quickReg
A manual to show the R package
quickReg.
##Introduction The quickReg package concentrates on a
set of functions to display and pry a dataset. More precisely, the
package can display statistical description for a dataset, build
regression models for lm, glm and cox regression based on specified
variables. More importantly, the package provides several seamless
functions to display these regressions. Several examples are used to
explain the idea.
##Getting started The example data is a hypothetical dataset extracting a subset from package PredictABEL. It has no practical implications and only be used to demostrate the main idea of the package.
# If you haven't install the package, you can download it from cran
# install.packages("quickReg")
library(quickReg)
library(ggplot2)
library(rlang)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## sex age smoking education diabetes BMI systolic diastolic CFHrs1061170 LOCrs10490924 CFHrs1410996 C2rs9332739 CFBrs641153 CFHrs2230199
## 1 1 44 1 0 1 40 129 91 1 2 2 1 1 0
## 2 0 53 0 0 0 29 137 98 2 1 1 1 0 0
## 3 1 46 1 0 0 29 136 93 1 1 2 1 1 1
## 4 1 63 0 0 0 29 176 119 1 0 1 1 0 0
## 5 0 60 NA 0 1 30 148 107 1 2 1 1 0 2
## 6 0 52 0 1 1 29 133 91 1 1 1 1 1 0We can use the function display_table or display_table_group to show statistical descriptions of the data.
display_1<-display_table(data=diabetes,variables=c("age","smoking","education"),group="CFHrs2230199")
display_1## variable level All sample CFHrs2230199 = 0 CFHrs2230199 = 1 CFHrs2230199 = 2 P.value1 P.value2 normality
## 1 age mean +- sd 58.98 +- 13.27 59.23 +- 13.36 58.34 +- 13.14 60.59 +- 13.16 0.36 0.47 6.45E-08; 3.01E-05; 0.24
## 2 NA 0 0 0 0
## 3 smoking 0 455 (49.14%) 259 (49.81%) 166 (48.4%) 30 (47.62%) 0.89 <NA>
## 4 1 471 (50.86%) 261 (50.19%) 177 (51.6%) 33 (52.38%)
## 5 NA 74 38 33 3
## 6 education 0 661 (66.1%) 370 (66.31%) 248 (65.96%) 43 (65.15%) 0.98 <NA>
## 7 1 339 (33.9%) 188 (33.69%) 128 (34.04%) 23 (34.85%)
## 8 NA 0 0 0 0# You could do a sub-group analysis by sex
display_2<-display_table_group(data=diabetes,variables=c("age","smoking"),group="CFHrs2230199",super_group = "sex")
display_2## # A tibble: 10 × 10
## sex variable level `All sample` `CFHrs2230199 = 0` `CFHrs2230199 = 1` `CFHrs2230199 = 2` P.value1 P.value2 normality
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 0 "age" mean +- sd 59.22 +- 13.72 59.94 +- 14.09 58.52 +- 13.38 57.54 +- 12.53 "0.36" "0.40" "2.71E-05; 1.59E-03; 0.08"
## 2 0 "" NA 0 0 0 0 "" "" ""
## 3 0 "smoking" 0 259 (49.05%) 142 (48.63%) 100 (50.51%) 17 (44.74%) "0.79" <NA> ""
## 4 0 "" 1 269 (50.95%) 150 (51.37%) 98 (49.49%) 21 (55.26%) "" "" ""
## 5 0 "" NA 44 20 21 3 "" "" ""
## 6 1 "age" mean +- sd 58.66 +- 12.65 58.33 +- 12.34 58.08 +- 12.84 65.60 +- 12.86 "0.02" "0.02" "3.56E-04; 0.01; 0.95"
## 7 1 "" NA 0 0 0 0 "" "" ""
## 8 1 "smoking" 0 196 (49.25%) 117 (51.32%) 66 (45.52%) 13 (52%) "0.53" <NA> ""
## 9 1 "" 1 202 (50.75%) 111 (48.68%) 79 (54.48%) 12 (48%) "" "" ""
## 10 1 "" NA 30 18 12 0 "" "" ""# You could do a sub-group analysis by two variables
display_3<-display_table_group(data=diabetes,variables=c("age","smoking"),group="CFHrs2230199",super_group = c("sex","education"))
