rms: Interfaces for rms package for data science pipelines.
In intubate: Interface to Popular R Functions for Data Science Pipelines
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Interfaces to rms functions that can be used
in a pipeline implemented by magrittr.
Usage
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Arguments
data
data frame, tibble, list, ...
...
Other arguments passed to the corresponding interfaced function.
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
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## Not run:
library(intubate)
library(magrittr)
library(rms)
## ntbt_bj: Buckley-James Multiple Regression Model
set.seed(1)
ftime <- 10*rexp(200)
stroke <- ifelse(ftime > 10, 0, 1)
ftime <- pmin(ftime, 10)
units(ftime) <- "Month"
age <- rnorm(200, 70, 10)
hospital <- factor(sample(c('a','b'),200,TRUE))
dd <- datadist(age, hospital)
options(datadist = "dd")
data_bj <- data.frame(ftime, stroke, age, hospital)
## Original function to interface
bj(Surv(ftime, stroke) ~ rcs(age,5) + hospital, data_bj, x = TRUE, y = TRUE)
## The interface puts data as first parameter
f <- ntbt_bj(data_bj, Surv(ftime, stroke) ~ rcs(age,5) + hospital, x = TRUE, y = TRUE)
anova(f)
## so it can be used easily in a pipeline.
data_bj %>%
ntbt_bj(Surv(ftime, stroke) ~ rcs(age,5) + hospital, x = TRUE, y = TRUE)
## ntbt_cph: Cox Proportional Hazards Model and Extensions
n <- 1000
set.seed(731)
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
sex <- factor(sample(c('Male','Female'), n,
rep=TRUE, prob=c(.6, .4)))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
dt <- -log(runif(n))/h
label(dt) <- 'Follow-up Time'
e <- ifelse(dt <= cens,1,0)
dt <- pmin(dt, cens)
units(dt) <- "Year"
dd <- datadist(age, sex)
options(datadist='dd')
S <- Surv(dt,e)
data_cph <- data.frame(S, age, sex)
## Original function to interface
cph(S ~ rcs(age,4) + sex, data_cph, x = TRUE, y = TRUE)
## The interface puts data as first parameter
ntbt_cph(data_cph, S ~ rcs(age,4) + sex, x = TRUE, y = TRUE)
## so it can be used easily in a pipeline.
data_cph %>%
ntbt_cph(S ~ rcs(age,4) + sex, x = TRUE, y = TRUE)
## ntbt_Glm
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
data_Glm <- data.frame(counts, outcome, treatment)
## Original function to interface
Glm(counts ~ outcome + treatment, family = poisson(), data = data_Glm)
## The interface puts data as first parameter
ntbt_Glm(data_Glm, counts ~ outcome + treatment, family = poisson())
## so it can be used easily in a pipeline.
data_Glm %>%
ntbt_Glm(counts ~ outcome + treatment, family = poisson())
## ntbt_lrm: Logistic Regression Model
n <- 1000 # define sample size
set.seed(17) # so can reproduce the results
age <- rnorm(n, 50, 10)
blood.pressure <- rnorm(n, 120, 15)
cholesterol <- rnorm(n, 200, 25)
sex <- factor(sample(c('female','male'), n,TRUE))
label(age) <- 'Age' # label is in Hmisc
label(cholesterol) <- 'Total Cholesterol'
label(blood.pressure) <- 'Systolic Blood Pressure'
label(sex) <- 'Sex'
units(cholesterol) <- 'mg/dl' # uses units.default in Hmisc
units(blood.pressure) <- 'mmHg'
#To use prop. odds model, avoid using a huge number of intercepts by
#grouping cholesterol into 40-tiles
ch <- cut2(cholesterol, g=40, levels.mean=TRUE) # use mean values in intervals
data_lrm <- data.frame(ch, age)
## Original function to interface
lrm(ch ~ age, data_lrm)
## The interface puts data as first parameter
ntbt_lrm(data_lrm, ch ~ age)
## so it can be used easily in a pipeline.
data_lrm %>%
ntbt_lrm(ch ~ age)
## ntbt_npsurv: Nonparametric Survival Estimates for Censored Data
tdata <- data.frame(time = c(1,1,1,2,2,2,3,3,3,4,4,4),
status = rep(c(1,0,2),4),
n = c(12,3,2,6,2,4,2,0,2,3,3,5))
## Original function to interface
f <- npsurv(Surv(time, time, status, type = 'interval') ~ 1, data = tdata, weights = n)
plot(f, fun = 'event', xmax = 20, mark.time = FALSE, col = 2:3)
## The interface puts data as first parameter
f <- ntbt_npsurv(tdata, Surv(time, time, status, type = 'interval') ~ 1, weights = n)
plot(f, fun = 'event', xmax = 20, mark.time = FALSE, col = 2:3)
## so it can be used easily in a pipeline.
tdata %>%
ntbt_npsurv(Surv(time, time, status, type = 'interval') ~ 1, weights = n) %>%
plot(fun = 'event', xmax = 20, mark.time = FALSE, col = 2:3)
## ntbt_ols: Linear Model Estimation Using Ordinary Least Squares
set.seed(1)
x1 <- runif(200)
x2 <- sample(0:3, 200, TRUE)
distance <- (x1 + x2/3 + rnorm(200))^2
d <- datadist(x1, x2)
options(datadist="d") # No d -> no summary, plot without giving all details
data_ols <- data.frame(distance, x1, x2)
## Original function to interface
ols(sqrt(distance) ~ rcs(x1, 4) + scored(x2), data_ols, x = TRUE)
## The interface puts data as first parameter
ntbt_ols(data_ols, sqrt(distance) ~ rcs(x1, 4) + scored(x2), x = TRUE)
## so it can be used easily in a pipeline.
data_ols %>%
ntbt_ols(sqrt(distance) ~ rcs(x1, 4) + scored(x2), x = TRUE)
## ntbt_orm: Ordinal Regression Model
set.seed(1)
n <- 300
x1 <- c(rep(0,150), rep(1,150))
y <- rnorm(n) + 3 * x1
data_orm <- data.frame(y, x1)
## Original function to interface
orm(y ~ x1, data_orm)
## The interface puts data as first parameter
ntbt_orm(data_orm, y ~ x1)
## so it can be used easily in a pipeline.
data_orm %>%
ntbt_orm(y ~ x1)
## ntbt_psm: Parametric Survival Model
n <- 400
set.seed(1)
age <- rnorm(n, 50, 12)
sex <- factor(sample(c('Female','Male'),n,TRUE))
dd <- datadist(age,sex)
options(datadist='dd')
# Population hazard function:
h <- .02*exp(.06*(age-50)+.8*(sex=='Female'))
d.time <- -log(runif(n))/h
cens <- 15*runif(n)
death <- ifelse(d.time <= cens,1,0)
d.time <- pmin(d.time, cens)
data_psm <- data.frame(d.time, death, sex, age)
## Original function to interface
psm(Surv(d.time, death) ~ sex * pol(age, 2), data_psm, dist = 'lognormal')
# Log-normal model is a bad fit for proportional hazards data
## The interface puts data as first parameter
ntbt_psm(data_psm, Surv(d.time, death) ~ sex * pol(age, 2), dist = 'lognormal')
