fitgrid
demo¶import fitgrid
%matplotlib inline
Here are the defaults we are using (current Kutas Lab defaults):
Single subject demonstration experiment at Kutas Lab.
epochs = fitgrid.epochs_from_hdf(
'../tests/data/sub000wr.epochs.h5',
key='wr',
epoch_id='Epoch_idx',
time='Time',
channels=['MiPf', 'MiCe', 'MiPa', 'MiOc']
)
epochs.table.head()
Time | Index | anchor_code | anchor_str | anchor_tick | anchor_tick_delta | category | congruity | crw_ticks | data_group | ... | RMOc | LLTe | RLTe | LLOc | RLOc | MiOc | A2 | HEOG | rle | rhz | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Epoch_idx | |||||||||||||||||||||
0 | -100 | 0 | 8010 | 8010 | 266277 | 0 | A form of government | incongruent | 266252 | sub000 | ... | 4.425781 | -14.320312 | 0.986816 | -7.949219 | 14.265625 | 8.500000 | 2.953125 | 0.000000 | -5.851562 | 1.457031 |
0 | -96 | 0 | 8010 | 8010 | 266277 | 0 | A form of government | incongruent | 266253 | sub000 | ... | 8.851562 | -15.328125 | -1.233398 | -3.478516 | 16.968750 | 13.359375 | 2.214844 | 2.203125 | -2.437500 | 1.457031 |
0 | -92 | 0 | 8010 | 8010 | 266277 | 0 | A form of government | incongruent | 266254 | sub000 | ... | 12.054688 | -13.312500 | 0.000000 | 0.993652 | 17.703125 | 14.335938 | 1.722656 | 0.979004 | -8.289062 | -3.886719 |
0 | -88 | 0 | 8010 | 8010 | 266277 | 0 | A form of government | incongruent | 266255 | sub000 | ... | 13.531250 | -11.054688 | -2.466797 | 3.974609 | 16.968750 | 17.000000 | -4.921875 | 3.425781 | -10.726562 | -6.800781 |
0 | -84 | 0 | 8010 | 8010 | 266277 | 0 | A form of government | incongruent | 266256 | sub000 | ... | 11.070312 | -11.554688 | -7.648438 | 2.236328 | 12.296875 | 10.687500 | -6.890625 | 3.181641 | -16.578125 | -9.718750 |
5 rows × 69 columns
Negative up is the default, but we use it here to show it's available:
_ = epochs.plot_averages(negative_up=True)
congruity
¶lm_grid = fitgrid.lm(epochs, RHS='congruity')
100%|██████████| 275/275 [00:07<00:00, 39.06it/s]
_ = lm_grid.plot_betas()
_ = lm_grid.plot_adj_rsquared()
pvalues = lm_grid.pvalues
tvalues = lm_grid.tvalues
rsquared_adj = lm_grid.rsquared_adj
import pandas as pd
idx_cols = ['Epoch_idx', 'Time']
lhs_cols = ['MiPf', 'MiCe', 'MiPa', 'MiOc']
rhs_cols = ['idx']
epochs_df = (
pd.read_hdf('../tests/data/sub000wr.epochs.h5', key='wr')
.loc[:, idx_cols + lhs_cols + rhs_cols]
.set_index(['Epoch_idx', 'Time'], append=True)
)
lmer_grid = fitgrid.lmer(
fitgrid.epochs_from_dataframe(
epochs_df,
time='Time',
epoch_id='Epoch_idx',
channels=lhs_cols
),
RHS = "1 + (1 | idx)",
LHS = lhs_cols
)
