All functions
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ClusterLhnData()
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A function for fitting Shahar's LHN data to the linear-nonlinear-poisson
model. |
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DetectVariableBaselineUsingBayesianModelSelection()
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A function that uses model selection to detect variability in the
baseline firing rate of a cell. The algorithm assumes Poisson
spiking in the baseline period and compares the posterior on a
single rate explaining the data to having two rates. |
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EstimateResponseDelayByMomentMatching()
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Estimates the response delay by cross correlating the observed response with the odor profile convolved with an exponential filter. |
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addnacols()
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Reorder odour response matrix adding nas as necessary |
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anyid2shortid() anyid2stack() anyid2longid()
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Convert any identifier / file path to Shahar's short id or stack id |
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baseline_subtract_allfreqs()
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Subtract baseline spike rate from list of smoothed psth data |
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basesubtract_heatmap_cor_dist()
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An attaempt to create a basesubtraction option first step at calculating lifetime sparsness |
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common_odours_for_cells()
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Find the common set of odours for which all cells have trials |
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createSummarySpikesMat() createSummarySpikesArray()
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Convert raw spike summary array into matrix or array without cells/odours missing data |
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create_raw_summary_array() create_simple_summary_arrays()
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Create the raw summary array for all spikes |
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heatmap_anatomy()
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heatmap for set of cells on nblast anatomy distance |
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heatmap_cor_dist() spike_cor_dist()
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heatmap for set of cells and odours based on correlation distance |
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jet.colors()
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Return a colour palette function |
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physplit.analysis
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Packaged and versioned analysis functions for Shahar's cells |
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plotcellsf()
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Plot spike/Vm trace for a cell using defaults appropriate for Frechter et al |
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poissonTestOdoursSF()
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Carry out Poisson test on absolute number of spikes in odour response |
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prop.ci()
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Approximate (1-alpha)100% confidence interval for proportion of a population |
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required.sample.size()
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Estimate sample size to find population proportion with given tolerance |
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sample_finite_population()
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Sample from finite population with known number of true positives |
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truepos_given_sample() summary(<truepos>)
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Estimate distribution of true positives given sampling resuts |