display_3## super_group super_group_level variable level All sample CFHrs2230199 = 0 CFHrs2230199 = 1 CFHrs2230199 = 2 P.value1 P.value2
## 1 sex 0 age mean +- sd 59.22 +- 13.72 59.94 +- 14.09 58.52 +- 13.38 57.54 +- 12.53 0.36 0.40
## 2 sex 0 NA 0 0 0 0
## 3 sex 0 smoking 0 259 (49.05%) 142 (48.63%) 100 (50.51%) 17 (44.74%) 0.79 <NA>
## 4 sex 0 1 269 (50.95%) 150 (51.37%) 98 (49.49%) 21 (55.26%)
## 5 sex 0 NA 44 20 21 3
## 6 sex 1 age mean +- sd 58.66 +- 12.65 58.33 +- 12.34 58.08 +- 12.84 65.60 +- 12.86 0.02 0.02
## 7 sex 1 NA 0 0 0 0
## 8 sex 1 smoking 0 196 (49.25%) 117 (51.32%) 66 (45.52%) 13 (52%) 0.53 <NA>
## 9 sex 1 1 202 (50.75%) 111 (48.68%) 79 (54.48%) 12 (48%)
## 10 sex 1 NA 30 18 12 0
## 11 education 0 age mean +- sd 58.68 +- 12.98 59.15 +- 12.95 57.73 +- 12.95 60.09 +- 13.36 0.31 0.49
## 12 education 0 NA 0 0 0 0
## 13 education 0 smoking 0 307 (49.68%) 166 (47.56%) 126 (55.26%) 15 (36.59%) 0.04 <NA>
## 14 education 0 1 311 (50.32%) 183 (52.44%) 102 (44.74%) 26 (63.41%)
## 15 education 0 NA 43 21 20 2
## 16 education 1 age mean +- sd 59.57 +- 13.81 59.37 +- 14.17 59.52 +- 13.48 61.52 +- 13.02 0.78 0.72
## 17 education 1 NA 0 0 0 0
## 18 education 1 smoking 0 148 (48.05%) 93 (54.39%) 40 (34.78%) 15 (68.18%) 7.35E-04 <NA>
## 19 education 1 1 160 (51.95%) 78 (45.61%) 75 (65.22%) 7 (31.82%)
## 20 education 1 NA 31 17 13 1
## normality
## 1 2.71E-05; 1.59E-03; 0.08
## 2
## 3
## 4
## 5
## 6 3.56E-04; 0.01; 0.95
## 7
## 8
## 9
## 10
## 11 1.18E-05; 7.28E-04; 0.39
## 12
## 13
## 14
## 15
## 16 5.73E-04; 4.11E-03; 0.84
## 17
## 18
## 19
## 20# Sub-group analysis can be a combination
display_4<-display_table_group(data=diabetes,variables=c("age","smoking"),group="CFHrs2230199",super_group = c("sex","education"),group_combine = TRUE)
display_4## # A tibble: 20 × 11
## sex education variable level `All sample` `CFHrs2230199 = 0` `CFHrs2230199 = 1` `CFHrs2230199 = 2` P.value1 P.value2 normality
## <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 0 0 "age" mean +- sd 59.15 +- 13.47 60.08 +- 13.66 58.03 +- 13.20 58.52 +- 13.54 "0.35" "0.40" "8.20E-04; 0.01; 0.…
## 2 0 0 "" NA 0 0 0 0 "" "" ""
## 3 0 0 "smoking" 0 181 (49.86%) 91 (46.91%) 81 (57.04%) 9 (33.33%) "0.04" <NA> ""
## 4 0 0 "" 1 182 (50.14%) 103 (53.09%) 61 (42.96%) 18 (66.67%) "" "" ""
## 5 0 0 "" NA 28 13 13 2 "" "" ""
## 6 0 1 "age" mean +- sd 59.38 +- 14.28 59.66 +- 14.99 59.72 +- 13.84 55.17 +- 9.77 "0.57" "0.63" "0.01; 0.07; 0.89"
## 7 0 1 "" NA 0 0 0 0 "" "" ""
## 8 0 1 "smoking" 0 78 (47.27%) 51 (52.04%) 19 (33.93%) 8 (72.73%) "0.02" <NA> ""
## 9 0 1 "" 1 87 (52.73%) 47 (47.96%) 37 (66.07%) 3 (27.27%) "" "" ""
## 10 0 1 "" NA 16 7 8 1 "" "" ""
## 11 1 0 "age" mean +- sd 58.00 +- 12.23 57.98 +- 11.94 57.24 +- 12.58 63.36 +- 12.83 "0.22" "0.25" "0.01; 0.01; 