drda {drda} | R Documentation |

Use the Newton's with a trust-region method to fit non-linear growth curves to observed data.

```
drda(
formula,
data,
subset,
weights,
na.action,
mean_function = "logistic4",
lower_bound = NULL,
upper_bound = NULL,
start = NULL,
max_iter = 1000
)
```

`formula` |
an object of class |

`data` |
an optional data frame, list or environment (or object coercible
by |

`subset` |
an optional vector specifying a subset of observations to be used in the fitting process. |

`weights` |
an optional vector of weights to be used in the fitting
process. If provided, weighted least squares is used with weights |

`na.action` |
a function which indicates what should happen when the data
contain |

`mean_function` |
the model to be fitted. See |

`lower_bound` |
numeric vector with the minimum admissible values of the
parameters. Use |

`upper_bound` |
numeric vector with the maximum admissible values of the
parameters. Use |

`start` |
starting values for the parameters. |

`max_iter` |
maximum number of iterations in the optimization algorithm. |

The 5-parameter logistic function can be selected by choosing
`mean_function = "logistic5"`

or `mean_function = "l5"`

. The function is
defined here as

`alpha + delta / (1 + nu * exp(-eta * (x - phi)))^(1 / nu)`

where `eta > 0`

and `nu > 0`

. When `delta`

is positive (negative) the curve
is monotonically increasing (decreasing).

Parameter `alpha`

is the value of the function when `x -> -Inf`

.
Parameter `delta`

is the (signed) height of the curve.
Parameter `eta`

represents the steepness (growth rate) of the curve.
Parameter `phi`

is related to the mid-value of the function.
Parameter `nu`

affects near which asymptote maximum growth occurs.

The value of the function when `x -> Inf`

is `alpha + delta`

. In
dose-response studies `delta`

can be interpreted as the maximum theoretical
achievable effect.

The 4-parameter logistic function is the default model of `drda`

. It can be
explicitly selected by choosing `mean_function = "logistic4"`

or
`mean_function = "l4"`

. The function is obtained by setting `nu = 1`

in the
generalized logistic function, that is

`alpha + delta / (1 + exp(-eta * (x - phi)))`

where `eta > 0`

. When `delta`

is positive (negative) the curve is
monotonically increasing (decreasing).

Parameter `alpha`

is the value of the function when `x -> -Inf`

.
Parameter `delta`

is the (signed) height of the curve.
Parameter `eta`

represents the steepness (growth rate) of the curve.
Parameter `phi`

represents the `x`

value at which the curve is equal to its
mid-point, i.e. `f(phi; alpha, delta, eta, phi) = alpha + delta / 2`

.

The value of the function when `x -> Inf`

is `alpha + delta`

. In
dose-response studies `delta`

can be interpreted as the maximum theoretical
achievable effect.

The 2-parameter logistic function can be selected by choosing
`mean_function = "logistic2"`

or `mean_function = "l2"`

. For a monotonically
increasing curve set `nu = 1`

, `alpha = 0`

, and `delta = 1`

:

`1 / (1 + exp(-eta * (x - phi)))`

For a monotonically decreasing curve set `nu = 1`

, `alpha = 1`

, and
`delta = -1`

:

`1 - 1 / (1 + exp(-eta * (x - phi)))`

where `eta > 0`

. The lower bound of the curve is zero while the upper bound
of the curve is one.

Parameter `eta`

represents the steepness (growth rate) of the curve.
Parameter `phi`

represents the `x`

value at which the curve is equal to its
mid-point, i.e. `f(phi; eta, phi) = 1 / 2`

.

The Gompertz function is the limit for `nu -> 0`

of the 5-parameter logistic
function. It can be selected by choosing `mean_function = "gompertz"`

or
`mean_function = "gz"`

. The function is defined in this package as

`alpha + delta * exp(-exp(-eta * (x - phi)))`

where `eta > 0`

.

Parameter `alpha`

is the value of the function when `x -> -Inf`

.
Parameter `delta`

is the (signed) height of the curve.
Parameter `eta`

represents the steepness (growth rate) of the curve.
Parameter `phi`

sets the displacement along the `x`

-axis.

The value of the function when `x -> Inf`

is `alpha + delta`

. In
dose-response studies `delta`

can be interpreted as the maximum theoretical
achievable effect.

The mid-point of the function, that is `alpha + delta / 2`

, is achieved at
`x = phi - log(log(2)) / eta`

.

The 5-parameter log-logistic function is selected by setting
`mean_function = "loglogistic5"`

or `mean_function = "ll5"`

. The function is
defined here as

`alpha + delta * (x^eta / (x^eta + nu * phi^eta))^(1 / nu)`

where `x >= 0`

, `eta > 0`

, `phi > 0`

, and `nu > 0`

. When `delta`

is
positive (negative) the curve is monotonically increasing (decreasing). The
function is defined only for positive values of the predictor variable `x`

.

Parameter `alpha`

is the value of the function at `x = 0`

.
Parameter `delta`

is the (signed) height of the curve.
Parameter `eta`

represents the steepness (growth rate) of the curve.
Parameter `phi`

is related to the mid-value of the function.
Parameter `nu`

affects near which asymptote maximum growth occurs.

`x -> Inf`

is `alpha + delta`

. In
dose-response studies `delta`

can be interpreted as the maximum theoretical
achievable effect.