## so it can be used easily in a pipeline.
data_psm %>%
ntbt_psm(Surv(d.time, death) ~ sex * pol(age, 2), dist = 'lognormal')
## End(Not run)
Example output
Loading required package: Hmisc
Loading required package: lattice
Loading required package: survival
Loading required package: Formula
Loading required package: ggplot2
Attaching package: 'Hmisc'
The following objects are masked from 'package:base':
format.pval, units
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
Buckley-James Censored Data Regression
bj(formula = Surv(ftime, stroke) ~ rcs(age, 5) + hospital, data = data_bj,
x = TRUE, y = TRUE)
Discrimination
Indexes
Obs 200 Regression d.f.5 g 0.325
Events 120 sigma0.8967 gr 1.384
d.f. 114
Coef S.E. Wald Z Pr(>|Z|)
Intercept -1.0590 1.9965 -0.53 0.5958
age 0.0554 0.0359 1.55 0.1222
age' -0.4562 0.1811 -2.52 0.0117
age'' 4.6885 1.6826 2.79 0.0053
age''' -6.6733 2.4011 -2.78 0.0054
hospital=b 0.2024 0.1695 1.19 0.2325
Wald Statistics Response: Surv(ftime, stroke)
Factor Chi-Square d.f. P
age 9.13 4 0.0580
Nonlinear 9.10 3 0.0279
hospital 1.43 1 0.2325
TOTAL 11.53 5 0.0418
Buckley-James Censored Data Regression
bj(formula = Surv(ftime, stroke) ~ rcs(age, 5) + hospital, x = TRUE,
y = TRUE)
Discrimination
Indexes
Obs 200 Regression d.f.5 g 0.325
Events 120 sigma0.8967 gr 1.384
d.f. 114
Coef S.E. Wald Z Pr(>|Z|)
Intercept -1.0590 1.9965 -0.53 0.5958
age 0.0554 0.0359 1.55 0.1222
age' -0.4562 0.1811 -2.52 0.0117
age'' 4.6885 1.6826 2.79 0.0053
age''' -6.6733 2.4011 -2.78 0.0054
hospital=b 0.2024 0.1695 1.19 0.2325
Cox Proportional Hazards Model
cph(formula = S ~ rcs(age, 4) + sex, data = data_cph, x = TRUE,
y = TRUE)
Model Tests Discrimination
Indexes
Obs 1000 LR chi2 78.28 R2 0.083
Events 183 d.f. 4 Dxy 0.378
Center -0.2861 Pr(> chi2) 0.0000 g 0.762
Score chi2 83.86 gr 2.143
Pr(> chi2) 0.0000
Coef S.E. Wald Z Pr(>|Z|)
age -0.0173 0.0286 -0.61 0.5443
age' 0.2040 0.0767 2.66 0.0079
age'' -0.7500 0.2679 -2.80 0.0051
sex=Male -0.6445 0.1488 -4.33 <0.0001
Cox Proportional Hazards Model
cph(formula = S ~ rcs(age, 4) + sex, x = TRUE, y = TRUE)
Model Tests Discrimination
Indexes
Obs 1000 LR chi2 78.28 R2 0.083
Events 183 d.f. 4 Dxy 0.378
Center -0.2861 Pr(> chi2) 0.0000 g 0.762
Score chi2 83.86 gr 2.143
Pr(> chi2) 0.0000
Coef S.E. Wald Z Pr(>|Z|)
age -0.0173 0.0286 -0.61 0.5443
age' 0.2040 0.0767 2.66 0.0079
age'' -0.7500 0.2679 -2.80 0.0051
sex=Male -0.6445 0.1488 -4.33 <0.0001
Cox Proportional Hazards Model
cph(formula = S ~ rcs(age, 4) + sex, x = TRUE, y = TRUE)
Model Tests Discrimination
Indexes
Obs 1000 LR chi2 78.28 R2 0.083
Events 183 d.f. 4 Dxy 0.378
Center -0.2861 Pr(> chi2) 0.0000 g 0.762
Score chi2 83.86 gr 2.143
Pr(> chi2) 0.0000
Coef S.E. Wald Z Pr(>|Z|)
age -0.0173 0.0286 -0.61 0.5443
age' 0.2040 0.0767 2.66 0.0079
age'' -0.7500 0.2679 -2.80 0.0051
sex=Male -0.6445 0.1488 -4.33 <0.0001
General Linear Model
Glm(formula = counts ~ outcome + treatment, family = poisson(),
data = data_Glm)
Model Likelihood
Ratio Test
Obs 9 LR chi2 5.45
Residual d.f.4 d.f. 4
g 0.2271276 Pr(> chi2) 0.2440
Coef S.E. Wald Z Pr(>|Z|)
Intercept 3.0445 0.1709 17.81 <0.0001
outcome=2 -0.4543 0.2022 -2.25 0.0246
outcome=3 -0.2930 0.1927 -1.52 0.1285
treatment=2 0.0000 0.2000 0.00 1.0000
treatment=3 0.0000 0.2000 0.00 1.0000
General Linear Model
Glm(formula = counts ~ outcome + treatment, family = poisson(),
data = data_Glm)
Model Likelihood
Ratio Test
Obs 9 LR chi2 5.45
Residual d.f.4 d.f. 4
g 0.2271276 Pr(> chi2) 0.2440
Coef S.E. Wald Z Pr(>|Z|)
Intercept 3.0445 0.1709 17.81 <0.0001
outcome=2 -0.4543 0.2022 -2.25 0.0246
outcome=3 -0.2930 0.1927 -1.52 0.1285
treatment=2 0.0000 0.2000 0.00 1.0000
treatment=3 0.0000 0.2000 0.00 1.0000
General Linear Model
Glm(formula = counts ~ outcome + treatment, family = poisson(),
data = .)
Model Likelihood
Ratio Test
Obs 9 LR chi2 5.45
Residual d.f.4 d.f. 4
g 0.2271276 Pr(> chi2) 0.2440
Coef S.E. Wald Z Pr(>|Z|)
Intercept 3.0445 0.1709 17.81 <0.0001
outcome=2 -0.4543 0.2022 -2.25 0.0246
outcome=3 -0.2930 0.1927 -1.52 0.1285
treatment=2 0.0000 0.2000 0.00 1.0000
treatment=3 0.0000 0.2000 0.00 1.0000
Logistic Regression Model
lrm(formula = ch ~ age, data = data_lrm)
Frequencies of Responses
143.16 155.16 162.68 166.81 170.68 173.56 176.95 179.04 181.17 182.83 184.91
25 25 25 25 25 25 25 25 25 25 25
186.74 188.48 190.09 191.54 193.23 195.04 196.62 198.16 199.93 201.07 202.78
25 25 25 25 25 25 25 25 25 25 25
204.92 206.49 208.08 209.53 211.16 212.71 214.12 215.80 217.42 219.50 221.76
25 25 25 25 25 25 25 25 25 25 25
224.28 227.32 230.02 233.76 238.77 245.69 255.30
25 25 25 25 25 25 25
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 1000 LR chi2 0.19 R2 0.000 C 0.511
max |deriv| 6e-13 d.f. 1 g 0.027 Dxy 0.021
Pr(> chi2) 0.6651 gr 1.027 gamma 0.021
gp 0.007 tau-a 0.021
Brier 0.250
Coef S.E. Wald Z Pr(>|Z|)
y>=155.16 3.7816 0.3398 11.13 <0.0001
y>=162.68 3.0626 0.3093 9.90 <0.0001
y>=166.81 2.6304 0.2983 8.82 <0.0001
y>=170.68 2.3153 0.2927 7.91 <0.0001
y>=173.56 2.0640 0.2894 7.13 <0.0001
y>=176.95 1.8528 0.2872 6.45 <0.0001
y>=179.04 1.6688 0.2857 5.84 <0.0001
y>=181.17 1.5045 0.2845 5.29 <0.0001
y>=182.83 1.3550 0.2835 4.78 <0.0001
