README.md

In lisaerein/nicereg: Suite of functions to format regression results

‘nicereg’ R package examples

Lisa Rein 2019-07-12

Load R packages

packs <- c("devtools",
           "knitr",
           "MASS",
           "geepack",
           "htmlTable")

lapply(packs, function(x) require(x, character.only = T))

install_github("lisaerein/nicereg")
library(nicereg)

help(nicereg)

options(knitr.kable.NA = '.')

Load survey example dataset from the MASS package

head(survey <- survey)

##      Sex Wr.Hnd NW.Hnd W.Hnd    Fold Pulse    Clap Exer Smoke Height
## 1 Female   18.5   18.0 Right  R on L    92    Left Some Never 173.00
## 2   Male   19.5   20.5  Left  R on L   104    Left None Regul 177.80
## 3   Male   18.0   13.3 Right  L on R    87 Neither None Occas     NA
## 4   Male   18.8   18.9 Right  R on L    NA Neither None Never 160.00
## 5   Male   20.0   20.0 Right Neither    35   Right Some Never 165.00
## 6 Female   18.0   17.7 Right  L on R    64   Right Some Never 172.72
##        M.I    Age
## 1   Metric 18.250
## 2 Imperial 17.583
## 3     <NA> 16.917
## 4   Metric 20.333
## 5   Metric 23.667
## 6 Imperial 21.000

Example: niceglm function for glm models

Run univariate linear regression and create a table

tab <- niceglm(df = survey,
                 out = "Height",
                 covs = c("Sex", "Age", "Clap"),
                 regtype = "uni",
                 title = "Univariate results",
                 estname = "Estimate",
                 family = "gaussian")

## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...

| Variable | Estimate | 95% CI | p-value | | :------------------ | :------: | :--------------: | -------: | | Sex (N = 208) | . | . | . | | * Male vs. Female | 13.14 | [11.14, 15.14] | \< 0.001 | | Age (N = 209) | -0.05 | [-0.26, 0.15] | 0.592 | | Clap (N = 208) | . | . | . | | * Neither vs. Left | -2.35 | [-6.87, 2.17] | 0.310 | | * Right vs. Left | -2.49 | [-6.30, 1.32] | 0.202 |

Univariate results; Family = Gaussian (Identity)

Run multivariable linear regression and create a table

tab <- niceglm(df = survey,
                 out = "Height",
                 covs = c("Sex", "Age", "Clap"),
                 regtype = "multi",
                 title = "Multivariable results",
                 estname = "Estimate",
                 family = "gaussian")

## Waiting for profiling to be done...

| Variable | Estimate | 95% CI | p-value | | :------------------ | :------: | :--------------: | -------: | | Sex | . | . | . | | * Male vs. Female | 13.33 | [11.33, 15.34] | \< 0.001 | | Age | -0.04 | [-0.19, 0.11] | 0.628 | | Clap | . | . | . | | * Neither vs. Left | -2.13 | [-5.50, 1.24] | 0.217 | | * Right vs. Left | -3.30 | [-6.14, -0.46] | 0.024 |

Multivariable results; Family = Gaussian (Identity), N obs = 207

Create a results table from an existing glm model object

survey$lefthanded <- 0
survey$lefthanded[survey$W.Hnd == "Left"] <- 1

fit <- glm(lefthanded ~ Age + Sex + Height + Exer, data = survey, family = "binomial")

tab <- niceglm(fit,
               estname = "aOR",
               overallp = T)

## Waiting for profiling to be done...

| Variable | aOR | 95% CI | Wald p-value | LR p-value | | :----------------- | :--: | :-------------: | -----------: | ---------: | | Age | 0.81 | [0.52, 1.00] | 0.218 | 0.069 | | Sex | . | . | . | 0.868 | | * Male vs. Female | 0.88 | [0.18, 4.07] | 0.868 | . | | Height | 1.05 | [0.97, 1.13] | 0.228 | 0.219 | | Exer | . | . | . | 0.235 | | * None vs. Freq | 4.31 | [0.78, 20.88] | 0.072 | . | | * Some vs. Freq | 1.49 | [0.43, 5.14] | 0.517 | . |

Family = Binomial (Logit), N obs = 208

Example: nicegee function for geeglm models

survey$clusterid <- rep(1:5, length = nrow(survey))

tab <- nicegee(df = survey,
                 out = "Height",
                 covs = c("Sex", "Age", "Clap"),
                 regtype = "uni",
                 title = "Uni GEE results",
                 estname = "Estimate",
                 family = "gaussian",
                 id = "clusterid",
                 corstr = "exch")

| Variable | Estimate | 95% CI | p-value | | :----------------------------- | :------: | :--------------: | -------: | | Sex (N = 208, N clusters = 5) | . | . | . | | * Male vs. Female | 13.14 | [11.16, 15.12] | \< 0.001 | | Age (N = 209, N clusters = 5) | -0.05 | [-0.18, 0.07] | 0.400 | | Clap (N = 208, N clusters = 5) | . | . | . | | * Neither vs. Left | -2.35 | [-6.97, 2.28] | 0.320 | | * Right vs. Left | -2.49 | [-6.40, 1.42] | 0.212 |

Uni GEE results; Family = Gaussian, Corstr = Exchangeable

survey$clusterid <- rep(1:5, length = nrow(survey))

tab <- nicegee(df = survey,
                 out = "Height",
                 covs = c("Sex", "Age", "Clap"),
                 regtype = "multi",
                 title = "Multi GEE results",
                 estname = "Estimate",
                 family = "gaussian",
                 id = "clusterid",
                 corstr = "exch")

| Variable | Estimate | 95% CI | p-value | | :------------------ | :------: | :--------------: | -------: | | Sex | . | . | . | | * Male vs. Female | 13.33 | [11.34, 15.33] | \< 0.001 | | Age | -0.04 | [-0.14, 0.07] | 0.480 | | Clap | . | . | . | | * Neither vs. Left | -2.13 | [-5.51, 1.26] | 0.218 | | * Right vs. Left | -3.30 | [-6.15, -0.44] | 0.023 |

Multi GEE results; Family = Gaussian, N obs = 207, N clusters = 5, Corstr = Exchangeable

lisaerein/nicereg documentation built on July 4, 2020, 9:04 p.m.