0%| | 0/275 [00:00<?, ?it/s]/home/turbach/.conda/envs/fitgrid_dev/lib/python3.6/site-packages/rpy2/robjects/pandas2ri.py:191: FutureWarning: from_items is deprecated. Please use DataFrame.from_dict(dict(items), ...) instead. DataFrame.from_dict(OrderedDict(items)) may be used to preserve the key order. res = PandasDataFrame.from_items(items) 100%|██████████| 275/275 [12:53<00:00, 3.00s/it]
Not everything is fully implemented
dir(lmer_grid)
['AIC', '_REML', '_conf_int', '_make_factors', '_permute', '_refit_orthogonal', 'anova', 'coefs', 'factors', 'factors_prev_', 'family', 'fit', 'fits', 'fitted', 'fixef', 'formula', 'grps', 'has_warning', 'logLike', 'marginal_contrasts', 'marginal_estimates', 'plot', 'plot_summary', 'post_hoc', 'predict', 'ranef', 'ranef_corr', 'ranef_var', 'resid', 'save', 'sig_type', 'simulate', 'summary', 'warning', 'warnings']
-2 * lmer_grid.logLike
MiPf | MiCe | MiPa | MiOc | |
---|---|---|---|---|
Time | ||||
-100 | 2536.414850 | 2471.318872 | 2469.826557 | 2285.498253 |
-96 | 2539.852654 | 2490.562126 | 2481.973029 | 2294.373932 |
-92 | 2542.086383 | 2486.489679 | 2481.269585 | 2285.843590 |
-88 | 2542.628112 | 2479.276322 | 2475.453334 | 2286.998378 |
-84 | 2531.346854 | 2460.998662 | 2465.016718 | 2266.686709 |
-80 | 2524.749147 | 2472.198967 | 2473.422182 | 2254.244120 |
-76 | 2532.009640 | 2474.156040 | 2473.684965 | 2240.643752 |
-72 | 2549.482992 | 2472.199660 | 2469.366029 | 2246.913954 |
-68 | 2549.214462 | 2460.209850 | 2458.206462 | 2250.552701 |
-64 | 2554.470058 | 2458.987153 | 2458.866832 | 2240.703608 |
-60 | 2556.969629 | 2456.677262 | 2448.758841 | 2221.267228 |
-56 | 2554.434316 | 2474.483558 | 2458.934383 | 2235.118548 |
-52 | 2543.685004 | 2469.242017 | 2462.654435 | 2241.316885 |
-48 | 2536.253918 | 2477.552316 | 2481.554752 | 2249.123782 |
-44 | 2536.222222 | 2486.338486 | 2495.176354 | 2230.171872 |
-40 | 2536.315499 | 2499.414149 | 2516.872396 | 2238.223030 |
-36 | 2537.210200 | 2504.234695 | 2526.532808 | 2240.483478 |
-32 | 2537.875358 | 2506.703958 | 2531.595899 | 2245.061478 |
-28 | 2530.303268 | 2496.667580 | 2526.260019 | 2264.713777 |
-24 | 2528.653774 | 2496.534509 | 2523.323142 | 2276.595390 |
-20 | 2529.100913 | 2485.566203 | 2521.022087 | 2289.158158 |
-16 | 2530.473846 | 2476.441590 | 2506.979486 | 2283.984950 |
-12 | 2527.720197 | 2467.971377 | 2484.465669 | 2258.797759 |
-8 | 2515.972864 | 2480.392955 | 2485.985355 | 2250.478571 |
-4 | 2523.518110 | 2484.515686 | 2481.006168 | 2229.989673 |
0 | 2538.727686 | 2494.945495 | 2484.337428 | 2239.945569 |
4 | 2534.771950 | 2510.484814 | 2492.783388 | 2258.944257 |
8 | 2546.111911 | 2500.485340 | 2488.017599 | 2271.787010 |
12 | 2550.690653 | 2488.991720 | 2486.117538 | 2274.925886 |
16 | 2543.389697 | 2486.055704 | 2488.453827 | 2263.069385 |
... | ... | ... | ... | ... |
880 | 2517.846383 | 2403.181882 | 2447.276151 | 2248.253600 |
884 | 2535.750830 | 2410.844593 | 2450.527111 | 2255.348989 |