y>=184.91 1.2168 0.2827 4.30 <0.0001
y>=186.74 1.0875 0.2821 3.86 0.0001
y>=188.48 0.9654 0.2814 3.43 0.0006
y>=190.09 0.8488 0.2808 3.02 0.0025
y>=191.54 0.7369 0.2803 2.63 0.0086
y>=193.23 0.6286 0.2799 2.25 0.0247
y>=195.04 0.5232 0.2797 1.87 0.0614
y>=196.62 0.4200 0.2795 1.50 0.1329
y>=198.16 0.3184 0.2793 1.14 0.2543
y>=199.93 0.2177 0.2791 0.78 0.4353
y>=201.07 0.1176 0.2789 0.42 0.6734
y>=202.78 0.0175 0.2788 0.06 0.9501
y>=204.92 -0.0832 0.2788 -0.30 0.7655
y>=206.49 -0.1848 0.2789 -0.66 0.5075
y>=208.08 -0.2880 0.2790 -1.03 0.3020
y>=209.53 -0.3933 0.2792 -1.41 0.1589
y>=211.16 -0.5015 0.2794 -1.79 0.0727
y>=212.71 -0.6134 0.2797 -2.19 0.0283
y>=214.12 -0.7298 0.2801 -2.61 0.0092
y>=215.80 -0.8519 0.2806 -3.04 0.0024
y>=217.42 -0.9811 0.2811 -3.49 0.0005
y>=219.50 -1.1192 0.2818 -3.97 <0.0001
y>=221.76 -1.2688 0.2827 -4.49 <0.0001
y>=224.28 -1.4331 0.2839 -5.05 <0.0001
y>=227.32 -1.6171 0.2855 -5.67 <0.0001
y>=230.02 -1.8284 0.2876 -6.36 <0.0001
y>=233.76 -2.0798 0.2909 -7.15 <0.0001
y>=238.77 -2.3950 0.2964 -8.08 <0.0001
y>=245.69 -2.8271 0.3073 -9.20 <0.0001
y>=255.30 -3.5462 0.3383 -10.48 <0.0001
age -0.0023 0.0054 -0.43 0.6651
Logistic Regression Model
lrm(formula = ch ~ age)
Frequencies of Responses
143.16 155.16 162.68 166.81 170.68 173.56 176.95 179.04 181.17 182.83 184.91
25 25 25 25 25 25 25 25 25 25 25
186.74 188.48 190.09 191.54 193.23 195.04 196.62 198.16 199.93 201.07 202.78
25 25 25 25 25 25 25 25 25 25 25
204.92 206.49 208.08 209.53 211.16 212.71 214.12 215.80 217.42 219.50 221.76
25 25 25 25 25 25 25 25 25 25 25
224.28 227.32 230.02 233.76 238.77 245.69 255.30
25 25 25 25 25 25 25
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 1000 LR chi2 0.19 R2 0.000 C 0.511
max |deriv| 6e-13 d.f. 1 g 0.027 Dxy 0.021
Pr(> chi2) 0.6651 gr 1.027 gamma 0.021
gp 0.007 tau-a 0.021
Brier 0.250
Coef S.E. Wald Z Pr(>|Z|)
y>=155.16 3.7816 0.3398 11.13 <0.0001
y>=162.68 3.0626 0.3093 9.90 <0.0001
y>=166.81 2.6304 0.2983 8.82 <0.0001
y>=170.68 2.3153 0.2927 7.91 <0.0001
y>=173.56 2.0640 0.2894 7.13 <0.0001
y>=176.95 1.8528 0.2872 6.45 <0.0001
y>=179.04 1.6688 0.2857 5.84 <0.0001
y>=181.17 1.5045 0.2845 5.29 <0.0001
y>=182.83 1.3550 0.2835 4.78 <0.0001
y>=184.91 1.2168 0.2827 4.30 <0.0001
y>=186.74 1.0875 0.2821 3.86 0.0001
y>=188.48 0.9654 0.2814 3.43 0.0006
y>=190.09 0.8488 0.2808 3.02 0.0025
y>=191.54 0.7369 0.2803 2.63 0.0086
y>=193.23 0.6286 0.2799 2.25 0.0247
y>=195.04 0.5232 0.2797 1.87 0.0614
y>=196.62 0.4200 0.2795 1.50 0.1329
y>=198.16 0.3184 0.2793 1.14 0.2543
y>=199.93 0.2177 0.2791 0.78 0.4353
y>=201.07 0.1176 0.2789 0.42 0.6734
y>=202.78 0.0175 0.2788 0.06 0.9501
y>=204.92 -0.0832 0.2788 -0.30 0.7655
y>=206.49 -0.1848 0.2789 -0.66 0.5075
y>=208.08 -0.2880 0.2790 -1.03 0.3020
y>=209.53 -0.3933 0.2792 -1.41 0.1589
y>=211.16 -0.5015 0.2794 -1.79 0.0727
y>=212.71 -0.6134 0.2797 -2.19 0.0283
y>=214.12 -0.7298 0.2801 -2.61 0.0092
y>=215.80 -0.8519 0.2806 -3.04 0.0024
y>=217.42 -0.9811 0.2811 -3.49 0.0005
y>=219.50 -1.1192 0.2818 -3.97 <0.0001
y>=221.76 -1.2688 0.2827 -4.49 <0.0001
y>=224.28 -1.4331 0.2839 -5.05 <0.0001
y>=227.32 -1.6171 0.2855 -5.67 <0.0001
y>=230.02 -1.8284 0.2876 -6.36 <0.0001
y>=233.76 -2.0798 0.2909 -7.15 <0.0001
y>=238.77 -2.3950 0.2964 -8.08 <0.0001
y>=245.69 -2.8271 0.3073 -9.20 <0.0001
y>=255.30 -3.5462 0.3383 -10.48 <0.0001
age -0.0023 0.0054 -0.43 0.6651
Logistic Regression Model
lrm(formula = ch ~ age)
Frequencies of Responses
143.16 155.16 162.68 166.81 170.68 173.56 176.95 179.04 181.17 182.83 184.91
25 25 25 25 25 25 25 25 25 25 25
186.74 188.48 190.09 191.54 193.23 195.04 196.62 198.16 199.93 201.07 202.78
25 25 25 25 25 25 25 25 25 25 25
204.92 206.49 208.08 209.53 211.16 212.71 214.12 215.80 217.42 219.50 221.76
25 25 25 25 25 25 25 25 25 25 25
224.28 227.32 230.02 233.76 238.77 245.69 255.30
25 25 25 25 25 25 25
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 1000 LR chi2 0.19 R2 0.000 C 0.511
max |deriv| 6e-13 d.f. 1 g 0.027 Dxy 0.021
Pr(> chi2) 0.6651 gr 1.027 gamma 0.021
gp 0.007 tau-a 0.021
Brier 0.250
Coef S.E. Wald Z Pr(>|Z|)
y>=155.16 3.7816 0.3398 11.13 <0.0001
y>=162.68 3.0626 0.3093 9.90 <0.0001
y>=166.81 2.6304 0.2983 8.82 <0.0001
y>=170.68 2.3153 0.2927 7.91 <0.0001
y>=173.56 2.0640 0.2894 7.13 <0.0001
y>=176.95 1.8528 0.2872 6.45 <0.0001
y>=179.04 1.6688 0.2857 5.84 <0.0001
y>=181.17 1.5045 0.2845 5.29 <0.0001
y>=182.83 1.3550 0.2835 4.78 <0.0001
y>=184.91 1.2168 0.2827 4.30 <0.0001
y>=186.74 1.0875 0.2821 3.86 0.0001
y>=188.48 0.9654 0.2814 3.43 0.0006
y>=190.09 0.8488 0.2808 3.02 0.0025
y>=191.54 0.7369 0.2803 2.63 0.0086
y>=193.23 0.6286 0.2799 2.25 0.0247
y>=195.04 0.5232 0.2797 1.87 0.0614
y>=196.62 0.4200 0.2795 1.50 0.1329
y>=198.16 0.3184 0.2793 1.14 0.2543
y>=199.93 0.2177 0.2791 0.78 0.4353
y>=201.07 0.1176 0.2789 0.42 0.6734
y>=202.78 0.0175 0.2788 0.06 0.9501
y>=204.92 -0.0832 0.2788 -0.30 0.7655
y>=206.49 -0.1848 0.2789 -0.66 0.5075
y>=208.08 -0.2880 0.2790 -1.03 0.3020
y>=209.53 -0.3933 0.2792 -1.41 0.1589
y>=211.16 -0.5015 0.2794 -1.79 0.0727
y>=212.71 -0.6134 0.2797 -2.19 0.0283
y>=214.12 -0.7298 0.2801 -2.61 0.0092
y>=215.80 -0.8519 0.2806 -3.04 0.0024
y>=217.42 -0.9811 0.2811 -3.49 0.0005
