def mixedModelTrace(dm, model, winSize=1, effectIndex=1, **traceParams):
"""
desc:
Perform a mixed model over a single trace. The dependent variable is
specifed through the signal and phase keywords.
arguments:
dm:
desc: A DataMatrix.
type: DataMatrix
model:
desc: An lmer-style model. This needs to be only the fixed and
random effects part of the model, so everything after
the `~` sign. For example `cond + (1|subject_nr)`.
type: str
keywords:
winSize:
desc: Indicates the number of samples that should be skipped
each time. For a real analysis, this should be 1, but
for a quick look, it can be increased (default=1)
type: int
keyword-dict:
*traceParams: See getTrace().
returns: |
A DataMatrix with nr-of-effects*nr-of-samples rows and the following
columns:
- `i`: sample number
- `effect`: name of effect
- `est`: estimated effect (slope/ intercept)
- `se`: standard error of effect
- `t`: t-value of effect
"""
if not model.startswith('mmdv__ ~ '):
model = 'mmdv__ ~ ' + model
global R
try:
R
except:
R = RBridge()
traceLen = traceParams['traceLen']
l = [ ['i', 'effect', 'est', 'se', 't'] ]
for i in range(0, traceLen, winSize):
# First calculate the mean value for the current signal slice for each
# trial and save that in a copy of the DataMatrix
_dm = dm.addField('mmdv__', dtype=float)
for trialId in range(len(_dm)):
aTrace = getTrace(_dm[trialId], **traceParams)
if i < len(aTrace):
sliceMean = aTrace[i:i+winSize].mean()
else:
sliceMean = np.nan
_dm['mmdv__'][trialId] = sliceMean
# Do mixed effects
R.load(_dm)
_dm = R.lmer(model)
_dm._print(sign=4, title='%d - %d' % (i, i+winSize))
for k in range(winSize):
if i+k >= traceLen:
break
for j in _dm.range():
l.append([i+k, _dm['effect'][j], _dm['est'][j], _dm['se'][j],
_dm['t'][j]])
return DataMatrix(l)
def lme(dm, dv='iRt'):
"""
Runs the overall LME.
Arguments:
dm -- A DataMatrix.
Keyword arguments:
dv -- The dependent variable. (default='iRt')
"""
assert(dv in ['iRt', 'correct'])
if dv == 'iRt':
dm = dm.select('correct == 1')
from exparser.RBridge import RBridge
R = RBridge()
R.load(dm)
# Run all lmers
lm = R.lmer('%s ~ postRotDelay*valid*cond + (1|subject_nr)' % dv, \
lmerVar='lmerFull')
lm._print(sign=5)
lm.save('output/lme.%s.full.csv' % dv)
lm = R.lmer('%s ~ postRotDelay*valid + (1|subject_nr)' % dv, \
lmerVar='lmerNoCond')
lm._print(sign=5)
lm.save('output/lme.%s.noCond.csv' % dv)
lm = R.lmer('%s ~ valid*cond + (1|subject_nr)' % dv, \
lmerVar='lmerNoDelay')
lm._print(sign=5)
lm.save('output/lme.%s.noDelay.csv' % dv)
lm = R.lmer('%s ~ postRotDelay*cond + (1|subject_nr)' % dv, \
lmerVar='lmerNoValid')
lm._print(sign=5)
lm.save('output/lme.%s.noValid.csv' % dv)
# Do the model comparisons
am = R.anova('lmerNoCond', 'lmerFull')
am._print(sign=5)
am.save('output/anova.%s.noCond.csv' % dv)
am = R.anova('lmerNoDelay', 'lmerFull')
am._print(sign=5)
am.save('output/anova.%s.noDelay.csv' % dv)
am = R.anova('lmerNoValid', 'lmerFull')
am._print(sign=5)
am.save('output/anova.%s.noValid.csv' % dv)
R.load(dm.select('postRotDelay == 0'))
lm = R.lmer('%s ~ valid + (1|subject_nr)' % dv)
lm._print(sign=5)
lm.save('output/lme.%s.0.noCond.csv' % dv)
R.load(dm.select('postRotDelay == 1000'))
lm = R.lmer('%s ~ valid + (1|subject_nr)' % dv)
lm._print(sign=5)
lm.save('output/lme.%s.1000.noCond.csv' % dv)
def lmePlot(dm, dv='iRt'):
"""
Plots the graph for the overall LME.
Arguments:
dm -- A DataMatrix.