888 | 2533.884037 | 2425.543655 | 2458.675508 | 2254.485554 |
892 | 2526.527449 | 2437.178605 | 2464.503458 | 2248.012236 |
896 | 2552.438056 | 2435.103238 | 2461.054614 | 2239.628184 |
900 | 2556.478602 | 2420.253754 | 2453.012100 | 2228.564278 |
904 | 2534.724027 | 2408.584392 | 2443.483352 | 2209.766480 |
908 | 2543.055166 | 2414.927204 | 2446.123354 | 2203.481706 |
912 | 2545.604313 | 2410.346496 | 2443.716828 | 2203.329853 |
916 | 2536.789144 | 2411.967670 | 2450.242480 | 2211.808399 |
920 | 2541.636956 | 2427.690872 | 2464.623103 | 2222.220016 |
924 | 2560.611208 | 2436.449489 | 2472.257338 | 2228.883399 |
928 | 2563.594246 | 2437.350948 | 2470.231984 | 2222.292629 |
932 | 2567.070032 | 2435.337904 | 2470.884678 | 2242.506803 |
936 | 2571.547147 | 2447.602152 | 2478.189896 | 2231.134497 |
940 | 2572.111979 | 2453.227529 | 2484.131288 | 2224.748161 |
944 | 2573.203404 | 2451.165212 | 2488.655858 | 2242.621784 |
948 | 2590.727317 | 2458.228997 | 2484.379736 | 2231.826230 |
952 | 2587.906072 | 2460.759792 | 2478.945819 | 2241.167883 |
956 | 2597.043173 | 2460.961044 | 2466.770287 | 2234.688529 |
960 | 2615.239258 | 2441.704982 | 2444.737471 | 2235.521236 |
964 | 2620.221893 | 2431.478995 | 2428.125535 | 2234.216418 |
968 | 2630.505477 | 2429.572114 | 2431.646229 | 2247.884297 |
972 | 2646.505229 | 2440.920157 | 2443.326897 | 2259.324855 |
976 | 2654.311373 | 2435.031485 | 2439.431511 | 2248.494336 |
980 | 2664.041351 | 2412.005329 | 2425.111451 | 2252.669174 |
984 | 2667.612947 | 2420.527837 | 2435.689871 | 2257.807937 |
988 | 2668.339163 | 2433.416601 | 2450.711620 | 2254.912652 |
992 | 2678.802395 | 2435.482588 | 2453.392558 | 2238.072566 |
996 | 2682.515821 | 2408.015801 | 2442.154126 | 2221.841839 |
275 rows × 4 columns
lmer_grid.coefs
MiPf | MiCe | MiPa | MiOc | |||
---|---|---|---|---|---|---|
Time | ||||||
-100 | (Intercept) | Estimate | -14.9653 | -6.06596 | -6.07264 | -3.06109 |
2.5_ci | -17.3188 | -8.11644 | -8.11779 | -4.5445 | ||
97.5_ci | -12.6118 | -4.01548 | -4.02748 | -1.57768 | ||
SE | 1.20078 | 1.04618 | 1.04347 | 0.756858 | ||
DF | 143 | 287 | 287 | 287 | ||
T-stat | -12.463 | -5.79818 | -5.81967 | -4.04447 | ||
P-val | 1.34475e-24 | 1.76581e-08 | 1.57424e-08 | 6.74511e-05 | ||
Sig | *** | *** | *** | *** | ||
-96 | (Intercept) | Estimate | -14.3767 | -5.6365 | -5.80687 | -2.7537 |
2.5_ci | -16.7464 | -7.75689 | -7.89577 | -4.26023 | ||
97.5_ci | -12.0071 | -3.51611 | -3.71797 | -1.24717 | ||
SE | 1.20904 | 1.08185 | 1.06578 | 0.768652 | ||
DF | 143 | 287 | 287 | 287 | ||
T-stat | -11.891 | -5.21005 | -5.44845 | -3.5825 | ||
P-val | 4.20049e-23 | 3.61114e-07 | 1.09411e-07 | 0.000399643 | ||
Sig | *** | *** | *** | *** | ||