y>=219.50 -1.1192 0.2818 -3.97 <0.0001
y>=221.76 -1.2688 0.2827 -4.49 <0.0001
y>=224.28 -1.4331 0.2839 -5.05 <0.0001
y>=227.32 -1.6171 0.2855 -5.67 <0.0001
y>=230.02 -1.8284 0.2876 -6.36 <0.0001
y>=233.76 -2.0798 0.2909 -7.15 <0.0001
y>=238.77 -2.3950 0.2964 -8.08 <0.0001
y>=245.69 -2.8271 0.3073 -9.20 <0.0001
y>=255.30 -3.5462 0.3383 -10.48 <0.0001
age -0.0023 0.0054 -0.43 0.6651
Linear Regression Model
ols(formula = sqrt(distance) ~ rcs(x1, 4) + scored(x2), data = data_ols,
x = TRUE)
Model Likelihood Discrimination
Ratio Test Indexes
Obs 200 LR chi2 44.91 R2 0.201
sigma0.7894 d.f. 6 R2 adj 0.176
d.f. 193 Pr(> chi2) 0.0000 g 0.449
Residuals
Min 1Q Median 3Q Max
-1.4630 -0.5879 -0.1007 0.5238 2.1320
Coef S.E. t Pr(>|t|)
Intercept 0.5050 0.2418 2.09 0.0380
x1 1.0738 0.9831 1.09 0.2761
x1' -0.5649 2.7791 -0.20 0.8391
x1'' 1.0240 9.0616 0.11 0.9101
x2 0.0637 0.1461 0.44 0.6631
x2=2 0.6089 0.2624 2.32 0.0214
x2=3 0.3897 0.3860 1.01 0.3139
Linear Regression Model
ols(formula = sqrt(distance) ~ rcs(x1, 4) + scored(x2), x = TRUE)
Model Likelihood Discrimination
Ratio Test Indexes
Obs 200 LR chi2 44.91 R2 0.201
sigma0.7894 d.f. 6 R2 adj 0.176
d.f. 193 Pr(> chi2) 0.0000 g 0.449
Residuals
Min 1Q Median 3Q Max
-1.4630 -0.5879 -0.1007 0.5238 2.1320
Coef S.E. t Pr(>|t|)
Intercept 0.5050 0.2418 2.09 0.0380
x1 1.0738 0.9831 1.09 0.2761
x1' -0.5649 2.7791 -0.20 0.8391
x1'' 1.0240 9.0616 0.11 0.9101
x2 0.0637 0.1461 0.44 0.6631
x2=2 0.6089 0.2624 2.32 0.0214
x2=3 0.3897 0.3860 1.01 0.3139
Linear Regression Model
ols(formula = sqrt(distance) ~ rcs(x1, 4) + scored(x2), x = TRUE)
Model Likelihood Discrimination
Ratio Test Indexes
Obs 200 LR chi2 44.91 R2 0.201
sigma0.7894 d.f. 6 R2 adj 0.176
d.f. 193 Pr(> chi2) 0.0000 g 0.449
Residuals
Min 1Q Median 3Q Max
-1.4630 -0.5879 -0.1007 0.5238 2.1320
Coef S.E. t Pr(>|t|)
Intercept 0.5050 0.2418 2.09 0.0380
x1 1.0738 0.9831 1.09 0.2761
x1' -0.5649 2.7791 -0.20 0.8391
x1'' 1.0240 9.0616 0.11 0.9101
x2 0.0637 0.1461 0.44 0.6631
x2=2 0.6089 0.2624 2.32 0.0214
x2=3 0.3897 0.3860 1.01 0.3139
Logistic (Proportional Odds) Ordinal Regression Model
orm(formula = y ~ x1, data = data_orm)
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 300 LR chi2 322.59 R2 0.659 rho 0.841
Distinct Y 300 d.f. 1 g 2.811
Median Y 1.51254 Pr(> chi2) <0.0001 gr 16.630
max |deriv| 9e-06 Score chi2 271.61 |Pr(Y>=median)-0.5| 0.442
Pr(> chi2) <0.0001
Coef S.E. Wald Z Pr(>|Z|)
x1 5.6036 0.4369 12.83 <0.0001
Logistic (Proportional Odds) Ordinal Regression Model
orm(formula = y ~ x1)
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 300 LR chi2 322.59 R2 0.659 rho 0.841
Distinct Y 300 d.f. 1 g 2.811
Median Y 1.51254 Pr(> chi2) <0.0001 gr 16.630
max |deriv| 9e-06 Score chi2 271.61 |Pr(Y>=median)-0.5| 0.442
Pr(> chi2) <0.0001
Coef S.E. Wald Z Pr(>|Z|)
x1 5.6036 0.4369 12.83 <0.0001
Logistic (Proportional Odds) Ordinal Regression Model
orm(formula = y ~ x1)
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 300 LR chi2 322.59 R2 0.659 rho 0.841
Distinct Y 300 d.f. 1 g 2.811
Median Y 1.51254 Pr(> chi2) <0.0001 gr 16.630
max |deriv| 9e-06 Score chi2 271.61 |Pr(Y>=median)-0.5| 0.442
Pr(> chi2) <0.0001
Coef S.E. Wald Z Pr(>|Z|)
x1 5.6036 0.4369 12.83 <0.0001
Parametric Survival Model: Log Normal Distribution
psm(formula = Surv(d.time, death) ~ sex * pol(age, 2), data = data_psm,
dist = "lognormal")
Model Likelihood Discrimination
Ratio Test Indexes
Obs 400 LR chi2 56.39 R2 0.154
Events 89 d.f. 5 Dxy 0.463
sigma 1.640564 Pr(> chi2) <0.0001 g 0.964
gr 2.622
Coef S.E. Wald Z Pr(>|Z|)
(Intercept) 9.0492 2.5313 3.57 0.0004
sex=Male -5.4095 3.9482 -1.37 0.1706
age -0.1815 0.0935 -1.94 0.0521
age^2 0.0011 0.0009 1.27 0.2029
sex=Male * age 0.2144 0.1518 1.41 0.1580
sex=Male * age^2 -0.0016 0.0014 -1.12 0.2640
Log(scale) 0.4950 0.0811 6.10 <0.0001
Parametric Survival Model: Log Normal Distribution
psm(formula = Surv(d.time, death) ~ sex * pol(age, 2), dist = "lognormal")
Model Likelihood Discrimination
Ratio Test Indexes
Obs 400 LR chi2 56.39 R2 0.154
Events 89 d.f. 5 Dxy 0.463
sigma 1.640564 Pr(> chi2) <0.0001 g 0.964
gr 2.622
Coef S.E. Wald Z Pr(>|Z|)
(Intercept) 9.0492 2.5313 3.57 0.0004
sex=Male -5.4095 3.9482 -1.37 0.1706
age -0.1815 0.0935 -1.94 0.0521
age^2 0.0011 0.0009 1.27 0.2029
sex=Male * age 0.2144 0.1518 1.41 0.1580
sex=Male * age^2 -0.0016 0.0014 -1.12 0.2640
Log(scale) 0.4950 0.0811 6.10 <0.0001
Parametric Survival Model: Log Normal Distribution
psm(formula = Surv(d.time, death) ~ sex * pol(age, 2), dist = "lognormal")
Model Likelihood Discrimination
Ratio Test Indexes
Obs 400 LR chi2 56.39 R2 0.154
Events 89 d.f. 5 Dxy 0.463
sigma 1.640564 Pr(> chi2) <0.0001 g 0.964
gr 2.622
Coef S.E. Wald Z Pr(>|Z|)
(Intercept) 9.0492 2.5313 3.57 0.0004
sex=Male -5.4095 3.9482 -1.37 0.1706
age -0.1815 0.0935 -1.94 0.0521
age^2 0.0011 0.0009 1.27 0.2029
sex=Male * age 0.2144 0.1518 1.41 0.1580
sex=Male * age^2 -0.0016 0.0014 -1.12 0.2640
Log(scale) 0.4950 0.0811 6.10 <0.0001
intubate documentation built on May 2, 2019, 2:46 p.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
Interfaces to rms functions that can be used
in a pipeline implemented by magrittr.
