0.3029 0.21110       3      3.253552  8.837928
#> 4  c01b000015717s 0.99820 0.2474 0.20290       3      3.253552  5.987185
#> 5  c01b000015721s 0.74750 0.2206 0.19540       3      3.253552  6.870263
#> 6  c01b000016805s 0.08051 0.1532 0.79100       3      3.253552  9.259684
#> 7  c01b000016809s 0.07062 0.2896 0.85790       3      3.253552  8.085928
#> 8  c01b000016856s 0.74300 0.5204 0.31930       3      3.253552  4.183682
#> 9  c01b000016946s 0.77860 0.6758 0.80840       3      3.253552  1.709628
#> 10 c01b000016963s 0.82460 0.7960 0.30990       3      3.253552  3.185037
#> 11 c01b000016968s 0.13200 0.5866 0.25170       3      3.253552  7.875766
#> 12 c01b000016977s 0.82080 0.7761 0.21520       3      3.253552  3.974274
#> 13 c01b000016993s 0.18290 0.6209 0.06663       3      3.253552  9.768003
#> 14 c01b000017041s 0.76820 0.8736 0.54980       3      3.253552  1.994077
#> 15 c01b000017101s 0.24760 0.3189 0.10090       3      3.253552  9.664888
#> 16 c01b000017147s 0.03534 0.9412 0.99310       3      3.253552  6.820527
#> 17 c01b000017181s 0.84080 0.7264 0.76440       3      3.253552  1.523440
#> 18 c01b000017375s 0.97000 0.2214 0.03283       3      3.253552  9.909312
#> 19 c01b000017379s 0.56130 0.5311 0.05570       3      3.253552  8.196160
#>         sum_z    pvalue     meta_z     meta_p meta_nlog10p
#> 1  -0.7616582 0.7549448 -0.4222612 0.66358283   0.17810486
#> 2  -0.6800542 0.7694257 -0.3770202 0.64692071   0.18914894
#> 3   2.2024960 0.1829002  1.2210578 0.11103206   0.95455159
#> 4  -1.3972360 0.4246272 -0.7746239 0.78071902   0.10750524
#> 5   0.9616926 0.3330121  0.5331598 0.29696150   0.52729986
#> 6   1.6145585 0.1594917  0.8951069 0.18536498   0.73197231
#> 7   0.9548107 0.2318753  0.5293445 0.29828326   0.52537112
#> 8  -0.2341224 0.6518348 -0.1297968 0.55163641   0.25834708
#> 9  -2.0954750 0.9443755 -1.1617257 0.87732654   0.05683873
#> 10 -1.2643232 0.7852901 -0.7009374 0.75832894   0.12014237
#> 11  1.5673289 0.2473471  0.8689229 0.19244465   0.71569416
#> 12 -0.8889986 0.6801580 -0.4928584 0.68894370   0.16181627
#> 13  2.0978928 0.1347681  1.1630661 0.12240135   0.91221381
#> 14 -2.0016627 0.9202425 -1.1097164 0.86643938   0.06226182
#> 15  2.4292787 0.1394923  1.3467855 0.08902466   1.05048968
#> 16 -2.2198268 0.3377643 -1.2306660 0.89077610   0.05023145
#> 17 -2.3202403 0.9579223 -1.2863350 0.90083691   0.04535383
#> 18  0.7274177 0.1285234  0.4032784 0.34337171   0.46423549
#> 19  1.3596307 0.2240816  0.7537756 0.22549200   0.64686886