Keyword arguments:
dv -- The dependent variable. (default='iRt')
"""
assert(dv in ['iRt', 'correct'])
if dv == 'iRt':
dm = dm.select('correct == 1')
from exparser.RBridge import RBridge
R = RBridge()
# Now plot!
if dv == 'iRt':
fig = plt.figure(figsize=(5,3))
else:
fig = plt.figure(figsize=(5,1.5))
plt.subplots_adjust(wspace=0, bottom=.15)
i = 1
for postRotDelay in (0, 1000):
_dm = dm.select('postRotDelay == %s' % postRotDelay)
_dmObj = _dm.select('cond == "object-based"')
_dmSpa = _dm.select('cond == "spatial"')
_dmVal = _dm.select('valid == "{0}valid"')
_dmInv = _dm.select('valid == "{1}invalid"')
_dmObjVal = _dmObj.select('valid == "{0}valid"')
_dmObjInv = _dmObj.select('valid == "{1}invalid"')
_dmSpaVal = _dmSpa.select('valid == "{0}valid"')
_dmSpaInv = _dmSpa.select('valid == "{1}invalid"')
R.load(_dm)
__dm = R.lmer('%s ~ valid + (1|subject_nr)' % dv)
__dm._print(sign=5)
if dv == 'iRt':
mVal = 1 / _dmVal[dv].mean()
mInv = 1 / _dmInv[dv].mean()
mObjVal = 1 / _dmObjVal[dv].mean()
mObjInv = 1 / _dmObjInv[dv].mean()
mSpaVal = 1 / _dmSpaVal[dv].mean()
mSpaInv = 1 / _dmSpaInv[dv].mean()
# Get the errorbars!
lo = ((__dm['est'][0]+__dm['ci95lo'][1])**-1 - \
(__dm['est'][0]+__dm['est'][1])**-1) / 2
up = ((__dm['est'][0]+__dm['ci95up'][1])**-1 - \
(__dm['est'][0]+__dm['est'][1])**-1) / 2
elif dv == 'correct':
mVal = 100. - 100.*_dmVal[dv].mean()
mInv = 100. - 100.*_dmInv[dv].mean()
mObjVal = 100. - 100.*_dmObjVal[dv].mean()
mObjInv = 100. - 100.*_dmObjInv[dv].mean()
mSpaVal = 100. - 100.*_dmSpaVal[dv].mean()
mSpaInv = 100. - 100.*_dmSpaInv[dv].mean()
# Get the errorbars!
lo = ((__dm['est'][0]+__dm['ci95lo'][1]) - \
(__dm['est'][0]+__dm['est'][1])) / 2
up = ((__dm['est'][0]+__dm['ci95up'][1]) - \
(__dm['est'][0]+__dm['est'][1])) / 2
lo *= 100.
up *= 100.
eVal = [lo, up]
eInv = [lo, up]
plt.subplot(1,2,i)
plt.errorbar([0,1], [mVal, mInv], yerr=[eVal, eInv], fmt='o-', \
label='Preferred model', color='black')
plt.plot([0,1], [mObjVal, mObjInv], '--', label='Object-centered', \
color='black')
plt.plot([0,1], [mSpaVal, mSpaInv], ':', label='Retinotopic', \
color='black')
plt.xlim(-.2, 1.2)
plt.xticks([0,1], ['Valid', 'Invalid'])
if dv == 'correct':
plt.ylim(9, 15)
plt.yticks([10,12,14])
else:
plt.ylim(595, 665)
plt.xlabel('Cue Validity')
if i == 2:
plt.title('Long SOA')
plt.gca().yaxis.set_ticklabels([])
if dv != 'correct':
plt.legend(frameon=False)
else:
plt.title('Short SOA')
if dv == 'correct':
plt.ylabel('Error rate (%)')
else:
plt.ylabel('Response time (ms)')
i += 1
plt.savefig('plot/lme.%s.png' % dv)
plt.savefig('plot/lme.%s.svg' % dv)
plt.show()
Plots landing position as a function of sacc latency (and other predictors, if
wanted), using LMM.
"""
from exparser.CsvReader import CsvReader
from exparser.RBridge import RBridge
src = "/home/lotje/Documents/PhD Marseille/Studies/004 - Single-object experiment - Handle-orientation effect/analysis 004/selected_dm_004B_WITH_drift_corr_onlyControl_True.csv"
dm = CsvReader(src).dataMatrix()
dm = dm.select("endX1NormToHandle == ''")
dvRaw = "endX1Norm"
dvNorm = "endX1NormToHandle"
nsim = 10
R = RBridge()
R.load(dm)
print "Condition in DV"
# Run a full LME
lmerDm = R.lmer(\
'%s ~ (1|file) + (1|object)'\
% (dvNorm), nsim=nsim, printLmer=True)
lmerDm._print(sign=5)
lmerDm.save('lme_condition_in_dv.csv')
print "Condition as factor"
lmerDm = R.lmer(\
'%s ~ handle_side + (1|file) + (1|object)'\
% (dvRaw), nsim=nsim, printLmer=True)
lmerDm._print(sign=5)
lmerDm.save('lme_condition_as_factor.csv')
def lmeBehaviorBrightness(dm):
"""
Analyzes the behavioral data with mixed effects, separately for the bright
and dark cue.
Arguments:
dm -- A DataMatrix.