Usage
1 2 3 4 5 6 7 8 |
Arguments
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | ## Not run:
library(intubate)
library(magrittr)
library(rms)
## ntbt_bj: Buckley-James Multiple Regression Model
set.seed(1)
ftime <- 10*rexp(200)
stroke <- ifelse(ftime > 10, 0, 1)
ftime <- pmin(ftime, 10)
units(ftime) <- "Month"
age <- rnorm(200, 70, 10)
hospital <- factor(sample(c('a','b'),200,TRUE))
dd <- datadist(age, hospital)
options(datadist = "dd")
data_bj <- data.frame(ftime, stroke, age, hospital)
## Original function to interface
bj(Surv(ftime, stroke) ~ rcs(age,5) + hospital, data_bj, x = TRUE, y = TRUE)
## The interface puts data as first parameter
f <- ntbt_bj(data_bj, Surv(ftime, stroke) ~ rcs(age,5) + hospital, x = TRUE, y = TRUE)
anova(f)
## so it can be used easily in a pipeline.
data_bj %>%
ntbt_bj(Surv(ftime, stroke) ~ rcs(age,5) + hospital, x = TRUE, y = TRUE)
## ntbt_cph: Cox Proportional Hazards Model and Extensions
n <- 1000
set.seed(731)
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
sex <- factor(sample(c('Male','Female'), n,
rep=TRUE, prob=c(.6, .4)))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
dt <- -log(runif(n))/h
label(dt) <- 'Follow-up Time'
e <- ifelse(dt <= cens,1,0)
dt <- pmin(dt, cens)
units(dt) <- "Year"
dd <- datadist(age, sex)
options(datadist='dd')
S <- Surv(dt,e)
data_cph <- data.frame(S, age, sex)
## Original function to interface
cph(S ~ rcs(age,4) + sex, data_cph, x = TRUE, y = TRUE)
## The interface puts data as first parameter
ntbt_cph(data_cph, S ~ rcs(age,4) + sex, x = TRUE, y = TRUE)
## so it can be used easily in a pipeline.
data_cph %>%
ntbt_cph(S ~ rcs(age,4) + sex, x = TRUE, y = TRUE)
## ntbt_Glm
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
data_Glm <- data.frame(counts, outcome, treatment)
## Original function to interface
Glm(counts ~ outcome + treatment, family = poisson(), data = data_Glm)
## The interface puts data as first parameter
ntbt_Glm(data_Glm, counts ~ outcome + treatment, family = poisson())
## so it can be used easily in a pipeline.
data_Glm %>%
ntbt_Glm(counts ~ outcome + treatment, family = poisson())
## ntbt_lrm: Logistic Regression Model
n <- 1000 # define sample size
set.seed(17) # so can reproduce the results
age <- rnorm(n, 50, 10)
blood.pressure <- rnorm(n, 120, 15)
cholesterol <- rnorm(n, 200, 25)
sex <- factor(sample(c('female','male'), n,TRUE))
label(age) <- 'Age' # label is in Hmisc
label(cholesterol) <- 'Total Cholesterol'
label(blood.pressure) <- 'Systolic Blood Pressure'
label(sex) <- 'Sex'
units(cholesterol) <- 'mg/dl' # uses units.default in Hmisc
units(blood.pressure) <- 'mmHg'
#To use prop. odds model, avoid using a huge number of intercepts by
#grouping cholesterol into 40-tiles
ch <- cut2(cholesterol, g=40, levels.mean=TRUE) # use mean values in intervals
data_lrm <- data.frame(ch, age)
## Original function to interface
lrm(ch ~ age, data_lrm)
## The interface puts data as first parameter
ntbt_lrm(data_lrm, ch ~ age)
## so it can be used easily in a pipeline.
data_lrm %>%
ntbt_lrm(ch ~ age)
## ntbt_npsurv: Nonparametric Survival Estimates for Censored Data
tdata <- data.frame(time = c(1,1,1,2,2,2,3,3,3,4,4,4),
status = rep(c(1,0,2),4),
n = c(12,3,2,6,2,4,2,0,2,3,3,5))
## Original function to interface
f <- npsurv(Surv(time, time, status, type = 'interval') ~ 1, data = tdata, weights = n)
plot(f, fun = 'event', xmax = 20, mark.time = FALSE, col = 2:3)
## The interface puts data as first parameter
f <- ntbt_npsurv(tdata, Surv(time, time, status, type = 'interval') ~ 1, weights = n)
plot(f, fun = 'event', xmax = 20, mark.time = FALSE, col = 2:3)
## so it can be used easily in a pipeline.
tdata %>%
ntbt_npsurv(Surv(time, time, status, type = 'interval') ~ 1, weights = n) %>%
plot(fun = 'event', xmax = 20, mark.time = FALSE, col = 2:3)
## ntbt_ols: Linear Model Estimation Using Ordinary Least Squares
set.seed(1)
x1 <- runif(200)
x2 <- sample(0:3, 200, TRUE)
distance <- (x1 + x2/3 + rnorm(200))^2
d <- datadist(x1, x2)
options(datadist="d") # No d -> no summary, plot without giving all details
data_ols <- data.frame(distance, x1, x2)
## Original function to interface
ols(sqrt(distance) ~ rcs(x1, 4) + scored(x2), data_ols, x = TRUE)
## The interface puts data as first parameter
ntbt_ols(data_ols, sqrt(distance) ~ rcs(x1, 4) + scored(x2), x = TRUE)
## so it can be used easily in a pipeline.
data_ols %>%
ntbt_ols(sqrt(distance) ~ rcs(x1, 4) + scored(x2), x = TRUE)
## ntbt_orm: Ordinal Regression Model
set.seed(1)
n <- 300
x1 <- c(rep(0,150), rep(1,150))
y <- rnorm(n) + 3 * x1
data_orm <- data.frame(y, x1)
## Original function to interface
orm(y ~ x1, data_orm)
## The interface puts data as first parameter
ntbt_orm(data_orm, y ~ x1)
## so it can be used easily in a pipeline.
data_orm %>%
ntbt_orm(y ~ x1)
## ntbt_psm: Parametric Survival Model
n <- 400
set.seed(1)
age <- rnorm(n, 50, 12)
sex <- factor(sample(c('Female','Male'),n,TRUE))
dd <- datadist(age,sex)
options(datadist='dd')
# Population hazard function:
h <- .02*exp(.06*(age-50)+.8*(sex=='Female'))
d.time <- -log(runif(n))/h
cens <- 15*runif(n)
death <- ifelse(d.time <= cens,1,0)
d.time <- pmin(d.time, cens)
data_psm <- data.frame(d.time, death, sex, age)
## Original function to interface
psm(Surv(d.time, death) ~ sex * pol(age, 2), data_psm, dist = 'lognormal')
# Log-normal model is a bad fit for proportional hazards data
## The interface puts data as first parameter
ntbt_psm(data_psm, Surv(d.time, death) ~ sex * pol(age, 2), dist = 'lognormal')
