BayesianFROC (version 1.0.0)

error_srsc_error_visualization: Visualization for Error of Estimator

Description

The function plot the graph of errors with respect to sample sizes.

Error plot

x-axis

Sample sizes

y-axis

Error for each parameter

Usage

error_srsc_error_visualization(
  return.value.of_error_srsc,
  log_scale_x.axis = TRUE
)

Arguments

return.value.of_error_srsc

A return value of the function error_srsc().

log_scale_x.axis

A logical, whether x axis is log scale or not.

Value

A long format dataframe of error and its parameter name

See Also

error_srsc_variance_visualization

Examples

# NOT RUN {
# General plot

df <- data.frame(x=runif(100),y=runif(100),g= as.factor(rep(1:5,10)))

ggplot(df, aes(x = x, y = y, shape = g)) +
  geom_point(size = 3) +
  scale_shape_manual(values = c(1,2,3,4,5,6,7,8,9))









df <- data.frame(x=runif(100),y=runif(100),g= as.factor(rep(1:25,4)))

  # Use slightly larger points and use custom values for the shape scale


ggplot(df, aes(x = x, y = y, shape = g)) +
  geom_point(size = 3) +
  scale_shape_manual(values = c(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))

# }
# NOT RUN {
 a <- error_srsc()

 error_srsc_error_visualization(a)


#========================================================================================
#              In case of C = 4, arbitrary C is available.
#========================================================================================

  a <-error_srsc(NLvector = c(
100,
10000,
1000000
),
ratio=2,
replicate.datset =2,
ModifiedPoisson = FALSE,
mean.truth=0.6,
sd.truth=5.3,
z.truth =c(-0.8,0.7,2.38,3), # Here we use the C=4
ite =500
)

error_srsc_error_visualization(a)
error_srsc_variance_visualization(a)











#========================================================================================
#              In case of C = 7, arbitrary C is available.
#========================================================================================







#'
a <-error_srsc(NLvector = c(
  100,
  10000,
  100000
),
ratio=2,
replicate.datset =2,
ModifiedPoisson = FALSE,
mean.truth=0.6,
sd.truth=5.3,
z.truth =c(-0.8,0.7,2.38,3,3.4,3.6,3.8), # Here we use the C=7
ite =500
)

error_srsc_error_visualization(a)
error_srsc_variance_visualization(a)













# }
# NOT RUN {
# }