0.94"
## 12 1 0 "" NA 0 0 0 0 "" "" ""
## 13 1 0 "smoking" 0 126 (49.41%) 75 (48.39%) 45 (52.33%) 6 (42.86%) "0.74" <NA> ""
## 14 1 0 "" 1 129 (50.59%) 80 (51.61%) 41 (47.67%) 8 (57.14%) "" "" ""
## 15 1 0 "" NA 15 8 7 0 "" "" ""
## 16 1 1 "age" mean +- sd 59.79 +- 13.29 59.01 +- 13.15 59.31 +- 13.22 68.45 +- 12.92 "0.08" "0.08" "0.04; 0.04; 0.92"
## 17 1 1 "" NA 0 0 0 0 "" "" ""
## 18 1 1 "smoking" 0 70 (48.95%) 42 (57.53%) 21 (35.59%) 7 (63.64%) "0.03" <NA> ""
## 19 1 1 "" 1 73 (51.05%) 31 (42.47%) 38 (64.41%) 4 (36.36%) "" "" ""
## 20 1 1 "" NA 15 10 5 0 "" "" ""Build regression models
# Apply univariate regression models
reg_1<-reg_x(data = diabetes, y = 5, factors = c(1, 3, 4), model = 'glm')
reg_1## x term estimate std.error statistic p.value OR OR.low OR.high N
## 1 sex sex_1 -0.0995619364 0.163419266 -0.60924234 5.423638e-01 0.9052339 0.6571403 1.246992 1000
## 2 age age -0.0016515166 0.006083056 -0.27149453 7.860107e-01 0.9983498 0.9865176 1.010324 1000
## 3 smoking smoking_1 0.2203884367 0.171356638 1.28613889 1.983946e-01 1.2465608 0.8909523 1.744105 926
## 4 education education_1 0.0072440035 0.169823173 0.04265615 9.659756e-01 1.0072703 0.7220916 1.405076 1000
## 5 BMI BMI -0.0205541093 0.021530295 -0.95465990 3.397497e-01 0.9796557 0.9391757 1.021880 994
## 6 systolic systolic -0.0001758354 0.004399858 -0.03996388 9.681219e-01 0.9998242 0.9912392 1.008484 995
## 7 diastolic diastolic -0.0010196342 0.007323325 -0.13923104 8.892676e-01 0.9989809 0.9847445 1.013423 995
## 8 CFHrs1061170 CFHrs1061170 0.1648181445 0.108731134 1.51583211 1.295618e-01 1.1791787 0.9528565 1.459257 1000
## 9 LOCrs10490924 LOCrs10490924 0.6243454613 0.112922906 5.52895320 3.221473e-08 1.8670235 1.4963378 2.329539 1000
## 10 CFHrs1410996 CFHrs1410996 0.3154310240 0.128347280 2.45763699 1.398545e-02 1.3708501 1.0659591 1.762947 1000
## 11 C2rs9332739 C2rs9332739 1.0717936770 0.433256076 2.47381107 1.336804e-02 2.9206134 1.2493549 6.827510 1000
## 12 CFBrs641153 CFBrs641153 0.1993582016 0.253688461 0.78583866 4.319620e-01 1.2206191 0.7424038 2.006874 1000
## 13 CFHrs2230199 CFHrs2230199 0.3402726917 0.125293121 2.71581303 6.611324e-03 1.4053308 1.0993320 1.796504 1000# Or a survial analysis
reg_2<-reg_x(data = diabetes, x = c(3:4, 6), y ="diabetes",time=2,factors = c(1, 3, 4), model = 'coxph')
reg_2## x term estimate std.error statistic p.value HR HR.low HR.high N
## 1 smoking smoking_1 0.17247504 0.15526447 1.1108468 0.266634305 1.1882422 0.8764832 1.6108916 926
## 2 education education_1 -0.06871785 0.15313905 -0.4487285 0.653627559 0.9335901 0.6915188 1.2604001 1000
## 3 BMI BMI -0.05564539 0.02111973 -2.6347584 0.008419718 0.9458745 0.9075203 0.9858496 994# adjust some covariates
reg_3<-reg_x(data = diabetes, x = c("sex","age"), y ="diabetes" ,cov=c("CFBrs641153","CFHrs2230199"), factors ="sex", model = 'glm',cov_show = TRUE)
reg_3## x term estimate std.error statistic p.value OR OR.low OR.high N
