Confidence Intervals for Replicated Means
repmeanCI.RdCalculates the confidence intervals for a repmean object.
Arguments
- x
an object produced by
repmean.- alpha
a numeric value indicating confidence level.
- add
a logical value indicating if the confidence intervals should be added to the object or not. Defaults is
TRUE.
Examples
# Creation of replicate weights
RW <- repcreate(df = repdata, # the data frame with all the information
wt = "wt", # the total weights column name
jkzone = "jkzones", # the jkzones column name
jkrep = "jkrep", # the jkreps column name
repwtname = "REPWT", # the desired name for the rep weights
reps = 50, # the number of replications
method = "ICILS") # the name of the method aka the study name
### Groups ----
# One variable
reme <- repmean(x = c("item01"),
PV = FALSE,
repwt = RW, wt = "wt", df = repdata,
method = "ICILS",var = "ML",
group = "GROUP",
exclude = "GR2") # GR2 will not be used for Pooled nor Composite
repmeanCI(reme)
#> group N mean se CIdown CIup sd sdse var
#> 1 Pooled 2992 3.62342 0.01165 3.60058 3.64626 0.65023 0.01427 0.42279
#> 2 Composite NA 3.62334 0.01168 3.60044 3.64623 0.65009 0.01427 0.42269
#> 3 GR1 1507 3.63936 0.01542 3.60914 3.66957 0.64117 0.02043 0.41110
#> 4 GR2 1491 3.60337 0.01979 3.56457 3.64216 0.69582 0.02070 0.48416
#> 5 GR3 1485 3.60732 0.01755 3.57292 3.64171 0.65900 0.01994 0.43429
#> varse
#> 1 0.01855
#> 2 0.01851
#> 3 0.02611
#> 4 0.02881
#> 5 0.02625
# One PV variable
reme <- repmean(x = paste0("Math",1:5),
PV = TRUE, # if set to TRUE, PVs will be treated as separate variables
repwt = RW, wt = "wt", df = repdata,
method = "ICILS",var = "ML",
group = "GROUP",
exclude = "GR2") # GR2 will not be used for Pooled nor Composite
repmeanCI(reme)
#> group N mean se CIdown CIup sd sdse var
#> 1 Pooled 3334 -0.00146 0.02130 -0.04321 0.04028 1.08185 0.01624 1.17050
#> 2 Composite NA -0.00205 0.02701 -0.05498 0.05088 0.90569 0.01462 0.82041
#> 3 GR1 1667 -0.59327 0.03943 -0.67056 -0.51599 0.90148 0.02101 0.81283
#> 4 GR2 1666 0.01857 0.02397 -0.02841 0.06555 0.89383 0.02093 0.79905
#> 5 GR3 1667 0.58917 0.03692 0.51682 0.66153 0.90989 0.02034 0.82799
#> varse
#> 1 0.03516
#> 2 0.02648
#> 3 0.03779
#> 4 0.03740
#> 5 0.03711
### Groups and By ----
# One variable
reme <- repmean(x = c("item01"),
PV = FALSE,
repwt = RW, wt = "wt", df = repdata,
method = "ICILS",var = "ML",
group = "GROUP",
by = "GENDER", # results will be separated by GENDER
exclude = "GR2") # GR2 will not be used for Pooled nor Composite
repmeanCI(reme)
#> $ALL
#> group N mean se CIdown CIup sd sdse var
#> 1 Pooled 2992 3.62342 0.01165 3.60058 3.64626 0.65023 0.01427 0.42279
#> 2 Composite NA 3.62334 0.01168 3.60044 3.64623 0.65009 0.01427 0.42269
#> 3 GR1 1507 3.63936 0.01542 3.60914 3.66957 0.64117 0.02043 0.41110
#> 4 GR2 1491 3.60337 0.01979 3.56457 3.64216 0.69582 0.02070 0.48416
#> 5 GR3 1485 3.60732 0.01755 3.57292 3.64171 0.65900 0.01994 0.43429
#> varse
#> 1 0.01855
#> 2 0.01851
#> 3 0.02611
#> 4 0.02881
#> 5 0.02625
#>
#> $`GENDER==0`
#> group N mean se CIdown CIup sd sdse var
#> 1 Pooled 1458 3.63697 0.01813 3.60143 3.67251 0.63377 0.02041 0.40166
#> 2 Composite NA 3.63698 0.01755 3.60258 3.67138 0.63385 0.02006 0.40179
#> 3 GR1 734 3.63042 0.02467 3.58206 3.67878 0.63847 0.02812 0.40764
#> 4 GR2 760 3.59046 0.02919 3.53324 3.64768 0.72062 0.02742 0.51929
#> 5 GR3 724 3.64354 0.02497 3.59460 3.69249 0.62923 0.02860 0.39594
