Compute Parameters for Proportional Odds Markov Model

```
intMarkovOrd(
y,
times,
initial,
absorb = NULL,
intercepts,
extra = NULL,
g,
target,
t,
ftarget = NULL,
onlycrit = FALSE,
constraints = NULL,
printsop = FALSE,
...
)
```

list containing two vectors named `intercepts`

and `extra`

unless `oncrit=TRUE`

in which case the best achieved sum of absolute errors is returned

- y
vector of possible y values in order (numeric, character, factor)

- times
vector of measurement times

- initial
initial value of

`y`

(baseline state; numeric, character, or factor matching`y`

). If length 1 this value is used for all subjects, otherwise it is a vector of length`n`

.- absorb
vector of absorbing states, a subset of

`y`

(numeric, character, or factor matching`y`

). The default is no absorbing states. Observations are truncated when an absorbing state is simulated.- intercepts
vector of initial guesses for the intercepts

- extra
an optional vector of intial guesses for other parameters passed to

`g`

such as regression coefficients for previous states and for general time trends. Name the elements of`extra`

for more informative output.- g
a user-specified function of three or more arguments which in order are

`yprev`

- the value of`y`

at the previous time, the current time`t`

, the`gap`

between the previous time and the current time, an optional (usually named) covariate vector`X`

, and optional arguments such as a regression coefficient value to simulate from. The function needs to allow`yprev`

to be a vector and`yprev`

must not include any absorbing states. The`g`

function returns the linear predictor for the proportional odds model aside from`intercepts`

. The returned value must be a matrix with row names taken from`yprev`

. If the model is a proportional odds model, the returned value must be one column. If it is a partial proportional odds model, the value must have one column for each distinct value of the response variable Y after the first one, with the levels of Y used as optional column names. So columns correspond to`intercepts`

. The different columns are used for`y`

-specific contributions to the linear predictor (aside from`intercepts`

) for a partial or constrained partial proportional odds model. Parameters for partial proportional odds effects may be included in the ... arguments.- target
vector of target state occupancy probabilities at time

`t`

. If`extra`

is specified,`target`

must be a matrix where row names are character versions of`t`

and columns represent occupancy probabilities corresponding to values of`y`

at the time given in the row.- t
target times. Can have more than one element only if

`extra`

is given.- ftarget
an optional function defining constraints that relate to transition probabilities. The function returns a penalty which is a sum of absolute differences in probabilities from target probabilities over possibly multiple targets. The

`ftarget`

function must have two arguments:`intercepts`

and`extra`

.- onlycrit
set to

`TRUE`

to only return the achieved objective criterion and not print anything- constraints
a function of two arguments: the vector of current intercept values and the vector of

`extra`

parameters, returning`TRUE`

if that vector meets the constrains and`FALSE`

otherwise- printsop
set to

`TRUE`

to print solved-for state occupancy probabilities for groups 1 and 2 and log odds ratios corresponding to them- ...
optional arguments to pass to

`stats::nlm()`

. If this is specified, the arguments that`intMarkovOrd`

normally sends to`nlm`

are not used.

Frank Harrell

Given a vector `intercepts`

of initial guesses at the intercepts in a Markov proportional odds model, and a vector `extra`

if there are other parameters, solves for the `intercepts`

and `extra`

vectors that yields a set of occupancy probabilities at time `t`

that equal, as closely as possible, a vector of target values.