Library
library(gtsummary)
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
Dataset
data("cars")
View(cars)
Correlation
r <- cor(cars$dist,cars$speed)
summary(r)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.8069 0.8069 0.8069 0.8069 0.8069 0.8069
r
## [1] 0.8068949
head(trial)
LM
model <- lm(dist~speed , data=cars)
summary(model)
##
## Call:
## lm(formula = dist ~ speed, data = cars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.069 -9.525 -2.272 9.215 43.201
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.5791 6.7584 -2.601 0.0123 *
## speed 3.9324 0.4155 9.464 1.49e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.38 on 48 degrees of freedom
## Multiple R-squared: 0.6511, Adjusted R-squared: 0.6438
## F-statistic: 89.57 on 1 and 48 DF, p-value: 1.49e-12
gtsummary
trial
modl <- lm(marker ~ age + trt + grade + response + death, data = trial)
summary(modl)
##
## Call:
## lm(formula = marker ~ age + trt + grade + response + death, data = trial)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1389 -0.5954 -0.2520 0.3992 2.7081
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0874113 0.2462034 4.417 1.8e-05 ***
## age 0.0001009 0.0045462 0.022 0.9823
## trtDrug B -0.1656895 0.1315334 -1.260 0.2096
## gradeII -0.3853005 0.1597109 -2.412 0.0169 *
## gradeIII -0.1063987 0.1588923 -0.670 0.5040
## response 0.2152273 0.1466634 1.467 0.1441
## death -0.0016161 0.1380194 -0.012 0.9907
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8564 on 166 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.05749, Adjusted R-squared: 0.02342
## F-statistic: 1.688 on 6 and 166 DF, p-value: 0.1269
tbl_regression(modl)
| Characteristic |
Beta |
95% CI |
p-value |
| Age |
0.00 |
-0.01, 0.01 |
>0.9 |
| Chemotherapy Treatment |
|
|
|
| Drug A |
— |
— |
|
| Drug B |
-0.17 |
-0.43, 0.09 |
0.2 |
| Grade |
|
|
|
| I |
— |
— |
|
| II |
-0.39 |
-0.70, -0.07 |
0.017 |
| III |
-0.11 |
-0.42, 0.21 |
0.5 |
| Tumor Response |
0.22 |
-0.07, 0.50 |
0.14 |
| Patient Died |
0.00 |
-0.27, 0.27 |
>0.9 |
| Abbreviation: CI = Confidence Interval |
trial$response <- as.factor(trial$response)
m <- glm(trial$response~trial$trt, family=binomial)
summary(m)
##
## Call:
## glm(formula = trial$response ~ trial$trt, family = binomial)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.8725 0.2250 -3.877 0.000106 ***
## trial$trtDrug B 0.1946 0.3104 0.627 0.530641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 240.81 on 192 degrees of freedom
## Residual deviance: 240.42 on 191 degrees of freedom
## (7 observations deleted due to missingness)
## AIC: 244.42
##
## Number of Fisher Scoring iterations: 4
exp(cbind(OR = coef(m), confint(m)))
## Waiting for profiling to be done...
## OR 2.5 % 97.5 %
## (Intercept) 0.4179104 0.2647887 0.6421547
## trial$trtDrug B 1.2148352 0.6617376 2.2413744