"""
global R
try:
R
except:
R = RBridge()
dm = dm.addField('targetLum', dtype=str, default='bright')
dm['targetLum'][(dm['cueLum'] == "dark") \
& (dm['cueValidity'] == "valid")] = 'dark'
dm['targetLum'][(dm['cueLum'] == "bright") \
& (dm['cueValidity'] == "invalid")] = 'dark'
print '*** Target luminance\n'
R.load(dm)
lm = R.lmer('irt ~ targetLum * cueValidity * soa + (1|subject_nr)')
lm._print('iRt')
lm = R.lmer('correct ~ targetLum * cueValidity * soa + (1|subject_nr)')
lm._print('Accuracy')
for soa in dm.unique('soa'):
R.load(dm.select('soa == %d' % soa, verbose=False))
lm = R.lmer('irt ~ targetLum * cueValidity + (1|subject_nr)')
lm._print('iRt (%d)' % soa)
lm = R.lmer('correct ~ targetLum * cueValidity + (1|subject_nr)')
lm._print('Accuracy (%d)' % soa)
print '*** Cue luminance\n'
R.load(dm)
lm = R.lmer('irt ~ cueLum * cueValidity * soa + (1|subject_nr)')
lm._print('iRt')
lm = R.lmer('correct ~ cueLum * cueValidity * soa + (1|subject_nr)')
lm._print('Accuracy')
for soa in dm.unique('soa'):
R.load(dm.select('soa == %d' % soa, verbose=False))
lm = R.lmer('irt ~ cueLum * cueValidity + (1|subject_nr)')
lm._print('iRt (%d)' % soa)
lm = R.lmer('correct ~ cueLum * cueValidity + (1|subject_nr)')
lm._print('Accuracy (%d)' % soa)
def lmeBehavior(dm, suffix=''):
"""
Analyzes the behavioral data with mixed effects.
Arguments:
dm -- A DataMatrix.
keywords:
suffix -- A filename suffix.
"""
global R
try:
R
except:
R = RBridge()
i = 1
Plot.new(size=Plot.ws)
for dv in ['correct', 'irt']:
# Also analyze the grouped 945 and 2445 SOAs, which gives more power
# than analyzing them separately.
R.load(dm.select('soa != 45'))
lm = R.lmer('%s ~ cueValidity * soa + (1|subject_nr)' % dv)
lm.save('output/lmeBehavior.longInteract.%s.csv' % dv)
lm._print(title='Long: %s' % dv, sign=10)
lm = R.lmer('%s ~ cueValidity + (1|subject_nr)' % dv)
lm.save('output/lmeBehavior.longNoInteract.%s.csv' % dv)
lm._print(title='Long: %s' % dv, sign=10)
# Loop over SOAs
lSoa = []
lVal = []
lInv = []
for soa in [45, 945, 2445]:
_dm = dm.select('soa == %d' % soa, verbose=False)
R.load(_dm)
lm = R.lmer('%s ~ cueValidity + (1|subject_nr)' % dv)
lm.save('output/lmeBehavior.%s.%s.csv' % (dv, soa))
lm._print(title='%s (%s)' % (dv, soa), sign=10)
mInv = lm['est'][0] # Invalid is reference
mVal = mInv + lm['est'][1] #4Add slope for validity effect
d = lm['est'][1]
m = mInv + .5*d
minD = d - lm['se'][1]
maxD = d + lm['se'][1]
# Determine error bars based on the slope CIs
cInv = [m-minD/2, m-maxD/2]
cVal = [m+minD/2, m+maxD/2]
if dv == 'irt':
mVal = 1./mVal
mInv = 1./mInv
cInv = [1./cInv[0], 1./cInv[1]]
cVal = [1./cVal[0], 1./cVal[1]]
else:
mVal = 100.*mVal
mInv = 100.*mInv
cInv = [100.*cInv[0], 100.*cInv[1]]
cVal = [100.*cVal[0], 100.*cVal[1]]
# We plot the errorbars separately, because it's a bit easier than
# passing them onto `plt.errorbar()`.
plt.plot([soa+55, soa+55], cVal, '-', color=valColor)
plt.plot([soa+55, soa+55], cInv, '-', color=invColor)
lSoa.append(soa+55)
lVal.append(mVal)
lInv.append(mInv)
plt.xlim(-100, 2750)
plt.xticks([100, 1000, 2500])
plt.xlabel('Cue-target interval (ms)')
if dv == 'irt':
plt.ylabel('Respone time (ms)')
plt.ylim(420, 630)
plt.yticks([450, 500, 550, 600])
else:
plt.ylabel('Accuracy (%)')
plt.ylim(80, 95)
plt.yticks([82.5, 85, 87.5, 90, 92.5])
i += 1
nVal = len(dm.select('cueValidity == "valid"', verbose=False))
nInv = len(dm.select('cueValidity == "invalid"', verbose=False))
plt.plot(lSoa, lVal, 'o-', color=valColor, label='Valid (N=%d)' % nVal)
plt.plot(lSoa, lInv, 'o-', color=invColor, label='Invalid (N=%d)' % \
nInv)
plt.legend(frameon=False)
Plot.save('behavior%s' % suffix, 'behavior', show=show)