-92 | (Intercept) | Estimate | -15.3229 | -6.48833 | -6.40865 | -3.24399 |
2.5_ci | -17.7096 | -8.59372 | -8.49499 | -4.73268 | ||
97.5_ci | -12.9362 | -4.38293 | -4.32231 | -1.75531 | ||
SE | 1.21773 | 1.0742 | 1.06448 | 0.759547 | ||
DF | 143 | 287 | 287 | 143 | ||
T-stat | -12.5832 | -6.04013 | -6.02046 | -4.27096 | ||
P-val | 6.52946e-25 | 4.76127e-09 | 5.30431e-09 | 3.53008e-05 | ||
Sig | *** | *** | *** | *** | ||
-88 | (Intercept) | Estimate | -15.75 | -6.8184 | -6.56754 | -3.21188 |
2.5_ci | -18.1574 | -8.8975 | -8.63284 | -4.70833 | ||
97.5_ci | -13.3426 | -4.73929 | -4.50223 | -1.71542 | ||
SE | 1.22829 | 1.06079 | 1.05375 | 0.76351 | ||
DF | 143 | 287 | 287 | 143 | ||
T-stat | -12.8227 | -6.42767 | -6.23256 | -4.20672 | ||
... | ... | ... | ... | ... | ... | ... |
984 | (Intercept) | 97.5_ci | -5.98327 | 1.70622 | 2.95792 | -0.288717 |
SE | 1.47274 | 0.957589 | 0.999627 | 0.765856 | ||
DF | 287 | 287 | 143 | 143 | ||
T-stat | -6.02265 | -0.178174 | 0.999057 | -2.33695 | ||
P-val | 5.24114e-09 | 0.858712 | 0.319455 | 0.0208297 | ||
Sig | *** | * | ||||
988 | (Intercept) | Estimate | -8.39931 | 0.223914 | 1.09357 | -1.2248 |
2.5_ci | -11.3176 | -1.72816 | -0.949679 | -2.7002 | ||
97.5_ci | -5.481 | 2.17599 | 3.13681 | 0.250597 | ||
SE | 1.48896 | 0.995976 | 1.04249 | 0.752767 | ||
DF | 143 | 143 | 143 | 143 | ||
T-stat | -5.64106 | 0.224819 | 1.04899 | -1.62706 | ||
P-val | 8.74728e-08 | 0.822441 | 0.295951 | 0.105925 | ||
Sig | *** | |||||
992 | (Intercept) | Estimate | -8.86458 | -0.572662 | 0.324593 | -1.58488 |
2.5_ci | -11.8889 | -2.5473 | -1.71941 | -3.01462 | ||
97.5_ci | -5.84028 | 1.40197 | 2.36859 | -0.15514 | ||
SE | 1.54304 | 1.00748 | 1.04288 | 0.729473 | ||
DF | 143 | 143 | 143 | 143 | ||
T-stat | -5.74489 | -0.568408 | 0.311248 | -2.17264 | ||
P-val | 5.32797e-08 | 0.570649 | 0.756065 | 0.031455 | ||
Sig | *** | * | ||||
996 | (Intercept) | Estimate | -8.69271 | -0.334893 | 0.548476 | -1.12444 |
2.5_ci | -11.7557 | -2.1918 | -1.42485 | -2.52832 | ||
97.5_ci | -5.62972 | 1.52201 | 2.52181 | 0.279437 | ||
SE | 1.56278 | 0.947417 | 1.00682 | 0.716277 | ||
DF | 143 | 143 | 143 | 143 | ||
T-stat | -5.56235 | -0.35348 | 0.544761 | -1.56984 | ||
P-val | 1.26932e-07 | 0.724249 | 0.586766 | 0.118662 | ||
Sig | *** |
2200 rows × 4 columns
lmer_grid.coefs
MiPf | MiCe | MiPa | MiOc | |||
---|---|---|---|---|---|---|
Time | ||||||
-100 | (Intercept) | Estimate | -14.9653 | -6.06596 | -6.07264 | -3.06109 |
2.5_ci | -17.3188 | -8.11644 | -8.11779 | -4.5445 | ||
97.5_ci | -12.6118 | -4.01548 | -4.02748 | -1.57768 | ||
SE | 1.20078 | 1.04618 | 1.04347 | 0.756858 | ||
DF | 143 | 287 | 287 | 287 | ||
T-stat | -12.463 | -5.79818 | -5.81967 | -4.04447 | ||
P-val | 1.34475e-24 | 1.76581e-08 | 1.57424e-08 | 6.74511e-05 | ||
