LMGroup.column_ttest(self, term, return_data=False, popmean=0, *args, **kwargs)

One-sample t-test on a single model column

term : str

Name of the term to test.

return_data : bool

Return the individual subjects’ coefficients along with test results.

popmean : scalar

Value to compare y against (default is 0).

tail : 0 | 1 | -1

Which tail of the t-distribution to consider: 0: both (two-tailed); 1: upper tail (one-tailed); -1: lower tail (one-tailed).

samples : None | int

Number of samples for permutation cluster test. For None, no clusters are formed. Use 0 to compute clusters without performing any permutations.

pmin : None | scalar (0 < pmin < 1)

Threshold for forming clusters: use a t-value equivalent to an uncorrected p-value.

tmin : None | scalar

Threshold for forming clusters.

tfce : bool

Use threshold-free cluster enhancement (Smith & Nichols, 2009). Default is False.

tstart, tstop : scalar

Restrict time window for permutation cluster test.

mintime : scalar

Minimum duration for clusters (in seconds).

minsource : int

Minimum number of sources per cluster.

result : ttest_1samp

T-test result.

data : Dataset (only with return_data=True)

Dataset with subjects’ coefficients.


Performs a one-sample t-test on coefficient estimates from all subjects to test the hypothesis that the coefficient is different from popmean in the population.