## so it can be used easily in a pipeline.
data_psm %>%
ntbt_psm(Surv(d.time, death) ~ sex * pol(age, 2), dist = 'lognormal')
## End(Not run)
|
Example output
Loading required package: Hmisc
Loading required package: lattice
Loading required package: survival
Loading required package: Formula
Loading required package: ggplot2
Attaching package: 'Hmisc'
The following objects are masked from 'package:base':
format.pval, units
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
Buckley-James Censored Data Regression
bj(formula = Surv(ftime, stroke) ~ rcs(age, 5) + hospital, data = data_bj,
x = TRUE, y = TRUE)
Discrimination
Indexes
Obs 200 Regression d.f.5 g 0.325
Events 120 sigma0.8967 gr 1.384
d.f. 114
Coef S.E. Wald Z Pr(>|Z|)
Intercept -1.0590 1.9965 -0.53 0.5958
age 0.0554 0.0359 1.55 0.1222
age' -0.4562 0.1811 -2.52 0.0117
age'' 4.6885 1.6826 2.79 0.0053
age''' -6.6733 2.4011 -2.78 0.0054
hospital=b 0.2024 0.1695 1.19 0.2325
Wald Statistics Response: Surv(ftime, stroke)
Factor Chi-Square d.f. P
age 9.13 4 0.0580
Nonlinear 9.10 3 0.0279
hospital 1.43 1 0.2325
TOTAL 11.53 5 0.0418
Buckley-James Censored Data Regression
bj(formula = Surv(ftime, stroke) ~ rcs(age, 5) + hospital, x = TRUE,
y = TRUE)
Discrimination
Indexes
Obs 200 Regression d.f.5 g 0.325
Events 120 sigma0.8967 gr 1.384
d.f. 114
Coef S.E. Wald Z Pr(>|Z|)
Intercept -1.0590 1.9965 -0.53 0.5958
age 0.0554 0.0359 1.55 0.1222
age' -0.4562 0.1811 -2.52 0.0117
age'' 4.6885 1.6826 2.79 0.0053
age''' -6.6733 2.4011 -2.78 0.0054
hospital=b 0.2024 0.1695 1.19 0.2325
Cox Proportional Hazards Model
cph(formula = S ~ rcs(age, 4) + sex, data = data_cph, x = TRUE,
y = TRUE)
Model Tests Discrimination
Indexes
Obs 1000 LR chi2 78.28 R2 0.083
Events 183 d.f. 4 Dxy 0.378
Center -0.2861 Pr(> chi2) 0.0000 g 0.762
Score chi2 83.86 gr 2.143
Pr(> chi2) 0.0000
Coef S.E. Wald Z Pr(>|Z|)
age -0.0173 0.0286 -0.61 0.5443
age' 0.2040 0.0767 2.66 0.0079
age'' -0.7500 0.2679 -2.80 0.0051
sex=Male -0.6445 0.1488 -4.33 <0.0001
Cox Proportional Hazards Model
cph(formula = S ~ rcs(age, 4) + sex, x = TRUE, y = TRUE)
Model Tests Discrimination
Indexes
Obs 1000 LR chi2 78.28 R2 0.083
Events 183 d.f. 4 Dxy 0.378
Center -0.2861 Pr(> chi2) 0.0000 g 0.762
Score chi2 83.86 gr 2.143
Pr(> chi2) 0.0000
Coef S.E. Wald Z Pr(>|Z|)
age -0.0173 0.0286 -0.61 0.5443
age' 0.2040 0.0767 2.66 0.0079
age'' -0.7500 0.2679 -2.80 0.0051
sex=Male -0.6445 0.1488 -4.33 <0.0001
Cox Proportional Hazards Model
cph(formula = S ~ rcs(age, 4) + sex, x = TRUE, y = TRUE)
Model Tests Discrimination
Indexes
Obs 1000 LR chi2 78.28 R2 0.083
Events 183 d.f. 4 Dxy 0.378
Center -0.2861 Pr(> chi2) 0.0000 g 0.762
Score chi2 83.86 gr 2.143
Pr(> chi2) 0.0000
Coef S.E. Wald Z Pr(>|Z|)
age -0.0173 0.0286 -0.61 0.5443
age' 0.2040 0.0767 2.66 0.0079
age'' -0.7500 0.2679 -2.80 0.0051
sex=Male -0.6445 0.1488 -4.33 <0.0001
General Linear Model
Glm(formula = counts ~ outcome + treatment, family = poisson(),
data = data_Glm)
Model Likelihood
Ratio Test
Obs 9 LR chi2 5.45
Residual d.f.4 d.f. 4
g 0.2271276 Pr(> chi2) 0.2440
Coef S.E. Wald Z Pr(>|Z|)
Intercept 3.0445 0.1709 17.81 <0.0001
outcome=2 -0.4543 0.2022 -2.25 0.0246
outcome=3 -0.2930 0.1927 -1.52 0.1285
treatment=2 0.0000 0.2000 0.00 1.0000
treatment=3 0.0000 0.2000 0.00 1.0000
General Linear Model
Glm(formula = counts ~ outcome + treatment, family = poisson(),
data = data_Glm)
Model Likelihood
Ratio Test
Obs 9 LR chi2 5.45
Residual d.f.4 d.f. 4
g 0.2271276 Pr(> chi2) 0.2440
Coef S.E. Wald Z Pr(>|Z|)
Intercept 3.0445 0.1709 17.81 <0.0001
outcome=2 -0.4543 0.2022 -2.25 0.0246
outcome=3 -0.2930 0.1927 -1.52 0.1285
treatment=2 0.0000 0.2000 0.00 1.0000
treatment=3 0.0000 0.2000 0.00 1.0000
General Linear Model
Glm(formula = counts ~ outcome + treatment, family = poisson(),
data = .)
Model Likelihood
Ratio Test
Obs 9 LR chi2 5.45
Residual d.f.4 d.f. 4
g 0.2271276 Pr(> chi2) 0.2440
Coef S.E. Wald Z Pr(>|Z|)
Intercept 3.0445 0.1709 17.81 <0.0001
outcome=2 -0.4543 0.2022 -2.25 0.0246
outcome=3 -0.2930 0.1927 -1.52 0.1285
treatment=2 0.0000 0.2000 0.00 1.0000
treatment=3 0.0000 0.2000 0.00 1.0000
Logistic Regression Model
lrm(formula = ch ~ age, data = data_lrm)
Frequencies of Responses
143.16 155.16 162.68 166.81 170.68 173.56 176.95 179.04 181.17 182.83 184.91
25 25 25 25 25 25 25 25 25 25 25
186.74 188.48 190.09 191.54 193.23 195.04 196.62 198.16 199.93 201.07 202.78
25 25 25 25 25 25 25 25 25 25 25
204.92 206.49 208.08 209.53 211.16 212.71 214.12 215.80 217.42 219.50 221.76
25 25 25 25 25 25 25 25 25 25 25
224.28 227.32 230.02 233.76 238.77 245.69 255.30
25 25 25 25 25 25 25
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 1000 LR chi2 0.19 R2 0.000 C 0.511
max |deriv| 6e-13 d.f. 1 g 0.027 Dxy 0.021
Pr(> chi2) 0.6651 gr 1.027 gamma 0.021
gp 0.007 tau-a 0.021
Brier 0.250
Coef S.E. Wald Z Pr(>|Z|)
y>=155.16 3.7816 0.3398 11.13 <0.0001
y>=162.68 3.0626 0.3093 9.90 <0.0001
y>=166.81 2.6304 0.2983 8.82 <0.0001
y>=170.68 2.3153 0.2927 7.91 <0.0001
y>=173.56 2.0640 0.2894 7.13 <0.0001
y>=176.95 1.8528 0.2872 6.45 <0.0001
y>=179.04 1.6688 0.2857 5.84 <0.0001
y>=181.17 1.5045 0.2845 5.29 <0.0001
