Examples of sigr formatting. Inspired by APA format (American Psychological Association), but not fully compliant. Discussed here.
Please see here for some notes.
Simple formatting.
library("sigr")
sigr::getRenderingFormat()[1] “html”
cat(render(wrapSignificance(1/300)))p=0.003333
F-test examples (quality of a numeric model of a numeric outcome).
cat(render(wrapFTestImpl(numdf=2,dendf=55,FValue=5.56)))F Test summary: (R2=0.1682, F(2,55)=5.56, p=0.006322).
d <- data.frame(x=0.2*(1:20))
d$y <- cos(d$x)
model <- lm(y~x,data=d)
d$prediction <- predict(model,newdata=d)
print(summary(model))##
## Call:
## lm(formula = y ~ x, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.34441 -0.24493 0.00103 0.18320 0.65001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.95686 0.12869 7.436 6.84e-07 ***
## x -0.56513 0.05371 -10.521 4.06e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.277 on 18 degrees of freedom
## Multiple R-squared: 0.8601, Adjusted R-squared: 0.8524
## F-statistic: 110.7 on 1 and 18 DF, p-value: 4.062e-09cat(render(wrapFTest(model),pSmallCutoff=1.0e-12))F Test summary: (R2=0.8601, F(1,18)=110.7, p=4.062e-09).
cat(render(wrapFTest(d,'prediction','y'),
pSmallCutoff=1.0e-12))F Test summary: (R2=0.8601, F(1,18)=110.7, p=4.062e-09).
Chi-squared test examples (quality of a probability model of a two category outcome).
d <- data.frame(x=c(1,2,3,4,5,6,7,7),
y=c(TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,TRUE,FALSE))
model <- glm(y~x,data=d,family=binomial)
model$converged## [1] TRUEsummary(model)##
## Call:
## glm(formula = y ~ x, family = binomial, data = d)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.37180 -1.09714 -0.00811 1.08024 1.42939
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7455 1.6672 -0.447 0.655
## x 0.1702 0.3429 0.496 0.620
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 11.090 on 7 degrees of freedom
## Residual deviance: 10.837 on 6 degrees of freedom
## AIC: 14.837
##
## Number of Fisher Scoring iterations: 4d$pred <- predict(model,type='response',newdata=d)cat(render(wrapChiSqTest(model),pLargeCutoff=1))Chi-Square Test summary: pseudo-R2=0.02282 (χ2(1,N=8)=0.2531, p=0.6149).
cat(render(wrapChiSqTest(d,'pred','y'),pLargeCutoff=1))Chi-Square Test summary: pseudo-R2=0.02282 (χ2(1,N=8)=0.2531, p=0.6149).
d <- data.frame(x=c(1,2,3,4,5,6,7,7),
y=c(1,1,2,2,3,3,4,4))
ct <- cor.test(d$x,d$y)cat(render(wrapCorTest(ct)))Pearson’s product-moment correlation: (r=0.9767, p=3.094e-05).
d <- data.frame(x=c('b','a','a','a','b','b','b'),
y=c('1','1','1','2','2','2','2'))
ft <- fisher.test(table(d))cat(render(wrapFisherTest(ft),pLargeCutoff=1))Fisher’s Exact Test for Count Data: (odds.ratio=4.45, p=0.4857).
d <- data.frame(x=c(1,2,3,4,5,6,7,7),
y=c(1,1,2,2,3,3,4,4))
ft <- t.test(d$x,d$y)cat(render(wrapTTest(ft),pLargeCutoff=1))Welch Two Sample t-test, two.sided: (t=2.072, df=10.62, p=0.06349).
parallelCluster <- NULL
#parallelCluster <- parallel::makeCluster(parallel::detectCores())
set.seed(25325)
d <- data.frame(x1=c(1,2,3,4,5,6,7,7),
y=c(FALSE,TRUE,FALSE,FALSE,
TRUE,TRUE,FALSE,TRUE))
d <- rbind(d,d,d,d)
sigr::resampleTestAUC(d,'x1','y',TRUE,
nrep=200,
parallelCluster=parallelCluster)[1] “AUC test alt. hyp. AUC>0.5: (AUC=0.6562, s.d.=0.09311, p=n.s.).”
set.seed(25325)
d <- data.frame(x1=c(1,2,3,4,5,6,7,7),
x2=1,
y=c(FALSE,TRUE,FALSE,FALSE,
TRUE,TRUE,FALSE,TRUE))
d <- rbind(d,d,d,d)
sigr::testAUCpair(d,'x1','x2','y',TRUE,
nrep=200,
parallelCluster=parallelCluster)[1] “AUC test resampled AUC1>AUC2: (AUCs=0.6562;0.5, s.d.=0.09694, e=n.s.).”
if(!is.null(parallelCluster)) {
parallel::stopCluster(parallelCluster)
}permutationScoreModel
set.seed(25325)
y <- 1:5
m <- c(1,1,2,2,2)
cor.test(m,y,alternative='greater')##
## Pearson's product-moment correlation
##
## data: m and y
## t = 3, df = 3, p-value = 0.02883
## alternative hypothesis: true correlation is greater than 0
## 95 percent confidence interval:
## 0.1526678 1.0000000
## sample estimates:
## cor
## 0.8660254f <- function(modelValues, yValues) { cor(modelValues, yValues) }
sigr::permutationScoreModel(m,y,f)## [1] "<b>Studentized permutation test</b>: is observed score greater than permuted score, summary: <i>p</i>=0.04162"resampleScoreModel
set.seed(25325)
y <- 1:5
m1 <- c(1,1,2,2,2)
cor.test(m1,y,alternative='greater')##
## Pearson's product-moment correlation
##
## data: m1 and y
## t = 3, df = 3, p-value = 0.02883
## alternative hypothesis: true correlation is greater than 0
## 95 percent confidence interval:
## 0.1526678 1.0000000
## sample estimates:
## cor
## 0.8660254f <- function(modelValues,yValues) {
if((sd(modelValues)<=0)||(sd(yValues)<=0)) {
return(0)
}
cor(modelValues,yValues)
}
s <- sigr::resampleScoreModel(m1,y,f)
print(s)## $fnName
## [1] "resampleScoreModel"
##
## $observedScore
## [1] 0.8660254
##
## $bias
## [1] -0.06201873
##
## $sd
## [1] 0.2768382
##
## $nNA
## [1] 0
##
## $n
## [1] 5z <- s$observedScore/s$sd # always check size of z relative to bias!
pValue <- pt(z,df=length(y)-2,lower.tail=FALSE)
pValue## [1] 0.0260677resampleScoreModelPair
set.seed(25325)
y <- 1:5
m1 <- c(1,1,2,2,2)
m2 <- c(1,1,1,1,2)
cor(m1,y)## [1] 0.8660254cor(m2,y)## [1] 0.7071068f <- function(modelValues,yValues) {
if((sd(modelValues)<=0)||(sd(yValues)<=0)) {
return(0)
}
cor(modelValues,yValues)
}
sigr::render(sigr::resampleScoreModelPair(m1,m2,y,f),
pLargeCutoff=1,format='ascii')## [1] "Studentized empirical test: is difference greater than zero on re-samples, summary: e=0.3331"