#> varse mean_1 meandiff_1 meandiffse_1 tvalue_1 df_1 pvalue_1
#> 1 0.02583 3.61060 0.02637 0.02540 1.03818 2990 0.29927
#> 2 0.02533 3.61028 0.02670 0.02492 1.07127 NA NA
#> 3 0.03585 3.64776 -0.01734 0.03674 -0.47194 1505 0.63704
#> 4 0.03963 3.61665 -0.02618 0.04066 -0.64399 1489 0.51968
#> 5 0.03581 3.57281 0.07074 0.03369 2.09948 1483 0.03594
#>
#> $`GENDER==1`
#> group N mean se CIdown CIup sd sdse var
#> 1 Pooled 1534 3.61060 0.01636 3.57853 3.64267 0.66531 0.01903 0.44264
#> 2 Composite NA 3.61028 0.01663 3.57768 3.64288 0.66426 0.01914 0.44166
#> 3 GR1 773 3.64776 0.02332 3.60205 3.69347 0.64386 0.02989 0.41456
#> 4 GR2 731 3.61665 0.02746 3.56283 3.67046 0.66940 0.02560 0.44809
#> 5 GR3 761 3.57281 0.02372 3.52632 3.61930 0.68466 0.02391 0.46875
#> varse mean_0 meandiff_0 meandiffse_0 tvalue_0 df_0 pvalue_0
#> 1 0.02532 3.63697 -0.02637 0.02540 -1.03818 2990 0.29927
#> 2 0.02521 3.63698 -0.02670 0.02492 -1.07127 NA NA
#> 3 0.03837 3.63042 0.01734 0.03674 0.47194 1505 0.63704
#> 4 0.03425 3.59046 0.02618 0.04066 0.64399 1489 0.51968
#> 5 0.03272 3.64354 -0.07074 0.03369 -2.09948 1483 0.03594
#>
# One PV variable
reme <- repmean(x = paste0("Math",1:5),
PV = TRUE, # if set to TRUE, PVs will be treated as separate variables
repwt = RW, wt = "wt", df = repdata,
method = "ICILS",var = "ML",
group = "GROUP",
by = "GENDER", # results will be separated by GENDER
exclude = "GR2") # GR2 will not be used for Pooled nor Composite
repmeanCI(reme)
#> $ALL
#> group N mean se CIdown CIup sd sdse var
#> 1 Pooled 3334 -0.00146 0.02130 -0.04321 0.04028 1.08185 0.01624 1.17050
#> 2 Composite NA -0.00205 0.02701 -0.05498 0.05088 0.90569 0.01462 0.82041
#> 3 GR1 1667 -0.59327 0.03943 -0.67056 -0.51599 0.90148 0.02101 0.81283
#> 4 GR2 1666 0.01857 0.02397 -0.02841 0.06555 0.89383 0.02093 0.79905
#> 5 GR3 1667 0.58917 0.03692 0.51682 0.66153 0.90989 0.02034 0.82799
#> varse
#> 1 0.03516
#> 2 0.02648
#> 3 0.03779
#> 4 0.03740
#> 5 0.03711
#>
#> $`GENDER==0`
#> group N mean se CIdown CIup sd sdse var
#> 1 Pooled 1637 0.43947 0.04440 0.35244 0.52649 1.00205 0.01584 1.00412
#> 2 Composite NA 0.43849 0.03804 0.36393 0.51305 0.78972 0.02293 0.62411
#> 3 GR1 819 -0.17761 0.04462 -0.26507 -0.09015 0.79348 0.03057 0.62996
#> 4 GR2 846 0.48279 0.03513 0.41394 0.55164 0.78112 0.03688 0.61083
#> 5 GR3 818 1.05458 0.06162 0.93380 1.17537 0.78595 0.03419 0.61826
#> varse mean_1 meandiff_1 meandiffse_1 tvalue_1 df_1 pvalue_1
#> 1 0.03173 -0.42518 0.86465 0.08929 9.68332 3332 0
#> 2 0.03611 -0.42542 0.86391 0.06456 13.38179 NA NA
#> 3 0.04847 -0.99226 0.81465 0.09314 8.74621 1665 0
#> 4 0.05727 -0.45673 0.93952 0.07151 13.13918 1664 0
#> 5 0.05353 0.14141 0.91317 0.08942 10.21235 1665 0
#>
#> $`GENDER==1`
#> group N mean se CIdown CIup sd sdse var
#> 1 Pooled 1697 -0.42518 0.05160 -0.52632 -0.32405 0.98078 0.02238 0.96213
#> 2 Composite NA -0.42542 0.04389 -0.51144 -0.33940 0.79967 0.02551 0.64022
#> 3 GR1 848 -0.99226 0.07127 -1.13195 -0.85256 0.81277 0.03952 0.66135
#> 4 GR2 820 -0.45673 0.04790 -0.55061 -0.36286 0.73724 0.03839 0.54426
#> 5 GR3 849 0.14141 0.05124 0.04099 0.24183 0.78658 0.03226 0.61908
#> varse mean_0 meandiff_0 meandiffse_0 tvalue_0 df_0 pvalue_0
#> 1 0.04410 0.43947 -0.86465 0.08929 -9.68332 3332 0
#> 2 0.04090 0.43849 -0.86391 0.06456 -13.38179 NA NA
#> 3 0.06388 -0.17761 -0.81465 0.09314 -8.74621 1665 0
#> 4 0.05673 0.48279 -0.93952 0.07151 -13.13918 1664 0
#> 5 0.05110 1.05458 -0.91317 0.08942 -10.21235 1665 0
#>