Fit learning curve for accuracy matrix

Fit learning curve for accuracy matrix

learningCurve(
  accMat,
  n,
  auto_initial = TRUE,
  a = NULL,
  b = NULL,
  c = NULL,
  d_list = NULL,
  fitmodel = c("nls", "nls_mix", "gam"),
  plot = TRUE,
  verbose = TRUE
)

Arguments

accMat	Matrix of accuracy rate where column indicate different sample size
n	Vector indicates the sample size
auto_initial	whether automatical intialise
a	input the parameter a starting point
b	input the parameter a starting point
c	input the parameter a starting point
d_list	range of d
fitmodel	"nls", "nls_mix", "gam"
plot	indicates whether plot or not
verbose	indicates whether verbose or not

Value

list of results

Examples

set.seed(2019)
n <- seq(20, 10000, 100)
accMat <- do.call(cbind, lapply(1:length(n), function(i){
tmp_n <- rep(n[i], 50)
y <- -2/(tmp_n^0.8) + 0.95 + rnorm(length(tmp_n), 0, 0.02)
}))
res <- learningCurve(accMat = accMat, n)
#> 
#> Formula: acc ~ I(1/n^(c) * a) + b
#> 
#> Parameters:
#>                                 Estimate Std. Error t value Pr(>|t|)    
#> a.(Intercept)                 -1.8730006  0.1480680  -12.65   <2e-16 ***
#> c.log(max(acc) + 0.001 - acc)  0.7759121  0.0248888   31.18   <2e-16 ***
#> b                              0.9496806  0.0005534 1716.21   <2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 0.003151 on 97 degrees of freedom
#> 
#> Number of iterations to convergence: 6 
#> Achieved convergence tolerance: 1.49e-08
#> 
#> 
#> Formula: acc ~ I(1/n^(c) * a) + b
#> 
#> Parameters:
#>                                 Estimate Std. Error t value Pr(>|t|)    
#> a.(Intercept)                 -1.9661577  0.1927579  -10.20   <2e-16 ***
#> c.log(max(acc) + 0.001 - acc)  0.7936628  0.0309733   25.62   <2e-16 ***
#> b                              0.9360079  0.0006414 1459.30   <2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 0.003764 on 97 degrees of freedom
#> 
#> Number of iterations to convergence: 11 
#> Achieved convergence tolerance: 1.49e-08
#> 
#> 
#> Formula: acc ~ I(1/n^(c) * a) + b
#> 
#> Parameters:
#>                                 Estimate Std. Error  t value Pr(>|t|)    
#> a.(Intercept)                 -1.8151635  0.1898262   -9.562 1.18e-15 ***
#> c.log(max(acc) + 0.001 - acc)  0.7702072  0.0328869   23.420  < 2e-16 ***
#> b                              0.9624993  0.0007365 1306.788  < 2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 0.004153 on 97 degrees of freedom
#> 
#> Number of iterations to convergence: 5 
#> Achieved convergence tolerance: 1.49e-08
#> 
N <- getN(res, acc = 0.9)