Sig | *** | *** | *** | *** | ||
-96 | (Intercept) | Estimate | -14.3767 | -5.6365 | -5.80687 | -2.7537 |
2.5_ci | -16.7464 | -7.75689 | -7.89577 | -4.26023 | ||
97.5_ci | -12.0071 | -3.51611 | -3.71797 | -1.24717 | ||
SE | 1.20904 | 1.08185 | 1.06578 | 0.768652 | ||
DF | 143 | 287 | 287 | 287 | ||
T-stat | -11.891 | -5.21005 | -5.44845 | -3.5825 | ||
P-val | 4.20049e-23 | 3.61114e-07 | 1.09411e-07 | 0.000399643 | ||
Sig | *** | *** | *** | *** | ||
-92 | (Intercept) | Estimate | -15.3229 | -6.48833 | -6.40865 | -3.24399 |
2.5_ci | -17.7096 | -8.59372 | -8.49499 | -4.73268 | ||
97.5_ci | -12.9362 | -4.38293 | -4.32231 | -1.75531 | ||
SE | 1.21773 | 1.0742 | 1.06448 | 0.759547 | ||
DF | 143 | 287 | 287 | 143 | ||
T-stat | -12.5832 | -6.04013 | -6.02046 | -4.27096 | ||
P-val | 6.52946e-25 | 4.76127e-09 | 5.30431e-09 | 3.53008e-05 | ||
Sig | *** | *** | *** | *** | ||
-88 | (Intercept) | Estimate | -15.75 | -6.8184 | -6.56754 | -3.21188 |
2.5_ci | -18.1574 | -8.8975 | -8.63284 | -4.70833 | ||
97.5_ci | -13.3426 | -4.73929 | -4.50223 | -1.71542 | ||
SE | 1.22829 | 1.06079 | 1.05375 | 0.76351 | ||
DF | 143 | 287 | 287 | 143 | ||
T-stat | -12.8227 | -6.42767 | -6.23256 | -4.20672 | ||
... | ... | ... | ... | ... | ... | ... |
984 | (Intercept) | 97.5_ci | -5.98327 | 1.70622 | 2.95792 | -0.288717 |
SE | 1.47274 | 0.957589 | 0.999627 | 0.765856 | ||
DF | 287 | 287 | 143 | 143 | ||
T-stat | -6.02265 | -0.178174 | 0.999057 | -2.33695 | ||
P-val | 5.24114e-09 | 0.858712 | 0.319455 | 0.0208297 | ||
Sig | *** | * | ||||
988 | (Intercept) | Estimate | -8.39931 | 0.223914 | 1.09357 | -1.2248 |
2.5_ci | -11.3176 | -1.72816 | -0.949679 | -2.7002 | ||
97.5_ci | -5.481 | 2.17599 | 3.13681 | 0.250597 | ||
SE | 1.48896 | 0.995976 | 1.04249 | 0.752767 | ||
DF | 143 | 143 | 143 | 143 | ||
T-stat | -5.64106 | 0.224819 | 1.04899 | -1.62706 | ||
P-val | 8.74728e-08 | 0.822441 | 0.295951 | 0.105925 | ||
Sig | *** | |||||
992 | (Intercept) | Estimate | -8.86458 | -0.572662 | 0.324593 | -1.58488 |
2.5_ci | -11.8889 | -2.5473 | -1.71941 | -3.01462 | ||
97.5_ci | -5.84028 | 1.40197 | 2.36859 | -0.15514 | ||
SE | 1.54304 | 1.00748 | 1.04288 | 0.729473 | ||
DF | 143 | 143 | 143 | 143 | ||
T-stat | -5.74489 | -0.568408 | 0.311248 | -2.17264 | ||
P-val | 5.32797e-08 | 0.570649 | 0.756065 | 0.031455 | ||
Sig | *** | * | ||||
996 | (Intercept) | Estimate | -8.69271 | -0.334893 | 0.548476 | -1.12444 |
2.5_ci | -11.7557 | -2.1918 | -1.42485 | -2.52832 | ||
97.5_ci | -5.62972 | 1.52201 | 2.52181 | 0.279437 | ||
SE | 1.56278 | 0.947417 | 1.00682 | 0.716277 | ||
DF | 143 | 143 | 143 | 143 | ||
T-stat | -5.56235 | -0.35348 | 0.544761 | -1.56984 | ||
P-val | 1.26932e-07 | 0.724249 | 0.586766 | 0.118662 | ||
Sig | *** |
2200 rows × 4 columns