# Random slopes

#### James Thorson

## Random slopes models

`dsem` can be specified to estimate variation over time in a slope parameter that measures the impact of one variable on another.

To show this, we predict sea surface temperature from Departure Bay based upon the Pacific Decadal Oscillation:

    library(dsem)

    # Load data
    data(pdo_departure_bay)

    # Format
    tsdata = ts(data.frame(
      Temp = pdo_departure_bay[,2],
      PDO = pdo_departure_bay[,3],
      slope = NA
    ), start = 1914 )

We first fit these data using a stationary slope parameter:

    # Model
    sem = "
      PDO -> Temp, 0, slope
      PDO -> PDO, 1, ar_PDO
      Temp -> Temp, 1, ar_Temp
    "

    # Fit
    fit0 = dsem(
      tsdata = tsdata[,1:2],
      sem = sem,
      estimate_mu = colnames(tsdata)[1:2],
      estimate_delta0 = FALSE,
      control = dsem_control(
        quiet = TRUE,
        use_REML = FALSE
      )
    )

We then re-fit while estimating the slope parameter as a model variable that follows a first-order autoregressive process:

    # Model
    sem = "
      PDO -> Temp, 0, slope
      slope -> slope, 1, ar_slope
      PDO -> PDO, 1, ar_PDO
      Temp -> Temp, 1, ar_Temp
    "

    # Fit
    fit = dsem(
      tsdata = tsdata,
      sem = sem,
      estimate_mu = colnames(tsdata),
      estimate_delta0 = FALSE,
      control = dsem_control(
        quiet = TRUE,
        use_REML = FALSE,
        gmrf_parameterization = "full"
      )
    )

We then plot the predicted state-variables:

    # get estimates and SEs for first model
    df = expand.grid( year = time(tsdata), var = colnames(tsdata))
    df$est = as.vector(as.list(fit$sdrep, what = "Estimate", report = TRUE)$z_tj)
    df$se = as.vector(as.list(fit$sdrep, what = "Std. Error", report = TRUE)$z_tj)
    df$obs = as.vector(tsdata)

    # get estimates and SEs for second model
    df0 = expand.grid( year = time(tsdata), var = "constant_slope" )
    df0$est =  subset( summary(fit0), path == "PDO -> Temp" )$Estimate
    df0$se =  subset( summary(fit0), path == "PDO -> Temp" )$Std_Error
    df0$obs = NA

    # Combine
    df = rbind( df, df0 )
    df$var = factor( df$var, levels = c("PDO","Temp","slope","constant_slope") )

    # Plot
    library(ggplot2)
    ggplot(df) +
      geom_line( aes( x=year, y = est) ) +
      geom_point( aes( x=year, y = obs) ) +
      geom_ribbon( aes( x = year, ymin = est - 1.96*se, ymax = est + 1.96*se), alpha = 0.4 ) +
      facet_grid( vars(var), scales = "free_y", labeller =
                  labeller(var = c(PDO = "PDO", Temp = "Temperature (C)", slope = "slope (varying)", constant_slope = "slope (constant)")) ) +
      geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
      labs(x = "Year", y = "Observation / Estimate")

And can also visualize the estimated graph

    library(igraph)
    library(ggraph)

    g = make_empty_graph(8)
    V(g)$name = c("P[t]", "T[t]", "z[t]", "b[t]", "P[t+1]", "T[t+1]", "z[t+1]", "b[t+1]")
    V(g)$shape = c( "square","square","diamond","circle", "square","square","diamond","circle" )
    V(g)$label = c("P[t]", "T[t]", "", "beta[t]", "P[t+1]", "T[t+1]", "", "beta[t+1]")

    #
    g <- add_edges(g, c("P[t]", "T[t]"), attr = list(label = "", type = "solid", col = "black"))
    g <- add_edges(g, c("P[t+1]", "T[t+1]"), attr = list(label = "", type = "solid", col = "black"))
    g <- add_edges(g, c("b[t]", "z[t]"), attr = list(label = "", type = "solid", col = "black"))
    g <- add_edges(g, c("b[t+1]", "z[t+1]"), attr = list(label = "", type = "solid", col = "black"))
    # ARs
    val = paste0( round(subset(summary(fit),path == "slope -> slope")$Estimate,2), " (", round(subset(summary(fit),path == "slope -> slope")$Std_Error,2),")")
    g <- add_edges(g, c("b[t]", "b[t+1]"), attr = list(label = val, type = "dotted", col = "grey"))
    val = paste0( round(subset(summary(fit),path == "PDO -> PDO")$Estimate,2), " (", round(subset(summary(fit),path == "PDO -> PDO")$Std_Error,2),")")
    g <- add_edges(g, c("P[t]", "P[t+1]"), attr = list(label = val, type = "dotted", col = "grey"))
    val = paste0( round(subset(summary(fit),path == "Temp -> Temp")$Estimate,2), " (", round(subset(summary(fit),path == "Temp -> Temp")$Std_Error,2),")")
    g <- add_edges(g, c("T[t]", "T[t+1]"), attr = list(label = val, type = "dotted", col = "grey"))

    loc_nodes = rbind(
      c(x = 1, y = 3),
      c(1,0),
      c(1,1),
      c(0,2),
      c(4,3),
      c(4,0),
      c(4,1),
      c(3,2)
    )

    layout = create_layout( g, loc_nodes[,c("x","y")] )
    ggraph(layout) +
      geom_edge_link2(
        arrow = arrow(length = unit(2, "mm")),
        end_cap = ggraph::circle(7, 'mm'),
        start_cap = ggraph::circle(0, 'mm'),
        aes( label = label, linetype = type, col = col ),  # , edge_width = ifelse(type == "dotted", 0.8, 0.8)
        vjust = -0.2,
        hjust = 0.4,
        label_parse = TRUE
      ) +
      geom_node_point(
        aes(shape = shape, size = ifelse(shape == "diamond",8,15) ),
        #size = 15,
        stroke = 1.2,
        color = "black",
        fill = "white"
      ) +
      geom_node_label(
        fill = "white",
        lwd = 0,
        aes(label = label),
        parse = TRUE
      ) +
      theme(panel.background = element_rect(fill = NA, color = NA)) +
      coord_cartesian( xlim = c(-0.5, 4.5), ylim = c(-0.5, 3.5) )  +
      scale_shape_manual(values = c("circle" = 21, "square" = 22, "diamond" = 23), guide = "none") +  # ?scale_shape
      scale_edge_linetype_manual(values = c("solid" = "solid", "dotted" = "solid"), guide = "none") +
      scale_edge_colour_manual(values = c("black" = "black", "grey" = "black"), guide = "none") +
      scale_size( range = c(6,15), guide = "none" )

Runtime for this vignette: 1.95 secs