with Standard Standard deviation of weighted sd(SEL of pings for 1-min sdsel.1hrw deviation of all SEL_dB values that fall within 60 minutes segments SEL_dB that occurred within before respective 1-min with 0 or (weighted) the last hour segment) using only 1 (weighted by time lag) sqrt(Hmisc:wtd.var()). ping in Same lag-based weighting as previous applied to individual SEL 60 values for calculating minutes where 1-min we use cumsel.1hrw segments with greater lag contribute less than 1-min with segments with smaller lags. Standard Standard deviation of sdsprop.1hr deviation of over 60 minutes over 1 hour 5. Methods for generalized estimating equations Modeling approach for GEEs The relationship between the coefficients and the response (presence of calls or clicks) is modeled using a logit-link function that can be expressed as , where p is the probability of presence, 1-p is the probability of absence, and represent the β intercept and coefficients associated with the k covariates x. We fitted GEEs using the function of the package in R (). We added smoothing geeglm geepack terms of 3 to 5 degrees of freedom using the function of the package. We used the bs splines default correlation structure where correlated observations were grouped using a ‘independence’ block identifier (argument from the function). Each block consisted of consecutive id geeglm observations of 1-min segments. The size of the blocks was determined using the function acf from the package. This function estimates the autocorrelation between consecutive residuals stats for various lags. We chose sizes of 20 and 41 for the blue whale and beaked whale models, respectively. Model selection for GEEs Our methods for selecting the final model included three main steps: 1) elimination of collinear covariates; 2) stepwise backwards selection based on marginal and 3) stepwise p-values; backwards selection by inspecting 95% confidence intervals around partial fit plots.
with Standard deviation of SEL_dB (weighted) sdsel.1hrw Standard deviation of all SEL_dB values that occurred within the last hour (weighted by time lag) weighted sd(SEL of pings that fall within 60 minutes before respective 1-min segment) using sqrt(Hmisc:wtd.var()). Same lag-based weighting as applied to individual SEL values for calculating cumsel.1hrw where 1-min segments with greater lag contribute less than 1-min segments with smaller lags. for 1-min segments with 0 or only 1 ping in previous 60 minutes we use with Standard deviation of over 1 hour sdsprop.1hr Standard deviation of over 60 minutes 5. Methods for generalized estimating equations Modeling approach for GEEs The relationship between the coefficients and the response (presence of calls or clicks) is modeled using a logit-link function that can be expressed as , where p is the probability of presence, 1-p is the probability of absence, and β represent the intercept and coefficients associated with the k covariates x. We fitted GEEs using the geeglm function of the geepack package in R (). We added smoothing terms of 3 to 5 degrees of freedom using the bs function of the splines package. We used the default correlation structure ‘independence’ where correlated observations were grouped using a block identifier (argument id from the geeglm function). Each block consisted of consecutive observations of 1-min segments. The size of the blocks was determined using the acf function from the stats package. This function estimates the autocorrelation between consecutive residuals for various lags. We chose sizes of 20 and 41 for the blue whale and beaked whale models, respectively. Model selection for GEEs Our methods for selecting the final model included three main steps: 1) elimination of collinear covariates; 2) stepwise backwards selection based on marginal p-values; and 3) stepwise backwards selection by inspecting 95% confidence intervals around partial fit plots.
BaumannPickeringMPLTM620-2018.docx