lm example

library('sigr')
set.seed(352532)
d <- data.frame(x=1:10, z=c(4,5))
d$y <- 2*d$x + 0.1*rnorm(nrow(d))

model <- lm(y~x+z, data=d)
d$pred <- predict(model, newdata = d)

s <- summary(model)
print(s)

## 
## Call:
## lm(formula = y ~ x + z, data = d)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.086937 -0.032358 -0.000563  0.052421  0.065637 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.175154   0.178216   0.983    0.358    
## x            2.020521   0.006953 290.602 1.51e-15 ***
## z           -0.054716   0.039941  -1.370    0.213    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06219 on 7 degrees of freedom
## Multiple R-squared:  0.9999, Adjusted R-squared:  0.9999 
## F-statistic: 4.347e+04 on 2 and 7 DF,  p-value: 4.681e-15

print(s$fstatistic)

##    value    numdf    dendf 
## 43473.84     2.00     7.00

cat(render(wrapFTest(model),
    pSmallCutoff=0))

F Test summary: (R²=0.9999, F(2,7)=4.347e+04, p=4.681e-15).

cat(render(wrapFTest(d, 'pred', 'y', nParameters=2),
    pSmallCutoff=0))

F Test summary: (R²=0.9999, F(2,7)=4.347e+04, p=4.681e-15).

Intentionally forget to inform wrapFTest of the true number of parameters:

cat(render(wrapFTest(d, 'pred', 'y'),
    pSmallCutoff=0))

F Test summary: (R²=0.9999, F(1,8)=9.937e+04, p=1.148e-17).

John Mount

2020-10-16