y>=182.83 1.3550 0.2835 4.78 <0.0001
y>=184.91 1.2168 0.2827 4.30 <0.0001
y>=186.74 1.0875 0.2821 3.86 0.0001
y>=188.48 0.9654 0.2814 3.43 0.0006
y>=190.09 0.8488 0.2808 3.02 0.0025
y>=191.54 0.7369 0.2803 2.63 0.0086
y>=193.23 0.6286 0.2799 2.25 0.0247
y>=195.04 0.5232 0.2797 1.87 0.0614
y>=196.62 0.4200 0.2795 1.50 0.1329
y>=198.16 0.3184 0.2793 1.14 0.2543
y>=199.93 0.2177 0.2791 0.78 0.4353
y>=201.07 0.1176 0.2789 0.42 0.6734
y>=202.78 0.0175 0.2788 0.06 0.9501
y>=204.92 -0.0832 0.2788 -0.30 0.7655
y>=206.49 -0.1848 0.2789 -0.66 0.5075
y>=208.08 -0.2880 0.2790 -1.03 0.3020
y>=209.53 -0.3933 0.2792 -1.41 0.1589
y>=211.16 -0.5015 0.2794 -1.79 0.0727
y>=212.71 -0.6134 0.2797 -2.19 0.0283
y>=214.12 -0.7298 0.2801 -2.61 0.0092
y>=215.80 -0.8519 0.2806 -3.04 0.0024
y>=217.42 -0.9811 0.2811 -3.49 0.0005
y>=219.50 -1.1192 0.2818 -3.97 <0.0001
y>=221.76 -1.2688 0.2827 -4.49 <0.0001
y>=224.28 -1.4331 0.2839 -5.05 <0.0001
y>=227.32 -1.6171 0.2855 -5.67 <0.0001
y>=230.02 -1.8284 0.2876 -6.36 <0.0001
y>=233.76 -2.0798 0.2909 -7.15 <0.0001
y>=238.77 -2.3950 0.2964 -8.08 <0.0001
y>=245.69 -2.8271 0.3073 -9.20 <0.0001
y>=255.30 -3.5462 0.3383 -10.48 <0.0001
age -0.0023 0.0054 -0.43 0.6651
Logistic Regression Model
lrm(formula = ch ~ age)
Frequencies of Responses
143.16 155.16 162.68 166.81 170.68 173.56 176.95 179.04 181.17 182.83 184.91
25 25 25 25 25 25 25 25 25 25 25
186.74 188.48 190.09 191.54 193.23 195.04 196.62 198.16 199.93 201.07 202.78
25 25 25 25 25 25 25 25 25 25 25
204.92 206.49 208.08 209.53 211.16 212.71 214.12 215.80 217.42 219.50 221.76
25 25 25 25 25 25 25 25 25 25 25
224.28 227.32 230.02 233.76 238.77 245.69 255.30
25 25 25 25 25 25 25
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 1000 LR chi2 0.19 R2 0.000 C 0.511
max |deriv| 6e-13 d.f. 1 g 0.027 Dxy 0.021
Pr(> chi2) 0.6651 gr 1.027 gamma 0.021
gp 0.007 tau-a 0.021
Brier 0.250
Coef S.E. Wald Z Pr(>|Z|)
y>=155.16 3.7816 0.3398 11.13 <0.0001
y>=162.68 3.0626 0.3093 9.90 <0.0001
y>=166.81 2.6304 0.2983 8.82 <0.0001
y>=170.68 2.3153 0.2927 7.91 <0.0001
y>=173.56 2.0640 0.2894 7.13 <0.0001
y>=176.95 1.8528 0.2872 6.45 <0.0001
y>=179.04 1.6688 0.2857 5.84 <0.0001
y>=181.17 1.5045 0.2845 5.29 <0.0001
y>=182.83 1.3550 0.2835 4.78 <0.0001
y>=184.91 1.2168 0.2827 4.30 <0.0001
y>=186.74 1.0875 0.2821 3.86 0.0001
y>=188.48 0.9654 0.2814 3.43 0.0006
y>=190.09 0.8488 0.2808 3.02 0.0025
y>=191.54 0.7369 0.2803 2.63 0.0086
y>=193.23 0.6286 0.2799 2.25 0.0247
y>=195.04 0.5232 0.2797 1.87 0.0614
y>=196.62 0.4200 0.2795 1.50 0.1329
y>=198.16 0.3184 0.2793 1.14 0.2543
y>=199.93 0.2177 0.2791 0.78 0.4353
y>=201.07 0.1176 0.2789 0.42 0.6734
y>=202.78 0.0175 0.2788 0.06 0.9501
y>=204.92 -0.0832 0.2788 -0.30 0.7655
y>=206.49 -0.1848 0.2789 -0.66 0.5075
y>=208.08 -0.2880 0.2790 -1.03 0.3020
y>=209.53 -0.3933 0.2792 -1.41 0.1589
y>=211.16 -0.5015 0.2794 -1.79 0.0727
y>=212.71 -0.6134 0.2797 -2.19 0.0283
y>=214.12 -0.7298 0.2801 -2.61 0.0092
y>=215.80 -0.8519 0.2806 -3.04 0.0024
y>=217.42 -0.9811 0.2811 -3.49 0.0005
y>=219.50 -1.1192 0.2818 -3.97 <0.0001
y>=221.76 -1.2688 0.2827 -4.49 <0.0001
y>=224.28 -1.4331 0.2839 -5.05 <0.0001
y>=227.32 -1.6171 0.2855 -5.67 <0.0001
y>=230.02 -1.8284 0.2876 -6.36 <0.0001
y>=233.76 -2.0798 0.2909 -7.15 <0.0001
y>=238.77 -2.3950 0.2964 -8.08 <0.0001
y>=245.69 -2.8271 0.3073 -9.20 <0.0001
y>=255.30 -3.5462 0.3383 -10.48 <0.0001
age -0.0023 0.0054 -0.43 0.6651
Logistic Regression Model
lrm(formula = ch ~ age)
Frequencies of Responses
143.16 155.16 162.68 166.81 170.68 173.56 176.95 179.04 181.17 182.83 184.91
25 25 25 25 25 25 25 25 25 25 25
186.74 188.48 190.09 191.54 193.23 195.04 196.62 198.16 199.93 201.07 202.78
25 25 25 25 25 25 25 25 25 25 25
204.92 206.49 208.08 209.53 211.16 212.71 214.12 215.80 217.42 219.50 221.76
25 25 25 25 25 25 25 25 25 25 25
224.28 227.32 230.02 233.76 238.77 245.69 255.30
25 25 25 25 25 25 25
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 1000 LR chi2 0.19 R2 0.000 C 0.511
max |deriv| 6e-13 d.f. 1 g 0.027 Dxy 0.021
Pr(> chi2) 0.6651 gr 1.027 gamma 0.021
gp 0.007 tau-a 0.021
Brier 0.250
Coef S.E. Wald Z Pr(>|Z|)
y>=155.16 3.7816 0.3398 11.13 <0.0001
y>=162.68 3.0626 0.3093 9.90 <0.0001
y>=166.81 2.6304 0.2983 8.82 <0.0001
y>=170.68 2.3153 0.2927 7.91 <0.0001
y>=173.56 2.0640 0.2894 7.13 <0.0001
y>=176.95 1.8528 0.2872 6.45 <0.0001
y>=179.04 1.6688 0.2857 5.84 <0.0001
y>=181.17 1.5045 0.2845 5.29 <0.0001
y>=182.83 1.3550 0.2835 4.78 <0.0001
y>=184.91 1.2168 0.2827 4.30 <0.0001
y>=186.74 1.0875 0.2821 3.86 0.0001
y>=188.48 0.9654 0.2814 3.43 0.0006
y>=190.09 0.8488 0.2808 3.02 0.0025
y>=191.54 0.7369 0.2803 2.63 0.0086
y>=193.23 0.6286 0.2799 2.25 0.0247
y>=195.04 0.5232 0.2797 1.87 0.0614
y>=196.62 0.4200 0.2795 1.50 0.1329
y>=198.16 0.3184 0.2793 1.14 0.2543
y>=199.93 0.2177 0.2791 0.78 0.4353
y>=201.07 0.1176 0.2789 0.42 0.6734
y>=202.78 0.0175 0.2788 0.06 0.9501
y>=204.92 -0.0832 0.2788 -0.30 0.7655
y>=206.49 -0.1848 0.2789 -0.66 0.5075
y>=208.08 -0.2880 0.2790 -1.03 0.3020
y>=209.53 -0.3933 0.2792 -1.41 0.1589
y>=211.16 -0.5015 0.2794 -1.79 0.0727
y>=212.71 -0.6134 0.2797 -2.19 0.0283