## 2 sex sex_1 -0.095195435 0.164518306 -0.5786313 0.562838007 0.9091952 0.6585957 1.255149 1000
## 3 sex CFBrs641153 0.230790886 0.255559041 0.9030825 0.366482136 1.2595958 0.7633065 2.078564 1000
## 4 sex CFHrs2230199 0.342102415 0.125633639 2.7230160 0.006468892 1.4079045 1.1006105 1.800996 1000
## 6 age age -0.001887778 0.006114376 -0.3087442 0.757516121 0.9981140 0.9862240 1.010147 1000
## 7 age CFBrs641153 0.224355970 0.255129981 0.8793791 0.379195768 1.2515164 0.7590485 2.063496 1000
## 8 age CFHrs2230199 0.344428164 0.125604894 2.7421556 0.006103742 1.4111827 1.1032354 1.805088 1000# How about regression on several dependent variables
reg_4<-reg_y(data = diabetes, x = c("sex","age","CFHrs1061170"), y =c("systolic","diastolic","BMI") ,cov=c("CFBrs641153","CFHrs2230199"), factors ="sex", model = 'lm')
reg_4## y x term estimate std.error statistic p.value coef coef.low coef.high N
## 1 systolic sex sex_1 -2.69428330 1.177660308 -2.2878272 2.235750e-02 -2.69428330 -5.00527758 -0.38328902 995
## 2 systolic age age 0.62409179 0.039210814 15.9163181 5.778111e-51 0.62409179 0.54714603 0.70103755 995
## 3 systolic CFHrs1061170 CFHrs1061170 0.49087994 0.783066116 0.6268691 5.308894e-01 0.49087994 -1.04577821 2.02753809 995
## 4 diastolic sex sex_1 -1.73296131 0.708148117 -2.4471735 1.457089e-02 -1.73296131 -3.12260333 -0.34331929 995
## 5 diastolic age age 0.15343916 0.025977166 5.9066936 4.793066e-09 0.15343916 0.10246259 0.20441573 995
## 6 diastolic CFHrs1061170 CFHrs1061170 -0.30611928 0.471042899 -0.6498756 5.159232e-01 -0.30611928 -1.23047534 0.61823679 995
## 7 BMI sex sex_1 -0.39143773 0.243573523 -1.6070619 1.083597e-01 -0.39143773 -0.86941742 0.08654197 994
## 8 BMI age age 0.05159255 0.008939646 5.7712071 1.052213e-08 0.05159255 0.03404972 0.06913538 994
## 9 BMI CFHrs1061170 CFHrs1061170 -0.10033051 0.161718714 -0.6204013 5.351364e-01 -0.10033051 -0.41768134 0.21702033 994# Cool, but I want to do a subgroup analysis
reg_5<-reg(data = diabetes, x = c("age","CFHrs1061170"), y =c("systolic","diastolic") ,cov=c("CFBrs641153","CFHrs2230199"), model = 'lm',group="sex")
reg_5## # A tibble: 8 × 12
## sex y x term estimate std.error statistic p.value coef coef.low coef.high N
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 systolic age age 0.648 0.0508 12.8 6.06e-33 0.648 0.548 0.747 569
## 2 0 systolic CFHrs1061170 CFHrs1061170 1.14 1.06 1.08 2.80e- 1 1.14 -0.933 3.22 569
## 3 0 diastolic age age 0.130 0.0336 3.88 1.15e- 4 0.130 0.0644 0.196 569
## 4 0 diastolic CFHrs1061170 CFHrs1061170 0.229 0.625 0.366 7.14e- 1 0.229 -0.998 1.46 569
## 5 1 systolic age age 0.593 0.0618 9.60 7.31e-20 0.593 0.472 0.715 426
## 6 1 systolic CFHrs1061170 CFHrs1061170 -0.465 1.16 -0.402 6.88e- 1 -0.465 -2.74 1.81 426
## 7 1 diastolic age age 0.194 0.0410 4.73 3.13e- 6 0.194 0.113 0.274 426
## 8 1 diastolic CFHrs1061170 CFHrs1061170 -1.09 0.711 -1.53 1.26e- 1 -1.09 -2.49 0.308 426# or two subgroup analysis
reg_6<-reg(data = diabetes, x = c("age","CFHrs1061170"), y =c("systolic","diastolic") ,cov=c("CFBrs641153","CFHrs2230199"), model = 'lm',group=c("sex","smoking"))
