print.mimcr {disprofas}

Print a summary of MIMCR estimation

Description

This is a method for the function print() for objects of class ‘mimcr’.

Usage

## S3 method for class 'mimcr'
print(x, ...)

Arguments

x

An object of class ‘mimcr’ returned by the mimcr() function.

...

Further arguments passed to or from other methods or arguments that can be passed down to the formatC() function.

Details

The most relevant information in an ‘mimcr’ object is printed.

Value

The ‘mimcr’ object passed to the x parameter is returned invisibly.

See Also

mimcr, formatC, methods.

Examples

# Assessment of data by aid of the mimcr() function
res1 <- mimcr(data = dip1, tcol = 3:10, grouping = "type")

# Print of a summary of the assessment
print(res1)

# Results of Model-Independent Multivariate Confidence Region (MIMCR)
# approach to assess equivalence of highly variable in-vitro
# dissolution profiles of two drug product formulations
#
# Did the Newton-Raphson search converge? Yes
#
# Parameters (general):
#   Significance level:                 0.05
# Degrees of freedom (1):               7
# Degrees of freedom (2):               4
# Mahalanobis distance (MD):            25.72
# (F) scaling factor K:                 0.1714
# (MD) scaling factor k:                3
# Hotelling's T2:                       1984
#
# Parameters specific for Tsong (1996) approach:
# Maximum tolerable average difference: 10
# Similarity limit:                     11.33
# Observed upper limit:                 31.68
#
# Parameters specific for Hoffelder (2016) approach:
# Noncentrality parameter:              385
# Critial F (Hoffelder):                23.16
# Probability p (Hoffelder):            0.7402
#
# Conclusions:
#       Tsong (1996):  Dissimilar
#   Hoffelder (2016):  Dissimilar

# Taking only the 15 and 90 minutes testing points into account produces a
# warning because profiles should comprise a minimum of three testing points.
## Not run: 
  res2 <- mimcr(data = dip1, tcol = c(5, 9), grouping = "type", mtad = 15,
                signif = 0.1)
  print(res2)

  # Warning:
  #   In mimcr(data = dip1, tcol = c(5, 9), grouping = "type", mtad = 15,  :
  # The profiles should comprise a minimum of 3 time points. The actual profiles
  # comprise 2 points only.

  # Results of Model-Independent Multivariate Confidence Region (MIMCR)
  # approach to assess equivalence of highly variable in-vitro
  # dissolution profiles of two drug product formulations
  #
  # Did the Newton-Raphson search converge? Yes
  #
  # Parameters (general):
  #   Significance level:                 0.1
  # Degrees of freedom (1):               2
  # Degrees of freedom (2):               9
  # Mahalanobis distance (MD):            10.44
  # (F) scaling factor K:                 1.35
  # (MD) scaling factor k:                3
  # Hotelling's T2:                       327
  #
  # Parameters specific for Tsong (1996) approach:
  # Maximum tolerable average difference: 15
  # Similarity limit:                     9.631
  # Observed upper limit:                 11.93
  #
  # Parameters specific for Hoffelder (2016) approach:
  # Noncentrality parameter:              278.3
  # Critial F (Hoffelder):                83.57
  # Probability p (Hoffelder):            0.4823
  #
  # Conclusions:
  #       Tsong (1996):  Dissimilar
  #   Hoffelder (2016):  Dissimilar

## End(Not run)

# A successful comparison:
res3 <- mimcr(data = dip3, tcol = 4:6, grouping = "batch")
print(res3)

# Results of Model-Independent Multivariate Confidence Region (MIMCR)
# approach to assess equivalence of highly variable in-vitro
# dissolution profiles of two drug product formulations
#
# Did the Newton-Raphson search converge? Yes
#
# Parameters (general):
#   Significance level:                 0.05
# Degrees of freedom (1):               3
# Degrees of freedom (2):               20
# Mahalanobis distance (MD):            0.2384
# (F) scaling factor K:                 1.818
# (MD) scaling factor k:                6
# Hotelling's T2:                       0.341
#
# Parameters specific for Tsong (1996) approach:
# Maximum tolerable average difference: 10
# Similarity limit:                     2.248
# Observed upper limit:                 1.544
#
# Parameters specific for Hoffelder (2016) approach:
# Noncentrality parameter:              30.32
# Critial F (Hoffelder):                4.899
# Probability p (Hoffelder):            2.891e-08
#
# Conclusions:
#       Tsong (1996):  Similar
#   Hoffelder (2016):  Similar