y>=214.12 -0.7298 0.2801 -2.61 0.0092
y>=215.80 -0.8519 0.2806 -3.04 0.0024
y>=217.42 -0.9811 0.2811 -3.49 0.0005
y>=219.50 -1.1192 0.2818 -3.97 <0.0001
y>=221.76 -1.2688 0.2827 -4.49 <0.0001
y>=224.28 -1.4331 0.2839 -5.05 <0.0001
y>=227.32 -1.6171 0.2855 -5.67 <0.0001
y>=230.02 -1.8284 0.2876 -6.36 <0.0001
y>=233.76 -2.0798 0.2909 -7.15 <0.0001
y>=238.77 -2.3950 0.2964 -8.08 <0.0001
y>=245.69 -2.8271 0.3073 -9.20 <0.0001
y>=255.30 -3.5462 0.3383 -10.48 <0.0001
age -0.0023 0.0054 -0.43 0.6651
Linear Regression Model
ols(formula = sqrt(distance) ~ rcs(x1, 4) + scored(x2), data = data_ols,
x = TRUE)
Model Likelihood Discrimination
Ratio Test Indexes
Obs 200 LR chi2 44.91 R2 0.201
sigma0.7894 d.f. 6 R2 adj 0.176
d.f. 193 Pr(> chi2) 0.0000 g 0.449
Residuals
Min 1Q Median 3Q Max
-1.4630 -0.5879 -0.1007 0.5238 2.1320
Coef S.E. t Pr(>|t|)
Intercept 0.5050 0.2418 2.09 0.0380
x1 1.0738 0.9831 1.09 0.2761
x1' -0.5649 2.7791 -0.20 0.8391
x1'' 1.0240 9.0616 0.11 0.9101
x2 0.0637 0.1461 0.44 0.6631
x2=2 0.6089 0.2624 2.32 0.0214
x2=3 0.3897 0.3860 1.01 0.3139
Linear Regression Model
ols(formula = sqrt(distance) ~ rcs(x1, 4) + scored(x2), x = TRUE)
Model Likelihood Discrimination
Ratio Test Indexes
Obs 200 LR chi2 44.91 R2 0.201
sigma0.7894 d.f. 6 R2 adj 0.176
d.f. 193 Pr(> chi2) 0.0000 g 0.449
Residuals
Min 1Q Median 3Q Max
-1.4630 -0.5879 -0.1007 0.5238 2.1320
Coef S.E. t Pr(>|t|)
Intercept 0.5050 0.2418 2.09 0.0380
x1 1.0738 0.9831 1.09 0.2761
x1' -0.5649 2.7791 -0.20 0.8391
x1'' 1.0240 9.0616 0.11 0.9101
x2 0.0637 0.1461 0.44 0.6631
x2=2 0.6089 0.2624 2.32 0.0214
x2=3 0.3897 0.3860 1.01 0.3139
Linear Regression Model
ols(formula = sqrt(distance) ~ rcs(x1, 4) + scored(x2), x = TRUE)
Model Likelihood Discrimination
Ratio Test Indexes
Obs 200 LR chi2 44.91 R2 0.201
sigma0.7894 d.f. 6 R2 adj 0.176
d.f. 193 Pr(> chi2) 0.0000 g 0.449
Residuals
Min 1Q Median 3Q Max
-1.4630 -0.5879 -0.1007 0.5238 2.1320
Coef S.E. t Pr(>|t|)
Intercept 0.5050 0.2418 2.09 0.0380
x1 1.0738 0.9831 1.09 0.2761
x1' -0.5649 2.7791 -0.20 0.8391
x1'' 1.0240 9.0616 0.11 0.9101
x2 0.0637 0.1461 0.44 0.6631
x2=2 0.6089 0.2624 2.32 0.0214
x2=3 0.3897 0.3860 1.01 0.3139
Logistic (Proportional Odds) Ordinal Regression Model
orm(formula = y ~ x1, data = data_orm)
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 300 LR chi2 322.59 R2 0.659 rho 0.841
Distinct Y 300 d.f. 1 g 2.811
Median Y 1.51254 Pr(> chi2) <0.0001 gr 16.630
max |deriv| 9e-06 Score chi2 271.61 |Pr(Y>=median)-0.5| 0.442
Pr(> chi2) <0.0001
Coef S.E. Wald Z Pr(>|Z|)
x1 5.6036 0.4369 12.83 <0.0001
Logistic (Proportional Odds) Ordinal Regression Model
orm(formula = y ~ x1)
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 300 LR chi2 322.59 R2 0.659 rho 0.841
Distinct Y 300 d.f. 1 g 2.811
Median Y 1.51254 Pr(> chi2) <0.0001 gr 16.630
max |deriv| 9e-06 Score chi2 271.61 |Pr(Y>=median)-0.5| 0.442
Pr(> chi2) <0.0001
Coef S.E. Wald Z Pr(>|Z|)
x1 5.6036 0.4369 12.83 <0.0001
Logistic (Proportional Odds) Ordinal Regression Model
orm(formula = y ~ x1)
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 300 LR chi2 322.59 R2 0.659 rho 0.841
Distinct Y 300 d.f. 1 g 2.811
Median Y 1.51254 Pr(> chi2) <0.0001 gr 16.630
max |deriv| 9e-06 Score chi2 271.61 |Pr(Y>=median)-0.5| 0.442
Pr(> chi2) <0.0001
Coef S.E. Wald Z Pr(>|Z|)
x1 5.6036 0.4369 12.83 <0.0001
Parametric Survival Model: Log Normal Distribution
psm(formula = Surv(d.time, death) ~ sex * pol(age, 2), data = data_psm,
dist = "lognormal")
Model Likelihood Discrimination
Ratio Test Indexes
Obs 400 LR chi2 56.39 R2 0.154
Events 89 d.f. 5 Dxy 0.463
sigma 1.640564 Pr(> chi2) <0.0001 g 0.964
gr 2.622
Coef S.E. Wald Z Pr(>|Z|)
(Intercept) 9.0492 2.5313 3.57 0.0004
sex=Male -5.4095 3.9482 -1.37 0.1706
age -0.1815 0.0935 -1.94 0.0521
age^2 0.0011 0.0009 1.27 0.2029
sex=Male * age 0.2144 0.1518 1.41 0.1580
sex=Male * age^2 -0.0016 0.0014 -1.12 0.2640
Log(scale) 0.4950 0.0811 6.10 <0.0001
Parametric Survival Model: Log Normal Distribution
psm(formula = Surv(d.time, death) ~ sex * pol(age, 2), dist = "lognormal")
Model Likelihood Discrimination
Ratio Test Indexes
Obs 400 LR chi2 56.39 R2 0.154
Events 89 d.f. 5 Dxy 0.463
sigma 1.640564 Pr(> chi2) <0.0001 g 0.964
gr 2.622
Coef S.E. Wald Z Pr(>|Z|)
(Intercept) 9.0492 2.5313 3.57 0.0004
sex=Male -5.4095 3.9482 -1.37 0.1706
age -0.1815 0.0935 -1.94 0.0521
age^2 0.0011 0.0009 1.27 0.2029
sex=Male * age 0.2144 0.1518 1.41 0.1580
sex=Male * age^2 -0.0016 0.0014 -1.12 0.2640
Log(scale) 0.4950 0.0811 6.10 <0.0001
Parametric Survival Model: Log Normal Distribution
psm(formula = Surv(d.time, death) ~ sex * pol(age, 2), dist = "lognormal")
Model Likelihood Discrimination
Ratio Test Indexes
Obs 400 LR chi2 56.39 R2 0.154
Events 89 d.f. 5 Dxy 0.463
sigma 1.640564 Pr(> chi2) <0.0001 g 0.964
gr 2.622
Coef S.E. Wald Z Pr(>|Z|)
(Intercept) 9.0492 2.5313 3.57 0.0004
sex=Male -5.4095 3.9482 -1.37 0.1706
age -0.1815 0.0935 -1.94 0.0521
age^2 0.0011 0.0009 1.27 0.2029
sex=Male * age 0.2144 0.1518 1.41 0.1580
sex=Male * age^2 -0.0016 0.0014 -1.12 0.2640
Log(scale) 0.4950 0.0811 6.10 <0.0001
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