reg_6## group level y x term estimate std.error statistic p.value coef coef.low coef.high N
## 1 sex 0 systolic age age 0.64773257 0.05076473 12.75949920 6.063847e-33 0.64773257 0.54802193 0.7474432 569
## 2 sex 0 systolic CFHrs1061170 CFHrs1061170 1.14409524 1.05733099 1.08205968 2.796876e-01 1.14409524 -0.93268422 3.2208747 569
## 3 sex 0 diastolic age age 0.13034768 0.03356128 3.88387043 1.149692e-04 0.13034768 0.06442756 0.1962678 569
## 4 sex 0 diastolic CFHrs1061170 CFHrs1061170 0.22861690 0.62463113 0.36600306 7.144998e-01 0.22861690 -0.99826579 1.4554996 569
## 5 sex 1 systolic age age 0.59306788 0.06179047 9.59804724 7.305923e-20 0.59306788 0.47161244 0.7145233 426
## 6 sex 1 systolic CFHrs1061170 CFHrs1061170 -0.46512605 1.15629722 -0.40225475 6.877002e-01 -0.46512605 -2.73794544 1.8076933 426
## 7 sex 1 diastolic age age 0.19371219 0.04099307 4.72548612 3.132224e-06 0.19371219 0.11313616 0.2742882 426
## 8 sex 1 diastolic CFHrs1061170 CFHrs1061170 -1.08988936 0.71130615 -1.53223665 1.262133e-01 -1.08988936 -2.48803370 0.3082550 426
## 9 smoking 0 systolic age age 0.56709310 0.05775625 9.81873047 9.527853e-21 0.56709310 0.45358764 0.6805986 454
## 10 smoking 0 systolic CFHrs1061170 CFHrs1061170 0.09315881 1.09475817 0.08509533 9.322234e-01 0.09315881 -2.05831432 2.2446319 454
## 11 smoking 0 diastolic age age 0.12266643 0.03938756 3.11434456 1.961246e-03 0.12266643 0.04526004 0.2000728 454
## 12 smoking 0 diastolic CFHrs1061170 CFHrs1061170 0.34433728 0.68460209 0.50297434 6.152284e-01 0.34433728 -1.00107674 1.6897513 454
## 13 smoking 1 systolic age age 0.70147846 0.05756805 12.18520429 8.091810e-30 0.70147846 0.58835143 0.8146055 467
## 14 smoking 1 systolic CFHrs1061170 CFHrs1061170 0.48555084 1.21375126 0.40004147 6.893105e-01 0.48555084 -1.89959281 2.8706945 467
## 15 smoking 1 diastolic age age 0.19687617 0.03742561 5.26046659 2.200772e-07 0.19687617 0.12333107 0.2704213 467
## 16 smoking 1 diastolic CFHrs1061170 CFHrs1061170 -0.97263707 0.70551713 -1.37861582 1.686790e-01 -0.97263707 -2.35904940 0.4137752 467
## 17 smoking NA systolic age age 0.53068925 0.14994046 3.53933329 7.175890e-04 0.53068925 0.23164244 0.8297361 74
## 18 smoking NA systolic CFHrs1061170 CFHrs1061170 2.87081804 2.97945574 0.96353774 3.385946e-01 2.87081804 -3.07151907 8.8131551 74
## 19 smoking NA diastolic age age 0.10466712 0.09433926 1.10947570 2.710221e-01 0.10466712 -0.08348661 0.2928208 74
## 20 smoking NA diastolic CFHrs1061170 CFHrs1061170 -0.23489246 1.75288157 -0.13400361 8.937842e-01 -0.23489246 -3.73090451 3.2611196 74# or subgroup combination analysis
reg_7<-reg(data = diabetes, x = c("age","CFHrs1061170"), y =c("systolic","diastolic") ,cov=c("CFBrs641153","CFHrs2230199"), model = 'lm',group=c("sex","smoking"),group_combine = TRUE)
reg_7## # A tibble: 24 × 13