The 4-parameter log-logistic function is selected by setting
`mean_function = "loglogistic4"`

or `mean_function = "ll4"`

. The function is
obtained by setting `nu = 1`

in the generalized log-logistic function, that
is

`alpha + delta * x^eta / (x^eta + phi^eta)`

where `x >= 0`

and `eta > 0`

. When `delta`

is positive (negative) the curve
is monotonically increasing (decreasing). The function is defined only for
positive values of the predictor variable `x`

.

Parameter `alpha`

is the value of the function at `x = 0`

.
Parameter `delta`

is the (signed) height of the curve.
Parameter `eta`

represents the steepness (growth rate) of the curve.
Parameter `phi`

represents the `x`

value at which the curve is equal to its
mid-point, i.e. `f(phi; alpha, delta, eta, phi) = alpha + delta / 2`

.

`x -> Inf`

is `alpha + delta`

. In
dose-response studies `delta`

can be interpreted as the maximum theoretical
achievable effect.

The 2-parameter log-logistic function is selected by setting
`mean_function = "loglogistic2"`

or `mean_function = "ll2"`

. For a
monotonically increasing curve set `nu = 1`

, `alpha = 0`

, and `delta = 1`

:

`x^eta / (x^eta + phi^eta)`

For a monotonically decreasing curve set `nu = 1`

, `alpha = 1`

, and
`delta = -1`

:

`1 - x^eta / (x^eta + phi^eta)`

where `x >= 0`

, `eta > 0`

, and `phi > 0`

. The lower bound of the curve is
zero while the upper bound of the curve is one.

Parameter `eta`

represents the steepness (growth rate) of the curve.
Parameter `phi`

represents the `x`

value at which the curve is equal to its
mid-point, i.e. `f(phi; eta, phi) = 1 / 2`

.

The log-Gompertz function is the limit for `nu -> 0`

of the 5-parameter
log-logistic function. It can be selected by choosing
`mean_function = "loggompertz"`

or `mean_function = "lgz"`

. The function is
defined in this package as

`alpha + delta * exp(-(phi / x)^eta)`

where `x > 0`

, `eta > 0`

, and `phi > 0`

. Note that the limit for `x -> 0`

is
`alpha`

. When `delta`

is positive (negative) the curve is monotonically
increasing (decreasing). The function is defined only for positive values of
the predictor variable `x`

.

Parameter `alpha`

is the value of the function at `x = 0`

.
Parameter `delta`

is the (signed) height of the curve.
Parameter `eta`

represents the steepness (growth rate) of the curve.
Parameter `phi`

sets the displacement along the `x`

-axis.

`x -> Inf`

is `alpha + delta`

. In
dose-response studies `delta`

can be interpreted as the maximum theoretical
achievable effect.

It is possible to search for the maximum likelihood estimates within pre-specified interval regions.

*Note*: Hypothesis testing is not available for constrained estimates
because asymptotic approximations might not be valid.

An object of class `drda`

and `model_fit`

, where `model`

is the
chosen mean function. It is a list containing the following components:

- converged
boolean value assessing if the optimization algorithm converged or not.

- iterations
total number of iterations performed by the optimization algorithm

- constrained
boolean value set to

`TRUE`

if optimization was constrained.- estimated
boolean vector indicating which parameters were estimated from the data.

- coefficients
maximum likelihood estimates of the model parameters.

- rss
minimum value (found) of the residual sum of squares.

- df.residuals
residual degrees of freedom.

- fitted.values
fitted mean values.

- residuals
residuals, that is response minus fitted values.

- weights
(only for weighted fits) the specified weights.

- mean_function
model that was used for fitting.

- n
effective sample size.

- sigma
corrected maximum likelihood estimate of the standard deviation.

- loglik
maximum value (found) of the log-likelihood function.

- fisher.info
observed Fisher information matrix evaluated at the maximum likelihood estimator.

- vcov
approximate variance-covariance matrix of the model parameters.

- call
the matched call.

- terms
the

`terms`

object used.- model
the model frame used.

- na.action
(where relevant) information returned by

`model.frame`

on the special handling of`NA`

s.

```
# by default `drda` uses a 4-parameter logistic function for model fitting
fit_l4 <- drda(response ~ log_dose, data = voropm2)
# get a general overview of the results
summary(fit_l4)
# compare the model against a flat horizontal line and the full model
anova(fit_l4)
# 5-parameter logistic curve appears to be a better model
fit_l5 <- drda(response ~ log_dose, data = voropm2, mean_function = "l5")
plot(fit_l4, fit_l5)
# fit a 2-parameter logistic function
fit_l2 <- drda(response ~ log_dose, data = voropm2, mean_function = "l2")
# compare our models
anova(fit_l2, fit_l4)
# use log-logistic functions when utilizing doses (instead of log-doses)
# here we show the use of other arguments as well
fit_ll5 <- drda(
response ~ dose, weights = weight, data = voropm2,
mean_function = "loglogistic5", lower_bound = c(0.5, -1.5, 0, -Inf, 0.25),
upper_bound = c(1.5, 0.5, 5, Inf, 3), start = c(1, -1, 3, 100, 1),
max_iter = 10000
)
# note that the maximum likelihood estimate is outside the region of
# optimization: not only the variance-covariance matrix is now singular but
# asymptotic assumptions do not hold anymore.
```

[Package *drda* version 2.0.3 Index]