<>$intro<>
We fit a hierarchical (mixed effects) linear regression model.
<>$predictorVariables<>
There are varying intercepts by subject, and the effects of all the predictors <>$includeInteractionInGroup<> are also allowed to vary <>$groupingVariable<> (these are the mixed effects). The full formula^[See the lme4 documentation (especially section 2) for explanation of the formula format.] for this model is:
<>$formula<>
These are the estimates of the effect sizes for the predictors (and interactions) in the model. The dot is the point estimate, the thick line is the 95% confidence interval, and the thin line the 99% confidence interval. One rule of thumb is: if the confidence intervals do not the effect is statistically significant.
mocapGrip:::CoefficientPlot(list(params$data[["<>$dataSet<>"]]$analyses[["<>$analysis<>"]]$bestModel[[1]]$modelObject))
These are predictions from the model for specific conditions. The dots are point estimates, and the lines are 95% confidence intervals.
ggplot(mocapGrip:::pred(params$data[["<>$dataSet<>"]]$analyses[["<>$analysis<>"]]$bestModel[[1]]$modelObject)) + aes(x=<>$plotPredictor1<>, y=<>$plotOutcome<>, ymin=plo, ymax=phi, group=fins, color=fins) + geom_pointrange(position = position_dodge(width=0.5)) + labs(title = "Model predictions for grip", x="stick size (in cm)", y="<>$outcomeVariable<>")
texreg::htmlreg(list(params$data[["<>$dataSet<>"]]$analyses[["<>$analysis<>"]]$bestModel[[1]]$modelObject), method = "boot", # only needed for the overriding of pvalues use.se=TRUE, # only needed for the overriding of pvalues # override.pval = list(lengthLMscaleSum$coefficients[,"Pr(>|t|)"]), float.pos = "p!", single.row=TRUE, caption="Hierarchical linear regression coefficient estimates and standard errors.", use.packas=FALSE, custom.model.names=c("est. (s.e.)"), stars = c(0.001, 0.01, 0.05, 0.1), star.symbol = "\\*" )
There are estimates of the intercept and slope adjustments by subject. and 95% confidence intervals around those estimates. This is also referred to as the mixed effects structure and is one way to see variability between subjects.
mocapGrip:::ggCaterpillar(lme4::ranef(params$data[["<>$dataSet<>"]]$analyses[["<>$analysis<>"]]$bestModel[[1]]$modelObject, condVar = TRUE))
These are the raw data that was used in the model.
ggplot(params$data[["<>$dataSet<>"]]$data) + aes(x=<>$plotPredictor1<>, y=<>$plotOutcome<>, group=fins, color=fins) + geom_point(position = position_dodge(width=0.5), alpha = 0.5) + labs(title = "Raw data for grip", x="stick size (in cm)", y="<>$outcomeVariable<>")
knitr::kable( params$data[["<>$dataSet<>"]]$data %>% dplyr::group_by(stick, fins) %>% dplyr::summarize(n=n(), subjs=length(unique(obsisSubj)), meanPerSubj=n/subjs) ) if("<>$dataSet<>" %in% c("gestMove")){ cat("#### Observations with gripType included *this is temporary* \n The vast majority of the movements are open, with very few being open/closed. Currently, all of these (including the closed grips) are included in the analysis. We probably should exclude the closed, and possibly even the open.closed ones in the future. Thoughts?") knitr::kable( params$data[["<>$dataSet<>"]]$data %>% dplyr::group_by(stick, fins, gripType) %>% dplyr::summarize(n=n(), subjs=length(unique(obsisSubj)), meanPerSubj=n/subjs) ) }
There were r length(params$data[["<>$dataSet<>"]]$warnings)
trials that were excluded because there was occlusion.
if(length(params$data[["<>$dataSet<>"]]$warnings)>0){ # don't print anythign if there are no occlusions. cat(paste0(" \n* ", params$data[["<>$dataSet<>"]]$warnings, collapse = "")) }
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.