## sex smoking y x term estimate std.error statistic p.value coef coef.low coef.high N
## <dbl> <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0 systolic age age 0.566 0.0730 7.74 2.30e-13 0.566 0.422 0.710 258
## 2 0 0 systolic CFHrs1061170 CFHrs1061170 0.0370 1.40 0.0265 9.79e- 1 0.0370 -2.71 2.79 258
## 3 0 0 diastolic age age 0.0525 0.0515 1.02 3.09e- 1 0.0525 -0.0488 0.154 258
## 4 0 0 diastolic CFHrs1061170 CFHrs1061170 0.0128 0.886 0.0144 9.89e- 1 0.0128 -1.73 1.76 258
## 5 0 1 systolic age age 0.706 0.0759 9.30 5.64e-18 0.706 0.557 0.856 267
## 6 0 1 systolic CFHrs1061170 CFHrs1061170 2.03 1.69 1.20 2.32e- 1 2.03 -1.31 5.36 267
## 7 0 1 diastolic age age 0.206 0.0481 4.28 2.62e- 5 0.206 0.111 0.301 267
## 8 0 1 diastolic CFHrs1061170 CFHrs1061170 0.428 0.964 0.444 6.58e- 1 0.428 -1.47 2.33 267
## 9 0 NA systolic age age 0.819 0.201 4.07 2.14e- 4 0.819 0.413 1.23 44
## 10 0 NA systolic CFHrs1061170 CFHrs1061170 2.32 4.56 0.508 6.14e- 1 2.32 -6.90 11.5 44
## 11 0 NA diastolic age age 0.129 0.126 1.02 3.14e- 1 0.129 -0.127 0.385 44
## 12 0 NA diastolic CFHrs1061170 CFHrs1061170 -0.178 2.45 -0.0726 9.43e- 1 -0.178 -5.13 4.77 44
## 13 1 0 systolic age age 0.580 0.0938 6.18 3.68e- 9 0.580 0.395 0.765 196
## 14 1 0 systolic CFHrs1061170 CFHrs1061170 -0.113 1.77 -0.0639 9.49e- 1 -0.113 -3.61 3.38 196
## 15 1 0 diastolic age age 0.228 0.0604 3.77 2.18e- 4 0.228 0.109 0.347 196
## 16 1 0 diastolic CFHrs1061170 CFHrs1061170 0.578 1.08 0.535 5.94e- 1 0.578 -1.55 2.71 196
## 17 1 1 systolic age age 0.693 0.0895 7.74 5.14e-13 0.693 0.516 0.869 200
## 18 1 1 systolic CFHrs1061170 CFHrs1061170 -1.14 1.72 -0.664 5.08e- 1 -1.14 -4.54 2.25 200
## 19 1 1 diastolic age age 0.182 0.0606 3.00 3.06e- 3 0.182 0.0622 0.301 200
## 20 1 1 diastolic CFHrs1061170 CFHrs1061170 -2.59 1.03 -2.52 1.27e- 2 -2.59 -4.62 -0.559 200
## 21 1 NA systolic age age 0.133 0.188 0.705 4.87e- 1 0.133 -0.254 0.519 30
## 22 1 NA systolic CFHrs1061170 CFHrs1061170 2.91 3.01 0.967 3.42e- 1 2.91 -3.28 9.10 30
## 23 1 NA diastolic age age 0.0596 0.137 0.436 6.66e- 1 0.0596 -0.221 0.340 30
## 24 1 NA diastolic CFHrs1061170 CFHrs1061170 -0.739 2.21 -0.335 7.40e- 1 -0.739 -5.27 3.80 30How about plot these regression models
## Some variables are duplicated in your regression result.
## Using cov_show = FALSE for covariate variables or facet for subgroup result.
## Some variables are duplicated in your regression result.
## Using cov_show = FALSE for covariate variables or facet for subgroup result.
# Actually, you can modify the plot like ggplot2
library(ggplot2);library(ggthemes)
plot(reg_1,limits=c(0.5,2))+
labs(list(title = "Regression Model", x = "variables"))+
theme_classic() %+replace%
theme(legend.position ="none",axis.text.x=element_text(angle=45,size=rel(1.5)))
Perspective
The quickReg package provides a flexible and convenient
way to dispaly data and the association between variables. This vignette
offers a glimpse of its use and features. The source code and help files
are more helpful. The package is ongoing. If you have any comments,
questions or bug reports, please contact me.